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Obesity Research

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Over the years, NHLBI-supported research on overweight and obesity has led to the development of evidence-based prevention and treatment guidelines for healthcare providers. NHLBI research has also led to guidance on how to choose a behavioral weight loss program.

Studies show that the skills learned and support offered by these programs can help most people make the necessary lifestyle changes for weight loss and reduce their risk of serious health conditions such as heart disease and diabetes.

Our research has also evaluated new community-based programs for various demographics, addressing the health disparities in overweight and obesity.

NHLBI research that really made a difference

• In 1991, the NHLBI developed an Obesity Education Initiative to educate the public and health professionals about obesity as an independent risk factor for cardiovascular disease and its relationship to other risk factors, such as high blood pressure and high blood cholesterol. The initiative led to the development of clinical guidelines for treating overweight and obesity.
• The NHLBI and other NIH Institutes funded the Obesity-Related Behavioral Intervention Trials (ORBIT) projects , which led to the ORBIT model for developing behavioral treatments to prevent or manage chronic diseases. These studies included families and a variety of demographic groups. A key finding from one study focuses on the importance of targeting psychological factors in obesity treatment.

Current research funded by the NHLBI

The Division of Cardiovascular Sciences , which includes the Clinical Applications and Prevention Branch, funds research to understand how obesity relates to heart disease. The Center for Translation Research and Implementation Science supports the translation and implementation of research, including obesity research, into clinical practice. The Division of Lung Diseases and its National Center on Sleep Disorders Research fund research on the impact of obesity on sleep-disordered breathing.

Find funding opportunities and program contacts for research related to obesity and its complications.

Current research on obesity and health disparities

Health disparities happen when members of a group experience negative impacts on their health because of where they live, their racial or ethnic background, how much money they make, or how much education they received. NHLBI-supported research aims to discover the factors that contribute to health disparities and test ways to eliminate them.

• NHLBI-funded researchers behind the RURAL: Risk Underlying Rural Areas Longitudinal Cohort Study want to discover why people in poor rural communities in the South have shorter, unhealthier lives on average. The study includes 4,000 diverse participants (ages 35–64 years, 50% women, 44% whites, 45% Blacks, 10% Hispanic) from 10 of the poorest rural counties in Kentucky, Alabama, Mississippi, and Louisiana. Their results will support future interventions and disease prevention efforts.
• The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is looking at what factors contribute to the higher-than-expected numbers of Hispanics/Latinos who suffer from metabolic diseases such as obesity and diabetes. The study includes more than 16,000 Hispanic/Latino adults across the nation.

Find more NHLBI-funded studies on obesity and health disparities at NIH RePORTER.

Read how African Americans are learning to transform soul food into healthy, delicious meals to prevent cardiovascular disease: Vegan soul food: Will it help fight heart disease, obesity?

Current research on obesity in pregnancy and childhood

• The NHLBI-supported Fragile Families Cardiovascular Health Follow-Up Study continues a study that began in 2000 with 5,000 American children born in large cities. The cohort was racially and ethnically diverse, with approximately 40% of the children living in poverty. Researchers collected socioeconomic, demographic, neighborhood, genetic, and developmental data from the participants. In this next phase, researchers will continue to collect similar data from the participants, who are now young adults.
• The NHLBI is supporting national adoption of the Bright Bodies program through Dissemination and Implementation of the Bright Bodies Intervention for Childhood Obesity . Bright Bodies is a high-intensity, family-based intervention for childhood obesity. In 2017, a U.S. Preventive Services Task Force found that Bright Bodies lowered children’s body mass index (BMI) more than other interventions did.
• The NHLBI supports the continuation of the nuMoM2b Heart Health Study , which has followed a diverse cohort of 4,475 women during their first pregnancy. The women provided data and specimens for up to 7 years after the birth of their children. Researchers are now conducting a follow-up study on the relationship between problems during pregnancy and future cardiovascular disease. Women who are pregnant and have obesity are at greater risk than other pregnant women for health problems that can affect mother and baby during pregnancy, at birth, and later in life.

Find more NHLBI-funded studies on obesity in pregnancy and childhood at NIH RePORTER.

Learn about the largest public health nonprofit for Black and African American women and girls in the United States: Empowering Women to Get Healthy, One Step at a Time .

Current research on obesity and sleep

• An NHLBI-funded study is looking at whether energy balance and obesity affect sleep in the same way that a lack of good-quality sleep affects obesity. The researchers are recruiting equal numbers of men and women to include sex differences in their study of how obesity affects sleep quality and circadian rhythms.
• NHLBI-funded researchers are studying metabolism and obstructive sleep apnea . Many people with obesity have sleep apnea. The researchers will look at the measurable metabolic changes in participants from a previous study. These participants were randomized to one of three treatments for sleep apnea: weight loss alone, positive airway pressure (PAP) alone, or combined weight loss and PAP. Researchers hope that the results of the study will allow a more personalized approach to diagnosing and treating sleep apnea.
• The NHLBI-funded Lipidomics Biomarkers Link Sleep Restriction to Adiposity Phenotype, Diabetes, and Cardiovascular Risk study explores the relationship between disrupted sleep patterns and diabetes. It uses data from the long-running Multiethnic Cohort Study, which has recruited more than 210,000 participants from five ethnic groups. Researchers are searching for a cellular-level change that can be measured and can predict the onset of diabetes in people who are chronically sleep deprived. Obesity is a common symptom that people with sleep issues have during the onset of diabetes.

Find more NHLBI-funded studies on obesity and sleep at NIH RePORTER.

Learn about a recent study that supports the need for healthy sleep habits from birth: Study finds link between sleep habits and weight gain in newborns .

Obesity research labs at the NHLBI

The Cardiovascular Branch and its Laboratory of Inflammation and Cardiometabolic Diseases conducts studies to understand the links between inflammation, atherosclerosis, and metabolic diseases.

NHLBI’s Division of Intramural Research , including its Laboratory of Obesity and Aging Research , seeks to understand how obesity induces metabolic disorders. The lab studies the “obesity-aging” paradox: how the average American gains more weight as they get older, even when food intake decreases.

Related obesity programs and guidelines

• Aim for a Healthy Weight is a self-guided weight-loss program led by the NHLBI that is based on the psychology of change. It includes tested strategies for eating right and moving more.
• The NHLBI developed the We Can! ® (Ways to Enhance Children’s Activity & Nutrition) program to help support parents in developing healthy habits for their children.
• The Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project standardizes data collected from the various studies of obesity treatments so the data can be analyzed together. The bigger the dataset, the more confidence can be placed in the conclusions. The main goal of this project is to understand the individual differences between people who experience the same treatment.
• The NHLBI Director co-chairs the NIH Nutrition Research Task Force, which guided the development of the first NIH-wide strategic plan for nutrition research being conducted over the next 10 years. See the 2020–2030 Strategic Plan for NIH Nutrition Research .
• The NHLBI is an active member of the National Collaborative on Childhood Obesity (NCCOR) , which is a public–private partnership to accelerate progress in reducing childhood obesity.
• The NHLBI has been providing guidance to physicians on the diagnosis, prevention, and treatment of obesity since 1977. In 2017, the NHLBI convened a panel of experts to take on some of the pressing questions facing the obesity research community. See their responses: Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents (PDF, 3.69 MB).
• In 2021, the NHLBI held a Long Non-coding (lnc) RNAs Symposium to discuss research opportunities on lnc RNAs, which appear to play a role in the development of metabolic diseases such as obesity.
• The Muscatine Heart Study began enrolling children in 1970. By 1981, more than 11,000 students from Muscatine, Iowa, had taken surveys twice a year. The study is the longest-running study of cardiovascular risk factors in children in the United States. Today, many of the earliest participants and their children are still involved in the study, which has already shown that early habits affect cardiovascular health later in life.
• The Jackson Heart Study is a unique partnership of the NHLBI, three colleges and universities, and the Jackson, Miss., community. Its mission is to discover what factors contribute to the high prevalence of cardiovascular disease among African Americans. Researchers aim to test new approaches for reducing this health disparity. The study incudes more than 5,000 individuals. Among the study’s findings to date is a gene variant in African Americans that doubles the risk of heart disease.

Explore more NHLBI research on overweight and obesity

The sections above provide you with the highlights of NHLBI-supported research on overweight and obesity . You can explore the full list of NHLBI-funded studies on the NIH RePORTER .

To find more studies:

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Obesity research: Moving from bench to bedside to population

* E-mail: [email protected]

Affiliation Diabetes Research Program, Department of Medicine, New York University Grossman School of Medicine, New York, New York, United States of America

• Ann Marie Schmidt

Published: December 4, 2023

• https://doi.org/10.1371/journal.pbio.3002448
• Reader Comments

Globally, obesity is on the rise. Research over the past 20 years has highlighted the far-reaching multisystem complications of obesity, but a better understanding of its complex pathogenesis is needed to identify safe and lasting solutions.

Citation: Schmidt AM (2023) Obesity research: Moving from bench to bedside to population. PLoS Biol 21(12): e3002448. https://doi.org/10.1371/journal.pbio.3002448

Copyright: © 2023 Ann Marie Schmidt. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: AMS received funding from U.S. Public Health Service (grants 2P01HL131481 and P01HL146367). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The author has declared that no competing interests exist.

Abbreviations: EDC, endocrine disruptor chemical; GIP, gastric inhibitory polypeptide; GLP1, glucagon-like peptide 1; HFCS, high-fructose corn syrup

This article is part of the PLOS Biology 20th anniversary collection.

Obesity is a multifaceted disorder, affecting individuals across their life span, with increased prevalence in persons from underrepresented groups. The complexity of obesity is underscored by the multiple hypotheses proposed to pinpoint its seminal mechanisms, such as the “energy balance” hypothesis and the “carbohydrate–insulin” model. It is generally accepted that host (including genetic factors)–environment interactions have critical roles in this disease. The recently framed “fructose survival hypothesis” proposes that high-fructose corn syrup (HFCS), through reduction in the cellular content of ATP, stimulates glycolysis and reduces mitochondrial oxidative phosphorylation, processes that stimulate hunger, foraging, weight gain, and fat accumulation [ 1 ]. The marked upswing in the use of HFCS in beverages and foods, beginning in the 1980s, has coincided with the rising prevalence of obesity.

The past few decades of scientific progress have dramatically transformed our understanding of pathogenic mechanisms of obesity ( Fig 1 ). Fundamental roles for inflammation were unveiled by the discovery that tumor necrosis factor-α contributed to insulin resistance and the risk for type 2 diabetes in obesity [ 2 ]. Recent work has ascribed contributory roles for multiple immune cell types, such as monocytes/macrophages, neutrophils, T cells, B cells, dendritic cells, and mast cells, in disturbances in glucose and insulin homeostasis in obesity. In the central nervous system, microglia and their interactions with hypothalamic neurons affect food intake, energy expenditure, and insulin sensitivity. In addition to cell-specific contributions of central and peripheral immune cells in obesity, roles for interorgan communication have been described. Extracellular vesicles emitted from immune cells and from adipocytes, as examples, are potent transmitters of obesogenic species that transfer diverse cargo, including microRNAs, proteins, metabolites, lipids, and organelles (such as mitochondria) to distant organs, affecting functions such as insulin sensitivity and, strikingly, cognition, through connections to the brain [ 3 ].

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Basic, clinical/translational, and epidemiological research has made great strides in the past few decades in uncovering novel components of cell-intrinsic, intercellular, and interorgan communications that contribute to the pathogenesis of obesity. Both endogenous and exogenous (environmental) stressors contribute to the myriad of metabolic perturbations that impact energy intake and expenditure; mediate innate disturbances in the multiple cell types affected in obesity in metabolic organelles and organs, including in immune cells; and impair beneficial interkingdom interactions of the mammalian host with the gut microbiome. The past few decades have also witnessed remarkable efforts to successfully treat obesity, such as the use of the incretin agonists and bariatric surgery. Yet, these and other strategies may be accompanied by resistance to weight loss, weight regain, adverse effects of interventions, and the challenges of lifelong implementation. Hence, through leveraging novel discoveries from the bench to the bedside to the population, additional strategies to prevent obesity and weight regain post-weight loss, such as the use of “wearables,” with potential for implementation of immediate and personalized behavior modifications, may hold great promise as complementary strategies to prevent and identify lasting treatments for obesity. Figure created with BioRender.

https://doi.org/10.1371/journal.pbio.3002448.g001

Beyond intercellular communication mediated by extracellular vesicles, the discovery of interactions between the host and the gut microbiome has suggested important roles for this interkingdom axis in obesity. Although disturbances in commensal gut microbiota species and their causal links to obesity are still debated, transplantation studies have demonstrated relationships between Firmicutes/Bacteroidetes ratios and obesity [ 4 ]. Evidence supports the concept that modulation of gut microbiota phyla modulates fundamental activities, such as thermogenesis and bile acid and lipid metabolism. Furthermore, compelling discoveries during the past few decades have illustrated specific mechanisms within adipocytes that exert profound effects on organismal homeostasis, such as adipose creatine metabolism, transforming growth factor/SMAD signaling, fibrosis [ 5 ], hypoxia and angiogenesis, mitochondrial dysfunction, cellular senescence, impairments in autophagy, and modulation of the circadian rhythm. Collectively, these recent discoveries set the stage for the identification of potential new therapeutic approaches in obesity.

Although the above discoveries focus largely on perturbations in energy metabolism (energy intake and expenditure) as drivers of obesity, a recently published study suggests that revisiting the timeline of obesogenic forces in 20th and 21st century society may be required. The authors tracked 320,962 Danish schoolchildren (born during 1930 to 1976) and 205,153 Danish male military conscripts (born during 1939 to 1959). Although the overall trend of the percentiles of the distributions of body mass index were linear across the years of birth, with percentiles below the 75th being nearly stable, those above the 75th percentile demonstrated a steadily steeper rise the more extreme the percentile; this was noted in the schoolchildren and the military conscripts [ 6 ]. The authors concluded that the emergence of the obesity epidemic might have preceded the appearance of the factors typically ascribed to mediating the obesogenic transformation of society by several decades. What are these underlying factors and their yet-to-be-discovered mechanisms?

First, in terms of endogenous factors relevant to individuals, stressors such as insufficient sleep and psychosocial stress may impact substrate metabolism, circulating appetite hormones, hunger, satiety, and weight gain [ 7 ]. Reduced access to healthy foods rich in vegetables and fruits but easy access to ultraprocessed ingredients in “food deserts” and “food swamps” caused excessive caloric intake and weight gain in clinical studies [ 8 ]. Second, exogenous environmental stresses have been associated with obesity. For example, air pollution has been directly linked to adipose tissue dysfunction [ 9 ], and ubiquitous endocrine disruptor chemicals (EDCs) such as bisphenols and phthalates (found in many items of daily life including plastics, food, clothing, cosmetics, and paper) are linked to metabolic dysfunction and the development of obesity [ 10 ]. Hence, factors specific to individuals and their environment may exacerbate their predisposition to obesity.

In addition to the effects of exposure to endogenous and exogenous stressors on the risk of obesity, transgenerational (passed through generations without direct exposure of stimulant) and intergenerational (direct exposure across generations) transmission of these stressors has also been demonstrated. A leading proposed mechanism is through epigenetic modulation of the genome, which then predisposes affected offspring to exacerbated responses to obesogenic conditions such as diet. A recent study suggested that transmission of disease risk might be mediated through transfer of maternal oocyte-derived dysfunctional mitochondria from mothers with obesity [ 11 ]. Additional mechanisms imparting obesogenic “memory” may be evoked through “trained immunity.”

Strikingly, the work of the past few decades has resulted in profound triumphs in the treatment of obesity. Multiple approved glucagon-like peptide 1 (GLP1) and gastric inhibitory polypeptide (GIP) agonists [ 12 ] (alone or in combinations) induce highly significant weight loss in persons with obesity [ 13 ]. However, adverse effects of these agents, such as pancreatitis and biliary disorders, have been reported [ 14 ]. Therefore, the long-term safety and tolerability of these drugs is yet to be determined. In addition to pharmacological agents, bariatric surgery has led to significant weight loss as well. However, efforts to induce weight loss through reduction in caloric intake and increased physical activity, pharmacological approaches, and bariatric surgery may not mediate long-term cures in obesity on account of resistance to weight loss, weight regain, adverse effects of interventions, and the challenges of lifelong implementation of these measures.

Where might efforts in combating obesity lie in the next decades? At the level of basic and translational science, the heterogeneity of metabolic organs could be uncovered through state-of-the-art spatial “omics” and single-cell RNA sequencing approaches. For example, analogous to the deepening understanding of the great diversity in immune cell subsets in homeostasis and disease, adipocyte heterogeneity has also been suggested, which may reflect nuances in pathogenesis and treatment approaches. Further, approaches to bolster brown fat and thermogenesis may offer promise to combat evolutionary forces to hoard and store fat. A better understanding of which interorgan communications may drive obesity will require intensive profiling of extracellular vesicles shed from multiple metabolic organs to identify their cargo and, critically, their destinations. In the three-dimensional space, the generation of organs-on-a-chip may facilitate the discovery of intermetabolic organ communications and their perturbations in the pathogenesis of obesity and the screening of new therapies.

Looking to prevention, recent epidemiological studies suggest that efforts to tackle obesity require intervention at multiple levels. The institution of public health policies to reduce air pollution and the vast employment of EDCs in common household products could impact the obesity epidemic. Where possible, the availability of fresh, healthy foods in lieu of highly processed foods may be of benefit. At the individual level, focused attention on day-to-day behaviors may yield long-term benefit in stemming the tide of obesity. “Wearable” devices that continuously monitor the quantity, timing, and patterns of food intake, physical activity, sleep duration and quality, and glycemic variability might stimulate on-the-spot and personalized behavior modulation to contribute to the prevention of obesity or of maintenance of the weight-reduced state.

Given the involvement of experts with wide-ranging expertise in the science of obesity, from basic science, through clinical/translational research to epidemiology and public health, it is reasonable to anticipate that the work of the next 2 decades will integrate burgeoning multidisciplinary discoveries to drive improved efforts to treat and prevent obesity.

Acknowledgments

The author is grateful to Ms. Latoya Woods of the Diabetes Research Program for assistance with the preparation of the manuscript and to Ms. Kristen Dancel-Manning for preparation of the Figure accompanying the manuscript.

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Overview of Obesity

Facts about obesity.

Overweight and obesity together make up one of the leading preventable causes of death in the U.S. Obesity is a chronic disease that can seriously affect your health.  

Overweight means that you have extra body weight, and obesity means having a high amount of extra body fat. Being overweight or obese raises your risk for health problems. These include coronary heart disease, type 2 diabetes, asthma, high cholesterol, osteoarthritis, high blood pressure, sleep apnea, and certain types of cancer.

Public health experts agree that overweight and obesity have reached epidemic proportions in this country and around the world. More than a third of U.S. adults are obese. People ages 60 and older are more likely to be obese than younger adults, according to the most recent data from the National Health and Nutrition Examination Survey. And the problem also affects children. Approximately 20%, of U.S. children and adolescents ages 2 to 19 are obese.

Who's obese?

Overweight and obesity are different points on a scale that ranges from being underweight to being morbidly obese. Where you fit on this scale is determined by your body mass index (BMI).

BMI is a measure of your weight as it relates to your height. BMI usually gives you a good idea of the amount of body fat you have. Your healthcare providers use BMI to find out your risk for obesity-related diseases. Occasionally, some very muscular people may have a BMI in the overweight range. But these people are not considered overweight because muscle tissue weighs more than fat tissue.

In general, a BMI from 20 to 24.9 in adults is considered ideal. A BMI of more than 25 is considered overweight. A person is considered obese if the BMI is greater than 30 and is considered to have morbid obesity if the BMI is 40 or greater.

In general, after the age of 50, a man's weight tends to stay the same and often decreases slightly between ages 60 and 74. In contrast, a woman's weight tends to increase until age 60, and then begins to decrease.

Obesity can also be measured by waist-to-hip ratio. This is a measurement tool that looks at the amount of fat on your waist, compared with the amount of fat on your hips and buttocks. The waist circumference tells the amount of stomach fat. Increased stomach fat is associated with type 2 diabetes, high cholesterol, high blood pressure, and heart disease. A waist circumference of more than 40 inches in men and more than 35 inches in women may increase the risk for heart disease and other diseases tied to being overweight.

Talk with your healthcare provider if you have questions about healthy body weight.

What causes obesity?

In many ways, obesity is a puzzling disease. Experts don't know exactly how your body regulates your weight and body fat. What they do know is that a person who eats more calories than he or she uses for energy each day will gain weight.

But the risk factors that determine obesity can be complex. They are usually a combination of your genes, socioeconomic factors, metabolism, and lifestyle choices. Some endocrine disorders, diseases, and medicines may also affect a person's weight.

Factors that may affect obesity include the following.

Studies show that the likelihood of becoming obese is passed down through a family's genes. Researchers have found several genes that appear to be linked with obesity. Genes, for instance, may affect where you store extra fat in your body. But most researchers think that it takes more than just one gene to cause an obesity epidemic. They are continuing to do more research to better understand how genes and lifestyle interact to cause obesity. Because families eat meals together and share other activities, environment and lifestyle also play a role.

Metabolism factors

How your body uses energy is different from how another person's uses it. Metabolism and hormones differ from person to person, and these factors play a role in how much weight you gain. One example is ghrelin, the "hunger hormone" that regulates appetite. Researchers have found that ghrelin may help trigger hunger. Another hormone called leptin can decrease appetite. Another example is polycystic ovary syndrome (PCOS), a condition in women caused by high levels of certain hormones. A woman with PCOS is more likely to be obese.

Socioeconomic factors

How much money you make may affect whether you are obese. This is especially true for women. Women who are poor and of lower social status are more likely to be obese than women of higher socioeconomic status. This is especially true among minority groups.

Lifestyle choices

Overeating and a lack of exercise both contribute to obesity. But you can change these lifestyle choices. If many of your calories come from refined foods or foods high in sugar or fat, you will likely gain weight. If you don't get much if any exercise, you'll find it hard to lose weight or maintain a healthy weight.

Medicines like corticosteroids, antidepressants, and antiseizure medicines can cause you to gain some extra weight.

Emotional eating–eating when you're bored or upset–can lead to weight gain. Too little sleep may also contribute to weight gain. People who sleep fewer than 5 hours a night are more likely to become obese than people who get 7 to 8 hours of sleep a night.

Health effects of obesity

Obesity has a far-ranging negative effect on health. Each year in the U.S., obesity-related conditions cost more than $100 billion and cause premature deaths. The health effects linked with obesity include:

High blood pressure

Excess weight needs more blood to circulate to the fat tissue and causes the blood vessels to become narrow (coronary artery disease). This makes the heart work harder, because it must pump more blood against more resistance from the blood vessels and can lead to a heart attack (myocardial infarction). More circulating blood and more resistance also means more pressure on the artery walls. Higher pressure on the artery walls increases the blood pressure. Excess weight also raises blood cholesterol and triglyceride levels and lowers HDL ("good") cholesterol levels, adding to the risk of heart disease.

Type 2 diabetes

Obesity is the major cause of type 2 diabetes. Obesity can make your body resistant to insulin, the hormone that regulates blood sugar. When obesity causes insulin resistance, your blood sugar level rises. Even moderate obesity dramatically increases the risk for diabetes.

Heart disease

Atherosclerosis, or hardening of the arteries, happens more often in obese people. Coronary artery disease is also more common in obese people because fatty deposits build up in arteries that supply the heart. Narrowed arteries and reduced blood flow to the heart can cause chest pain called angina or a heart attack. Blood clots can also form in narrowed arteries and travel to the brain, causing a stroke.

Joint problems, including osteoarthritis

Obesity can affect the knees and hips because extra weight stresses the joints. Joint replacement surgery may not be a good choice for an obese person because the artificial joint has a higher risk of loosening and causing more damage.

Sleep apnea and respiratory problems are also related to obesity

Sleep apnea causes people to stop breathing for brief periods during sleep. Sleep apnea interrupts sleep and causes sleepiness during the day. It also causes heavy snoring. Sleep apnea is also linked to high blood pressure. Breathing problems tied to obesity happen when added weight of the chest wall squeezes the lungs. This restricts breathing.

Being overweight or obese increases your risk for a variety of cancers, according to the American Cancer Society. Among obese women, the risk increases for cancer of the endometrium or the lining of the uterus in younger women. Obese women also increase their risk for breast cancers in those who have gone through menopause. Men who are overweight have a higher risk for prostate cancer. Both men and women who are obese are at increased risk for colorectal cancer.

Metabolic syndrome

The National Cholesterol Education Program says that metabolic syndrome is a risk factor for cardiovascular disease. Metabolic syndrome has several major risk factors. These are stomach obesity, high blood triglyceride levels, low HDL cholesterol levels, high blood pressure, and insulin resistance (severe type 2 diabetes). Having at least three of these risk factors confirms the diagnosis of metabolic syndrome. 

Psychosocial effects

People who are overweight or obese can have problems socially or psychologically. This is because the culture in the U.S. often values a body image that's overly thin. Overweight and obese people are often blamed for their condition. Other people may think of them as lazy or weak-willed. It is not uncommon for people who are overweight or obese to earn less than other people or to have fewer or no romantic relationships. Some people's disapproval and bias against those who are overweight may progress to discrimination, and even torment. Depression is more common in people who are overweight and obese. 

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October 17, 2023

Research in Context: Obesity and metabolic health

The complexities of metabolism and body weight.

Losing weight and reversing obesity might seem straightforward: eat fewer calories than you burn. But that’s not as easy as it sounds. This special Research in Context feature explores the many factors that affect the body’s metabolism and weight, some of which are difficult to control.

Obesity has been a growing problem in the U.S. More than 40% of adults and 19% of children now have obesity. Some people may think of obesity as a consequence of lifestyle choices. But there are many factors affecting body weight that are beyond our conscious control. Researchers are only beginning to understand them.

The rise in obesity has serious implications. Excess body fat can trigger inflammation, high blood sugar, and high blood pressure. Levels of fat and cholesterol in the blood increase, raising the risk of heart disease and stroke. Fat can also get stored in places it normally wouldn’t be, such as the liver and kidneys.

Obesity and some of its associated symptoms, such as high blood pressure and high blood sugar, contribute to a condition known as metabolic syndrome. People with metabolic syndrome have a higher risk of developing many chronic diseases, including type 2 diabetes, cardiovascular disease, and even certain cancers.

NIH-funded researchers have been working for a deeper understanding of metabolic health. They are trying to learn how our bodies regulate how many calories we take in and burn, and how our behavior and environment can promote obesity. Research has also led to a more nuanced understanding of fat itself. This research may result in better ways to prevent and treat metabolic syndrome.

Body weight, diet, and appetite

Losing weight and reversing obesity might seem straightforward: eat fewer calories than you burn. But many of us know that’s not as easy as it sounds.

“I think that most people overestimate how much conscious control we have, over long periods of time, over both the amount of food that we eat as well as the type of food that we eat,” says NIH metabolism researcher Dr. Kevin Hall.

If you cut down how many calories you eat and do more physical activity, you will begin to lose weight. But as you do so, your body adjusts. You start burning fewer calories, so the calorie deficit shrinks. Your appetite may also go up, driving you to eat more. Both of these changes happen on an unconscious level. So, failure to lose a substantial amount of weight through diet and exercise can’t just be blamed on a lack of willpower. There’s a fundamental biological drive that has to be overcome.

“People perceive hunger in their brain,” explains Dr. Aaron Cypess, another NIH metabolism researcher. “And if the brain is not getting the right amount of signal saying that there's enough calories, then the brain will lead one to eat more.” Research on the recently approved weight-loss drugs semaglutide (Wegovy) and liraglutide (Saxenda) supports this view. These drugs work by targeting areas of the brain that regulate appetite.

But researchers have also discovered that the foods available to us are not created equal when it comes to appetite control. Hall’s research has found that people unconsciously eat more calories’ worth of certain foods than others. In one study, 20 people stayed at the NIH Clinical Center’s Metabolic Clinical Research Unit for four weeks. They were told they could eat as much or as little as they wanted. Each participant was given a diet that was low in fat and high in carbohydrates for two weeks and one that was low in carbohydrates and high in fat for two weeks. Both diets were based on minimally processed foods.

The participants reported no differences in hunger, satisfaction, or fullness between the diets. But when provided with the low-fat diet, they ended up eating almost 700 calories less per day than when given the low-carb diet. They also lost more body fat. Participants still lost weight on both diets, suggesting that both of these restricted food patterns caused people to eat fewer calories than their habitual diets, at least in the short term.

