research paper about obesity

A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity

Affiliations.

• 1 Centre for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia.
• 2 Centre for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia. Electronic address: [email protected].
• 3 RIADI Laboratory, University of Manouba, Manouba, Tunisia; College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia.
• 4 Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia.
• PMID: 34426171
• DOI: 10.1016/j.compbiomed.2021.104754

Obesity is considered a principal public health concern and ranked as the fifth foremost reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular diseases. The World Health Organization also predicted that 30% of death in the world will be initiated with lifestyle diseases in 2030 and can be stopped through the suitable identification and addressing of associated risk factors and behavioral involvement policies. Thus, detecting and diagnosing obesity as early as possible is crucial. Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors and condition likelihoods. The present study conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020. Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity. Next, the main diseases and health consequences of obesity and overweight are investigated. Ultimately, this study recognized the machine learning methods that can be used for the prediction of obesity. Finally, this study seeks to support decision-makers looking to understand the impact of obesity on health in the general population and identify outcomes that can be used to guide health authorities and public health to further mitigate threats and effectively guide obese people globally.

Keywords: Diseases; Machine learning; Obesity; Overweight; Risk factors.

Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Publication types

• Research Support, Non-U.S. Gov't
• Systematic Review
• Machine Learning
• Metabolic Syndrome*
• Obesity* / epidemiology
• Risk Factors

REVIEW article

Obesity: epidemiology, pathophysiology, and therapeutics.

• Department of Endocrinology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China

Obesity is a complex multifactorial disease that accumulated excess body fat leads to negative effects on health. Obesity continues to accelerate resulting in an unprecedented epidemic that shows no significant signs of slowing down any time soon. Raised body mass index (BMI) is a risk factor for noncommunicable diseases such as diabetes, cardiovascular diseases, and musculoskeletal disorders, resulting in dramatic decrease of life quality and expectancy. The main cause of obesity is long-term energy imbalance between consumed calories and expended calories. Here, we explore the biological mechanisms of obesity with the aim of providing actionable treatment strategies to achieve a healthy body weight from nature to nurture. This review summarizes the global trends in obesity with a special focus on the pathogenesis of obesity from genetic factors to epigenetic factors, from social environmental factors to microenvironment factors. Against this background, we discuss several possible intervention strategies to minimize BMI.

There has been a significant global increase in obesity rate during the last 50 years. Obesity is defined as when a person has a body mass index [BMI (kg/m 2 ), dividing a person’s weight by the square of their height] greater than or equal to 30, overweight is defined as a BMI of 25.0-29.9. Being overweight or obesity is linked with more deaths than being underweight and is a more common global occurrence than being underweight. This is a global phenomenon occurring in every region except parts of sub-Saharan Asia and Africa ( 1 ), and also countries with low obesity rates (i.e., Sri Lanka, Indonesia, Sudan, Singapore, Djibouti, etc.) ( 2 ).

Obesity increases the likelihood of various diseases and conditions which are linked to increased mortality. These include Type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), metabolic syndrome (MetS), chronic kidney disease (CKD), hyperlipidemia, hypertension, nonalcoholic fatty liver disease (NAFLD), certain types of cancer, obstructive sleep apnea, osteoarthritis, and depression ( 3 ).Treating these conditions can place an additional load on healthcare systems: for example, it is estimated that obese have a 30% higher medical cost than those with a normal BMI ( 4 