Food processing may also play a role in food intake. Hall’s team tested two food patterns with the same number of calories, salt, sugar, fat, fiber, and carbohydrates. But one diet was high in minimally processed foods while the other was high in “ultra-processed” foods. These are ready-to-heat or ready-to-eat foods that have undergone extensive industrial processing. They have additives and other ingredients not typically used in home cooking. For example, those on the ultra-processed diet received a bagel and cream cheese with turkey bacon for breakfast one day. Those on the unprocessed diet received oatmeal with walnuts, bananas, and coconut.  

As in the first study, participants reported no differences in hunger, fullness, or satisfaction between the diets. But during the ultra-processed dietary period, participants ate about 500 calories more per day than during the unprocessed period. The ultra-processed foods caused participants to gain an average of 2 pounds of weight, much of it from increased body fat. When participants were exposed to the unprocessed diet, they lost weight and body fat. This finding suggests that the increasing amount of ultra-processed foods in our environment may have helped to drive the rise in obesity.

Hall’s group is now trying to figure out what it is about ultra-processed foods that leads people to eat more calories, and how this happens. He notes that getting rid of ultra-processed foods entirely wouldn’t be practical. “We’re not going to go from a country where half of the food supply is ultra-processed foods to a country where a very small fraction of the food supply is ultra-processed foods. We rely on them too much. I rely on them still. They’re tasty, they're convenient, and it doesn’t take much time or effort or skill to prepare them.”

But understanding what makes these foods problematic could lead to healthier eating. Manufacturers might be able to reformulate ultra-processed foods to be less likely to cause overeating. Consumers could learn what products to avoid. And government regulators could craft policies to help improve people’s metabolic health.

Sleep and metabolic health

Diet and exercise aren’t the only activities that affect your metabolic health. Our metabolism, like many aspects of our biology, varies with the time of day. So besides what you eat, when you eat can affect your body and your health.

Research has found that sleep has a major effect on the body’s metabolism. Experts recommend that adults get at least seven hours of sleep each night. Yet many American adults regularly get less than that—and some aren't able to get all their sleep at night.

Poor sleep is linked to the risk of obesity and diabetes. Our bodies are most capable of taking in nutrients during the daytime when we’re awake. People who spend too much time awake end up eating more—particularly at night, when their bodies aren’t ready for it. Eating at night tends to lead to more energy being stored as fat. This is particularly a problem for night shift workers, who have an increased risk of metabolic disorders. Not getting enough sleep also impairs the body’s ability to respond to insulin, the hormone your body uses to regulate blood sugar. Over time, impaired insulin sensitivity can lead to type 2 diabetes.

Some people try to sleep longer on the weekends to make up for the sleep they didn’t get during the work week. But a recent NIH-funded study suggests that this “catch-up” sleep can’t counteract the effects of sleep deprivation. People who were limited to five hours of sleep per night ate more after-dinner snacks than those allowed to sleep normally. During the two-week study, they gained about 3 pounds on average, and their insulin sensitivity declined.

After five days, some of the sleep-deprived participants were allowed two days of unrestricted sleep. But this barely helped. They fell far short of making up for the more than 12 hours they lost during the preceding five days. After-dinner snacking decreased during the two-day recovery period, but it went back up upon returning to restricted sleep. In the end, they didn’t gain any less weight than people who didn’t get weekend recovery sleep, and their insulin sensitivity decreased even more.

“In the case of smoking, we wouldn’t say, ‘oh, you can smoke for five days, just take the weekend off, and it’ll be okay,’” explains study leader Dr. Kenneth Wright, Jr. of the University of Colorado Boulder. “We’re not trying to say, don't sleep in on the weekend. We’re trying to say get adequate sleep as much as you can on a consistent basis.”

Limiting the times you eat through brief intentional periods of fasting may also have benefits for metabolic health. Studies in animals have found that repeated cycles of fasting, even without reducing total calories, can improve a range of metabolic and immune functions. Examples include lower cholesterol levels, blood pressure, and inflammation. Small clinical studies have also found that periodic fasting can have health benefits in people. Some of the benefits seen include lower inflammation and blood pressure, and better control of blood sugar. Researchers are now studying whether such approaches might be safely used to improve human health over the long term, and whom they might benefit.

Different kinds of fat

Researchers have discovered that it isn’t only how much fat you have that matters; it’s the kind of fat. The human body contains two main kinds. Most is white fat. This stores excess energy in the form of molecules called triglycerides. White fat is found throughout the body, usually in a layer under the skin and around internal organs. Having too much white fat is what makes someone have obesity.

But there’s another kind of fat, called brown fat. Its job is to help maintain body temperature by burning triglycerides to generate heat. Until about 15 years ago, humans were believed to lose most of their brown fat after infancy. It was thought that any brown fat that remained in adults didn’t serve any function. Since then, researchers have learned that nearly every adult human has some functioning brown fat. It’s found only in certain places in people: the neck and shoulders, along the spine, and around the kidneys.

Brown fat may have a big impact on our metabolic health. In mice, activating brown fat reduces levels of triglycerides and cholesterol in the blood. It also prevents atherosclerosis, a sticky buildup along the artery walls that contributes to heart disease. In people, more brown fat is associated with lower rates of type 2 diabetes, cardiovascular disease, high blood pressure, and heart failure. So, having more brown fat, or more active brown fat, may be good for metabolic health. This suggests that increasing brown fat activity might protect against metabolic syndrome.

The simplest way to activate brown fat is long exposure to cold temperatures. But people might not be willing to spend hours a day sitting in the cold. It would be more practical if we could take a drug to activate our brown fat.

Cypess and his team are studying one potential drug, mirabegron, that is currently approved by the Food and Drug Administration for treating overactive bladder. It works by binding to and activating a protein found on the surface of certain cells, including brown fat cells.

In a small trial, Cypess and his team showed that mirabegron could activate brown fat at least as well as cold exposure. In a follow-up study, mirabegron increased the amount and activity of brown fat over the course of the study. The amount of energy used increased, and some measures of metabolism improved, although there were no changes in body weight or the percentage of body fat.

These changes were comparable to those caused by mild exercise, bariatric surgery, or anti-diabetic drugs. The only side effect was a small increase in resting heart rate. This is a known side effect of mirabegron and was not large enough to put the participants’ safety at risk.

Cypess’s team now seeks to figure out how brown fat activation leads to these metabolic changes. They’re also looking at whether mirabegron can help people at risk for metabolic disease.

“This field is interesting, because there are a lot of people with very strong opinions who will tell you that they already know the answers,” Hall says. “It seems to me that there are still a lot of really important open questions. We’re getting closer to understanding why obesity has increased. We thankfully have some seemingly effective ways to deal with people who have severe obesity, or complications of obesity. But there's still a lot of work to do.”

—by Brian Doctrow, Ph.D.

Related Links

• Brown Fat Associated with Less Heart and Metabolic Disease
• Drug Activates Brown Fat, Improves Glucose Metabolism in Healthy Women
• How Brown Fat Improves Metabolism
• Microneedle Patch Shrinks Fat Tissue in Mice
• Drug Activates Brown Fat and Increases Metabolism
• Low-Fat Diet Compared to Low-Carb Diet
• Eating Highly Processed Foods Linked to Weight Gain
• Prescribing Healthy Foods Could Bring Cost-Effective Benefits
• Biological Factors and Weight Loss Methods
• How Dietary Factors Influence Disease Risk
• Dietary Fat vs. Carbohydrate for Reducing Body Fat
• Diet Beverages and Body Weight
• Weekend Catch-Up Can’t Counter Chronic Sleep Deprivation
• How Disrupted Sleep May Lead to Heart Disease
• Fasting Increases Health and Lifespan in Male Mice
• To Fast or Not to Fast: Does When You Eat Matter?
• Plan Your Plate: Shifting to a Healthy Eating Style
• What Is Metabolic Syndrome?
• Understanding Adult Overweight & Obesity
• Healthy Eating Plan
• What Are Sleep Deprivation and Deficiency?
• Brain Basics: Understanding Sleep
• Insulin Resistance & Prediabetes

References:  Effect of a plant-based, low-fat diet versus an animal-based, ketogenic diet on ad libitum energy intake . Hall KD, Guo J, Courville AB, Boring J, Brychta R, Chen KY, Darcey V, Forde CG, Gharib AM, Gallagher I, Howard R, Joseph PV, Milley L, Ouwerkerk R, Raisinger K, Rozga I, Schick A, Stagliano M, Torres S, Walter M, Walter P, Yang S, Chung ST.  Nat Med.  2021 Jan 21. doi: 10.1038/s41591-020-01209-1. PMID: 33479499. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of  Ad Libitum  Food Intake . Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, Chung ST, Costa E, Courville A, Darcey V, Fletcher LA, Forde CG, Gharib AM, Guo J, Howard R, Joseph PV, McGehee S, Ouwerkerk R, Raisinger K, Rozga I, Stagliano M, Walter M, Walter PJ, Yang S, Zhou M.  Cell Metabolism.  2019 May 10. pii: S1550-4131(19)30248-7. doi: 10.1016/j.cmet.2019.05.008. PMID: 31105044. Ad libitum Weekend Recovery Sleep Fails to Prevent Metabolic Dysregulation during a Repeating Pattern of Insufficient Sleep and Weekend Recovery Sleep . Depner CM, Melanson EL, Eckel RH, Snell-Bergeon JK, Perreault L, Bergman BC, Higgins JA, Guerin MK, Stothard ER, Morton SJ, Wright KP Jr.  Curr Biol . 2019 Feb 11. pii: S0960-9822(19)30098-3. doi: 10.1016/j.cub.2019.01.069. PMID:30827911. Chronic mirabegron treatment increases human brown fat, HDL cholesterol, and insulin sensitivity . O'Mara AE, Johnson JW, Linderman JD, Brychta RJ, McGehee S, Fletcher LA, Fink YA, Kapuria D, Cassimatis TM, Kelsey N, Cero C, Abdul-Sater Z, Piccinini F, Baskin AS, Leitner BP, Cai H, Millo CM, Dieckmann W, Walter M, Javitt NB, Rotman Y, Walter PJ, Ader M, Bergman RN, Herscovitch P, Chen KY, Cypess AM.  J Clin Invest . 2020 May 1;130(5):2209-2219. doi: 10.1172/JCI131126. PMID: 31961826. Effects of Intermittent Fasting on Health, Aging, and Disease . de Cabo R, Mattson MP. N Engl J Med . 2019 Dec 26;381(26):2541-2551. doi: 10.1056/NEJMra1905136. PMID: 31881139.

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Genetics of Obesity: What We Have Learned Over Decades of Research

Affiliation.

• 1 Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA.
• PMID: 33899337
• DOI: 10.1002/oby.23116

There is a genetic component to human obesity that accounts for 40% to 50% of the variability in body weight status but that is lower among normal weight individuals (about 30%) and substantially higher in the subpopulation of individuals with obesity and severe obesity (about 60%-80%). The appreciation that heritability varies across classes of BMI represents an important advance. After controlling for BMI, ectopic fat and fat distribution traits are characterized by heritability levels ranging from 30% to 55%. Defects in at least 15 genes are the cause of monogenic obesity cases, resulting mostly from deficiencies in the leptin-melanocortin signaling pathway. Approximately two-thirds of the BMI heritability can be imputed to common DNA variants, whereas low-frequency and rare variants explain the remaining fraction. Diminishing allele effect size is observed as the number of obesity-associated variants expands, with most BMI-increasing or -decreasing alleles contributing only a few grams or less to body weight. Obesity-promoting alleles exert minimal effects in normal weight individuals but have larger effects in individuals with a proneness to obesity, suggesting a higher penetrance; however, it is not known whether these larger effect sizes precede obesity or are caused by an obese state. The obesity genetic risk is conditioned by thousands of DNA variants that make genetically based obesity prevention and treatment a major challenge.

© 2021 The Obesity Society.

Publication types

• Child, Preschool
• Genetic Predisposition to Disease / genetics*
• Obesity / genetics*
• Young Adult

• Open access
• Published: 24 January 2011

Weight Science: Evaluating the Evidence for a Paradigm Shift

• Linda Bacon 1 &
• Lucy Aphramor 2 , 3  

Nutrition Journal volume  10 , Article number:  9 ( 2011 ) Cite this article

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Current guidelines recommend that "overweight" and "obese" individuals lose weight through engaging in lifestyle modification involving diet, exercise and other behavior change. This approach reliably induces short term weight loss, but the majority of individuals are unable to maintain weight loss over the long term and do not achieve the putative benefits of improved morbidity and mortality. Concern has arisen that this weight focus is not only ineffective at producing thinner, healthier bodies, but may also have unintended consequences, contributing to food and body preoccupation, repeated cycles of weight loss and regain, distraction from other personal health goals and wider health determinants, reduced self-esteem, eating disorders, other health decrement, and weight stigmatization and discrimination. This concern has drawn increased attention to the ethical implications of recommending treatment that may be ineffective or damaging. A growing trans-disciplinary movement called Health at Every Size (HAES) challenges the value of promoting weight loss and dieting behavior and argues for a shift in focus to weight-neutral outcomes. Randomized controlled clinical trials indicate that a HAES approach is associated with statistically and clinically relevant improvements in physiological measures (e.g., blood pressure, blood lipids), health behaviors (e.g., eating and activity habits, dietary quality), and psychosocial outcomes (such as self-esteem and body image), and that HAES achieves these health outcomes more successfully than weight loss treatment and without the contraindications associated with a weight focus. This paper evaluates the evidence and rationale that justifies shifting the health care paradigm from a conventional weight focus to HAES.

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Introduction

Concern regarding "overweight" and "obesity" is reflected in a diverse range of policy measures aimed at helping individuals reduce their body mass index (BMI) 1 . Despite attention from the public health establishment, a private weight loss industry estimated at $58.6 billion annually in the United States [ 1 ], unprecedented levels of body dissatisfaction [ 2 ] and repeated attempts to lose weight [ 3 , 4 ], the majority of individuals are unable to maintain weight loss over the long term and do not achieve the putative benefits of improved morbidity and mortality [ 5 ]. Concern has arisen that this weight focused paradigm is not only ineffective at producing thinner, healthier bodies, but also damaging, contributing to food and body preoccupation, repeated cycles of weight loss and regain, distraction from other personal health goals and wider health determinants, reduced self-esteem, eating disorders, other health decrement, and weight stigmatization and discrimination [ 6 – 8 ]. As evidence-based competencies are more firmly embedded in health practitioner standards, attention has been given to the ethical implications of recommending treatment that may be ineffective or damaging [ 5 , 9 ].

A growing trans-disciplinary movement called Health at Every Size SM (HAES) 2 shifts the focus from weight management to health promotion. The primary intent of HAES is to support improved health behaviors for people of all sizes without using weight as a mediator; weight loss may or may not be a side effect.

HAES is emerging as standard practice in the eating disorders field: The Academy for Eating Disorders, Binge Eating Disorder Association, Eating Disorder Coalition, International Association for Eating Disorder Professionals, and National Eating Disorder Association explicitly support this approach [ 10 ]. Civil rights groups including the National Association to Advance Fat Acceptance and the Council on Size and Weight Discrimination also encourage HAES. An international professional organization, the Association for Size Diversity and Health, has developed, composed of individual members across a wide span of professions who are committed to HAES principles.

Health at Every Size: A Review of Randomized Controlled Trials

Several clinical trials comparing HAES to conventional obesity treatment have been conducted. Some investigations were conducted before the name "Health at Every Size" came into common usage; these earlier studies typically used the terms "non-diet" or "intuitive eating" and included an explicit focus on size acceptance (as opposed to weight loss or weight maintenance). A Pub Med search for "Health at Every Size" or "intuitive eating" or "non-diet" or "nondiet" revealed 57 publications. Randomized controlled trials (RCTs) were vetted from these publications, and additional RCTs were vetted from their references. Only studies with an explicit focus on size acceptance were included.

Evidence from these six RCTs indicates that a HAES approach is associated with statistically and clinically relevant improvements in physiological measures (e.g. blood pressure, blood lipids), health behaviors (e.g. physical activity, eating disorder pathology) and psychosocial outcomes (e.g, mood, self-esteem, body image) [ 11 – 20 ]. (See Table 1 .) All studies indicate significant improvements in psychological and behavioral outcomes; improvements in self-esteem and eating behaviors were particularly noteworthy [ 11 – 14 , 16 , 17 , 19 , 20 ]. Four studies additionally measured metabolic risk factors and three of these studies indicated significant improvement in at least some of these parameters, including blood pressure and blood lipids [ 11 , 12 , 16 , 17 , 19 , 20 ]. No studies found adverse changes in any variables.

A seventh RCT reported at a conference also found significantly positive results [ 18 ], as did a non-randomized controlled study [ 21 ] and five studies conducted without a control [ 22 – 26 ].

All of the controlled studies showed retention rates substantially higher than, or, in one instance, as high, as the control group, and all of the uncontrolled studies also showed high retention rates. Given the well-documented recidivism typical of weight loss programs [ 5 , 27 , 28 ] and the potential harm that may arise[ 29 , 30 ], this aspect is particularly noteworthy.

Assumptions underlying the conventional (weight-focused) paradigm

Dieting and other weight loss behaviors are popular in the general population and widely encouraged in public health policy and health care practice as a solution for the "problem" of obesity. There is increasing concern about the endemic misrepresentation of evidence in these weight management policies [ 5 , 8 ]. Researchers have demonstrated ways in which bias and convention interfere with robust scientific reasoning such that obesity research seems to "enjoy special immunity from accepted standards in clinical practice and publishing ethics" [ 5 , 8 , 31 ]. This section discusses the assumptions that underlie the current weight-focused paradigm, presenting evidence that contests their scientific merit and challenges the value of promoting weight management as a public health measure.

Assumption: Adiposity poses significant mortality risk

Evidence: Except at statistical extremes, body mass index (BMI) - or amount of body fat - only weakly predicts longevity [ 32 ]. Most epidemiological studies find that people who are overweight or moderately obese live at least as long as normal weight people, and often longer [ 32 – 35 ]. Analysis of the National Health and Nutrition Examination Surveys I, II, and III, which followed the largest nationally representative cohort of United States adults, determined that greatest longevity was in the overweight category [ 32 ]. As per the report, published in the Journal of the American Medical Association and reviewed and approved by the Centers for Disease Control and Prevention and the National Cancer Institute, "[this] finding is consistent with other results reported in the literature." Indeed, the most comprehensive review of the research pooled data for over 350,000 subjects from 26 studies and found overweight to be associated with greater longevity than normal weight [ 36 ]. More recently, Janssen analyzed data in the elderly (among whom more than 70 percent of all deaths occur) - also from 26 published studies - and similarly found no evidence of excess mortality associated with overweight [ 37 ]. The Americans' Changing Lives study came to a similar conclusion, indicating that "when socioeconomic and other risk factors are controlled for, obesity is not a significant risk factor for mortality; and... for those 55 or older, both overweight and obesity confer a significant decreased risk of mortality." [ 38 ] The most recent analysis, published in the New England Journal of Medicine, concluded that overweight was associated with increased risk, but only arrived at this conclusion after restricting the analysis by excluding 78 percent of the deaths [ 39 ]. They also used a reference category much narrower than the entire "normal weight" category used by most other studies, which also contributed to making the relative risk for overweight higher.

There is a robust pattern in the epidemiological literature that has been named the "obesity paradox" [ 40 , 41 ]: obesity is associated with longer survival in many diseases. For example, obese persons with type 2 diabetes [ 42 ], hypertension [ 43 , 44 ], cardiovascular disease[ 41 , 45 ], and chronic kidney disease [ 46 ] all have greater longevity than thinner people with these conditions [ 47 – 49 ]. Also, obese people who have had heart attacks, coronary bypass[ 50 ], angioplasty[ 51 ] or hemodialysis [ 52 ] live longer than thinner people with these histories [ 49 ]. In addition, obese senior citizens live longer than thinner senior citizens [ 53 ].

The idea that "this is the first generation of children that may have a shorter life expectancy than their parents" is commonly expressed in scientific journals [ 54 ] and popular press articles [ 55 ], even appearing in Congressional testimony by former Surgeon General Richard Carmona [ 56 ] and a 2010 report from the White House Task Force on Childhood Obesity[ 57 ]. When citation is provided, it refers to an opinion paper published in the New England Journal of Medicine [ 54 ], which offered no statistical evidence to support the claim. Life expectancy increased dramatically during the same time period in which weight rose (from 70.8 years in 1970 to 77.8 years in 2005) [ 58 ]. Both the World Health Organization and the Social Security Administration project life expectancy will continue to rise in coming decades [ 59 , 60 ].

Assumption: Adiposity poses significant morbidity risk

Evidence: While it is well established that obesity is associated with increased risk for many diseases, causation is less well-established. Epidemiological studies rarely acknowledge factors like fitness, activity, nutrient intake, weight cycling or socioeconomic status when considering connections between weight and disease. Yet all play a role in determining health risk. When studies do control for these factors, increased risk of disease disappears or is significantly reduced [ 61 ]. (This is less true at statistical extremes.) It is likely that these other factors increase disease risk at the same time they increase the risk of weight gain.

Consider weight cycling as an example. Attempts to lose weight typically result in weight cycling, and such attempts are more common among obese individuals [ 62 ]. Weight cycling results in increased inflammation, which in turn is known to increase risk for many obesity-associated diseases [ 63 ]. Other potential mechanisms by which weight cycling contributes to morbidity include hypertension, insulin resistance and dyslipidemia [ 64 ]. Research also indicates that weight fluctuation is associated with poorer cardiovascular outcomes and increased mortality risk [ 64 – 68 ]. Weight cycling can account for all of the excess mortality associated with obesity in both the Framingham Heart Study [ 69 ] and the National Health and Nutrition Examination Survey (NHANES) [ 70 ]. It may be, therefore, that the association between weight and health risk can be better attributed to weight cycling than adiposity itself [ 63 ].

As another example, consider type 2 diabetes, the disease most highly associated with weight and fat distribution. There is increasing evidence that poverty and marginalization are more strongly associated with type 2 diabetes than conventionally-accepted risk factors such as weight, diet or activity habits [ 30 , 71 – 73 ]. A large Canadian report produced in 2010, for example, found that low income was strongly associated with diabetes even when BMI (and physical activity) was accounted for [ 73 ]. Also, much evidence suggests that insulin resistance is a product of an underlying metabolic disturbance that predisposes the individual to increased fat storage due to compensatory insulin secretion [ 61 , 74 – 78 ]. In other words, obesity may be an early symptom of diabetes as opposed to its primary underlying cause.

Hypertension provides another example of a condition highly associated with weight; research suggests that it is two to three times more common among obese people than lean people [ 79 ]. To what extent hypertension is caused by adiposity, however, is unclear. That BMI correlates more strongly with blood pressure than percent body fat [ 80 ] indicates that the association between BMI and blood pressure results from higher lean mass as opposed to fat mass. Also, the association may have more to do with the weight cycling that results from trying to control weight than the actual weight itself [ 48 , 81 , 82 ]. One study conducted with obese individuals determined that weight cycling was strongly positively associated with incident hypertension [ 82 ]. Another study showed that obese women who had dieted had high blood pressure, while those who had never been on a diet had normal blood pressure [ 67 ]. Rat studies also show that obese rats that have weight cycled have very high blood pressures compared to obese rats that have not weight cycled [ 83 , 84 ]. This finding could also explain the weak association between obesity and hypertension in cultures where dieting is uncommon[ 48 , 85 ]. Additionally, it is well documented that obese people with hypertension live significantly longer than thinner people with hypertension [ 43 , 86 – 88 ] and have a lower risk of heart attack, stroke, or early death [ 45 ]. Rather than identifying health risk, as it does in thinner people, hypertension in heavier people may simply be a requirement for pumping blood through their larger bodies [ 89 ].

It is also notable that the prevalence of hypertension dropped by half between 1960 and 2000, a time when average weight sharply increased, declining much more steeply among those deemed overweight and obese than among thinner individuals [ 90 ]. Incidence of cardiovascular disease also plummeted during this time period and many common diseases now emerge at older ages and are less severe [ 90 ]. (The notable exception is diabetes, which showed a small, non-significant increase during this time period [ 90 ].) While the decreased morbidity can at least in part be attributed to improvements in medical care, the point remains that we are simply not seeing the catastrophic disease consequences predicted to result from the "obesity epidemic."

Assumption: Weight loss will prolong life

Evidence: Most prospective observational studies suggest that weight loss increases the risk of premature death among obese individuals, even when the weight loss is intentional and the studies are well controlled with regard to known confounding factors, including hazardous behavior and underlying diseases [ 91 – 96 ]. Recent review of NHANES, for example, a nationally representative sample of ethnically diverse people over the age of fifty, shows that mortality increased among those who lost weight [ 97 ].

While many short-term weight loss intervention studies do indicate improvements in health measures, because the weight loss is always accompanied by a change in behavior, it is not known whether or to what extent the improvements can be attributed to the weight loss itself. Liposuction studies that control for behavior change provide additional information about the effects of weight (fat) loss itself. One study which explicitly monitored that there were no changes in diet and activity for 10-12 weeks post abdominal liposuction is a case in point. Participants lost an average of 10.5 kgs but saw no improvements in obesity-associated metabolic abnormalities, including blood pressure, triglycerides, cholesterol, or insulin sensitivity [ 98 ]. (Note that liposuction removes subcutaneous fat, not the visceral fat that is more highly associated with disease, and these results should be interpreted carefully.)

In most studies on type 2 diabetes, the improvement in glycemic control is seen within days, before significant weight or fat is lost. Evidence also challenges the assumption that weight loss is associated with improvement in long-term glycemic control, as reflected in HbA1c values [ 99 , 100 ]. One review of controlled weight-loss studies for people with type 2 diabetes showed that initial improvements were followed by a deterioration back to starting values six to eighteen months after treatment, even when the weight loss was maintained [ 101 ].

Furthermore, health benefits associated with weight loss rarely show a dose response (in other words, people who lose small amounts of weight generally get as much health benefit from the intervention as those who lose larger amounts).

These data suggest that the behavior change as opposed to the weight loss itself may play a greater role in health improvement.

Assumption: Anyone who is determined can lose weight and keep it off through appropriate diet and exercise

Evidence: Long-term follow-up studies document that the majority of individuals regain virtually all of the weight that was lost during treatment, regardless of whether they maintain their diet or exercise program [ 5 , 27 ]. Consider the Women's Health Initiative, the largest and longest randomized, controlled dietary intervention clinical trial, designed to test the current recommendations. More than 20,000 women maintained a low-fat diet, reportedly reducing their calorie intake by an average of 360 calories per day [ 102 ] and significantly increasing their activity [ 103 ]. After almost eight years on this diet, there was almost no change in weight from starting point (a loss of 0.1 kg), and average waist circumference, which is a measure of abdominal fat, had increased (0.3 cm) [ 102 ].

A panel of experts convened by the National Institutes of Health determined that "one third to two thirds of the weight is regained within one year [after weight loss], and almost all is regained within five years." [ 28 ] More recent review finds one-third to two-thirds of dieters regain more weight than was lost on their diets; "In sum," the authors report, "there is little support for the notion that diets lead to lasting weight loss or health benefits [ 5 ]." Other reviews demonstrate the unreliability of conventional claims of sustained weight loss [ 104 , 105 ]. There is a paucity of long term data regarding surgical studies, but emerging data indicates gradual post-surgery weight regain as well [ 106 , 107 ]. Weight loss peaks about one year postoperative, after which gradual weight regain is the norm.

Assumption: The pursuit of weight loss is a practical and positive goal

Evidence: As discussed earlier, weight cycling is the most common result of engaging in conventional dieting practices and is known to increase morbidity and mortality risk. Research identifies many other contraindications to the pursuit of weight loss. For example, dieting is known to reduce bone mass, increasing risk for osteoporosis [ 108 – 111 ]; this is true even in an obese population, though obesity is typically associated with reduced risk for osteoporosis[ 108 ]. Research also suggests that dieting is associated with increased chronic psychological stress and cortisol production, two factors known to increase disease risk [ 112 ]. Also, there is emerging evidence that persistent organic pollutants (POPs), which bioaccumulate in adipose tissue and are released during its breakdown, can increase risk of various chronic diseases including type 2 diabetes [ 113 , 114 ], cardiovascular disease [ 115 ] and rheumatoid arthritis [ 116 ]; two studies document that people who have lost weight have higher concentration of POPs in their blood [ 117 , 118 ]. One review of the diabetes literature indicates "that obese persons that (sic) do not have elevated POPs are not at elevated risk of diabetes, suggesting that the POPs rather than the obesity per se is responsible for the association" [ 114 ].

Positing the value of weight loss also supports widespread anxiety about weight [ 119 , 120 ]. Evidence from the eating disorder literature indicates an emphasis on weight control can promote eating disordered behaviors [ 7 ]. Prospective studies show that body dissatisfaction is associated with binge eating and other eating disordered behaviors, lower levels of physical activity and increased weight gain over time [ 121 , 122 ]. Many studies also show that dieting is a strong predictor of future weight gain [ 66 , 123 – 128 ].

Another unintended consequence of the weight loss imperative is an increase in stigmatization and discrimination against fat individuals. Discrimination based on weight now equals or exceeds that based on race or gender [ 129 ]. Extensive research indicates that stigmatizing fat demotivates, rather than encourages, health behavior change [ 130 ]. Adults who face weight stigmatization and discrimination report consuming increased quantities of food [ 131 – 134 ], avoiding exercise [ 133 , 135 – 137 ], and postponing or avoiding medical care (for fear of experiencing stigmatization) [ 138 ]. Stigmatization and bias on the part of health care practitioners is well-documented, resulting in lower quality care [ 139 , 140 ].

Assumption: The only way for overweight and obese people to improve health is to lose weight

Evidence: That weight loss will improve health over the long-term for obese people is, in fact, an untested hypothesis. One reason the hypothesis is untested is because no methods have proven to reduce weight long-term for a significant number of people. Also, while normal weight people have lower disease incidence than obese individuals, it is unknown if weight loss in individuals already obese reduces disease risk to the same level as that observed in those who were never obese [ 91 , 93 ].

As indicated by research conducted by one of the authors and many other investigators, most health indicators can be improved through changing health behaviors, regardless of whether weight is lost [ 11 ]. For example, lifestyle changes can reduce blood pressure, largely or completely independent of changes in body weight [ 11 , 141 – 143 ]. The same can be said for blood lipids [ 11 , 143 – 145 ]. Improvements in insulin sensitivity and blood lipids as a result of aerobic exercise training have been documented even in individuals who gained body fat during the intervention [ 145 , 146 ].

Assumption: Obesity-related costs place a large burden on the economy, and this can be corrected by focused attention to obesity treatment and prevention

Evidence: The health cost attributed to obesity in the United States is currently estimated to be $147 billion annually [ 147 ] and this cost estimate has been used to justify efforts at obesity treatment and prevention. Although this estimate has been granted credence by health experts, the word "estimate" is important to note: as the authors state, most of the cost changes are not "statistically different from zero." Also, the estimate fails to account for many potentially confounding variables, among them physical activity, nutrient intake, history of weight cycling, degree of discrimination, access to (quality) medical care, etc. All are independently correlated with both weight and health and could play a role in explaining the costs associated with having a BMI over 30. Nor does it account for costs associated with unintended consequences of positing the value of a weight focus, which may include eating disorders, diet attempts, weight cycling, reduced self-esteem, depression, and discrimination.

Because BMI is considered a risk factor for many diseases, obese persons are automatically relegated to greater testing and treatment, which means that positing BMI as a risk factor results in increased costs, regardless of whether BMI itself is problematic. Yet using BMI as a proxy for health may be more costly than addressing health directly. Consider, for example, the findings of a study which examined the "healthy obese" and the "unhealthy normal weight" populations [ 148 ]. The study identified six different risk factors for cardiometabolic health and included subjects in the "unhealthy" group if they had two or more risk factors, making it a more stringent threshold of health than that used in categorizing metabolic syndrome or diabetes. The study found a substantial proportion of the overweight and obese population, at every age, who were healthy and a substantial proportion of the "normal weight" group who were unhealthy. Psychologist Deb Burgard examined the costs of overlooking the normal weight people who need treatment and over-treating the obese people who do not (personal communication, March 2010). She found that BMI profiling overlooks 16.3 million "normal weight" individuals who are not healthy and identifies 55.4 million overweight and obese people who are not ill as being in need of treatment (see Table 2 ). When the total population is considered, this means that 31 percent of the population is mis-identified when BMI is used as a proxy for health.

The weight bias inherent in BMI profiling may actually result in higher costs and sicker people. As an example, consider a 2009 study published in the American Journal of Public Health (96). The authors compared people of similar age, gender, education level, and rates of diabetes and hypertension, and examined how often they reported feeling sick over a 30-day period. Results indicated that body image had a much bigger impact on health than body size. In other words, two equally fat women would have very different health outcomes, depending on how they felt about their bodies. Likewise, two women with similar body insecurities would have similar health outcomes, even if one were fat and the other thin. These results suggest that the stigma associated with being fat is a major contributor to obesity-associated disease. BMI and health are only weakly related in cultures where obesity is not stigmatized, such as in the South Pacific [ 48 , 149 ].

Health at every size: shifting the paradigm from weight to health

This section explains the rationale supporting some of the significant ways in which the HAES paradigm differs from the conventional weight-focused paradigm. The following topics are addressed:

HAES encourages body acceptance as opposed to weight loss or weight maintenance;

HAES supports reliance on internal regulatory processes, such as hunger and satiety, as opposed to encouraging cognitively-imposed dietary restriction; and

HAES supports active embodiment as opposed to encouraging structured exercise.

Encouraging Body Acceptance

Conventional thought suggests that body discontent helps motivate beneficial lifestyle change [ 150 , 151 ]. However, as discussed previously in the section on the pursuit of weight loss, evidence suggests the opposite: promoting body discontent instead induces harm [ 122 , 133 , 134 , 152 ], resulting in less favorable lifestyle choices. A common aphorism expressed in the HAES community is that "if shame were effective motivation, there wouldn't be many fat people." Mounting evidence suggests this belief is unfounded and detrimental[ 8 , 152 ]. Promoting one body size as more favorable than another also has ethical consequences [ 120 ], contributing to shaming and discrimination.

Compassion-focused behavior change theory emerging from the eating disorders field suggests that self-acceptance is a cornerstone of self-care, meaning that people with strong self-esteem are more likely to adopt positive health behaviors [ 153 , 154 ]. The theory is borne out in practice: HAES research shows that by learning to value their bodies as they are right now, even when this differs from a desired weight or shape or generates ambivalent feelings, people strengthen their ability to take care of themselves and sustain improvements in health behaviors [ 8 , 11 ].

Critics of HAES express concern that encouraging body acceptance will lead individuals to eat with abandon and disregard dietary considerations, resulting in weight gain. This has been disproven by the evidence; no randomized controlled HAES study has resulted in weight gain, and all studies that report on dietary quality or eating behavior indicate improvement or at least maintenance [ 11 , 14 – 23 ]. This is in direct contrast to dieting behavior, which is associated with weight gain over time [ 66 , 123 – 128 ].

Supporting Intuitive Eating

Conventional recommendations view conscious efforts to monitor and restrict food choices as a necessary aspect of eating for health or weight control [ 155 ]. The underlying belief is that cognitive monitoring is essential for keeping appetite under control and that without these injunctions people would make nutritionally inadvisable choices, including eating to excess. The evidence, however, disputes the value of encouraging external regulation and restraint as a means for weight control: several large scale studies demonstrate that eating restraint is actually associated with weight gain over time [ 66 , 123 – 126 ].

In contrast, HAES teaches people to rely on internal regulation, a process dubbed intuitive eating [ 156 ], which encourages them to increase awareness of their body's response to food and learn how to make food choices that reflect this "body knowledge." Food is valued for nutritional, psychological, sensual, cultural and other reasons. HAES teaches people to make connections between what they eat and how they feel in the short- and medium-term, paying attention to food and mood, concentration, energy levels, fullness, ease of bowel movements, comfort eating, appetite, satiety, hunger and pleasure as guiding principles.

The journey towards adopting intuitive eating is typically a process one engages in over time. Particularly for people with a long history of dieting, other self-imposed dietary restriction, or body image concerns, it can feel very precarious to let go of old habits and attitudes and risk trying new ways of relating to food and self. Coming to eat intuitively happens gradually as old beliefs about food, nutrition and eating are challenged, unlearned and replaced with new ones.

A large popular literature has accumulated that supports individuals in developing intuitive eating skills [ 8 , 156 – 160 ]. (Intuitive eating is also known in the literature as "attuned eating" or "mindful eating." Note that intuitive eating is sometimes promoted as a means to weight loss and in that context is inconsistent with a HAES approach.)

There is considerable evidence that intuitive eating skills can be learned [ 11 , 18 , 161 ], and that intuitive eating is associated with improved nutrient intake [ 162 ], reduced eating disorder symptomatology [ 17 , 18 , 163 – 165 ] - and not with weight gain [ 11 , 13 , 16 – 18 ]. Several studies have found intuitive eating to be associated with lower body mass [ 162 , 163 , 166 , 167 ].

Supporting Active Embodiment

HAES encourages people to build activity into their day-to-day routines and focuses on helping people find enjoyable ways of being active. The goal is to promote well-being and self-care rather than advising individuals to meet set guidelines for frequency and intensity of exercise. Active living is promoted for a range of physical, psychological and other synergistic benefits which are independent of weight loss. Myths around weight control and exercise are explicitly challenged. Physical activity is also used in HAES as a way of healing a sense of body distrust and alienation from physicality that may be experienced when people are taught to over-ride embodied internal signals in pursuit of externally derived goals, such as commonly occurs in dieting. In addition, some HAES programs have used physical activity sessions, along with other activities such as art and relaxation, to further a community development agenda, creating volunteer, training and employment opportunities and addressing issues of isolation, poor self-esteem and depression among course participants.

Clinical Ethics

There are serious ethical concerns regarding the continued use of a weight-centered paradigm in current practice in relation to beneficence and nonmaleficence. Beneficence concerns the requirement to effect treatment benefit. There is a paucity of literature to substantiate that the pursuit of weight control is beneficial, and a similar lack of evidence to support that weight loss is maintained over the long term or that programs aimed at prevention of weight gain are successful. Nonmaleficence refers to the requirement to do no harm. Much research suggests damage results from a weight-centered focus, such as weight cycling and stigmatization. Consideration of several dimensions of ethical practice - veracity, fidelity, justice and a compassionate response - suggests that the HAES paradigm shift may be required for professional ethical accountability [ 168 ].

Public Health Ethics

The new public health ethics advocates scrutiny of the values and structure of medical care, recognizing that the remedy to poor health and health inequalities does not lie solely in individual choices.

This ethicality has been adopted by HAES in several ways. HAES academics have highlighted the inherent limitations of an individualistic approach to conceptualizing health. Individual self-care is taken as a starting point for HAES programs, but, unlike more conventional interventions, the HAES ethos recognizes the structural basis of health inequities and understands empowerment as a process that effects collective change in advancing social justice [ 169 ]. HAES advocates have also stressed the need for action to challenge the thinness privilege and to better enable fat people's voices to be heard in and beyond health care [ 8 , 170 ].

The hallmark theme of the new public health agenda is that it emphasizes the complexity of health determinants and the need to address systemic health inequities in order to improve population-wide health outcomes and reduce health disparities, making use of the evidence on the strong relationship between a person's social positioning and their health. For example, research since the 1950s has documented huge differences in cardiac health between and across socioeconomic gradients which has come to be recognized as arising from disparities in social standing and is articulated as the status syndrome [ 171 ]. Since weight tracks closely with socioeconomic class, obesity is a particularly potent marker of social disparity [ 172 ].

There is extensive research documenting the role of chronic stress in conditions conventionally described as obesity-associated, such as hypertension, diabetes and coronary heart disease [ 173 ]. These conditions are mediated through increased metabolic risk seen as raised cholesterol, raised blood pressure, raised triglycerides and insulin resistance. The increase in metabolic risk can in part be explained by a change in eating, exercise and drinking patterns attendant on coping with stress. However, changes in health behaviors do not fully account for the metabolic disturbances. Instead, stress itself alters metabolism independent of a person's lifestyle habits [ 174 ]. Thus, it has been suggested that psychological distress is the antecedent of high metabolic risk [ 175 ], which indicates the need to ensure health promotion policies utilize strategies known to reduce, rather than increase, psychological stress. In addition to the impact of chronic stress on health, an increasing body of international research, discussed earlier, recognizes particular pathways through which weight stigmatization and discrimination impact on health, health-seeking behaviors, and quality of health care [ 125 – 133 ].

Policies which promote weight loss as feasible and beneficial not only perpetuate misinformation and damaging stereotypes [ 176 ], but also contribute to a healthist, moralizing discourse which mitigates against socially-integrated approaches to health [ 155 , 168 , 177 , 178 ]. While access to size acceptance practitioners can ameliorate the harmful effects of discrimination in health care for individuals, systemic change is required to address the iatrogenic consequences of institutional size discrimination in and beyond health care, discrimination that impacts on people's opportunities and health.

Quite aside from the ethical arguments underscoring inclusive, non-discriminatory health care and civil rights, there are plausible metabolic pathways through which reducing weight stigma, by reducing inequitable social processes, can help alleviate the burden of poor health.

From the perspective of efficacy as well as ethics, body weight is a poor target for public health intervention. There is sufficient evidence to recommend a paradigm shift from conventional weight management to Health at Every Size. More research that considers the unintended consequences of a weight focus can help to clarify the associated costs and will better allow practitioners to challenge the current paradigm. Continued research that includes larger sample sizes and more diverse populations and examines how best to deliver a Health at Every Size intervention, customized to specific populations, is called for.

We propose the following guidelines, which are supported by the Association for Size Diversity and Health (ASDAH), to assist professionals in implementing HAES. Our proposed guidelines are modified, with permission, from guidelines developed by the Academy for Eating Disorders for working with children [ 7 ].

Interventions should meet ethical standards. They should focus on health, not weight, and should be referred to as "health promotion" and not marketed as "obesity prevention." Interventions should be careful to avoid weight-biased stigma, such as using language like "overweight" and "obesity."

Interventions should seek to change major determinants of health that reside in inequitable social, economic and environmental factors, including all forms of stigma and oppression.

Interventions should be constructed from a holistic perspective, where consideration is given to physical, emotional, social, occupational, intellectual, spiritual, and ecological aspects of health.

Interventions should promote self-esteem, body satisfaction, and respect for body size diversity.

Interventions should accurately convey the limited impact that lifestyle behaviors have on overall health outcomes.

Lifestyle-oriented elements of interventions that focus on physical activity and eating should be delivered from a compassion-centered approach that encourages self-care rather than as prescriptive injunctions to meet expert guidelines.

Interventions should focus only on modifiable behaviors where there is evidence that such modification will improve health. Weight is not a behavior and therefore not an appropriate target for behavior modification.

Lay experience should inform practice, and the political dimensions of health research and policy should be articulated.

These guidelines outline ways in which health practitioners can shift their practice towards a HAES approach and, in so doing, uphold the tenets of their profession in providing inclusive, effective, and ethical care consistent with the evidence base.

1 Critics challenge the value of using BMI terminology, suggesting that BMI is a poor determinant of health and the categories medicalize and pathologize having a certain body. We accept this argument; we have used "overweight" and "obese" throughout this paper when necessary to report research where these categories were used. We recognize, however, that "normal" does not reflect a normative or optimal value; that "overweight" falsely implies a weight over which one is unhealthy; and that the etymology of the word "obese" mistakenly implies that a large appetite is the cause.

2 Health at Every Size/HAES is a pending trademark of the Association for Size Diversity and Health.

Conflict of interests Disclosure

Linda Bacon and Lucy Aphramor are HAES practitioners. Both also speak and write on the topic of Health at Every Size and sometimes receive financial remuneration for this work.

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Acknowledgements

Deb Burgard conceptualized the obesity cost analysis. The authors thank Deb Burgard, Sigrún Daníelsdóttir, Paul Ernsberger, Janell Mensinger, Elise Paradis, Jon Robison, Camerin Ross, Abigail Saguy, and Evelyn Tribole for their contributions and critical review. Lucy Aphramor thanks the WM NMAHP Research Training Awards for financial support.

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Bacon, L., Aphramor, L. Weight Science: Evaluating the Evidence for a Paradigm Shift. Nutr J 10 , 9 (2011). https://doi.org/10.1186/1475-2891-10-9

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How common is overweight and obesity?

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Overweight and obesity refers to excess body weight. It is a risk factor for many chronic conditions and is associated with higher rates of death (AIHW 2019).

The interactive graphs in the following sections allow you to explore the prevalence of overweight and obesity in Australian children and adults, and the variations by population groups, and across remoteness and socioeconomic areas.

For more information on how to measure overweight and obesity, please see Measuring overweight and obesity and Causes of overweight and obesity .

Why is the most recent data from 2017–18?

Estimates of Body Mass Index (BMI) are based on nationally representative measured height and weight data from the Australian Bureau of Statistics (ABS) 2017–18 National Health Survey (NHS).

Due to the COVID-19 pandemic, physical measurements (including height, weight and waist circumference) were not taken at the time of the most recent NHS 2020–21. While self-reported height and weight were collected as part of the survey, self-reported data underestimates actual levels of overweight or obesity based on objective measurements (ABS 2018f).

As self-reported and measured rates of overweight and obesity should not be directly compared, the figures presented on this page reflect the latest nationally representative data based on measured height, weight and waist circumference.

For more information, please see Technical notes .

Children and adolescents

Based on the latest available data, of children and adolescents aged 2–17 (ABS 2018c):

• One in 4 (25%) are living with overweight or obesity. This is approximately 1.2 million children and adolescents.
• 17% are living with overweight but not obesity.
• 8.2% are living with obesity.

The rates of overweight and obesity are similar for boys and girls across age groups (Figure 1).

See Overweight and obesity among Australian children and adolescents for more information on this age group.

Figure 1: Proportion of children and adolescents aged 2–17 living with overweight and obesity, by age group and sex, 2017–18

This bar chart shows the prevalence of 3 measures of overweight and obesity for children and adolescents in 2017–18: overweight and obesity combined, obesity alone, and overweight but not obese. Data are shown for boys, girls and all children in the following age groups: 2–4, 5–9, 10–14 and 15–17. The chart shows similar rates of overweight and obesity across age groups for boys and girls.

Based on the latest available data, of adults aged 18 and over (ABS 2018e):

• Two in 3 (67%) are living with overweight or obesity. This is approximately 12.5 million adults.
• 36% are living with overweight but not obesity.
• 31% are living with obesity.
• 12% are living with severe obesity, which is defined in this report as having a BMI of 35 or more.

For all measures of overweight and obesity, men had higher rates than women did:

• 75% of men and 60% of women are living with overweight or obesity.
• 42% of men and 30% of women are living with overweight but not obesity.
• 33% of men and 30% of women are living with obesity.

Overweight and obesity is distributed differently among men and women, as shown in the BMI calculator .

The proportion of adults living with overweight or obesity generally increases with age. This is seen in both men and women (Figure 2):

• For men, the proportion increases from 52% at 18–24 to 83% at 45–54. It then plateaus until 65–74, and then decreases to 65% at age 85 years and over.
• For women, the proportion increases from 40% at 18–24 to 73% at 65–74. It then decreases to 61% at age 85 years and over.

Obesity is also more common in older age groups – 18% of men and 14% of women aged 18–24 year are living with obesity, compared with 42% of men and 39% of women aged 65–74 (Figure 2).

Figure 2: Proportion of adults living with overweight and obesity, by age group and sex, 2017–18

This bar chart shows the prevalence of 3 measures of overweight and obesity for adults aged 18 and over in 2017–18: overweight and obesity combined, obesity alone, and overweight but not obese. Data are shown for men and women in 8 age groups, from 18 to 85 and over. Across age groups for men, overweight and obesity increased with age from 52% at 18–24 to 83% at 45–54, plateaued until 65–74, and then decreased to 65% at age 85 years and over. Across age groups for women, the proportion who were overweight or obese increased with age from 40% at 18–24 to 73% at 65–74, and decreased to 61% at age 85 years and over.

Waist circumference

Based on the latest available data, 60% of men and 66% of women aged 18 and over have a waist circumference that indicated an increased or substantially increased risk of metabolic complications. The proportion of adults with a waist circumference that indicate a substantially increased risk of metabolic complications tends to increase with age, up until about age 65–74 for men and 75–84 for women (Figure 3).

Figure 3: Proportion of adults with a waist circumference indicating increased risk of metabolic complications, by age group and sex, 2017–18

This stacked bar chart shows the proportion of men and women who had a waist circumference indicating an increased or a substantially increased risk of metabolic complication, in 8 age groups, from 18 to 85 and over. It shows that the proportion of persons with a substantially increased risk tend to increase with age, up until about age 65–74 for men and 75–84 for women.

Aboriginal and Torres Strait Islander Australians

Among Aboriginal and Torres Strait Islander children and adolescents aged 2–17, 38% are living with overweight or obesity, according to the latest data from the 2018–19 ABS National Aboriginal and Torres Strait Islander Health Survey. This is an increase from the 31% estimated from the previous Australian Aboriginal and Torres Strait Islander Health Survey in 2012–13 (ABS 2015a, ABS 2019c). It is also higher than the 24% of non-Indigenous children and adolescents estimated from the National Health Survey in 2017–18 (ABS 2019b).

The proportion of Indigenous boys living with overweight or obesity increases with age from 21% of those aged 2–4 years to 33% of those aged 5–9 and 45% of those aged 10–14. For girls, there are no significant differences in the proportion living with overweight or obesity across age groups (ABS 2019a) (Figure 4).

See Overweight and obesity among Australian children and adolescents for more information.

Figure 4: Proportion of Indigenous children and adolescents aged 2–17 living with overweight and obesity, by age group and sex, 2018–19

This bar chart shows the prevalence of 3 measures of overweight and obesity for Indigenous children and adolescents in 2018–19: overweight and obesity combined, obesity alone, and overweight but not obese. Data are shown for boys, girls and all children in the following age groups: 2–4, 5–9, 10–14 and 15–17. The chart shows more variation across age groups for boys than for girls, with overweight and obesity increasing from 21% at age 2–4, to 45% at age 10–14 for boys, and ranging between 37% and 42% for girls.

Based on the latest available data, of Indigenous Australians aged 18 and over:

• 74% are living with overweight or obesity, increasing from 69% in 2012–13.
• 45% are living with obesity, increasing from 40% in 2012–13 (ABS 2014a, ABS 2019c).

After adjusting for differences in the age structure of Indigenous and non-Indigenous populations, Indigenous adults are 1.2 times as likely to be living with overweight or obesity as non-Indigenous adults (77% compared with 66%), and 1.5 times as likely to be living with obesity (47% compared with 31%) (ABS 2019c).

When comparing between Indigenous men and women, there are no statistically significant differences between the proportion living with overweight or obesity, and the proportion living with overweight alone. However, slightly more Indigenous women are living with obesity (48%) than Indigenous men (43%) (ABS 2019c).

The proportion of overweight or obesity generally increases with age. This was seen in both Indigenous men and women (ABS 2019c) (Figure 5):

• For Indigenous men, the proportion increases from 56% at 18–24 to 72% at 25–34 and 81% at 35–44. It peaks at 84% at 55 years and over.
• For Indigenous women, the proportion increases from 60% at 18–24 to 73% at 25–34. It peaks at 83% at 45–54.

For obesity alone, the proportion also increases with age. The proportion of Indigenous adults aged 18–24 living with obesity is 32% compared with the 51% of Indigenous adults aged 45 and over (Figure 5) (ABS 2019c).

Figure 5: Proportion of Indigenous adults living with overweight and obesity, by age group and sex, 2018–19

This bar chart shows the prevalence of 3 measures of overweight and obesity for Indigenous adults aged 18 and over in 2018–19: overweight and obesity combined, obesity alone, and overweight but not obese. Data are shown for men and women in 5 age groups, from 18 to 55 and over. For both men and women, the prevalence of overweight and obesity was lower for younger Indigenous adults aged 18–24 (59% overall) compared with those aged 55 and over (82% overall).

An important factor associated with poorer health outcomes, including higher rates of overweight and obesity is the area in which an individual lives, such as remoteness area and the relative socioeconomic advantage and disadvantage of the area (AIHW 2018a).

Remoteness areas

Based on the latest available data, a higher proportion of Australian children and adolescents aged 2–17 in Inner regional areas is living with overweight or obesity compared with those in Major cities (29% and 24%, respectively) (Figure 6). For children and adolescents in Outer regional and remote areas, the proportion is 27%. This pattern is similar for boys and girls (ABS 2019e).

Figure 6: Proportion of children and adolescents aged 2–17 living with overweight and obesity, by remoteness area, 2017–18

This bar chart shows that, in 2017–18, for both boys and girls, the proportion of children and adolescents aged 2–17 who were overweight or obese was generally highest in Inner regional areas (29% overall), followed by Outer regional and remote areas, and lowest in Major cities (23% overall). However, not all of these differences were statistically significant.

The proportion of Australians aged 18 and over who are living with overweight or obesity varies by remoteness areas (Figure 7). After adjusting for age (ABS 2019e):

• a greater proportion in the Outer regional and remote (70%) and Inner regional  areas (71%) are living with overweight and obesity, compared with those in Major cities  (65%).
• a greater proportion of men in Inner regional  areas (78%) is living with overweight and obesity, compared with those in Major cities  (73%).
• a greater proportion of women in Outer regional and remote (65%) and Inner regional  areas (64%) are living with overweight and obesity, compared with those in Major cities  (57%).

Figure 7: Age-standardised proportion of adults living with overweight and obesity, by remoteness area, 2017–18

This bar chart shows that after adjusting for age, in 2017–18, women living in Outer regional and remote areas, or Inner regional areas, were more likely to be overweight or obese than those living in Major cities. Men living in Inner regional areas were more likely to be overweight or obese than men living in Major cities.

Socioeconomic areas

Based on the latest available data, children and adolescents aged 2–17 in the lowest socioeconomic areas are more likely to be living with overweight or obesity (28%) than those in the highest socioeconomic areas (21%) (Figure 8). The proportion of those living with obesity in this age group is also higher for those in the lowest socioeconomic areas (11%) compared with the highest socioeconomic areas (4.4%) (ABS 2019e).

Among girls, the proportion living with overweight or obesity is higher for those in the lowest socioeconomic areas (31%) compared with those living in the highest socioeconomic areas (21%). Among boys, the proportion living with overweight or obesity does not differ significantly between the lowest and highest socioeconomic areas (Figure 8).

Figure 8: Proportion of children and adolescents aged 2–7 living with overweight and obesity, by socioeconomic area, 2017–18

This bar chart shows the prevalence of overweight and obesity across 5 socioeconomic areas (with area 1 representing the most disadvantaged areas, and area 5 representing the lease disadvantaged areas). It shows that, in 2017–18, for boys and girls, the most disadvantaged areas had a higher prevalence of overweight and obesity than the least disadvantaged areas (although the differences weren’t statistically significant).

The proportion of Australians aged 18 and over who are living with overweight or obesity varies by socioeconomic areas. After adjusting for age (Figure 9):

• a greater proportion in the lowest socioeconomic areas (72%) are living with overweight or obesity, compared with those in the highest socioeconomic areas (62%).
• a greater proportion of men in the lowest socioeconomic areas (77%) are living with overweight or obesity, compared with those in the highest socioeconomic areas (73%).
• a greater proportion of women in the lowest socioeconomic areas (66%) are living with overweight or obesity, compared with those in the highest socioeconomic areas (50%).

For both men and women, the prevalence of obesity is the underlying reason for the difference by socioeconomic areas. Among men, the age-adjusted proportion of those living with obesity is 37% in the lowest socioeconomic areas, compared with 26% in the highest areas. Among women, 38% are living with obesity in the lowest socioeconomic areas, compared with 22% in the highest areas, after adjusting for age (Figure 9).

Figure 9: Age-standardised proportion of adults living with overweight and obesity, by socioeconomic area, 2017–18

This bar chart shows the prevalence of overweight and obesity, after adjusting for age, across 5 socioeconomic areas (with area 1 representing the most disadvantaged areas, and area 5 representing the lease disadvantaged areas). It shows that, in 2017–18, for men and women, the most disadvantaged areas had a higher prevalence of overweight and obesity than the least disadvantaged areas, with a larger difference observed for women.

Primary Health Networks

In 2017–18, after adjusting for age, the Western New South Wales PHN area had the highest prevalence of overweight an obesity, with 4 in 5 adults living with overweight or obesity (83%). The Gold Coast PHN area had the lowest prevalence, with about 3 in 5 adults living with overweight or obesity (59%) (Figure 10).

Figure 10: Age-standardised proportion of overweight and obesity in persons aged 18 and over, by Primary Health Network (PHN) areas, 2017–18

This figure shows a map of Australia with the proportion of overweight and obese adults displayed for each Primary Health Network (PHN) area. It also shows a list of PHNs with their prevalence of overweight and obese adults shown in relation to the Australian average. It shows that of 31 PHN areas, the Western NSW PHN area had the highest prevalence of overweight and obesity (83%), while the Gold Coast PHN area had the lowest (59%).

The proportion of children and adolescents living with overweight or obesity increased between 1995 and 2007–08 (from 20% to 25%), then remained relatively stable from 2007–08 to 2017–18 (ABS 2009b, ABS 2013a, ABS 2013b, ABS 2015b, ABS 2019b) (Figure 11).

Similarly, the proportion of children and adolescents living with obesity increased from 4.9% in 1995 to 7.5% in 2007–08, then remained relatively stable to 2017–18 (81%). The proportion living with overweight (but not obesity) in children rose between 1995 and 2014–15 (from 15% to 20%), then declined to 17% in 2017–18.

Figure 11: Proportion of overweight and obesity in children and adolescents aged 5–17, 1995 to 2017–18

This line chart shows 3 separate lines for the proportion of children and adolescents who were overweight or obese, overweight but not obese, and obese in 1995, 2007–08, 2011–12, 2014–15 and 2017–18. Overweight and obesity increased between 1995 and 2007–08 (from 20% to 25%), then remained relatively stable from 2007–08 to 2017–18.

After adjusting for different population age structures over time, the proportion of adults aged 18 and over living with overweight or obesity increased from 57% in 1995 to 67% in 2017–18. Over this time, the proportion of the population living with obesity almost doubled, from 19% in 1995 to 31% in 2017–18. The proportion living with overweight but not obesity declined from 38% to 36% (Figure 12).

Figure 12: Age-standardised proportion of overweight and obesity in persons aged 18 and over, 1995 to 2017–18

This line chart shows 3 separate lines for the age-standardised proportion of adults who were overweight or obese, overweight but not obese, and obese in 1995, 2007–08, 2011–12, 2014–15 and 2017–18. The prevalence of overweight and obesity increased from 57% in 1995 to 67% in 2017–18, while the prevalence of obesity increased from 19% to 31% over this period.

The distribution of BMI in adults shifted towards higher BMIs from 1995 to 2017–18, due to an increase in obesity in the population over time (Figure 13).

Figure 13: Distribution of BMI among persons aged 18 and over, 1995 and 2017–18

This chart shows the smoothed distributions of BMI among adults in 1995 and 2017–18, with the BMI cut-off points for underweight, normal weight, overweight and obese also shown on the chart. Compared with 1995, the 2017­–18 distribution has shifted to the right, indicating an increase in obesity over time.

Birth cohorts

Birth cohort analysis looks at how health outcomes differ between birth cohorts, which is a group of people born in the same year or years. This type of analysis can be used to identify groups of people more at risk of a health outcome (AIHW 2020a). Analyses comparing the prevalence of overweight or obesity in Australians across three different time points (1995, 2007–08 and 2017–18) show that those born more recently are more likely to be living with overweight or obesity than people at the same age in previous years (AIHW 2020a, AIHW 2020b).

For the age group 5–14, those born most recently (in 2003–2012) are (Figure 14):

• more likely to be living with overweight or obesity (25%), compared with those born in 1981–1990 at the same age (20%)
• more likely to be living with obesity (7.7%), compared with those born in 1981–1990 at the same age (5.1%)
• not significantly more likely to be living with overweight or obesity than those born in 1993–2002.

For the age group 15–24, those born most recently (in 1993–2002) are (Figure 14):

• more likely to be living with overweight or obesity (41%) than those born in 1983–1992 at the same age (36%) and those born in 1971–1980 at the same age (28%)
• more likely to be living with obesity than those born in 1971–1980 at the same age (8.4%).

When comparing the 1993–2002 birth cohort as they aged from 5–14 to 15–24, the prevalence of overweight and obesity increased (from 23% to 41%). Obesity also increased, from 6.4% to 14% (Figure 14) (ABS 2009a).

Figure 14: Proportion of overweight and obesity in children, adolescents and young adults aged 5–24, by birth cohort and age group; measured at 1995, 2007–08 and 2017–18

This bar chart shows the prevalence of overweight and obesity for different birth cohorts at age 5–14 and age 15–24, for males and females. It shows that at age 5–14, boys born in 2003–2012 were more likely to be overweight or obese than boys born in 1981–1990 at the same age (25% compared with 19%). At age 15–24, males born in 1993–2002 were more likely to be overweight or obese than males born in 1971–1980 at the same age (46% compared with 32%), as were females (35% of females born in 1993–2002, compared with 24% of females born in 1971–1980). However, only males had a significant increase in overweight and obesity between the 1983–1992 and 1993–2002 cohorts (increasing from 38% to 46%). When comparing the 1993–2002 birth cohort as they aged from 5–14 to 15–24, the prevalence of overweight and obesity increased with age for males (from 24% to 46%) and females (from 22% to 35%).

For adults aged 18 and over, those born most recently are more likely to be living with obesity than those born 10 years earlier. The largest absolute difference was at age 75–84, where an additional 11 in every 100 adults were living with obesity at age 75–84 in 2017–18 (37%) compared with those at the same age in 2007–08 (26%) (Figure 15).

Between 1995, 2007–08 and 2017–18, the prevalence of obesity increased for almost all birth cohorts. The largest absolute change in the prevalence of obesity over the 22 years was among the 1973–1982 birth cohort. The prevalence of obesity in this birth cohort nearly tripled from 6.5% when they were aged 13–22 (in 1995) to 19% when they were aged 25–34 (in 2007–08), then increased to 31% when they were aged 35–44 (in 2017–18) (Figure 15).

To learn more on birth cohort analyses, see Overweight and obesity in Australia: an updated birth cohort analysis and Overweight and obesity among Australian children and adolescents .

Figure 15: Prevalence of overweight and obesity over time, by birth cohort and age group; 1995, 2007–08 and 2017–18

This line chart shows the prevalence of overweight and obesity and obesity for 9 birth cohorts, with data for each cohort shown at the midpoint of the cohort’s age group at up to 3 time points (1995, 2007–08 and 2017–18). For most birth cohorts, the prevalence of overweight and obesity generally increased with age over time, and the group with the highest prevalence is 65–74-year-olds in 2017–18 with 40% having obesity and 78% being overweight or obese.

International comparisons of the prevalence of overweight and obesity can be made for member countries of the Organisation for European Co-operation and Development. Comparisons for measured body weight are based on data from 2021 or the latest available year  (OECD 2022).

Australia ranked 9th out of 21 countries with available data for the proportion of people aged 15 and over who were living with overweight or obesity (65%) – this was greater than the OECD average of 60%.

When comparing the proportion of obesity in men and women across OECD countries, Australia had the 4th highest proportion of men living with obesity (32%), behind New Zealand (33%), Hungary (36%) and the United States (44%). The proportion of obesity in women in Australia was 9th highest out of 21 countries (29%) – higher than the OECD average of 26% for women (Figure 16).

Figure 16: Proportion of overweight or obesity in persons aged 15 years and over, OECD countries, 2021 or nearest year

This bar chart shows the proportion of people aged 15 and over who were overweight or obese in OECD countries in 2021 or the nearest year data were available, for males, females and persons. It shows Australia had the 6 th highest proportion for overweight and obesity combined (65%), and the 5 th highest proportion for obesity (30%). These were higher than the OECD averages of 59% and 24% respectively.

Self-reported overweight and obesity data have been omitted due to concerns about reliability of estimates.

Results are for 2021 or the nearest available year of data, for countries with available data. All data are sourced from the OECD Health Statistics 2022 website, published on 30 November 2022.

The ‘OECD average’ for each indicator has been calculated by the AIHW from the latest year of data available for each of the 37 OECD member countries with available data for that indicator. It was not possible to calculate confidence intervals to indicate variability around estimates from the published data available.

Variation between indicator results for each country may occur due to differences in data collection, the data quality and the years of data available. For more information on indicator methodology and country-specific data sources used, please see OECD Health Statistics 2022 Definitions, Sources and Methods document .

Australia is among a number of OECD member countries in which the prevalence of overweight and obesity has increased over recent decades (Figure 17). These increases have been driven by the increased proportion of people who are living with obesity (OECD 2022). This upward trend is expected to continue – OECD projections show a steady increase in obesity rates until at least 2030.

Figure 17: Proportion of overweight or obesity in persons aged 15 years and over, OECD countries, 2000 to 2021

This line chart shows the proportion of people aged 15 and over who were overweight or obese in OECD countries each year from 2000 to 2021 (or the nearest year data were available). It shows that Japan and Korea had much lower rates of overweight and obesity than other countries, with Japan having the lowest prevalence across all years of data (ranging from about 24% to 27%). The country with the highest prevalence of overweight and obesity is Mexico at 75% in 2018.

• Unconnected markers represent countries for which data were available for only 1 of the years presented
• Data are sourced from the OECD Health Statistics 2022 website, published on 30 November 2022. Results are presented for years of available data for each country, between 2000 and 2021.
• Results are based on overweight and obesity classifications based on measured height and weight only (self-reported data have been excluded due to concerns about reliability).
• Variation in results between countries may occur due to differences in data collection and data quality. For more information on indicator methodology and country-specific data sources used, please see OECD Health Statistics 2022 Definitions, Sources and Methods document .

For more information, see International health data comparisons, 2022 .

Burden of disease is a measure of the years of healthy life lost from living with ill health or dying prematurely from disease and injury. A portion of this burden is due to modifiable risk factors. Burden of disease analysis estimates the contribution of these risk factors to this burden.

Overweight (including obesity) is the 2 nd leading risk factor (after tobacco use) contributing to ill health and death, responsible for 8.4% of the total disease burden in Australia, in 2018 (AIHW 2019). Overweight (including obesity) is linked to 30 diseases, including 17 types of cancers, 4 cardiovascular diseases, 3 musculoskeletal conditions, type 2 diabetes, dementia, asthma and chronic kidney disease.

In 2018, overweight (including obesity) was responsible for:

• 55% of type 2 diabetes disease burden.
• 51% of hypertensive heart disease.
• 49% of uterine cancer.
• 43% of gout.
• 42% of chronic kidney disease.

Overweight (including obesity) contributed to around 16,400 deaths (10% of all deaths) (AIHW 2019).

The total disease burden attributable to overweight (including obesity) in 2018 was 2.2 times greater in the lowest socioeconomic group compared with the highest socioeconomic group (AIHW 2019).

See Australian Burden of Disease Study 2018: Interactive data on risk factor burden for more information on the burden of disease associated with overweight and obesity.

Nationally representative data on people’s weight in Australia during COVID-19 are not currently available. However, emerging research suggests that COVID-19 might have had an impact on the weight of some Australians.

Data from SiSU health check stations across Australia between January 2020 to December 2020 showed that on average, more people gained weight than lost weight. An average weight of 3.0 kgs per member was gained, by those who gained weight during 2020. This is a swing from net weight loss of 3.0 kgs per member during 2019. The prevalence of people with a BMI greater than 25 (classified as overweight or obese) has also increased when comparing the pre-COVID period (January 2017 to March 2020) and COVID period (April 2020-August 2021). This was seen across all age groups, except for the 65–74 and 75 and over age groups, indicating widespread population weight gain (Hannebery, et al. 2021).

It should be noted that users of SiSU health check stations tend to be younger, female and more socioeconomically advantaged than the general Australian population.

To learn more about how the pandemic has affected the population's health in the context of longer-term trends, please see ‘Chapter 2 Changes in the health of Australians during the COVID-19 period' in  Australia's health 2022: data insights .

For more information on overweight and obesity, see:

• Overweight and obesity in Australia: a birth cohort analysis
• Overweight and obesity in Australia: an updated birth cohort analysis
• Overweight and obesity among Australian children and adolescents

Visit  Overweight and obesity to see more on this topic.  

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• Open access
• Published: 27 April 2024

Obesity-induced blood-brain barrier dysfunction: phenotypes and mechanisms

• Ziying Feng 1 , 2 ,
• Cheng Fang 1 ,
• Yinzhong Ma 1 , 3 &
• Junlei Chang 1 , 2 , 3  

Journal of Neuroinflammation volume  21 , Article number:  110 ( 2024 ) Cite this article

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Obesity, a burgeoning global health issue, is increasingly recognized for its detrimental effects on the central nervous system, particularly concerning the integrity of the blood-brain barrier (BBB). This manuscript delves into the intricate relationship between obesity and BBB dysfunction, elucidating the underlying phenotypes and molecular mechanisms. We commence with an overview of the BBB’s critical role in maintaining cerebral homeostasis and the pathological alterations induced by obesity. By employing a comprehensive literature review, we examine the structural and functional modifications of the BBB in the context of obesity, including increased permeability, altered transport mechanisms, and inflammatory responses. The manuscript highlights how obesity-induced systemic inflammation and metabolic dysregulation contribute to BBB disruption, thereby predisposing individuals to various neurological disorders. We further explore the potential pathways, such as oxidative stress and endothelial cell dysfunction, that mediate these changes. Our discussion culminates in the summary of current findings and the identification of knowledge gaps, paving the way for future research directions. This review underscores the significance of understanding BBB dysfunction in obesity, not only for its implications in neurodegenerative diseases but also for developing targeted therapeutic strategies to mitigate these effects.

Introduction

Obesity, characterized by excessive fat accumulation, represents a burgeoning health issue of global populations. The World Health Organization reports an alarming rise in obesity rates worldwide, with an estimated 2.5 billion adults being overweight (body mass index, BMI > 25), of which approximately 890 million are obese (BMI > 30) as of 2022 [ 1 ]. This pervasive phenomenon profoundly impacts human health, inciting a multitude of disorders including cardiovascular disease, stroke, diabetes, and certain types of cancer.

Of particular interest, however, is the intricate relationship between obesity and neurological disorders, a link that has increasingly captivated scientific attention. Alzheimer’s disease [ 2 , 3 , 4 ] and stroke [ 5 , 6 , 7 ], for instance, are two representative neurological disorders that have been recurrently associated with obesity. Recent evidence points to obesity as a critical risk factor for mitochondrial dysfunction, as astrocytic fatty acid oxidation (FAO) and oxidative phosphorylation (OxPhos) are involved in these lipid-involving neurodegenerative disorders, implicating obesity’s role in influencing the disease onset and progression [ 8 , 9 ]. This escalating prevalence of obesity and its potential implications for neurological disorders presents a grave concern for global health. It necessitates a comprehensive understanding of obesity’s detrimental consequences towards the central nervous system (CNS), specifically its effects on the cerebral vascular system.

A critical component of the cerebrovascular system is the blood-brain barrier (BBB), a critical function of brain blood microvessels that maintains CNS homeostasis. The BBB strictly regulates the exchange of substances between the blood and the brain. This dynamic barrier ensures the stability of brain microenvironment, protects against neurotoxins, and facilitates proper neuronal function [ 10 , 11 ]. However, in pathological states such as obesity, this pivotal barrier can be compromised, leading to various neurological disorders. Understanding the intricate relationship between obesity and BBB dysfunction therefore becomes a matter of paramount importance.

We aim to delve into the complexities of this relationship, exploring the phenotypes and underlying mechanisms of BBB dysfunction in obesity. Through a comprehensive examination of the current literature, we provide an in-depth analysis of the potential changes in BBB structure and function induced by obesity. Additionally, we elucidate the mechanistic pathways that may be implicated in BBB dysfunction in obesity. This review ultimately seeks to underscore the significance of understanding BBB dysfunction in obesity and its implications for neurological health, with the goal of guiding future research and clinical practice.

Blood-brain barrier: cellular and molecular components

The blood-brain barrier (BBB) is a sophisticated and highly specialized biological construct, known for its selective permeability and protective role within the CNS. It owes its unique functionality to its intricately assembled cellular and molecular components, which work together to maintain CNS homeostasis. The structural and functional foundation of the BBB is the neurovascular unit (NVU) [ 12 ], a complex ensemble encompassing brain endothelial cells (BECs), pericytes, astrocytes, extracellular matrix (ECM), microglia, and neurons (Fig.  1 ).

BBB integrated within the NVU . This diagram depicts the structural components of the blood-brain barrier (BBB) within the neurovascular unit (NVU), illustrating the tight junctions of endothelial cells, the supportive pericytes, and the encircling astrocytic end-feet. It highlights the BBB’s integral structural role in the NVU, which is essential for maintaining cerebral integrity and function

Endothelial cells

As the primary component of the BBB, brain endothelial cells (BECs), line the interior surfaces of the brain’s capillaries. These cells are distinctive in their unique morphology and possess attributes that separate them from peripheral ECs [ 13 , 14 , 15 ]. BECs are closely interconnected through tight junctions (TJs), which restrict the paracellular pathway and control the ionic balance across the barrier. This feature is instrumental in preventing the free passage of substances from the blood into the brain. BECs also exhibit limited pinocytic activity and lack fenestrations, thereby further restricting transcellular transport. In order to provide brain with abundant nutrients and a healthy environment, specific transporters which allows for the selective passage of molecules are expressed in BECs. Through analyzing single-cell RNA-sequencing data of mouse ECs from different organs provided by the Tabula Muris consortium, Paik et al. found that BECs possess not only its own unique transcriptomic identities, but also the most specialized differentially expressed genes (DEGs) profiles that expressed primarily solute carrier transporters [ 16 ]. For instance, glucose transporter protein-1(Glut-1) is responsible for transporting glucose, which being a crucial energy fuel, from blood to the brain [ 17 ]. On the other hand, P-glycoprotein (P-gp) acts as an efflux pump that removes harmful substances out of the brain to keep a safe environment [ 18 ].

Worth noticed, BECs can also transport molecules through low rates of transcytosis, a process that plays a vital role in endothelial transport [ 19 , 20 ]. Transcytosis allows for selective or non-selective passage of molecules across the endothelial cells. In BECs, transcytosis can be divided into receptor-mediated (selective) transcytosis, which includes the transport of transferrin and insulin, and bulk-phase or fluid-phase (non-selective) transcytosis, typically exemplified by the transport of albumin [ 21 ]. Notably, while native albumin crosses the BBB via non-selective bulk-phase transcytosis, cationic albumin is transported more efficiently as it engages in adsorptive transcytosis, highlighting the distinct pathways for different forms of albumin. A key regulator in this process is Major Facilitator Superfamily Domain Containing 2 A (Mfsd2a), which specifically inhibits bulk-phase transcytosis in BECs [ 22 ]. Mfsd2a modifies the lipid composition of the cell membrane, thereby limiting the formation of caveolae vesicles and reducing caveolae-mediated transcytosis. This action helps maintain the selective permeability and overall integrity of the BBB. Contrary to inhibiting all forms of transcytosis, Mfsd2a’s role is particularly pivotal in restraining non-selective transcytosis or bulk-phase transcytosis. Research indicates that overexpression of Mfsd2a in CNS endothelial cells leads to a decreased number of transcytotic vesicles, which in turn reduces hematoma levels and alleviates BBB injury in mouse models of intracerebral hemorrhage (ICH) [ 23 ]. This highlights the potential therapeutic value of Mfsd2a in mitigating BBB disruption by specifically targeting non-selective transcytosis mechanisms post-injury.

In comparison to endothelial cells found elsewhere in the body, brain endothelial cells (BECs) demonstrate a unique profile of leukocyte adhesion molecule (LAM) expression. This profile contributes to the CNS’s immune privilege by limiting the extravasation of leukocytes under normal conditions. While LAMs are integral to leukocyte adhesion and migration — key steps in the immune response — their regulated expression in BECs ensures a controlled interaction with leukocytes, thereby maintaining CNS homeostasis [ 24 ]. Contrary to the constant expression in peripheral tissues, the presence and activity of LAMs in the BBB vary contextually. Notably, BECs typically lack selectins in their Weibel-Palade bodies, which reduces their ability for rapid leukocyte recruitment compared to peripheral endothelial cells. This does not equate to a complete suppression of immune response but represents a finely tuned regulation ensuring selective leukocyte passage without compromising BBB integrity [ 25 ]. During certain pathological conditions, leukocytes such as neutrophils and T cells may interact with the BBB in ways that can affect its permeability, through the release of cytokines, reactive oxygen species, and other mediators. However, these interactions do not universally result in BBB disruption and can occur in the absence of significant leakage, depending on the specific disease context and the nature of leukocyte engagement. In the context of neuroinflammatory diseases, such as multiple sclerosis (MS), alterations in the expression and functionality of LAMs have been observed. These changes can facilitate increased adhesion of leukocytes to BECs, potentially leading to BBB disruption under these pathological states [ 26 ]. However, this process reflects a departure from the normative regulatory mechanisms in place under healthy conditions and underscores the complex role of LAMs in both maintaining CNS immunity and contributing to neuroinflammation when dysregulated.

Pericytes is a kind of multi-functional cells embedded within the walls of capillaries throughout the body, including the brain, and play a crucial role in maintaining the integrity of the BBB. They were first identified in the 1870s, and more recently, numerous vascular functions of pericytes have been identified. These include regulation of cerebral blood flow, maintenance of the BBB integrity, and control of vascular development and angiogenesis [ 27 , 28 , 29 ]. In addition to these roles, pericytes have been found to play an active role during neuroinflammation in the adult brain [ 28 , 30 , 31 ]. They can respond differentially, depending on the degree of inflammation, by secreting a set of neurotrophic factors to promote cell survival and regeneration, or by potentiating inflammation through the release of inflammatory mediators (e.g., cytokines and chemokines), and the overexpression of pattern recognition receptors [ 32 ]. In neuroinflammatory conditions like multiple sclerosis, pericytes undergo morphological changes, elongating their processes within inflamed perivascular cuffs [ 33 ]. Exposure to cytokines and extracellular matrix proteins like chondroitin sulfate proteoglycans enhances pericyte secretion of chemokines and promotes macrophage migration [ 33 ]. This implicates pericytes in propagating neuroinflammation through immune cell recruitment. However, pericytes also have neuroprotective capacities dependent on the degree of inflammation [ 34 ]. Through release of neurotrophic factors like BDNF, pericytes can promote neuronal survival and regeneration [ 35 ]. Their expression of cell adhesion molecules likewise facilitates interactions with endothelial cells and astrocytes to maintain cerebrovascular stability [ 36 ]. The dual functionality of pericytes is highlighted by their differential secretome profiles in response to IL-1β [ 37 ]. While pericytes secrete certain pro-inflammatory genes like CCL2 [ 38 ], they also show expression of vascular-stabilized mediators like TIMP3 [ 34 , 39 ]. This nuanced, context-dependent pericyte reactivity fine-tunes neuroinflammatory responses.

Pericyte dysfunction is increasingly recognized as a contributor to the progression of vascular diseases such as stroke and neurodegenerative diseases [ 40 , 41 ]. The therapeutic potential of pericytes to repair cerebral blood vessels and promote angiogenesis due to their ability to possess stem cell-like properties has recently been brought to light. In the context of cerebral blood vessels repair, pericytes can migrate to the site of injury and differentiate into cells that are needed for repair [ 42 , 43 ]. As for promoting angiogenesis, pericytes play a vital role in the formation of new blood vessels from pre-existing ones, a process known as angiogenesis [ 44 ]. They stabilize the newly formed endothelial tubes, modulate blood flow and vascular permeability, and regulate endothelial proliferation, differentiation, migration and survival. However, research has shown that pericytes and endothelial cells have overlapping but distinct secretome profiles in response to IL-1β [ 45 ]. This indicates that these two cell types may respond differently to inflammatory stimuli, which could have implications for understanding how inflammation affects the cerebrovasculature.

In conclusion, pericytes play a critical role in maintaining BBB integrity by controlling various processes. Understanding these mechanisms could provide valuable insights into BBB function and CNS immunity. Future research directions could include exploring the role of other proteins involved in these processes, further investigating the function of pericytes in disease states, and studying how changes in pericyte function could impact BBB integrity and CNS health.

Astrocytes, another critical component of the NVU, also play a crucial role in BBB maintenance. Their end-foot processes enwrap the brain capillaries and provide physical support to the BBB. Through their extensive contact with endothelial cells, astrocytes are vital in the formation and maintenance of the BBB. More specifically, astrocytes can secrete Sonic hedgehog (Shh), thereby stimulating the expression of TJs proteins and junctional adhesion molecule-A (JAM-A) while promoting immune quiescence of BBB by decreasing the expression of chemokines and LAMs [ 46 ]. The Hedgehog (Hh) pathway is involved in embryonic morphogenesis, neuronal guidance, and angiogenesis [ 47 ]. In adult tissues, it participates in vascular proliferation, differentiation, and tissue repair. The Hh pathway provides a barrier-promoting effect and an endogenous anti-inflammatory balance to CNS-directed immune attacks [ 48 ]. In terms of cerebrovascular accidents, astrocytes have been found to play a crucial role in maintaining blood-brain barrier function [ 49 ]. They support neurons and other glia, and react to changes in both the local and external environment. Beyond these homeostatic functions, astrocytes can respond to several stimuli and subsequently display profound genetic, morphological, and functional changes in a process termed reactive astrogliosis.

In addition to their role in maintaining BBB integrity, astrocytes also play a crucial role in controlling water homeostasis in the brain. Aquaporins (AQPs) are transmembrane proteins responsible for fast water movement across cell membranes, including those of astrocytes [ 50 ]. Various subtypes of AQPs (AQP1, AQP3, AQP4, AQP5, AQP8 and AQP9) have been reported to be expressed in astrocytes. The expressions and subcellular localizations of AQPs in astrocytes are highly correlated with both their physiological and pathophysiological functions [ 51 , 52 ].

Basement membrane

The basement membrane (BM) was secreted and maintained by BECs, pericytes and astrocytes. The BM is composed of various proteins, including Laminins, Collagen-IV, Perlecan, and Fibronectin [ 53 , 54 ]. These components interact with each other and with other cells at the BBB. For example, Laminins provide structural support to the BBB and are involved in processes such as cell adhesion, differentiation, migration, and apoptosis, while Collagen-IV provides mechanical strength to the BBB and contributes to its stability [ 53 , 54 ]. Any alterations in these components can lead to BBB dysfunction, which is associated with various neurological disorders. For instance, in conditions such as stroke, the BBB undergoes significant changes, including alterations in the expression of integrins and degradation of surrounding ECM, which indirectly affect the vascular barrier function [ 55 ]. In an animal model of autoimmunity, considerable information has been revealed about the function of different BMs in maintaining BBB integrity and causing neuroinflammation [ 56 , 57 , 58 ].

ECM receptors, such as integrins and dystroglycan, are also expressed at the brain microvasculature and mediate the connections between cellular and matrix components in physiology and disease [ 59 , 60 , 61 ]. These proteins and receptors elicit diverse molecular signals that allow cell adaptation to environmental changes and regulate growth and cell motility [ 62 ].

Microglia and neurons

Finally, microglia and neurons, while not directly forming the BBB, significantly contribute to its function and maintenance. Microglia, the resident immune cells of the brain, play a crucial role in maintaining the integrity of the BBB. Following ischemic stroke, microglia interact with endothelial cells in a paracrine manner to promote angiogenesis and barrier repair [ 63 ]. However, this reparative crosstalk is impeded in aging, implicating declined microglial function in age-related BBB disruption [ 64 ]. Accordingly, experimental depletion of microglia exacerbates injury and edema in aged mice subjected to ischemic stroke, further confirming microglia’s protective influence [ 65 ]. Beyond strokes, microglia also maintain BBB stability in neuroinflammatory conditions by modulating astrocyte reactivity and phagocytosing synapses [ 66 ]. In a model of multiple sclerosis, microglia limited astrocyte activation and protected against BBB leakage, stressing their homeostatic role [ 67 ]. During reactive gliosis induced by stroke, microglia and macrophages preferentially eliminated excitatory synapses while astrocytes cleared inhibitory synapses, preventing excessive imbalance of excitatory/inhibitory tone [ 67 ].These findings underscore diverse mechanisms whereby microglia maintain cerebrovascular equilibrium, ranging from paracrine support of angiogenesis to controlled synapse engulfment. Their protective influence likely declines with aging, permitting BBB hyperpermeability. Boosting microglial function represents a promising strategy to strengthen BBB integrity in neurological disorders.

Neurons also play a significant role in BBB’s function and maintenance. Neurons communicate with other components of the neurovascular unit (NVU), such as endothelial cells, pericytes, and astrocytes, to regulate BBB properties. This communication is also critical for maintaining the CNS homeostasis and for responding to changes in neural activity. In the early postnatal mouse barrel cortex, it was demonstrated that manipulations of sensory inputs resulted in vascular structural changes [ 68 ]. Specifically, this study showed that local sensory-related neural activity promoted the formation of cerebrovascular networks.

Neurovascular coupling refers to the relationship between local neural activity and subsequent changes in cerebral blood flow (CBF) [ 69 ]. The magnitude and spatial location of blood flow changes are tightly linked to changes in neural activity through a complex sequence of coordinated events involving neurons, glia, and vascular cells. This mechanism is crucial as it matches the high energy demand of the brain with a supply of energy substrates from the blood. Evoked the neural activity by high-intensity visual stimulation could drive macroscopic cerebrospinal fluid (CSF) flow in the human brain [ 70 ]. The timing and amplitude of CSF flow were matched to the visually evoked hemodynamic responses, suggesting neural activity can modulate CSF flow via neurovascular coupling.

In addition to their role in neurovascular coupling, neurons also contribute to BBB function through some transporters. It was found that neuronal activity regulates BBB efflux transporter expression and function [ 71 ], which is critical for excluding many small lipophilic molecules from the brain parenchyma. These findings suggest that sensory-related neural activity can influence both vascular structure and BBB function, which could have significant implications for understanding neuro-vascular interaction. An understanding of these intricate structures and their functions is pivotal in comprehending how pathological states, such as obesity, could affect the BBB and ultimately lead to neurological disorders.

Obesity-induced BBB leakage: phenotypes and mechanisms

The impact of obesity on BBB integrity has been an area of increasing scientific scrutiny, given the relationship between obesity and various neurological disorders. The disruption of BBB integrity or non-specific leakage of the BBB, as it is commonly referred to, is a recurrent phenotype observed in obesity, especially in the context of high-fat diet (HFD) intake with a wide duration from 8 to 36 w [ 72 , 73 , 74 , 75 ]. This section seeks to elucidate the molecular mechanisms and phenotypic changes underlying obesity-induced BBB leakage and associated BBB markers of this pathological state.

Changes in tight junctions

A key factor implicated in obesity-induced BBB leakage is the dysregulation of tight junction proteins (TJs). TJs, as earlier stated, are critical in maintaining the restrictive properties of the BBB. However, obesity, particularly under conditions of HFD, can alter the expression and function of these proteins. For instance, Ouyang et al. observed alterations in the expression levels of ZO-1 in microvessels from obese mice modeling by 8 weeks (w) of HFD [ 76 ]. In line with this observation, HFD significantly decreased the protein levels of ZO-1, Claudin-5 and Occludin along with the leakage of brain microvessles after 8 weeks (w) of HFD [ 77 ]. In adult rats, 90 days of high-energy diet (high in saturated fat and glucose) consumption decreased mRNA expression of TJs, particularly Claudin-5 and − 12, in the choroid plexus and the BBB. Consequently, an increased blood-to-brain permeability of sodium fluorescein was observed in the hippocampus [ 78 ]. This underscores the potential for obesity-induced modifications to BBB structure and function.

The adenosine receptor 2a (Adora2a) is increasingly recognized for its significant role in the modulation of neurovascular and neuroinflammatory responses. Activation of Adora2a receptors has been linked to heightened inflammatory responses, contributing to the disruption of the blood-brain barrier (BBB) and subsequent neuronal damage, conditions often exacerbated by obesity and metabolic syndrome. In a rodent model of diet-induced insulin resistance by 16 w of HFD, it was found that chronic activation of Adora2a eroded TJs between BECs, as evidenced by diminished Occludin and Claudin-5 in hippocampal lysates. Considering the detrimental effects associated with Adora2a activation on BBB integrity, antagonism of this receptor presents a promising therapeutic strategy. By inhibiting Adora2a, it is possible to mitigate the receptor-mediated exacerbation of inflammatory processes within the CNS, thereby preserving BBB function and reducing neuroinflammatory sequelae. This premise is supported by several studies demonstrating that Adora2a antagonists can effectively reduce BBB permeability and alleviate inflammatory damage in various neuroinflammatory and neurovascular disorders [ 79 , 80 , 81 ].

In addition, it is noteworthy that obesity not only affects one’s own BBB function, but also has an impact on its offspring. A recent study showed that maternal obesity during pregnancy could impaired BBB formation of the fetal, leading to changes in TJ components of the arcuate nucleus region in offspring’s brain, thereby significantly increasing BBB permeability [ 82 ]. This dysregulation of TJs compromises the integrity of the BBB, increasing its permeability and enabling the passage of potentially harmful substances from the blood into the CNS. Interestingly, recent studies have also linked prolonged HFD intake for 32 w to anxiety-like and depression-like behaviors in mice [ 83 ]. It was found that 24 weeks of HFD consumption induced neurobehavioral deterioration, including increased anxiety-like and depression-like behavior. These behavioral changes were associated with impaired gut microbiota homeostasis and inflammation. Long term HFD may induce certain behavioral phenotypes related to neurological disorders through the gut-brain axis [ 83 ]. In line with this, treatment with the anti-inflammatory molecule palmitoylethanolamide was found to reduce anxiety-like behavior in obese mice modeling by 19 w of HFD, along with dampening systemic and central inflammation [ 84 ]. Taken together, these studies suggest prolonged HFD may not only directly disrupt TJs and BBB integrity through inflammatory and other mechanisms, but also trigger neurobehavioral changes that could secondarily impact BBB function.

Changes in fenestration

As mentioned above, BECs are characterized by a lack of fenestrations, a characteristic that contributes to the high selectivity and restrictive nature of the BBB. Stan et al. identified PV-1 (also known as PLVAP, plasmalemma vesicle-associated protein; or MEGA-32 antigen) as a component of fenestral diaphragms in endothelial cells [ 85 ]. As PV-1 comprises these structures, its regulation could influence fenestration numbers. While PLVAP expressed on fenestrated endothelia and associated with the formation of diaphragms in vesicular structures, it does not serve as a marker for fenestrations within the BBB context. Instead, increases in PLVAP expression may reflect alterations in the molecular composition associated with transcellular pathways rather than the formation of true fenestrae. In a single-cell profiling study, analysis of BECs revealed that among eight major clusters, fenestrated BECs in areas such as the choroid plexus showed the most unique obesity-induced DEGs, where fenestrated endothelia are typical, rather than suggesting the emergence of fenestrations within the BBB due to obesity modeling by 12 w of HFD [ 86 ]. In line with this, Previous study have demonstrated that HFD intake can lead to an increase in endothelial fenestration in the BBB, such as the observed changes in the offspring of gestational obesity in mice [ 82 ]. Worth noticed, these findings, particularly those related to increased permeability in regions like the arcuate nucleus, may reflect localized alterations in BBB properties rather than systemic induction of fenestrations akin to those in inherently fenestrated structures like the choroid plexus.

Therefore, while the effects of HFD on endothelial cell biology are undeniable, the notion that these effects lead to the formation of fenestrations within the typical BBB structure remains debatable. This perspective allows for the possibility that PLVAP-regulated alterations may occur regionally within specific areas of the brain under certain conditions, rather than implying a global change across the entire barrier. The DEGs identified in obesity models, particularly those affecting endothelial cells of the BBB, should be interpreted with an understanding that obesity-induced stress may lead to changes in molecular signaling and barrier properties without necessitating the creation of actual fenestral openings.

Changes in matrix metalloproteinases

Matrix metalloproteinases (MMPs) are a family of endopeptidases that function to degrade and remodel the extracellular matrix (ECM). MMPs are secreted by various cell types including epithelial cells, fibroblasts, and inflammatory cells, playing important roles in physiological processes as well as disease states characterized by tissue damage and inflammation. However, excessive MMPs can lead to pathological ECM degradation and impairment of tissue structure and function, as in the case of blood-brain barrier (BBB) disruption by MMP-2 and MMP-9 [ 87 , 88 , 89 ]. Obesity has been linked to increased expression and activity of certain MMPs, which can impair BBB integrity. For example, plasma MMP-9 levels were found to be elevated in obese subjects and decreased with anti-diabetic treatment [ 90 , 91 ]. Cotemporally, lipocalin-2 was also increased in adipose tissue of obese individuals and correlated with MMP-2 and MMP-9 activity [ 92 ]. MMP-8 levels were similarly increased in obesity and associated with insulin resistance [ 93 ]. Additional studies have assessed levels of MMPs like MMP-2 and MMP-9 along with their inhibitors TIMPs in obese children [ 94 ]. Together, these findings suggest obesity creates a pro-inflammatory state characterized by upregulation of MMPs like MMP-8 and MMP-9, potentially driven by increases in mediators like lipocalin-2. By degrading ECM proteins, these MMPs can impair BBB structural integrity. A general description of obesity-induced BBB leakage was shown in Fig.  2 .

Mechanisms of Obesity-Induced BBB Leakage. Illustration of the complex alterations in the BBB induced by obesity, highlighting the loosening of endothelial cell junctions, increased permeability, and the unusual occurrence of fenestrations. These structural changes are further exacerbated by the activity of MMPs, which degrade extracellular matrix components, facilitating the infiltration of peripheral immune cells, such as neutrophils. This cascade of events underscores the multifaceted and interconnected impact of obesity on BBB integrity and the resulting immune response within the cerebral vasculature

Obesity-induced BBB transport dysfunction: phenotypes and mechanisms

In addition to disrupting the BBB integrity, obesity is associated with alterations in the BBB’s transport functions. These changes not only affect the nutrient supply to the brain but can also influence the entry and clearance of toxins, signaling molecules, and therapeutics. The transport across the BBB is highly regulated, with specific receptors and transporters expressed in BECs mediating the process. However, obesity can induce significant changes in these receptors and transporters, leading to BBB transport dysfunction.

Glucose transport-1 (Glut-1)

One of the key alterations in BBB transport function under obesity conditions is the dysregulation of glucose transport. The primary transporter responsible for glucose transport across the BBB is the glucose transporter 1 (Glut-1), which ensures the supply of glucose from the blood to the brain. However, in the state of obesity, particularly in HFD-induced obesity, the expression and function of Glut-1 have been found to be altered. Specifically, studies in mouse models have demonstrated that acute high-fat feeding for 2–3 days suppressed Glut-1 expression and glucose uptake in the brain [ 95 , 96 ]. Similarly, the mouse insulin resistance model showed that when 2 weeks of HFD significantly downregulated the expression of Glut-1 in the brain, 10 weeks of HFD normalized the expression of Glut-1, suggesting the duration of insulin resistance may influenced the regulation of Glut-1 [ 97 ]. In humans, genetic factors were found to impact Glut-1 levels, which in turn modulates cognitive effects of high-fat intake [ 98 ].

Interestingly, upon more prolonged high-fat feeding for 10 weeks, Glut-1 expression was restored, which was related to the initiation of compensatory mechanisms of vascular endothelial growth factor (VEGF), a key regulator of angiogenesis and vascular function [ 95 ]. Recent studies have illuminated the role of VEGF in respond to metabolic stresses and hypoxic conditions often prevalent in obesity. This response includes the potential to upregulate Glut-1 expression, ostensibly to compensate for altered metabolic demands and ensure sufficient glucose transport across the BBB. However, the interplay between VEGF and Glut-1 in the setting of obesity is multifaceted. While moderate increases in VEGF can be beneficial, aiding in the restoration of Glut-1 levels and maintaining cerebral glucose metabolism, chronic elevations—common in prolonged obesity—may lead to adverse effects [ 95 , 96 ]. Excessive VEGF can contribute to vascular abnormalities and exacerbate BBB disruption, complicating the metabolic landscape of the CNS. Therefore, understanding the dynamic between VEGF-induced Glut-1 modulation and obesity provides insight into the broader implications of metabolic syndrome on BBB health and brain metabolism. This section explores how the nuanced changes in VEGF and Glut-1 expression influenced by obesity underscore the complexity of maintaining BBB integrity and highlight the need for targeted therapeutic strategies to address these metabolic challenges.

Taken together, while short-term high-fat intake appears to impair Glut-1 function and glucose transport at the BBB, compensation may occur with prolonged obesity to normalize Glut-1 expression. However, the brain is the most energy-consuming organ, requiring constant high energy to maintain its function, thereby the temporary disruption in glucose delivery to the brain may be sufficient to impact neuronal health and cognition. Further research into the kinetics of Glut-1 regulation in response to high-fat feeding could delineate the timeframe of BBB transport deficits. This may provide insights into critical windows where impaired brain glucose uptake contributes to neurological disorders associated with obesity.

Alterations in insulin transport have also been observed in obesity, which can profoundly impact neuronal function. Insulin enters the brain via saturation transport and binds to insulin receptors (IR) on BECs, triggering receptor autophosphorylation and downstream signaling cascades involved in glucose uptake, metabolism, neuronal plasticity and survival [ 99 ]. However, studies in obese animal models and human subjects indicate that obesity and HFD feeding can impair insulin transport across the BBB. In preclinical studies using mouse and canine models, HFD feeding for 7 w reduced transport of intravenously injected insulin from the circulation into the brain parenchyma [ 100 , 101 ]. This reduction is associated with central insulin resistance, as evidenced by impaired insulin receptor signaling cascades in the brain from mice subjected to 4 w of HFD [ 102 ]. In human clinical studies, obese subjects were found to have lower cerebrospinal fluid (CSF) insulin levels and attenuated CSF insulin increases after systemic insulin infusion compared to healthy controls [ 103 ]. Together, these animal and human studies demonstrate that obesity disrupts brain endothelial insulin transport, reducing insulin delivery to the CNS. This transport dysfunction contributes to central insulin resistance, a phenomenon often linked with cognitive dysfunction and neurological disorders [ 104 ]. Consistence with this, brain insulin resistance has been recognized as an early characteristic of Alzheimer’s disease [ 105 ]. Elucidating the mechanisms by which high-fat feeding alters insulin receptor expression, trafficking and downstream signaling at the BBB will be critical for developing therapeutic strategies to overcome CNS insulin resistance.

In addition to insulin, the transport of the hormone leptin across the BBB is also altered in obesity. Leptin is also transported into the brain via a saturable transport system and binds to leptin receptors on neurons involved in regulating food intake and energy expenditure. However, multiple studies indicate HFD-induced obesity impairs leptin transport across the BBB. For instance, in obese individuals, the transport level of leptin to the brain is downregulated, as evidenced by a significantly lower ratio of the leptin cerebrospinal fluid (CSF)/serum compared to the healthy lean individuals [ 106 , 107 ]. In obese animal models including rodents subjected to 10 w of HFD and sheep subjected to 40 w of HFD, there is a significant decrease in the rate of leptin transport from blood to brain compared to lean controls [ 108 , 109 , 110 ]. Obesity can inhibit the transport of leptin across the BBB, making it impossible for the brain to receive the “satiety signal” emitted by leptin, leading to overeating and worsening of obesity, which may lead to a series of metabolic diseases. Of notice, with the development of obesity, obese mice modeling by 56 days of HFD respond to leptin for central administration (intracerebroventricularly) rather than peripheral administration (intraperitoneally or subcutaneously) [ 111 , 112 ]. This suggests that. the impairment in leptin transport does not appear to be due to altered leptin receptor expression at the BBB. Rather, transport may be inhibited due to saturation of the carrier system and interactions with other circulating factors, such as the high-level triglycerides [ 113 , 114 , 115 ]. Overall, these findings indicate obese states inhibit leptin’s ability to enter the CNS and bind neuronal targets, despite normal BBB leptin receptor levels. Overcoming the transport block could potentiate leptin’s effects on appetite and weight regulation.

P-glycoprotein (P-gp)

Obesity also affects the function of efflux transporters at the BBB, such as the P-glycoprotein (P-gp). P-gp is an ATP-dependent transporter that functions to pump foreign substances and metabolites out of the brain back into the bloodstream. This helps protect the brain from accumulation of potentially toxic compounds. P-gp is encoded by the ABCB1 gene. A human study found a negative correlation between BMI values and the expression levels of ABCB1 in the brain, suggesting P-gp levels are reduced in obesity [ 116 ]. While the mechanisms linking obesity to P-gp regulation require further elucidation, systemic inflammation appears to play a role. In obese pregnant mice, placental P-gp expression was decreased in tandem with increases in inflammatory cytokines like tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), and interleukin-6 (IL-6) [ 117 ]. Changes in P-gp likely impair efflux of substrates from the brain back into circulation. Overall, although there is limited research on obesity and P-gp, current evidence indicates obesity can suppress P-gp expression and function at the BBB, at least in the obese human. This impairment in a key transporter for xenobiotic clearance may enable accumulation of toxins and drugs in the brain.

L-type amino acid transporter-1 (LAT1)

Amino acid transporters are also affected by obesity. The system L amino acid transporter 1 (LAT1) expressed at the BBB is responsible for transport of large neutral amino acids like leucine into the brain. LAT1 plays a key role in regulating mTORC1 signaling, which controls processes like protein synthesis and autophagy [ 118 ]. Recent studies have found that LAT1 function is altered in obesity models. Mice lacking neuronal LAT1 develop obesity phenotypes including increased adiposity [ 119 ]. LAT1 expression and amino acid uptake are reduced in the hypothalamus of obese, diabetic mice [ 120 ]. In humans, lower expression of the related transporter SLC7A10/ASC-1 in adipose tissue is associated with increased visceral fat, insulin resistance, and adipocyte hypertrophy [ 121 ]. Together, these findings indicate obesity impairs the function of multiple amino acid transporters at the BBB and periphery. This likely dysregulates mTORC1 signaling and other nutrient-sensing pathways, contributing to metabolic dysfunction. The alterations of transporters expressed in endothelial cells was summarized in Fig.  3 .

Obesity-Induced Alterations in BBB Transporters. This figure demonstrates the effects of obesity on BBB transporters: Glut-1 function is initially impaired by high-fat intake but may be normalized with prolonged obesity, despite potential impacts on neuronal health and cognition. Brain endothelial insulin transport is disrupted, contributing to central insulin resistance, and often associated with cognitive dysfunction and neurological disorders. Leptin transport impairment is linked to carrier system saturation, independent of leptin receptor expression changes. Obesity suppresses P-glycoprotein expression and function, potentially increasing brain accumulation of toxins and drugs. LAT1 expression and amino acid uptake are reduced in the hypothalamus of obese, diabetic mice, reflecting impaired amino acid transporter function at the BBB.

Evidence of BBB disruption in obesity and type 2 diabetes

Recent evidence has increasingly indicated that metabolic disorders such as obesity and Type 2 Diabetes (T2D) are closely linked to the disruption of the BBB. This section aims to elucidate the relationship between these conditions and BBB integrity, underpinned by recent scientific findings.

BBB Permeability in Type 2 Diabetes: Among the most compelling evidence in this domain is the study conducted by Starr and colleagues [ 122 ], which has demonstrated a significant increase in BBB permeability to gadolinium in patients with well-controlled T2D. This landmark study utilized advanced magnetic resonance imaging techniques to compare BBB integrity between diabetic patients and healthy individuals. The results indicated a pronounced increase in BBB permeability, notably in the basal ganglia region, suggesting a direct impact of T2D on neurovascular integrity. These findings are critical as they highlight the potential for well-controlled T2D to contribute to BBB dysfunction independently of other comorbidities often associated with metabolic syndrome.

The link between obesity and BBB disruption has been further substantiated by studies investigating the levels of specific BBB markers in obese individuals. A longitudinal perspective explores how factors related to adiposity in mid-life can have long-lasting effects on BBB integrity. The study highlights the enduring impact of obesity on neurovascular health, suggesting that the consequences of increased body weight extend far beyond the immediate metabolic disturbances commonly associated with obesity [ 123 ]. Another study focused on Adipsin, a complement system component known to be influenced by adiposity levels, and studied within the cerebrospinal fluid, providing insights into the biochemical pathways through which obesity might mediate changes in BBB integrity. This research suggests a direct link between metabolic health markers and the biochemical status of the BBB, illustrating the intricate connection between systemic metabolic health and neurovascular function [ 124 ]. These studies have shown elevated levels of certain markers, indicating compromised BBB integrity in the context of excessive body weight and associated metabolic derangements. The disruption is believed to be multifactorial, involving mechanisms such as increased systemic inflammation, altered lipid metabolism, and hypertension, all of which are prevalent in obesity and can adversely affect the endothelial cells constituting the BBB.

The mechanistic pathways through which obesity and T2D exacerbate BBB disruption are complex and multifaceted. Inflammatory cytokines, often elevated in obesity and T2D, are known to compromise BBB integrity by altering tight junction protein expression and endothelial cell function. Additionally, the hyperglycemic environment in T2D can induce oxidative stress and microvascular complications, further impairing BBB function. These alterations not only have direct implications for neurovascular health but also predispose individuals to a range of neurological disorders. Takechi et al. [ 125 ] shows that BBB dysfunction precedes cognitive decline and neurodegeneration in a diabetic insulin-resistant mouse modeled by 24 w of high fat and fructose fed, which may imply a causal link. Although this study is more centered around diabetes, the intersection with obesity (through insulin resistance) makes it pertinent. The above findings emphasizing the need for comprehensive management of these metabolic conditions to maintain BBB integrity and overall brain health.

In summary, the accumulating evidence underscores a significant association between obesity, T2D, and BBB disruption. This relationship highlights the importance of managing these metabolic disorders not only for cardiovascular health but also for maintaining the integrity of the neurovascular unit. As research in this field continues to evolve, understanding the specific pathways and impacts will be crucial for developing targeted interventions aimed at preserving BBB integrity in the face of growing obesity and T2D prevalence.

Obesity-induced neuroinflammation: phenotypes and mechanisms

Neuroinflammation, characterized by the activation of resident brain cells (microglia and astrocytes) and infiltration of peripheral immune cells, has been widely recognized as a critical pathological feature of obesity. The increased BBB permeability induced by obesity, as discussed earlier, not only allows harmful substances to penetrate into the CNS but also paves the way for peripheral immune cells to infiltrate the brain. Once in the CNS, these immune cells can instigate an inflammatory response, contributing significantly to obesity-induced neuroinflammation.

Microglia, the primary immune cells of the CNS, show enhanced activation in obesity. Once activated, microglia release a plethora of pro-inflammatory factors, including cytokines like TNF-α, IL-1β, and IL-6, thereby promoting a pro-inflammatory environment within the CNS [ 84 , 126 , 127 , 128 ]. In the research employed bone marrow chimerism mouse subjected to 15 or 30 w of HFD, it was also found that the aggregated inflammatory monocytes/macrophages located in the parenchyma and expressed the microglial marker Iba1 [ 129 ]. Notably, while microglia often exhibit a pro-inflammatory phenotype in the early stages of HFD-induced obesity, their activation state appears to change over time with prolonged exposure. As the study by Baufeld et al. [ 130 ] demonstrated, the initial microglial reaction in the hypothalamus of mice with 3 days of HFD was not accompanied by sustained increased pro-inflammatory cytokines with prolonged 20 w of HFD consumption. Rather, anti-inflammatory genes were upregulated while microglial sensing genes were downregulated [ 130 ]. This indicates that microglia may shift to a more anti-inflammatory or homeostatic phenotype after longer-term HFD consumption.

The regional heterogeneity of microglial responses is another important consideration. Microglia in the hypothalamic arcuate nucleus, for instance, displayed a markedly reaction to 8 weeks of HFD in a region-specific manner [ 130 ]. This underscores how microglia in different brain regions may uniquely adapted to their specific microenvironments and react differently to the metabolic challenges imposed by obesity. Furthermore, the plasticity and ability of microglia to respond to additional stimuli was preserved even after prolonged HFD feeding. When stimulated with LPS ex vivo after 8 weeks of HFD, hypothalamic microglia upregulated inflammatory genes comparable to microglia from control diet mice [ 130 ]. This indicates the microglia retain responsiveness despite adapting to the HFD conditions. Mechanically, Kim et al. [ 131 ] has been instrumental in highlighting the dynamic increase in uncoupling protein 2 ( Ucp2 ) mRNA expression in the hypothalamic microglia of mice following an 8 w-HFD regimen. This increase influences mitochondrial modifications that activate microglia, further contributing to hypothalamic inflammation and the overall susceptibility to obesity. In summary, emerging evidence indicates microglial phenotypes and functions are altered in a temporal and spatial manner by obesity and HFD consumption. While often displaying pro-inflammatory features acutely, microglia may adapt with anti-inflammatory or homeostatic responses over time. Their heterogeneous phenotypes across brain regions and retained ability to respond to stimuli highlight the complexity of microglial reactions in obesity.

Astrocytes, another critical cell type in the CNS, undergo reactive astrogliosis in obesity, characterized by changes in their morphology, proliferation, and function. Similar to microglia, activated astrocytes can also secrete pro-inflammatory cytokines, further exacerbating the neuroinflammatory response. Astrocytes in 16 w-HFD consumption displayed reactive astrogliosis, characterized by altered morphology and upregulation of intermediate filaments like glial fibrillary acidic protein (GFAP) [ 132 ]. This phenotypic shift was observable early during HFD feeding, even preceding substantial weight gain. Lin et al. [ 133 ] elucidates the upregulation of disease-associated astrocyte (DAA) and microglia markers in response to an 12 w-HFD that are similar to the pathogenesis of Alzheimer’s disease. providing a direct link between dietary habits, neuroinflammation, and neurodegeneration.

The functional profile of astrocytes was also altered by obesity-induced astrogliosis. Activated astrocytes upregulate expression of pro-inflammatory cytokines, including IL-6, IL-1β, and TNF-α in mice following an 11 w-HFD regimen [ 134 ]. This creates a self-perpetuating cycle, as increased cytokine levels can further stimulate astrogliosis. Additionally, aberrant release of gliotransmitters like glutamate from reactive astrocytes can also occur, potentially impacting neuronal excitability after a 16 w of HFD [ 126 ].

Multiple signaling pathways have been implicated in driving the astrogliotic transformation of astrocytes in obesity. As Thaler et al. demonstrated, astrocyte-specific inhibition of IKKβ/NF-κB signaling mitigated weight gain, glucose intolerance, and hypothalamic inflammation induced by 11 w of HFD consumption [ 134 ]. This suggests the IKKβ/NF-κB pathway is critical for obesity-related astrogliosis and its metabolic consequences. Calcineurin signaling has also been linked to astrocyte reactivity in response to HFD for 16 w [ 132 ]. Calcineurin inhibition attenuated gliosis in the arcuate nucleus, ventromedial hypothalamus, and dorsomedial hypothalamus of HFD mice. This implicates calcineurin/NFAT as another important mediator of astrocyte activation. In summary, the morphological and functional changes accompanying astrogliosis position astrocytes as key propagators of neuroinflammatory responses in obesity models induced by high-fat feeding. Delineating the intracellular signaling pathways driving these astrocyte alterations, such as IKKβ/NF-κB and calcineurin/NFAT, will contribute to uncover therapeutic targets for mitigating obesity-associated hypothalamic inflammation.

Monocytes/macrophages

Peripheral immune cells, particularly monocytes, have been reported to infiltrate the CNS in obesity. These monocytes can differentiate into macrophages, producing a variety of pro-inflammatory cytokines that exacerbate neuroinflammation. For instance, a bone marrow chimerism mouse model demonstrated that 15 or 30 w of HFD-induced obesity led to a 30% increase of immune cells in the CNS compared to controls [ 129 ]. Most of these cells exhibited a microglia/macrophage phenotype, being CD45 + CD11b + . The ratio of CD11b + CD45 hi to CD11b + CD45 lo cells was elevated, indicating an inflammatory state. In addition to the infiltration, HFD also promotes the differentiation of monocytes into macrophages. It was demonstrated that 14 w of HFD consumption induced the Ly6c high monocytes to differentiate into macrophages in the brain [ 135 ]. Another study showed that prolonged HFD consumption for 4 and 20 w led to expansion of the monocyte-derived macrophage pool in the hypothalamic arcuate nucleus, attributed to enhanced macrophage proliferation [ 136 ]. In mouse models of leptin receptor deficiency, breakdown of the BBB was found to enable macrophage infiltration into the brain of db/db mice [ 137 ]. Leptin resistance, glucose intolerance, and elevated cytokines like IL-1β and TNF-α accompanied the accumulation of macrophages. Notably, this study also suggested that IL-1β potentially play an important role in trafficking of peripheral monocytes into the brain. Overall, these studies indicate HFD-induced obesity facilitates the recruitment and proliferation of macrophages in the brain, propagating inflammation.

While the involvement of monocytes and macrophages in mediating neuroinflammatory responses under obese conditions is evident, it is imperative to scrutinize the methodologies employed in these studies for potential confounding factors. Specifically, investigations utilizing GFP bone marrow chimeras, such as those by Buckman et al. [ 129 ] and Baufeld et al. [ 130 ] mention above, provide critical insights into the trafficking of these immune cells in the context of obesity. However, the integral role of radiation treatment in these experimental designs warrants a careful evaluation of its impact on the observed outcomes. Radiation used to establish bone marrow chimeras, as a preparatory step for tracking immune cell migration, is known to independently alter BBB permeability and trigger pro-inflammatory responses, as detailed by Kierdorf et al. [ 138 ] This raises important considerations for interpreting studies on obesity-associated neuroinflammation: the increased permeability of the BBB and the augmented infiltration of monocytes/macrophages observed could be confounded by the radiation treatment itself, rather than being solely attributable to the effects of obesity.

Acknowledging these potential confounding effects is crucial for a comprehensive understanding of the dynamics between obesity and neurovascular integrity. Future research aimed at elucidating the specific roles of monocytes and macrophages in obesity-induced BBB disruption and brain inflammation should consider employing alternative methodologies that circumvent the need for radiation-induced bone marrow ablation. This approach will ensure a clearer delineation of the direct consequences of obesity on neuroimmune interactions and BBB integrity, free from the complicating effects of experimental interventions.

By carefully considering these methodological nuances, we can enhance our understanding of the complex interplay between obesity, BBB integrity, and the role of monocytes/macrophages in neuroinflammation, thereby paving the way for more targeted and effective therapeutic strategies.

Pro-inflammatory factors

Obesity-induced neuroinflammation is also marked by elevated expression levels of pro-inflammatory factors. In addition to TNF-α, IL-1β, and IL-6, other pro-inflammatory molecules such as inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) show increased expression in obesity. These molecules, produced by both infiltrating peripheral immune cells and activated resident brain cells, not only propagate inflammation but can also contribute to BBB disruption and neuronal damage. In human frontal cortex, it was reported that increased BMI has been found to cause iNOS-mediated inflammatory activity [ 139 ]. A recent finding demonstrated that iNOS promotes hypothalamic insulin resistance in obese rats. Aberrant nitrosative stress such as S-nitrosatiion (also referred to as S-nitrosylation) is closely associate with various neurological disorders such as AD and Parkinson’s Disease (PD) [ 140 , 141 ]. Inhibition of central iNOS ameliorated not only glucose metabolism, but also macrophage activation induced inflammation in hypothalamus of HFD-induced obesity mice [ 136 ]. In addition, 18 or 20 w of HFD consumption also can significantly enhance the expression of COX-2 in the hippocampus of the mice [ 142 , 143 ]. An increased activity of COX-2-PEG2 signaling pathway has been considered to play a key role in impairing hippocampal neuronal function and cognition [ 144 , 145 ].

Additionally, the aberrant activation of the inflammasome complexes, especially NOD-like receptor thermal protein domain associated protein 3 (NLRP3), plays a vital role in obesity-induced neuroinflammatory [ 146 ]. Within the CNS, microglia accumulate lipid droplets and activate NLRP3 inflammasomes under hyperglycemic conditions due to impaired lipophagy [ 147 ]. The microglial specific inflammatory amplifier TREM1 (triggering receptor expressed on myeloid cells), resulting in the buildup of microglial TREM1, was found to aggravates the HG-induced lipophagy damage and subsequently promoted HG-induced neuroinflammatory cascades via NLRP3 (NLR family pyrin domain containing 3) inflammasome. Pharmacological blockade of TREM1 with LP17 in db/db mice and HFD/STZ mice inhibited accumulation of lipid droplets and TREM1, reduced hippocampal neuronal inflammatory damage, and consequently improved cognitive functions [ 147 ].

Together, these diverse peripheral and central inflammatory pathways contribute to BBB disruption, neurotoxicity, and cognitive deficits associated with obesity. Targeting shared processes like nitrosative stress, which interacts with iNOS signaling, may simultaneously mitigate obesity-related neuroinflammation. The upregulation of pivotal inflammatory enzymes like iNOS and COX-2 highlights the multi-faceted nature of obesity-induced inflammation and the need for strategies that address key underlying pathways.

Mechanisms of Obesity-Induced Neuroinflammation . Enhanced microglial activation in obesity leads to increased release of pro-inflammatory cytokines (TNF-α, IL-1β, IL-6), contributing to a pro-inflammatory CNS environment. Concurrently, obesity triggers reactive astrogliosis in astrocytes characterized by morphological and functional changes, and secretion of pro-inflammatory cytokines. The infiltration of peripheral monocytes into the CNS, and differentiation into macrophages further exacerbates neuroinflammation through additional cytokine production. Elevated expression of pro-inflammatory factors such as iNOS and COX-2 in obesity are also depicted, highlighting their potential role in neuroinflammation, BBB disruption, and neuronal damage

Dietary influences on BBB integrity and obesity outcomes

The intricate relationship between obesity and inflammation underscores the multifaceted nature of BBB dysfunction. While the systemic inflammation associated with obesity undoubtedly impacts BBB integrity, it is imperative to address the direct and indirect effects of dietary patterns on neurovascular health. The link between diet, obesity, and BBB integrity provides a unique lens through which to understand the broader implications of nutritional habits on cerebral health and disease susceptibility. Freeman et al. [ 148 ] highlights the adverse effects of a 6 month consumption of high-fat/high-cholesterol (HFHC) diet on BBB function, demonstrating a reduction in BBB integrity markers and an increase in inflammatory responses within the hippocampus in middle-aged rats. This suggests that HFHC diets, beyond contributing to obesity, may directly compromise the BBB, thereby facilitating a neuroinflammatory state that could underpin cognitive decline. Similarly, Davidson et al. [ 149 ] reported on the effects of high-energy diets on hippocampal-dependent cognitive functions and BBB integrity. Their findings emphasized that diets high in saturated fats and sugars, hallmarks of the “Western” diet, not only contribute to obesity but also impair types of learning and memory dependent on the hippocampus. The unique aspect of this research is the differentiation between diet-induced obese and diet-resistant rats, highlighting how diet affects cognitive function and BBB integrity differently based on individual susceptibility to obesity. This is particularly concerning as it implies that dietary components can directly influence cognitive functions by altering hippocampal integrity and possibly disrupting the BBB. The dietary components and direct influences on BBB was detailed in Table  1 , which underscores the potential for dietary modifications as preventive or mitigative strategies against obesity-induced neurovascular alterations.

Emerging therapeutic strategies in tackling obesity-induced BBB dysfunction

In addressing the complexities of obesity-induced metabolic syndrome and its impact on the central nervous system, a spectrum of experimental therapies is currently under investigation, each targeting distinct pathological mechanisms. One promising approach involves the decrease of BBB permeability. Agents such as Palmitoylethanolamide, Topiramate, and Nicotine have demonstrated potential in modulating BBB dynamics, suggesting a pivotal role in mitigating obesity-related neurological sequelae in mice subjected to 10–36 w of HFD or 10 w of high saturated fatty acids diet consumption [ 72 , 73 , 84 ]. Among these, Palmitoylethanolamide has shown promise, not only in attenuating anxiety-like behavior but also in modulating neurotransmitter levels such as dopamine turnover and γ-aminobutyric acid (GABA) levels in the amygdala from mice subjected to 19 w of HFD consumption [ 84 ]. Additionally, it has been found to reduce systemic inflammation markers like TNF-α and IL-1β, attenuate hypothalamic injury, and decrease neuroinflammation and BBB permeability in the hippocampus. However, while its effects on BBB permeability are notable, further investigation is necessary to fully understand its long-term efficacy and safety profile, particularly in relation to chronic administration and potential systemic effects.

Topiramate, another therapeutic agent, has been demonstrated its capability to decrease BBB permeability in mice subjected to 13 and 36 w of HFD [ 72 ]. It achieves this through increasing the expression of tight junction proteins like ZO-1 and Claudin-12, which are crucial in maintaining BBB integrity. Topiramate also exhibits properties that inhibit oxidative stress, a common pathological feature in obesity-related neurological disorders. In this regard, agents like Dapsone and Resveratrol have garnered attention for their potential to protect tight junctions and reduce BBB disruption in mice following 8 w of HFD [ 74 , 77 ]. Dapsone, for instance, has been found to decrease brain microvascular leakage, which is often exacerbated in obesity. It does so by inhibiting the oxidation of low-density lipoproteins (LDL), a process that is detrimental to the BBB [ 77 ]. Additionally, Dapsone has shown efficacy in protecting tight junction proteins such as ZO-1, Claudin-5, and Occludin, further enhancing its role in preserving BBB integrity in mice following 8 w of HFD. Similarly, Resveratrol, a naturally occurring polyphenolic compound, has been demonstrated its capacity to fortify BBB tight junctions. Its neuroprotective properties extend beyond just maintaining the BBB; Resveratrol also exhibits antioxidative and anti-inflammatory effects, which are beneficial in addressing the multifactorial aspects of obesity-induced neural damage in mice subjected 8 w of HFD [ 74 ]. However, while these agents show promise, their clinical application faces challenges. The variability in individual responses and potential side effects, such as hypersensitivity reactions with Dapsone, require careful consideration. Future research must focus on optimizing these therapies, possibly through targeted delivery systems or combination therapies, to enhance their protective effects on the BBB while minimizing adverse reactions. Despite these benefits, the challenge with Topiramate lies in its potential side effects, such as cognitive disturbances and weight loss, which might limit its use in certain patient populations. Future research should focus on optimizing its dosage and delivery mechanisms to maximize its therapeutic benefits while minimizing adverse effects. Besides the aforementioned strategies, other pharmacological effects pivotal in managing obesity-induced complications include the modulation of oxidative stress and cellular death, the regulation of metabolic pathways, and the enhancement of neural regeneration, each contributing uniquely towards mitigating the multifaceted challenges posed by obesity.

In conclusion, the emerging therapeutic strategies discussed in this section underscore the complexity and multidimensionality of tackling obesity-induced metabolic syndrome and its neurological implications. From enhancing BBB integrity to addressing oxidative stress, metabolic dysregulation, and promoting neuroregeneration, each approach offers a unique angle in combating the extensive impact of obesity [ 150 , 151 ]. This multifaceted approach not only broadens our understanding but also paves the way for innovative and comprehensive treatments in the ongoing battle against obesity-related neurological disorders. The major experimental therapies are summarized in Table  2 , which presents a curated list of compounds demonstrated to exert protective effects against the adverse consequences of obesity on the brain, particularly focusing on maintaining or restoring BBB integrity. Each compound included has been selected based on empirical evidence from studies highlighting its efficacy in counteracting obesity-induced neurovascular dysfunction.

Conclusion and future directions

The intricate association between obesity and the BBB has been the primary focus of this review. We have delved into the fundamental understanding of BBB dysfunction in the context of obesity, detailing the altered BBB permeability, transport dysfunction, and neuroinflammation, each contributing to a multifaceted pathophysiological landscape that paves the way for obesity-related neurological disorders. Despite the progress made, several gaps persist in our understanding of obesity-induced BBB dysfunction. For instance, more in-depth studies on the temporal relationship between obesity and BBB dysfunction could provide insight into the initial triggers of these changes. Furthermore, it remains unclear whether BBB dysfunction is a universal feature of obesity or if it varies with factors such as the degree and duration of obesity, age, sex, and genetic predisposition. Moreover, the potential reversibility of obesity-induced BBB changes and the optimal strategies for achieving such reversibility warrant exploration.

Recognizing BBB dysfunction’s role in obesity-related neurological diseases holds significant promise for future therapeutic advancements. By better understanding the interactions between BBB dysfunction and neurodegeneration, we may discover new strategies to mitigate or even prevent the deleterious effects of obesity on the CNS. There lies immense potential in targeting the BBB for therapeutic intervention, with the promise of not only alleviating obesity-induced BBB dysfunction but also mitigating its downstream effects, including neuroinflammation and neurodegeneration.

In conclusion, obesity-induced BBB dysfunction represents an area of research with implications extending beyond the realm of obesity to a broad spectrum of neurological disorders. A more comprehensive understanding of obesity’s influence on the CNS would ultimately benefit those affected by obesity and its neurological consequences.

Data availability

No datasets were generated or analysed during the current study.

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This work was supported by the National Natural Science Foundation of China (32170985, 82273923), National Key Research and Development Program of China (2023YFE0202200 and 2021YFA0910000), Guangdong Province Basic and Applied Basic Research Grant (2021B1515120089), Shenzhen Science and Technology Program (JCYJ20210324115800003, ZDSYS20190902093409851 JCYJ20220531100203008), International collaboration project of Chinese Academy of Sciences (172644KYSB20200045), CAS-Croucher Funding Scheme for Joint Laboratories, and Guangdong Innovation Platform of Translational Research for Cerebrovascular Diseases.

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Feng, Z., Fang, C., Ma, Y. et al. Obesity-induced blood-brain barrier dysfunction: phenotypes and mechanisms. J Neuroinflammation 21 , 110 (2024). https://doi.org/10.1186/s12974-024-03104-9

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• Published: 30 April 2024

Inhibition of mammalian mtDNA transcription acts paradoxically to reverse diet-induced hepatosteatosis and obesity

• Shan Jiang 1   na1 ,
• Taolin Yuan 1   na1 ,
• Florian A. Rosenberger   ORCID: orcid.org/0000-0003-4604-6170 2 ,
• Arnaud Mourier   ORCID: orcid.org/0000-0002-5413-611X 3 ,
• Nathalia R. V. Dragano 4 , 5 ,
• Laura S. Kremer 1 ,
• Diana Rubalcava-Gracia   ORCID: orcid.org/0000-0002-4615-7375 1 ,
• Fynn M. Hansen 2 ,
• Melissa Borg 6 ,
• Mara Mennuni 1 ,
• Roberta Filograna 1 ,
• David Alsina 1 ,
• Jelena Misic 1 ,
• Camilla Koolmeister 1 ,
• Polyxeni Papadea   ORCID: orcid.org/0000-0002-5131-8196 1 ,
• Martin Hrabe de Angelis 4 , 5 , 7 ,
• Lipeng Ren 8 ,
• Olov Andersson   ORCID: orcid.org/0000-0001-6715-781X 8 ,
• Anke Unger   ORCID: orcid.org/0000-0003-1288-3763 9 ,
• Tim Bergbrede   ORCID: orcid.org/0000-0002-0930-1884 9 ,
• Raffaella Di Lucrezia   ORCID: orcid.org/0000-0001-6510-9945 9 ,
• Rolf Wibom 10 ,
• Juleen R. Zierath   ORCID: orcid.org/0000-0001-6891-7497 6 , 11 ,
• Anna Krook   ORCID: orcid.org/0000-0002-0891-0258 6 ,
• Patrick Giavalisco   ORCID: orcid.org/0000-0002-4636-1827 12 ,
• Matthias Mann   ORCID: orcid.org/0000-0003-1292-4799 2 &
• Nils-Göran Larsson   ORCID: orcid.org/0000-0001-5100-996X 1  

Nature Metabolism ( 2024 ) Cite this article

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• Energy metabolism
• Fat metabolism

The oxidative phosphorylation system 1 in mammalian mitochondria plays a key role in transducing energy from ingested nutrients 2 . Mitochondrial metabolism is dynamic and can be reprogrammed to support both catabolic and anabolic reactions, depending on physiological demands or disease states. Rewiring of mitochondrial metabolism is intricately linked to metabolic diseases and promotes tumour growth 3 , 4 , 5 . Here, we demonstrate that oral treatment with an inhibitor of mitochondrial transcription (IMT) 6 shifts whole-animal metabolism towards fatty acid oxidation, which, in turn, leads to rapid normalization of body weight, reversal of hepatosteatosis and restoration of normal glucose tolerance in male mice on a high-fat diet. Paradoxically, the IMT treatment causes a severe reduction of oxidative phosphorylation capacity concomitant with marked upregulation of fatty acid oxidation in the liver, as determined by proteomics and metabolomics analyses. The IMT treatment leads to a marked reduction of complex I, the main dehydrogenase feeding electrons into the ubiquinone (Q) pool, whereas the levels of electron transfer flavoprotein dehydrogenase and other dehydrogenases connected to the Q pool are increased. This rewiring of metabolism caused by reduced mtDNA expression in the liver provides a principle for drug treatment of obesity and obesity-related pathology.

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The first attempts to target mitochondria to treat obesity were reported in the 1930s when more than 100,000 individuals were treated with the uncoupler dinitrophenol (DNP) 7 , 8 , 9 . Although this treatment increased the metabolic rate and reduced obesity, serious side effects prevented DNP from becoming an established treatment 7 , 8 . Metformin provides an alternate way to inhibit oxidative phosphorylation (OXPHOS) and this mild complex I inhibitor is widely used as an anti-diabetic medication and also protects against cancer 10 , 11 , 12 , 13 , 14 , 15 . The possible connection between beneficial metabolic effects and anti-cancer activity of drugs targeting mitochondria prompted us to investigate whether inhibitor of mitochondrial transcription (IMT) treatment, which is known to impair tumour metabolism and growth in mouse models 6 , also may have beneficial metabolic effects. Treatment of tumour cell lines with IMT induces a dose-dependent impairment of OXPHOS and cellular metabolic starvation, with progressively reduced levels of a range of critical metabolites and eventually cell death 6 . Despite the drastic effects on metabolism in cancer cell lines and cancer xenografts, treatment of whole animals is well tolerated 6 . We therefore decided to test the hypothesis that IMT treatment aiming to moderately impair the OXPHOS capacity in whole animals may induce beneficial metabolic effects in healthy and metabolically challenged mice.

Male C57BL/6N mice at the age of 4 weeks were randomly chosen to be fed a chow diet or high-fat diet (HFD) for 8 weeks. Thereafter, the two groups were subdivided for oral treatment (gavage) with either IMT (LDC4857, 30 mg kg −1 ) or vehicle for 4 weeks while continuing the respective diets (Fig. 1a ). The IMT compound used in this study was developed within an optimization programme based on the structurally closely related IMT1B compound. IMT treatment of mice on HFD causes a rapid marked reduction of body weight after 1 week, with a cumulative weight loss of ~7 g after 4 weeks (Fig. 1b ). Measurements of body composition with non-invasive magnetic resonance imaging (EchoMRI-100) after 4 weeks of IMT treatment showed markedly reduced fat mass without any change of lean mass (Fig. 1c ). Haematoxylin and eosin (H&E) staining of tissue sections of epididymal white adipose tissue (eWAT) showed that HFD results in large lipid-filled adipocytes and that IMT treatment leads to a drastic decrease in adipocyte size (Extended Data Fig. 1a ).

a , Experimental strategy for diet intervention and IMT treatment. Male 4-week-old C57BL/6N mice were randomly fed either a chow diet or HFD for 8 weeks. Thereafter, the diet was continued and mice were orally treated with IMT (30 mg kg −1 ) or vehicle for 4 weeks. Six independent cohorts of mice were used in this study; total mice n  = 260. b , Body weight in mice on a chow diet or HFD treated with vehicle or IMT compound; n  = 22 mice per group. Asterisk indicates a significant difference between HFD IMT and HFD vehicle. # Indicates a significant difference between chow vehicle and HFD vehicle. P values are indicated. c , Body composition showing fat mass and lean mass after 4 weeks of IMT treatment; n  = 17 mice per group. d , g , Measurement of whole-body metabolism during the fourth week of gavage treatment with vehicle or IMT compound using Oxymax/CLAMS. Food intake during the fourth day ( d ). The average RER over 42 h during the light and dark cycles ( g ). The RER in the four groups of mice. Chow vehicle, n  = 10; chow IMT, n  = 10; HFD vehicle, n  = 8; and HFD IMT, n  = 11 mice. e , Mouse faeces was collected for 4 days during the fourth week of IMT or vehicle treatment using the Single Mouse Metabolic Cage System. Faecal lipids were extracted using Folch’s method; n  = 9 mice per group. f , Total faecal energy was analysed using bomb calorimetry; n  = 9 mice per group. Data are presented as mean ± s.e.m. ( b – g ). Statistical significance was assessed by a two-way ANOVA with Tukey’s test for multiple comparisons. P values are indicated. Part of the image in a was created with BioRender.com .

Source data

We next assessed whole-body energy homoeostasis in mice on chow diet or HFD treated with vehicle or the IMT compound using the Oxymax/Comprehensive Lab Animal Monitoring System (CLAMS). The four groups of mice were subjected to five continuous days of CLAMS analysis during the fourth week of gavage treatment with vehicle or IMT compound. The first 3 days were used to acclimate the animals to the CLAMS system, followed by measurements during the fourth day. Day five included a 12-h period of fasting followed by 6 h of refeeding. Notably, IMT treatment did not alter food intake (Fig. 1d ) or physical activity (Extended Data Fig. 1b ). We found that IMT treatment did not increase the lipid content in faeces (Fig. 1e ) or the total diurnal lipid excretion in faeces (Extended Data Fig. 1c,d ). We performed bomb calorimetry and found a higher energy content in faeces in mice on a chow diet in comparison with mice on HFD, consistent with results from other studies 16 , 17 . IMT treatment did not additionally alter the total energy content in faeces (Fig. 1f ). These analyses of faeces thus exclude that drug-induced malabsorption explains the weight loss.

IMT-treated mice on HFD showed enhanced oxygen consumption during both the light and dark cycle (Extended Data Fig. 2a ). Regression-based analysis of covariance (ANCOVA) 18 , 19 with either total mass or lean mass as a covariate did not clearly link increased energy expenditure to IMT treatment (Extended Data Fig. 2b,c ). Although these results indicate that IMT treatment may not exert its effect through increasing energy expenditure, subtle differences in energy expenditure can be hard to detect by indirect calorimetry despite having a profound long-term impact on body weight 18 , 20 . We therefore proceeded to assess the respiratory exchange ratio (RER) as this parameter needs no normalization to body weight or body composition. Mice on a standard chow diet had a RER of ~0.9–1.1, whereas it was decreased to ~0.8 on HFD (Fig. 1g and Extended Data Fig. 2d ), as expected. Upon refeeding after fasting, IMT treatment resulted in a lower RER in comparison with vehicle treatment, regardless of the diet (Fig. 1g and Extended Data Fig. 2d ), consistent with drug-induced activation of fat metabolism. These data provide evidence that IMT treatment reverses HFD-induced obesity by promoting metabolism of fat at the organismal level.

We found normal fasting blood glucose levels accompanied by markedly increased fasting serum insulin levels (Extended Data Fig. 3a,b ) and pathological intraperitoneal glucose tolerance tests (ipGTT; Extended Data Fig. 3c–e ) with an increased peak concentration of serum insulin (Extended Data Fig. 3e ) in mice on HFD, consistent with a pre-diabetic state and insulin resistance. Glucose homoeostasis was markedly improved when mice on HFD were treated with an IMT compound for 4 weeks; the fasting blood glucose was reduced (Extended Data Fig. 3a ), serum insulin levels were decreased (Extended Data Fig. 3b ) and the ipGTT responses were normalized (Extended Data Fig. 3c–e ). IMT treatment leads to reduced circulating insulin levels (Extended Data Fig. 3b ), but ex vivo glucose-stimulated insulin secretion (GSIS) assays showed that IMT treatment did not impair insulin secretion or insulin biosynthesis in isolated pancreatic islets (Extended Data Fig. 3f,g ). The reduced circulating insulin levels and normalized glucose homoeostasis in IMT-treated mice on HFD are, thus, probably explained by increased insulin sensitivity.

We observed a large macrovesicular steatosis in the liver of mice on HFD (Fig. 2a ). Notably, IMT treatment markedly reduced hepatosteatosis (Fig. 2a ), leading to a decreased lipid content in the liver (Fig. 2b ) and reduced liver weight (Fig. 2c ). We performed lipidomics and found a large accumulation of diglycerides and triglycerides in the liver of mice on HFD, which was reversed by 4 weeks of IMT treatment (Fig. 2d ). In contrast, the phospholipid and sphingolipid levels in the liver were mainly affected by the diet and not markedly impacted by IMT treatment (Extended Data Fig. 4 ). IMT treatment of mice on HFD was accompanied by an improvement of the liver function, as demonstrated by decreased aminotransferase activities in the serum (Fig. 2e,f ). The serum albumin levels were normal in all investigated groups (Fig. 2g ). Taken together these data show that IMT treatment can reverse diet-induced hepatosteatosis and normalize liver function.

a , Representative images of H&E staining showing liver structure and morphology in mice on a chow diet or HFD treated with either vehicle or IMT compound. Scale bars, 100 µm. n  = 5 mice per group. b , Quantitative measurement of triglycerides in mouse liver after 4 weeks of IMT treatment; n  = 12 mice per group. c , Liver weight in mice treated with vehicle or IMT for 4 weeks; n  = 30 mice per group. d , The levels of diglycerides and triglycerides in mouse liver after 4 weeks of IMT treatment. Chow vehicle, chow IMT and HFD vehicle, n  = 8 mice per group; HFD IMT, n  = 7 mice. Veh, vehicle. e – g , Serum alanine aminotransferase (ALT) activity ( e ) aspartate aminotransferase (AST) activity ( f ) and albumin levels ( g ) measured in mice after 4 weeks of vehicle or IMT treatment; n  = 18 mice per group. h , Mitochondrial transcript levels in the liver after 4 weeks of IMT treatment; n  = 12 mice per group. i , IMT concentration in plasma and mouse tissues. Plasma, n  = 5 mice per group; liver chow IMT, n  = 7 mice; HFD IMT, n  = 8 mice; heart, skeletal muscle, eWAT, n  = 8 mice per group; BAT, n  = 3 mice per group. Data are presented as mean ± s.e.m. ( b , c , e – i ). Statistical significance was assessed by a two-way ANOVA with Tukey’s test for multiple comparisons ( b , c , e , g , i ) and a Mann–Whitney U -test ( f , h ). P values are indicated.

IMT treatment resulted in marked reduction in levels of mtDNA-encoded transcripts (Fig. 2h ) and mtDNA (Extended Data Fig. 5a ) in the livers of mice on a chow diet or HFD. The decrease of mtDNA is probably due to a decreased formation of the RNA primers needed for initiation of mtDNA replication, because IMT inhibits POLRMT, which is not only necessary for gene expression but also serves as the primase for mammalian mtDNA replication 21 , 22 , 23 . Treatment with IMT resulted in a moderate decrease of mtDNA-encoded transcripts and mtDNA levels in eWAT (Extended Data Fig. 5b,c ), but there was no decrease in levels of OXPHOS subunits (Extended Data Fig. 5d ). No significant changes in the levels of mtDNA-encoded transcripts or mtDNA levels were observed in skeletal muscle after IMT treatment (Extended Data Fig. 5e,f ). No significant changes in levels of mtDNA-encoded transcripts were observed in the heart (Extended Data Fig. 5g ) and brown adipose tissue (BAT) (Extended Data Fig. 5h ) after IMT treatment.

To gain further insights into the differences in inhibition of mtDNA transcription between tissues, we measured IMT concentrations 24 h after the last dose in mice treated with IMT for 4 weeks (Fig. 2i ). The IMT concentrations were much higher in the plasma and liver than in the heart, skeletal muscle, eWAT and BAT (Fig. 2i ), which shows that the IMT compound is preferentially accumulated in the liver due to the oral route of administration and the first-passage effect. The skewed tissue distribution of IMT, thus, explains the preferential strong inhibitory effect on mtDNA transcription in liver.

We used label‐free quantitative proteomics to identify differentially expressed proteins in the homogenates from liver tissue or ultra-purified liver mitochondria. In total, 4,408 proteins were identified in the liver tissue proteome of mice on a chow diet or HFD, and IMT treatment caused a significant change in the levels of ~15–20% of these proteins (false discovery rate (FDR) < 0.05, Extended Data Fig. 6a ). A high proportion (68.7% at FDR < 0.05) of the proteins whose levels changed significantly after IMT treatment were classified as mitochondrial proteins, according to MitoCarta 3.0 (ref. 24 ). We performed principal-component analyses (PCA) and found that changes in both the total and mitochondrial liver proteome were mainly determined by the IMT treatment (Extended Data Fig. 6b ).

A detailed inspection of the OXPHOS subunits in the mitochondrial proteome showed that IMT treatment significantly decreased the levels of subunits of complex I, III, IV and the membrane portion (F 0 ) of complex V, whereas subunits of the matrix portion (F 1 ) of complex V were less affected or even increased (Extended Data Fig. 6c and Extended Data Fig. 7a ). In contrast, the levels of subunits of complex II (succinate dehydrogenase) were increased (Extended Data Fig. 7a ), consistent with the lack of mtDNA-encoded subunits in this complex. Western blot analyses confirmed the reduction in the levels of OXPHOS complexes containing mtDNA-encoded subunits (Extended Data Fig. 7b ). We also observed an increase in the levels of many OXPHOS complex assembly factors (Extended Data Fig. 7c ). Levels of most of the mitochondrial ribosomal proteins were drastically decreased (Extended Data Fig. 8a ) because IMT treatment decreases the 12S and 16S rRNA levels (Extended Data Fig. 8b ) necessary for assembly of the mitochondrial ribosome.

We measured the activity of respiratory chain enzymes in liver mitochondria and found that IMT treatment caused a marked decrease in the activity of complex I, I/III and IV, whereas complex II and complex II/III activities were maintained (Extended Data Fig. 9a ). Consistently, in-gel activities of OXPHOS complexes resolved by blue native polyacrylamide gel electrophoresis (BN-PAGE) showed a marked decrease in the levels and activities of assembled complex I and IV, and an increase in the level and activity of complex II in the liver mitochondria from IMT-treated mice irrespective of diet (Extended Data Fig. 9b ). Furthermore, IMT treatment decreased the levels of fully assembled complex V and induced the appearance of a subassembly with ATPase activity (Extended Data Fig. 9b ), consistent with proteomics analyses (Extended Data Fig. 7a ) and previous observations of this sub-assembled complex in mouse models with reduced mtDNA gene expression 25 , 26 .

To further assess the impact of IMT treatment on bioenergetics, we performed high-resolution respiration experiments (Oroboros) on freshly isolated liver mitochondria from mice on a chow diet or HFD treated with vehicle or IMT. In the liver mitochondria from vehicle-treated mice, we observed additive effects on the oxygen consumption rate (OCR) when multiple substrates (palmitoylcarnitine, pyruvate + glutamate + malate, succinate and glycerol-1-phosphate) were sequentially added, but this additive effect was substantially reduced after IMT treatment (Extended Data Fig. 9c ). In contrast, the oxidation of palmitoylcarnitine as a single substrate was similar in mouse liver mitochondria from animals treated with vehicle or IMT (Extended Data Fig. 9d ). To assess OXPHOS function in more detail, respiration was measured using different combinations of substrates fuelling complex I (pyruvate, glutamate and malate), complex II (succinate and rotenone) and β-oxidation (palmitoylcarnitine) under phosphorylating (state 3), non-phosphorylating (pseudo-state 4 with oligomycin) and uncoupled states. In agreement with the first set of experiments (Extended Data Fig. 9d ), we found maintained state 3 fatty acid oxidation in IMT-treated mice (Fig. 3a ). Of note, despite the substantially impaired enzyme activity of complex I and IV (Extended Data Fig. 9a ), the maximal uncoupled respiration was only mildly affected (Fig. 3b–d ). In contrast, respiration assessed under phosphorylating conditions was severely impaired by the IMT treatment, suggesting that the phosphorylating respiration is highly controlled and impacted by ATP synthase deficiency (Fig. 3b–d ). The IMT-induced ATP synthase deficiency selectively impaired complex I- and complex II-driven phosphorylating respiration (Fig. 3b–d ), reducing the OCR to a similar level as β-oxidation-driven respiration (Fig. 3a ). To summarize, the bioenergetic characterization of liver mitochondria from IMT-treated mice showed maintained fatty acid oxidation capacity despite markedly reduced OXPHOS capacity.

a – d , Respiration of fresh liver mitochondria on malate/palmitoylcarnitine ( a ), succinate/rotenone ( b ), pyruvate/malate/glutamate (PGM) ( c ) and glutamate/malate (GM) ( d ) at state 3, state 4 and the uncoupled state. Chow vehicle and chow IMT groups, n  = 5 mice per group; HFD vehicle, n  = 7 mice; HFD IMT, n  = 8 mice. Data are presented as mean ± s.e.m. Statistical significance was assessed by a two-way ANOVA with Tukey’s test for multiple comparisons. P values are indicated. J O 2 , oxygen consumption flux; natO, nanoatom oxygen. e , Volcano plot presenting all quantified proteins in mouse liver on a chow diet or HFD and subjected to vehicle or IMT treatment. The differentially expressed subunits of different OXPHOS complexes are highlighted in different colours. f , GSEA of total tissue and mitochondrial proteomes from the liver. g , Heatmaps illustrating the protein density of enzymes involved in fatty acid oxidation in mouse livers after 4 weeks of vehicle or IMT treatment; n  = 3 mice per group ( e – g ).

To further analyse the proteomics dataset, we used volcano plots and highlighted differentially expressed OXPHOS subunits in liver protein extracts from IMT-treated mice (Fig. 3e ). The majority of subunits of complexes I, III and IV were downregulated, complex II subunits were upregulated, subunits of the F 1 portion of complex V were increased, and subunits if the F 0 portion of complex V were decreased (Fig. 3e ), consistent with results from BN-PAGE (Extended Data Fig. 9b ) and proteomics heatmaps (Extended Data Fig. 7a ). To identify pathways influenced by IMT treatment, we performed gene set enrichment analysis (GSEA) of the total liver tissue proteome and found that enzymes involved in fatty acid metabolism/degradation were markedly enriched in the liver of IMT-treated mice, regardless of diet (Fig. 3f ). The GSEA findings indicate that IMT rewires liver metabolism to favour fatty acid degradation, as documented by the increased levels of several key fatty acid oxidation enzymes, for example, the heterodimeric electron transfer flavoprotein subunits (ETFA and ETFB), electron transfer flavoprotein dehydrogenase (ETF-DH) and the carnitine acyltransferases (CPT1a and CPT2) (Fig. 3g ).

We proceeded to investigate whether fasting can induce changes in protein expression that are similar to those seen after IMT treatment. Age-matched C57BL/6N mice were fasted for 16 h and total liver protein extracts were used for label-free proteomics analyses (Extended Data Fig. 10a,b ). We found nearly no correlation of liver protein expression between fasting and IMT-treated mice under chow diet ( R  = −0.01) or HFD ( R  = −0.07). Thus, the reversal of obesity, depending on IMT-induced activation of fatty acid oxidation in liver, does not mimic induction of a fasting-like response.

Next, we performed metabolomics to identify metabolites in the extracts from liver tissue after 4 weeks of IMT treatment in mice on a chow diet or HFD. We found that the levels of quinones (Q9 and Q10) were normal or increased (Extended Data Fig. 10c ). When human cancer cell lines are treated with IMT1B, there is a time-dependent, marked decrease of triphosphate nucleotides (accompanied by an increase in mono- and diphosphate nucleotides) and a decrease in amino acids, which leads to a cellular energy crisis, activation of AMPK and cell death 6 . In contrast, metabolite analyses in the livers of IMT-treated mice on a chow diet or HFD showed normal levels of key mono-, di- and triphosphate nucleotides (Extended Data Fig. 10d ) and amino acids (Extended Data Fig. 10e ), consistent with the observation that liver function is not impaired by IMT treatment (Fig. 2e–g ). The calculated ratio of AMP to ATP was not changed by IMT treatment (Extended Data Fig. 10f ) and consistently, we found no difference in the phosphorylation of AMPK and the downstream acetyl CoA carboxylase (ACC) enzyme in the liver (Extended Data Fig. 10g ). We proceeded with a comparison of proteomics and metabolomics data and found an overall pattern consistent with the activation of fatty acid oxidation in the liver of IMT-treated mice on HFD (Fig. 4a ).

a , An integrated view of the changes of metabolite and protein levels in liver of mice on a chow diet or HFD and subjected to vehicle or IMT treatment. The protein levels are represented by circles and the metabolite levels are represented by diamonds. b , Substrate oxidation and electron transfer pathways to Q under normal conditions. Glucose is metabolized and produces pyruvate. Pyruvate is imported to the mitochondria and is converted to acetyl-CoA, which enters the tricarboxylic acid (TCA) cycle and generates NADH. Complex I oxidizes NADH and functions as the primary entry point for electrons into the Q pool. Electrons, thereafter, flow through complex III, then through cytochrome c and finally reach complex IV where they reduce molecular oxygen to water. c , IMT impairs the OXPHOS capacity and rewires the pathways for electron transfer to Q. IMT reduces the activities of complex I, III and IV. As a consequence, the capacity of complex I to oxidize NADH is reduced and the electron transfer through electron-transfer flavoprotein dehydrogenase (ETF-DH) to the Q pool is increased. IMT treatment, thus, impairs the OXPHOS system leading to a rewiring of liver metabolism that decreases OXPHOS capacity and maintains fatty acid oxidation. Images in b and c were created with BioRender.com . CI, complex I; CII, complex II; CIII, complex III; CIV, complex IV; CV, complex V; α-KG, α-ketoglutarate; Gpdm, mitochondrial glycerol phosphate dehydrogenase; CoQ9, ubiquinone biosynthesis protein COQ9, mitochondrial; SCAD, short chain acyl-CoA dehydrogenase; MCAD, medium chain acyl-CoA dehydrogenase; VLCAD, very long chain acyl-CoA dehydrogenase.

We traditionally think of the mammalian respiratory chain as consisting of complexes I–IV, where complex I and II both are dehydrogenases that directly deliver electrons to the Q pool 1 (Fig. 4b ); however, there are at least four additional dehydrogenases that are connected to the matrix- or intermembrane-space side of the inner mitochondrial membrane 27 . The OXPHOS system thus integrates many metabolic pathways through several different dehydrogenases that directly deliver electrons to the Q pool for subsequent electron translocation by complex III and IV, leading to the final reduction of molecular oxygen to water. Although the biogenesis of complex I is critically dependent on mtDNA expression 26 , 28 , the other dehydrogenases that directly deliver electrons to the Q pool are exclusively nucleus-encoded and are therefore not directly impacted by IMT treatment. The final step of fatty acid oxidation depends on the ETF dehydrogenase that directly delivers electrons to the Q pool for transfer to complex III and IV. Complex I is the main dehydrogenase in the OXPHOS system and it is possible that a reduction of its levels and/or activity allows better access for ETF-DH to directly deliver electrons to the Q pool (Fig. 4b,c ). This type of competition between different dehydrogenases for electron delivery to the Q pool has been documented in budding yeast 29 and recently between complex I and II in mammals 30 and may also occur for other Q-oxidoreductases fuelling the respiratory chain.

We present a range of findings showing that IMT treatment has beneficial metabolic effects, causing (1) decreased weight (fat content) of mice on HFD (Fig. 1b,c and Extended Data Fig. 1a ) without affecting food intake (Fig. 1d ), physical activity (Extended Data Fig. 1b ) or intestinal nutrient uptake (Fig. 1e,f and Extended Data Fig. 1c,d ); (2) decreased RER consistent with a shift towards fatty acid oxidation at the organismal level (Fig. 1g and Extended Data Fig. 2d ); (3) decreased OXPHOS protein levels and impaired OXPHOS activities in liver, but not in other organs, (Extended Data Figs. 6c , 7a,b and 9a ) consistent with an accumulation of IMT in liver (Fig. 2i ); and (4) rewiring of liver metabolism towards fatty acid oxidation (Fig. 4a ), concomitant with maintained fatty acid oxidation in the liver mitochondria despite markedly reduced OXPHOS capacity (Fig. 3a–d ). We hypothesize that IMT treatment leads to a metabolic reprogramming in the liver that shifts the balance of dehydrogenases that deliver electrons to the Q pool (Fig. 4a,b ) thereby favouring fatty acid oxidation.

The present study strongly suggests that IMT treatment increases fatty acid oxidation in the liver in mice on HFD. Future studies with isotope-labelled substrates in isolated liver cells or whole animals will be necessary to further validate this observation.

IMT substance and vehicle for daily gavage

The IMT substance used in this study (LDC4587) is closely related to the previously published IMT1B 6 . The IMT compound was suspended in 0.5% ( w / v ) hydroxypropyl methylcellulose (Hypromellose, Sigma-Aldrich, H3785) and a dose of 30 mg kg −1 body weight was given by gavage once per day.

Pharmacokinetics analyses

LDC4857 was extracted from plasma and tissues by protein precipitation using acetonitrile. Tissue samples were homogenized using two parts ( w / v ) of PBS before extraction with acetonitrile. Following filtration, samples were analysed by liquid chromatography–tandem mass spectrometry (LC–MS/MS) using a Prominence UFLC system (Shimadzu) coupled to a Qtrap 5500 instrument (ABSciex). Test articles were separated on a C18 column using a gradient elution with an acetonitrile/water mixture containing 0.1% formic acid as the mobile phase. Chromatographic conditions and MS parameters were optimized for the tested compound before sample analysis. Concentrations of LDC4857 were calculated by means of a standard curve.

Mouse models

C57BL/6N male mice (Charles River Laboratories) were maintained from 4 weeks of age under a 12-h light–dark cycle, temperature of 22 °C, humidity of 50% and with free access to water and standard chow diet (4% kcal from fat; Special Diet Service) or HFD (60% kcal from fat, TD.06414; Envigo). Animal studies were approved by the animal welfare ethics committee (Stockholms Djurförsöksetiska Nämnd) and performed in compliance with National and European law.

Indirect calorimetry

The CLAMS cage system (Columbus) was used to measure food intake, O 2 consumption, CO 2 production, energy expenditure, RER and physical activity. Mice were individually put into metabolic cages and after 24–48 h acclimation they were visually checked for signs of stress and food/drink consumption was recorded from 9:00. If mice were not eating and/or drinking (determined as less than 1 g food or 1 ml water), they were removed from the experiment ( n  = 1 excluded mouse). From 18:00 of day three to 12:00 of day five, the data collection phase was performed. From 18:00 of day four to the 6:00 of day five, food was removed from the cage and mice were underwent a 12-h fasting period. At 6:00 of day five, food was given to the mice and data were collected for the refeeding phase. Caloric consumption was calculated using the following values: HFD 5.24 kcal g −1 and chow diet 3.18 kcal g −1 .

Energy expenditure analysis

We performed a regression-based analysis of covariance (ANCOVA) 18 , 19 , with total body mass as a covariate, on the energy expenditure data obtained from indirect calorimetry using the online tool CalR ( https://calrapp.org ).

BN-PAGE and OXPHOS capacity

Isolated mitochondria were analysed by BN-PAGE as previously described 32 , 33 . The OCRs in the liver mitochondria were assessed with high-resolution respirometry Oxygraph-2k at 37 °C. The experiments were performed by diluting 100 µg crude mitochondria diluted in 2.0 ml mitochondrial respiration medium MiR05. Oxidation of 0.2 mM malate/0.04 mM palmitoylcarnitine was monitored at state 3 with 2.5 mM ADP. To assess additive effects, the following substrates were added in addition to palmitoylcarnitine: 5 mM pyruvate/2 mM malate/10 mM glutamate, 10 mM succinate, and 10 mM glycerol-1-phosphate. To measure the succinate respiration separately, 0.5 µM rotenone/10 mM succinate was used. To measure the complex I respiration separately, two sets of substrates were used: (1) 5 mM pyruvate/2 mM malate/10 mM glutamate and (2) 2 mM malate/10 mM glutamate. Non-phosphorylating respiration (pseudo-state 4) was induced by 0.025 µM oligomycin. Mitochondrial quality was checked by 10 µM cytochrome c. The specificity of mitochondrial respiration was controlled with antimycin (0.025 µM) sensitivity. All chemicals were obtained from Sigma-Aldrich. The measurement of respiratory chain enzyme activities and citrate synthase activity was performed as previously described 32 .

Intraperitoneal glucose tolerance test

Experiments were performed following 4 h of fasting starting at ~6:00. Blood glucose was monitored using the Contour XT glucometer (Bayer) from samples collected at the distal tail vein. Following an initial blood glucose measurement, glucose (2 g kg −1 body weight) was injected intraperitoneally. Blood glucose was measured 15, 30, 60, 90 and 120 min after the injection.

Insulin secretion in isolated islets

Mouse islets were isolated as previously described 34 and incubated in RPMI medium (11875093, Thermo Fisher Scientific) with 11 mM glucose, 10% FBS and 1% penicillin-streptomycin overnight to recover from the isolation. GSIS was performed as previously described 35 . In brief, 15 islets from each group were equilibrated in KRBH solution containing 2.8 mM glucose for 2 h, then transferred to be incubated in KRBH containing 2.8 mM glucose for 1 h, 16.7 mM glucose for another hour and the supernatant from each incubation was collected. Islets were lysed with RIPA (R0278, Sigma) containing protease inhibitor cocktail (11697498001, Roche) and phosphatase inhibitor cocktail (4906845001, Roche) to determine protein concentration. The measurement of insulin in the islet lysate, the supernatant and the fasting and 15 min ipGTT serum was performed using a Rat/Mouse Insulin ELISA kit (EZRMI-13K, Sigma-Aldrich).

Histology and hepatic lipid quantification

One liver lobe and eWAT were collected and fixed in 4% paraformaldehyde at 4 °C for 24 h. The tissues were embedded in paraffin and sectioned to 5-μm thickness. H&E staining was performed and the morphology of the tissues was analysed by microscopy. Liver triglycerides were quantified with a kit (ab65336, Abcam) according to the manufacturer’s instructions.

Mitochondrial isolation

Crude mitochondria from the liver were isolated by differential centrifugation in mitochondrial isolation buffer (320 mM Sucrose, 10 mM Tris-HCl, pH 7.4, 1 mM EDTA and 0.2% BSA), supplemented with EDTA‐free complete protease inhibitor cocktail and PhosSTOP Tablets (Roche). Liver tissue was homogenized using a Potter homogenizer on ice (13 strokes at 500 rpm). Nuclei and cell debris were pelleted at 1,000 g for 10 min at 4 °C. Mitochondria were pelleted from the supernatant by centrifugation at 10,000 g for 10 min at 4 °C. The mitochondrial pellet was carefully resuspended in mitochondrial isolation buffer without BSA and the differential centrifugations were repeated to obtain crude mitochondria.

Ultrapure mitochondria were prepared as previously described 34 . In brief, crude mitochondrial pellets from mouse liver were washed in 1xM buffer (220 mM mannitol, 70 mM sucrose, 5 mM HEPES, pH 7.4 and 1 mM EGTA, pH 7.4); pH was adjusted with potassium hydroxide, supplemented with EDTA‐free complete protease inhibitor cocktail and PhosSTOP Tablets (Roche) and purified on a Percoll density gradient (12%:19%:40%) via centrifugation in a SW41 rotor at 42,000 g at 4 °C for 1 h in a Beckman Coulter Optima L‐100 XP ultracentrifuge using 14 × 89-mm Ultra‐Clear Centrifuge Tubes (Beckman Instruments). Mitochondria were collected at the interphase between 19 and 40% Percoll and washed three times with buffer 1xM. The mitochondrial pellets were then frozen at −80 °C.

Western blots

Mitochondrial proteins (10 μg) were resuspended in 1× NuPAGE LDS sample buffer. Mitochondrial proteins were thereafter separated by SDS–PAGE (4–12% Bis‐Tris Protein Gels; Invitrogen) and transferred onto polyvinylidene difluoride membranes (Merck Millipore). Immunoblotting was performed using standard procedures with ECL reagent detection as previously decribed 32 .

RNA extraction and RT–qPCR

RNA was extracted from mouse liver, skeletal muscle, eWAT, heart and BAT using Trizol reagent (Invitrogen) according to the manufacturer’s instructions and then treated with TURBO DNA‐free DNase (Invitrogen). For RT–qPCR expression analysis, complementary DNA was reversed transcribed from 1 μg total RNA using the High‐Capacity cDNA Reverse Transcription kit (Invitrogen). The qPCR was performed in a QuantStudio 6 Flex Real‐Time PCR System (Life Technologies), using TaqMan Universal Master Mix II with UNG (Applied Biosystems) to quantify mitochondrial transcripts (mt‐rRNAs and mt‐mRNAs), actin and 18S rRNA.

DNA isolation and mtDNA quantification

Genomic DNA was isolated from mouse liver, eWAT and skeletal muscle using the DNeasy Blood and Tissue kit (QIAGEN) following the manufacturer’s instructions and treated with RNase A. Levels of mtDNA were measured by quantitative PCR using 5 ng DNA in a QuantStudio 6 Flex Real‐Time PCR System using TaqMan Universal Master Mix II with UNG. Nd1, Cox1 and Cyb probes were used for TaqMan assays to measure mtDNA levels and 18S was used for normalization.

Label‐free quantitative proteomics

Proteomics sample preparation.

Frozen tissue pieces were placed in precooled ‘Lysing Matrix D’ tubes, followed by addition of 400 µl lysis buffer (1% SDC in 100 mM Tris-HCl, pH 8.5). Tissue pieces were lysed at 4 °C by three cycles of 40 s bead beating (6.0 setting) and 20 s pause in the FastPrep-24 (MP Biomedicals). Thereafter, lysates were transferred into reaction tubes and boiled for 10 min at 95 °C. Similarly, ultrapure mitochondria pellets were resuspended in 150 µl lysis buffer and boiled for 10 min at 95 °C. After lysate boiling, the protein concentration was estimated by tryptophan assay and 30 µg of each sample were diluted with lysis buffer to a protein concentration of 0.75 µg µl −1 . Proteins were reduced and alkylated by adding chloroacetamide (CAA) and Tris(2-carboxyethyl)phosphine (TCEP) to a final concentration of 40 mM and 10 mM, respectively in a 5-min incubation at 45 °C. After adding trypsin (1:100 ( w / w ), Sigma-Aldrich) and LysC (1:100 ( w / w ), Wako), proteins were digested overnight at 37 °C. Protein digestion was quenched by adding 200 µl 1% TFA in isopropanol to the samples. Subsequently, peptides were loaded onto SDB-RPS StageTips (Empore) followed by washes with 200 µl 1% TFA in isopropanol and 200 µl 0.2% TFA in 2% ACN. Peptides were eluted with 60 µl 1.25% NH 4 OH in 80% ACN and dried in a SpeedVac centrifuge (Eppendorf, Concentrator Plus). Dried peptides were resuspended in A* (0.2% TFA in 2% ACN) and subjected to measurement by LC–MS/MS.

LC–MS/MS and proteomics data analysis

Peptide concentration was estimated by NanoDrop and 250 ng peptide material was used for individual measurements. Peptides were loaded onto a 50-cm, in-house packed, reversed-phase column (75-μm inner diameter, ReproSil-Pur C18-AQ 1.9 μm resin, Dr. Maisch) and separated with a binary buffer system consisting of buffer A (0.1% FA) and buffer B (0.1% FA in 80% ACN) with an EASY-NLC 1,200 (Thermo Fisher Scientific). The LC system was directly coupled online with the mass spectrometer (Exploris 480, Thermo Fisher Scientific) via a nano-electrospray source. Peptide separation was performed at a flow rate of 300 µl min −1 and an elution gradient starting at 5% B increasing to 30% B in 80 min, 60% in 4 min and 95% in 4 min.

Data were acquired in DIA mode with a scan range of 300–1,650  m / z at a resolution of 120,000. The AGC was set to 3 × 10 6 at a maximum injection time of 60 ms. Precursor fragmentation was achieved via HCD (NCD 25.5%, 27.5% and 30%) and fragment ions were analysed in 33 DIA windows at a resolution of 30,000, while the AGC was kept at 1 × 10 6 .

DIA raw files were processed using Spectronaut (v.14) with default settings. Perseus (v.1.6.7.0) 36 was used on data with three valid values in at least one treatment group. PCA was performed on missing-values imputed matrices and analysis of variance (ANOVA) testing with permutation-based FDR correction. GSEA was computed with WebGestalt 2019 (ref. 37 ) in an R environment (v.4.1.2) correcting for multiple library testing and normalized enrichment scores were reported. Statistical analyses and FDR calculations were performed with limma and an FDR cutoff of 0.05 was defined as significant. Heatmaps were generated on filtered, imputed and z -transformed data matrices in R with the pheatmap package.

Metabolomics and lipidomics

Samples extraction of polar and lipid metabolites.

Metabolites were extracted from 10–15 mg of frozen tissue. The frozen tissue samples were homogenized to a fine tissue powder using a ball mill (MM400, Retsch).

After the tissue was pulverized in 2-ml round-bottom microcentrifuge tubes, a 1-ml −20 °C methyl-tert butyl-ether:methanol:water (5:3:2 ( v / v / v )) mixture, containing 0.2 µl ml −1 deuterated EquiSplash lipidomix (Avanti), 0.2 µl ml −1 U- 13 C 15 N amino acid mix (Cambridge Isotopes, MSK_A2-1.2), 0.1 μl ml −1 1 mg ml −1 13 C 10 ATP, 15 N 5 ADP and 13 C 10 15 N 5 AMP (Sigma) and 0.2 µl ml −1 100 µg ml −1 of deuterated citric acid as internal standards was added to each sample. After addition of the extraction buffer, the samples were immediately vortexed before they were incubated for additional 30 min at 4 °C on an orbital shaker. Proteins were removed by a 10-min 21,000 g centrifugation at 4 °C and the supernatant was transferred to a fresh 2-ml Eppendorf tube. To separate the organic from the polar phase, 150 μl MTBE and 100 µl UPC/MS-grade water was added to the cleared supernatant, which was briefly vortexed before mixing it for 15 min at 15 °C on an orbital shaker. Phase separation was obtained after a 5 min centrifugation at 16,000 g at 15 °C. The upper MTBE phase, which contains the hydrophobic compounds (lipids), was sampled to a fresh 1.5-ml microcentrifuge tube (~600 µl), while the remaining polar phase (~600 µl) was kept in the initial 2-ml tube. These two fractions were then immediately concentrated to dryness in a speed vacuum concentrator (LaboGene, MaxiVac) at room temperature. Thereafter, samples were then either stored at −80 °C or processed immediately for LC–MS analysis.

LC–high-resolution MS-based analysis of anionic and amine-containing metabolites from the polar fraction

The polar fraction of the extracted metabolites was resuspended in 400 µl ULC–MS-grade water (Biosolve). After 15 min of incubation on a thermomixer at 4 °C and a 5-min centrifugation at 16,000 g at 4 °C, 100 µl of the cleared supernatant were transferred to polypropylene autosampler vials (Chromatography Accessories Trott) and analysed using anion-exchange chromatography MS (AEX-MS), described in detail previously 6 , 38 . For the analysis of amine-containing compounds, 50 µl of the above-mentioned resuspended 400 µl polar phase were mixed with 25 µl 100 mM sodium carbonate (Sigma), followed by the addition of 25 µl 2% ( v / v ) benzoylchloride (Sigma) in acetonitrile (UPC–MS-grade, Biosolve). Derivatized samples were thoroughly mixed and analysed as previously described 6 , 38 .

LC–high-resolution MS-based analysis of lipids

The dried lipid fractions were resuspended in 400 µl UPLC-grade acetonitrile: isopropanol (70:30 ( v / v ), Biosolve). Samples were vortexed for 10 s and incubated for 10 min on a thermomixer at 4 °C. Resuspended samples were centrifuged for 5 min at 10,000 g and 4 °C, before transferring the cleared supernatant to 2-ml glass vials with 200-µl glass inserts (Chromatography Zubehör Trott). All samples were placed in a UHPLC sample manager (Vanquish, Thermo Fisher Scientific), which was set to 6 °C. The UHPLC was connected to a Tribrid Orbitrap HRMS, equipped with a heated ESI source (ID-X, Thermo Fisher Scientific).

A volume of 1 µl of each lipid sample was injected into a 100 × 2.1-mm BEH C8 UPLC column, packed with 1.7-µm particles (Waters). The flow rate of the UPLC was set to 400 µl min −1 and the buffer system consisted of buffer A (10 mM ammonium acetate, 0.1% acetic acid in UPLC-grade water) and buffer B (10 mM ammonium acetate, 0.1% acetic acid in UPLC-grade acetonitrile/isopropanol 7:3 ( v / v )). The UPLC gradient was as follows: 0–1 min in 45% A, 1–4 min in 45–25% A, 4–12 min in 25–11% A, 12–15 min in 11–1% A, 15–20 min in 1% A, 20–20.1 min in 1–45% A and 20.1–24 min re-equilibrating at 45% A, which leads to a total runtime of 24 min per sample and polarity.

The ID-X mass spectrometer was operating for the first injection in positive ionization mode and for the second injection in negative ionization mode. In both cases, the analysed mass range was between m / z 150–1,500. The resolution was set to 120,000, leading to approximately four scans per second. The RF lens was set to 50% and the AGC target was set to 100%. The maximal ion time was set to 100 ms and the heated ESI source was operating with a spray voltage of 3.6 kV in positive ionization mode, while 3.2 kV was applied in negative ionization mode. The ion tube transfer capillary temperature was 300 °C, the sheath gas flow was 60 arbitrary units (AU), the auxiliary gas flow was 20 AU and the sweep gas flow was set to 1 AU at 330 °C.

To obtain positive ionization mode and negative ionization mode MS/MS-based lipid annotations, we performed five iterative MS/MS deep-sequencing runs on a pooled sample of each tissue type using the AcquireX algorithm (Xcalibur v.4.3, Thermo Fisher Scientific). Lipids from these MS/MS spectra were then automatically annotated using LipidSearch (v.4.2, Thermo Fisher Scientific). The annotated lipids were filtered for quality grades (A,B and C were accepted) and the resulting lipid IDs, m / z and retention time values were exported into a TraceFinder compound database (v.4.1, Thermo Fisher Scientific). From this compound database we generated a TraceFinder method for each sample set and extracted the corresponding peaks from each full-scan MS spectrum.

For data analysis, the area of each monoisotopic mass peak was extracted and integrated using a mass accuracy of <5 ppm and a retention time tolerance of <0.05 min compared with the independently measured reference compounds. Areas of the cellular pool sizes were normalized to the internal standards, followed by a normalization to the fresh weight/volume of the analysed sample.

Statistical analysis

Experiments were replicated across multiple batches. Within each batch, mice were randomized into groups and treated according to the experimental design. This approach ensured that the results were not dependent on a single cohort and increased the generalizability and confidence in our findings. Due to body size differences, complete blindness during data collection and analysis was challenging for HFD-fed mice. Biochemical experiments were independently performed at least three times and results represent n  > 5 independent biological replicates, unless indicated otherwise. No data points were excluded. All values are presented as mean ± s.e.m. Statistical analyses were conducted using GraphPad Prism software (v.9.4.0). Before statistical analysis, data were tested for normal distribution using the Kolmogorov–Smirnov test with Lillifors correction or D’Agostino–Pearson omnibus test. Statistical significance was assessed by a two-way ANOVA with Tukey’s test for multiple comparisons in normal distributed data. For non-normal distribution, data were analysed using a Mann–Whitney U -test, as indicated in the figure legends. P values are shown in the figures. No statistical methods were used to predetermine sample sizes but our sample sizes are similar to those reported in previous publications 32 , 39 .

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository 40 with the dataset identifier PXD034771 . All data and materials used in this study, including standard code with no custom code generated, are available in Source Data with no restrictions. Source data are provided with this paper.

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Acknowledgements

We thank the Morphological Phenotype Analysis Core Facility (FENO) at the Karolinska Institutet for assistance with histology and imaging. We thank M. Moedas and G. Gao for technical assistance. Figures 1a and 4b,c were created using BioRender.com . N.G.L. was supported by the Swedish Research Council (2015-00418), the Swedish Cancer Foundation (21 1409 Pj), the Knut and Alice Wallenberg foundation (2016.0050 and 2019.0109), the Swedish Brain Foundation (FO2021-0080), the Swedish Diabetes Foundation (DIA2023-804), the Novo Nordisk Foundation (NNF20OC006316 and NNF22OC0078444) and grants from the Swedish state under the agreement between the Swedish government and the county councils (RS2020-0731). L.S.K. and F.A.S. were supported by EMBO long-term fellowships (ALTF 570-2019 and ALTF 399-2021). J.R.Z. was supported by the Knut and Alice Wallenberg Foundation (2021.0249), Swedish Research Council (2015-00165). A.K was supported by the Swedish Research council (2018-02389; 2022-00609) and the Swedish Diabetes Foundation (DIA2018-336; DIA2021-641), and the Strategic Research Programme in Diabetes at the Karolinska Institutet supported animal phenotyping (Swedish Research Council 2009-1068).

Open access funding provided by Karolinska Institute.

Author information

These authors contributed equally: Shan Jiang, Taolin Yuan.

Authors and Affiliations

Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden

Shan Jiang, Taolin Yuan, Laura S. Kremer, Diana Rubalcava-Gracia, Mara Mennuni, Roberta Filograna, David Alsina, Jelena Misic, Camilla Koolmeister, Polyxeni Papadea & Nils-Göran Larsson

Department of Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Martinsried, Germany

Florian A. Rosenberger, Fynn M. Hansen & Matthias Mann

University of Bordeaux, CNRS, Institut de Biochimie et Génétique Cellulaires (IGBC) UMR, Bordeaux, France

Arnaud Mourier

Institute of Experimental Genetics - German Mouse Clinic, Helmholtz Zentrum, Munich, Germany

Nathalia R. V. Dragano & Martin Hrabe de Angelis

German Center for Diabetes Research (DZD), Oberschleißheim-Neuherberg, Neuherberg, Germany

Department of Physiology and Pharmacology, Section for Integrative Physiology, Karolinska Institutet, Stockholm, Sweden

Melissa Borg, Juleen R. Zierath & Anna Krook

Chair of Experimental Genetics, TUM School of Life Sciences, Technische Universität München, Freising, Germany

Martin Hrabe de Angelis

Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden

Lipeng Ren & Olov Andersson

Lead Discovery Center, Dortmund, Germany

Anke Unger, Tim Bergbrede & Raffaella Di Lucrezia

Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden

Department of Molecular Medicine and Surgery, Section for Integrative Physiology, Karolinska Institutet, Stockholm, Sweden

Juleen R. Zierath

Metabolomics Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany

Patrick Giavalisco

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Contributions

N.G.L. and S.J. conceived the project, designed the experiments and wrote the manuscript. S.J. and T.Y. performed, interpreted the majority of the experiments and revised the final version of the figures and manuscript. N.G.L., S.J., L.S.K., C.K., O.A., P.G., A.K., J.R.Z., P.P. and M. Mann advised on methodology. L.S.K., D.R.-G., M. Mennuni, R.F., D.A., J.M., M.B., P.P. and L.R. performed experiments and analysed the data. F.A.R., F.M.H., L.S.K. and P.G. performed and interpreted the proteomics and metabolomics experiments. A.M., T.Y. and R.W. performed bioenergetic characterization of mitochondria, N.R.V.D. and M.H.A. performed and analysed bomb calorimetry of faeces, A.U., T.B. and R.D.L. supervised the inhibitor generation and profiling, established the structure–activity and property relationships and developed the proper formulation and dosing regimen for the in vivo study. N.G.L. supervised the project. All authors commented on the manuscript.

Corresponding author

Correspondence to Nils-Göran Larsson .

Ethics declarations

Competing interests.

N.G.L. is a scientific founder and holds stock in Pretzel Therapeutic. T.B., A.U. and R.D.L. are employees of Lead Discovery Center and are co-inventors of the patent application WO 2019/057821. The remaining authors declare no competing interests.

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Nature Metabolism thanks Navdeep Chandel, David Nicholls and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Revati Dewal and Isabella Samuelson, in collaboration with the Nature Metabolism team.

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Extended data

Extended data fig. 1 imt treatment reduces adiposity without affecting physical activity and total faecal content..

a , Representative images of H&E staining of eWAT. Scale bars, 200 µm. n = 5 mice per group. b , Measurement of whole-body metabolism during the fourth week of gavage treatment with vehicle or IMT compound by using the Oxymax/Comprehensive Lab Animal Monitoring System (CLAMS). The first three days were used to acclimate the animals to the CLAMS system, followed by measurements during the fourth day. Physical activity during day four and day five. Chow vehicle n = 10, Chow IMT n = 10, HFD Vehicle n = 8, and HFD IMT n = 11 mice. Total faecal amount ( c ) and total faecal lipid content ( d ) in mice. c and d , Mouse faeces was collected using the Single Mouse Metabolic Cage System. Faecal lipids were extracted using Folch’s method. n = 9 mice per group. All data are presented as mean ± SEM. Statistical significance was assessed by a two-way ANOVA with Tukey’s test for multiple comparisons. P values are shown in the figure.

Extended Data Fig. 2 Measurement of whole-body metabolism in mice.

Measurement of whole-body metabolism during the fourth week of gavage treatment with vehicle or IMT using CLAMS. a , The average oxygen consumption rate during day four and day five. Regression plot of energy expenditure versus total mass ( b ), or lean mass ( c ). d , Respiratory exchange ratio during day four and day five. a - d , Chow vehicle n = 9, Chow IMT n = 10, HFD Vehicle n = 8, and HFD IMT n = 10 mice. All data are presented as mean ± SEM. Statistical significance was assessed by a two-way ANOVA with Tukey’s test for multiple comparisons. P values are shown in the figure.

Extended Data Fig. 3 IMT improves glucose homoeostasis and does not impair islet insulin secretion.

a , b , Fasting blood glucose ( a ) and fasting serum insulin ( b ) levels in mice after four weeks of IMT treatment. n = 10 mice per group. c, d , Blood glucose levels ( c ) and the area under the curve (AUC, d ) during intraperitoneal glucose tolerance tests (ipGTT) with 2 g/kg glucose in mice after four weeks of vehicle or IMT treatment. n = 10 mice per group. Data are presented as mean ± SEM. Statistical significance was assessed by a two-way ANOVA with Tukey’s test for multiple comparisons. ∗ indicates a significant difference between HFD IMT and HFD Vehicle; # indicates a significant difference between Chow Vehicle and HFD Vehicle. P values are shown in the figure. e , Serum insulin levels at the 15-min of ipGTT. n = 10 mice per group. f , Ex vivo glucose-stimulated insulin secretion assays performed on isolated pancreatic islets. Glucose (2.8 mM and 16.7 mM) was added to the medium to recapitulate basal and glucose-stimulated insulin secretion conditions. Three independent GSIS assays were performed with three replicates per group. g , Islet insulin content. Three independent GSIS assays were performed with three replicates per group. All data are presented as mean ± SEM. Statistical significance was assessed by a two-way ANOVA with Tukey’s test for multiple comparisons. P values are shown in the figure.

Extended Data Fig. 4 IMT treatment does not change levels of phospholipids and sphingolipids.

Levels of phospholipids and sphingolipids in mouse liver after four weeks of vehicle or IMT treatment. n = 8 mice per group.

Extended Data Fig. 5 Levels of mitochondrial transcripts and mtDNA in different tissues of IMT-treated mice.

a-f , Levels of representative mitochondrial transcripts and mtDNA were measured in tissues of mice on chow diet or HFD treated with vehicle or IMT compound for four weeks. Levels of mtDNA in liver after four weeks of IMT treatment. n = 12 mice per group ( a ). The mtDNA transcript ( b ) and mtDNA ( c ) levels in eWAT. n = 10 mice per group. d , Representative western blot analyses of OXPHOS protein levels in eWAT after four weeks of vehicle or IMT treatment. Subunits of complex I (NDUFB8), complex II (SDHA and SDHB), complex IV (MTCOX1), and complex V (ATP5A) were analysed. VDAC was used as loading control. A representative image of n = 3 independent experiments is shown. The mtDNA transcript, n = 10 mice per group ( e ) and mtDNA, n = 6 mice per group ( f ) levels in skeletal muscle. The mtDNA transcript levels in heart ( g ) and brown adipose tissue (BAT, h ). n = 6 mice per group. All data are presented as mean ± SEM. a , e , Statistical significance was assessed by the Mann–Whitney U-test. b , c , f - h , Statistical significance was assessed by a two-way ANOVA with Tukey’s test for multiple comparisons. P values are shown in the figure.

Extended Data Fig. 6 Analysis of the total and mitochondrial proteome.

a , Venn diagram showing number of quantified and significantly changed proteins at a given FDR cutoff. The liver tissue proteome and the proteome of isolated liver mitochondria (Mitoproteome) are shown. b , Principal-component analyses (PCA) of the liver tissue and liver mitochondrial proteomes. c , Hierarchical clustering analysis of the total proteome (ANOVA-significant proteins at FDR < 5%; left) with biological replicates as individual lanes (CV: control vehicle; HV: HFD Vehicle; HIMT: HFD IMT-treated, CIMT: control IMT-treated), z-score normalised fold changes in indicated clusters (middle) with the same sample order as in the heatmap, and significant KEGG terms in each cluster (Fisher exact test at FDR < 5%; right).

Extended Data Fig. 7 IMT treatment rewires OXPHOS.

a , Heatmaps illustrating the protein density of subunits of OXPHOS complexes in mouse liver after four weeks of vehicle or IMT treatment. n = 3 mice per group. b , Representative western blot analyses of OXPHOS protein levels in liver mitochondria after four weeks of vehicle or IMT treatment. Subunits of complex I (NDUFB8), complex II (SDHB), complex III (UQCRC2), complex IV (MTCOX1), and complex V (ATP5A) were analysed. VDAC was used as loading control. A representative image of n = 3 independent experiments is shown. c , Heatmaps depicting the protein density of different mitochondrial OXPHOS assembly factors in mouse liver after four weeks of vehicle or IMT treatment. n = 3 mice per group.

Extended Data Fig. 8 IMT decreases mitoribosomal proteins and rRNAs.

a , Heatmaps depicting the protein density of mitoribosomal proteins in mouse liver after four weeks of vehicle or IMT treatment. n = 3 mice per group. b , The mtDNA-encoded 12S and 16S rRNA transcripts in mouse liver after four weeks of vehicle or IMT treatment. n = 8 mice per group. Data are presented as mean ± SEM. Statistical significance was assessed by a two-way ANOVA with Tukey’s test for multiple comparisons. P values are shown in the figure.

Extended Data Fig. 9 Characterization of liver OXPHOS function.

a , Respiratory chain complex activities normalized to citrate synthase activity in liver mitochondria after four weeks of vehicle or IMT treatment. n = 3 mice per group. The analysed enzyme activities are NADH coenzyme Q reductase (complex I, CI), NADH cytochrome c reductase (complex I/III, CI/III), succinate dehydrogenase (complex II, CII), and cytochrome c oxidase (complex IV, CIV). b , In-gel activities of OXPHOS complexes resolved by BN-PAGE. Digitonin-solubilized liver mitochondria (60 μg) from mice on chow diet or HFD and subjected to vehicle or IMT treatment were resolved by native gel electrophoresis (BN-PAGE). Gels were stained with Coomassie or incubated with substrates for detecting the in-gel activity of the indicated OXPHOS complexes. Complex V subassembly intermediates containing F 1 subunits (Vsub) are observed in the IMT-treated subunits. The image is representative of 3 biological replicates from each group. Oxygen consumption rate (OCR) of liver mitochondria in the state 3 with ( c ) multiple substrates (malate/palmitoylcarnitine, pyruvate/malate/glutamate, succinate and glycerophosphate), ( d ) malate/palmitoylcarnitine only. c and d, n = 5 mice/group. a, c, and d, data are presented as mean ± SEM. Statistical significance was assessed by a two-way ANOVA with Tukey’s test for multiple comparisons. P values are shown in the figure.

Extended Data Fig. 10 Analysis of the liver metabolites and fasting mouse proteomics correlation.

a, b , Liver proteomics correlation between IMT/vehicle and the fed/fasted mice on chow diet ( a ) or HFD ( b ). n = 3 mice per group. c , Fold change in the levels of quinones (Q9 and Q10) in mouse liver after four weeks of vehicle or IMT treatment. Chow Vehicle, Chow IMT and HFD Vehicle n = 8 mice per group, HFD IMT n = 7 mice per group. d , Fold change in nucleotide levels in mouse liver after four weeks of vehicle or IMT treatment. n = 8 mice per group. e , Fold change in amino acid levels in mouse liver after four weeks of vehicle or IMT treatment. Chow Vehicle, Chow IMT and HFD Vehicle n = 8 mice per group, HFD IMT n = 6 mice. f , The ratio of AMP to ATP in mouse liver after four weeks of vehicle or IMT treatment. n = 8 mice per group. c - f , All data are presented as mean ± SEM. c , Statistical significance was assessed by a two-way ANOVA with Tukey’s test for multiple comparisons. d-f , Statistical significance was assessed by the Mann–Whitney U-test. P values are shown in the figure. g , Western blot analyses of the levels of AMPK phosphorylated at T172 (pAMPK(T172)) and ACC1 phosphorylated at (pACC1(S79)). A representative image of n = 2 independent experiments is shown.

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Table with TaqMan probes used in manuscript.

Source Data Fig. 1

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Source data fig. 3, source data extended data fig. 1, source data extended data fig. 2, source data extended data fig. 3, source data extended data fig. 4, source data extended data fig. 5.

Unprocessed western blots.

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Jiang, S., Yuan, T., Rosenberger, F.A. et al. Inhibition of mammalian mtDNA transcription acts paradoxically to reverse diet-induced hepatosteatosis and obesity. Nat Metab (2024). https://doi.org/10.1038/s42255-024-01038-3

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A gut health dietitian shares her 4 favorite fiber-filled, minimally processed Costco snacks

• When dietitian Megan Hilbert buys snacks she looks for minimally processed whole foods. 
• She buys most of her snacks at Costco because they're good value and healthy. 
• Hilbert tries to eat fiber-rich, plant-based snacks to support her gut microbiome.

A gut health dietitian shared the four healthy snacks she buys on repeat from Costco .

Megan Hilbert, a registered dietitian who helps clients eat in a way that benefits the gut-brain axis , or the signaling between the gut and brain, eats a healthy diet with a focus on fiber and nutrients but also loves to snack.

When buying a snack, she tends to go for minimally processed, plant-based, whole foods, and products made with ingredients you'd find in a regular kitchen, and little or no added sugar.

"Typically the shorter that ingredient list, the better," Hilbert told Business Insider.

Around 73% of the US food supply is ultra-processed, according to a 2024 research paper by Northeastern University's Network Science Institute, which hasn't been peer-reviewed, and a diet high in UPFs has been linked to health conditions including cancer, type 2 diabetes, and obesity.

Research suggests eating a wide range of plants can help to cultivate a diverse gut microbiome — the trillions of microbes that live in the colon lining. Studies have found that a more diverse microbiome is a healthier one, and this is important because research increasingly shows that gut health is linked to overall health.

With this in mind, Hilbert always has nuts, seeds, and dried fruit on hand because they're convenient and healthy. She likes to buy them in bulk from Costco.

"I go through these types of things pretty quickly, and if you go to normal grocery stores, little bags of dried fruit and seeds can get really pricey," she said.

Some of Costco's items do contain additives and added sugar, like the dried mango, which she avoids, but there are plenty of minimally processed options , and that's why it's important to check food labels, she said. Here are her four favorite snacks from Costco.

Dried blueberries

Hilbert loves Costco's dried blueberries because they're good value for money. You can get a 20-ounce bag for $10.49, according to their website.

She goes for the dried version because they last much longer and provide pretty much the same health benefits as fresh berries, she said.

Related stories

Blueberries contain fiber, which feeds the good bacteria in the gut, and anthocyanin, a chemical typically found in purple and blue foods that's associated with many health benefits. Studies suggest it could lower blood pressure and reduce the risk of heart disease by managing blood sugar and reducing inflammation, according to Cleveland Clinic.

Pumpkin seeds

Hilbert's favorite type of seeds to buy at Costco are pumpkin seeds because they're nutrient-dense, and she loves the taste. A 22-ounce bag costs $12.99 on their website.

Pumpkin seeds are a really great source of fiber and healthy fats, she said. One ounce of shelled pumpkin seeds also contains 8.5 grams of protein, and they're high in vitamins and minerals such as magnesium and potassium.

Seeds are considered plants alongside fruits, vegetables, nuts, herbs, and spices, so adding them to your diet is a great way to boost your plant intake, she said.

'That's It' dried fruit bar

Hilbert and her partner are big fans of Costco's "That's It" fruit bars.

As the name suggests, they contain only dried fruit with no added sugar or other ingredients. They come in three flavors: strawberry and apple, mango and apple, or blueberry and apple. You can get a pack of 24 for $15.99 on the Costco website.

"These are really great, especially if I need a quick snack before a workout or even after a workout," she said.

Healthy carbohydrates such as fruits can help you perform better, she said, and the bars go well with a protein source like seeds.

Like seeds, nuts are a good source of healthy fats and plant-based protein. They're a convenient and healthy snack, Hilbert said. She keeps packets of her favorite nuts in her desk drawer. The Kirkland Signature Organic range of nuts, which are Hilbert's favorite, have no added ingredients.

A 2023 study found that eating lots of nuts , as well as whole grains and fruit in middle age could add years to a person's life.

Hilbert's favorites are almonds, pistachios, cashews, and walnuts.

Watch: How to spot ultra-processed foods we mistake for healthy

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Exercise and fitness effect on obesity.

Grace M. Niemiro ; Ayesan Rewane ; Amit M. Algotar .

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Last Update: November 17, 2023 .

• Introduction

Obesity represents a significant public health concern, with one-third of adults classified as living with obesity in the United States. Obesity correlates with cardiometabolic comorbidities that can decrease the quality of life. [1] [2] Researchers have proposed that exercise is an important lifestyle measure to maintain a healthy weight. This review will cover the role of exercise in obesity and fitness.

Obesity is an excessive fat accumulation in adipose tissues defined by a body mass index (BMI) of 30 kg/m 2 and above. Individuals in the BMI range of 25 to 30 kg/m 2 are categorized as overweight, while a BMI of 40 kg/m 2 and above is regarded as morbid obesity. [3] Obesity correlates with an individual’s increased risk of cancers, stroke, metabolic disease, heart failure, and other cardiovascular conditions, highlighting the need to reduce the incidence and prevalence of obesity. [4] [5] [6] Chronic low-grade inflammation associated with obesity is hypothesized to have associations with adverse cardiometabolic adverse effects. [7] Although short-term inflammation is beneficial to initiate an immune response, chronically elevated levels of inflammation exhaust the immune system and contribute to immune dysfunction. [2] Researchers posit that this inflammation is stimulated by the excess adipose tissue, which has consistently been shown to play a role as an active endocrine organ. [8]

Reducing adipose tissue is one of the ways to reduce weight in individuals with obesity and is necessary to mitigate negative cardio-metabolic comorbidities in obesity. Two methods exist that can effectively decrease adipose tissue and include:

• Dietary modification 
• Energy expenditure modification (ie, exercise)

Thus, increasing energy expenditure can help reduce excess adipose tissue and obesity. The current guidelines by the American College of Sports Medicine (ACSM) include aerobic or anaerobic exercise. Aerobic exercise (eg, running, cycling, rowing) is an exercise that exhausts the oxygen in the muscles. Still, oxygen consumption is sufficient to supply the energy demands placed on the muscles and does not need to derive energy from another source. [9] On the other hand, anaerobic exercise or resistance exercise, eg, weight lifting)is oxygen consumption insufficient to supply the energy demands placed on the muscles, and muscles must break down other energy supplies, such as sugars, to produce energy and lactic acid. [9] Physical activity is included in the exercise, although it does not necessarily include structured exercise plans/sessions.

The measurement of exercise is conducted in “metabolic equivalent tasks” (METs), which roughly equate to the effort and energy expenditure it takes for an individual to sit quietly. Physical activity is frequently incorporated into different lifestyle interventions, highlighting the need for regular daily physical activity. Physical activity in the general lifestyle includes goal setting, problem-solving, leisure-time physical activity, and activity used for commuting. Outcomes of interest include cardiorespiratory fitness, body composition, and muscular fitness. Recently, much literature has shown the positive effects of exercise on physical health and cognitive and emotional well-being in people of all ages. [10]

• Issues of Concern

Overweight and obese people can partake in the same exercise prescriptions as individuals with normal weight. Special considerations are essential, accounting for prevalent obesity-related comorbidities like orthopedic risks (eg, arthritis) and pulmonary and cardiac conditions. However, this should not deter individuals from participating in exercise programs, as exercise is essential for overall health. [11] [12] Currently, there are several exercise guidelines for individuals living with obesity, including the American College of Sports Medicine (ACSM), the Obesity Medical Association (OMA), and the Obesity Society (TOS), which are all clinically available to aid individuals in prescribing exercise. Here, we outline the general recommendations for individuals living with obesity as follows:

A . Patients must be cleared by their healthcare provider for any comorbid conditions by history and physical examination to maximize patient safety. [13]  Examples include the Physical Activity Readiness Questionnaire (PAR-Q) and the Health/Fitness Facility Preparticipation Screening Questionnaire. [14] [15]

B . A minimum of 150 to 300 minutes of moderate physical activity per week or 75 to 150 minutes of vigorous physical activity weekly is essential to prevent weight regain, increase weight loss, and improve fitness. [14] However, for individuals who wish to lose weight, at least 200 to 300 minutes of moderate to vigorous physical activity each week is recommended to encourage long-term weight loss. [14] [15]

• The recommendation for inactive individuals is to “start low and go slow” by starting with lower-intensity activities and gradually increasing the frequency and duration of the activity. 
• It is an excellent idea to spread out aerobic activity over the week versus all the time in one day.
• Utilize appropriate gear and sports equipment and choose safe environments.
• Adjust exercises to decrease orthopedic risk or is nonambulatory (if applicable). This can include cycling instead of running if an individual has arthritis. The exercise guidelines still apply if individuals are not ambulatory or may have to modify exercise due to particular circumstances. However, the patient can get creative to find ways to achieve them, such as utilizing more ambulatory limbs (eg, moving arms faster to get the heart rate up if legs cannot be used, upper body ergometer, etc.)
• Anaerobic training can be implemented and may even increase muscle mass. Anaerobic exercise is not practical in altering energy expenditure or absolute weight loss. [13] However, anaerobic exercise is highly encouraged if the patient's goal is to increase muscle mass. Furthermore, each muscle group should be exercised at least 10 sets per week to increase muscle mass, with one set of 8 to 10 reps. Also, ensure proper form to avoid injuries. Individuals who are not ambulatory or may have limited movement can still participate in an anaerobic exercise. Individuals must ensure proper form but can modify exercises as needed, such as upper body-only exercises, lower body-only exercises, using a neutral grip, keeping stable movements, etc.
• Clinical Significance

Utilizing exercise to reduce obesity (ie, reducing fat mass) has benefits beyond reducing fat mass. In many instances, fitness is associated with more desirable clinical outcomes, such as decreasing metabolic disease, cardiovascular disease, Alzheimer's disease risk, inflammation, and many other disease states not listed here. [14] [15] [16]

Exercise/physical activity is a proven modality for treating the disease of overweight and obesity. However, managing this disease is best through dietary interventions and regular exercise. Exercise is an integral part of not only weight loss but overall health as well. A balanced hypocaloric diet, aerobic training, and cognitive behavioral therapy (CBT) help reduce weight. Weight-reducing pharmacotherapy is indicated in individuals with a BMI greater than 30 kg/m2 with or without comorbidities. Bariatric surgery is only needed to reduce weight in BMI greater than 40 kg/m2, especially with comorbidity.

The Food Drug Administration (FDA) approved medications and their mechanism of action:

• Orlistat inhibits pancreatic gastric lipase
• The phentermine/topiramate combination is unknown, but it is believed to inhibit Norepinephrine (NE) release and GABA gamma-aminobutyric acid transmission
• Bupropion/naltrexone combination, NE/dopamine reuptake inhibitor (NDRI), naltrexone (an opioid antagonist)
• Liraglutide, a glucagon-like peptide- GLP-1 agonist, decreases dipeptidyl peptidase-IV metabolism and appetite.

Aerobic exercise is a form of physical activity proven to be efficacious in managing obesity. Moderate- or high-intensity aerobics involving larger groups of muscles is recommended. Aerobic exercise should be practiced for a long duration to appreciate the effect. Hence, a weekly aerobic exercise of at least 150 to 180 minutes can increase physical fitness. Resistance exercise has also been shown to have some meaningful impact on weight. [17] [18] [19] [20] [21]

• Enhancing Healthcare Team Outcomes

The healthcare team (nurse practitioner, primary care provider, internist, endocrinologist, bariatric surgeon, pharmacist, and obesity nurse) should implement many strategies to increase physical activity and fitness for individuals living with obesity, including utilizing “exercise vital signs,” tracking exercise, motivational interviewing, and periodic check-ins. Currently, the following could potentially be implemented into practice to encourage patients living with obesity to exercise.

Utilizing exercise as a vital sign in individuals with obesity: Obtaining current exercise and physical activity habits from patients could serve as another vital sign and would include understanding the intensity, mode, and duration of the exercise performed weekly by the patient. Providers could have electronic medical records (EMRs) to prompt patients who are living with obesity to have discussions with the patient regarding their physical activity. These prompts on the EMR can be input by the medical assistants who may ask at the beginning of the appointment, just like taking blood pressure and pulse.

Utilizing exercise trackers: Several devices can track heart rate, motion, exercise, moderate to vigorous physical activity (MVPA), and beyond. Providers could potentially use these data to ensure that the patient is exercising and could point to potential problems that may arise from abnormal heart or exercise responses. Examples include smartwatches, cellular smartphones, pedometers, heart rate monitors, etc.

Motivational Interviewing: To drive the point home further, nurses, CNAs, physicians, and anyone else involved in the healthcare setting for this patient could employ/use motivational interviewing techniques with the patient to reflect, plan, and execute different action plans to ensure that patients are meeting their exercise goals.

Check-Ins: Technology is allowing individuals to interact now more than ever. Physicians and patients could potentially use these technological advances to develop relationships further. Utilizing technology to have doctor-patient check-ins regarding their exercise may increase the adherence of obese individuals to exercise programs. These could include developing an app that alerts patients and the doctor when exercise habits are not sufficient, thus prompting a check-in from the physician using motivational interviewing and asking why the patient has or hasn’t exercised according to plan.

• Nursing, Allied Health, and Interprofessional Team Interventions

If the patient can exercise, exercise may be the preferred route to decrease disease symptoms and future risk compared to alternative pharmaceuticals that may exacerbate symptoms. An open and communicative relationship between the physician, healthcare team, and the patient must be present to suggest adding exercise to the patient's lifestyle to decrease obesity and improve adverse side effects. [22]  Obesity disproportionately affects individuals with a lower socioeconomic status, and these individuals may not have access to a safe exercise space, may not understand the importance of exercise, or may not have the time during the day to exercise due to other obligations. Therefore, the relationship between the care providers and the patient becomes significant in implementing exercise in obese individuals.

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Disclosure: Grace Niemiro declares no relevant financial relationships with ineligible companies.

Disclosure: Ayesan Rewane declares no relevant financial relationships with ineligible companies.

Disclosure: Amit Algotar declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Niemiro GM, Rewane A, Algotar AM. Exercise and Fitness Effect on Obesity. [Updated 2023 Nov 17]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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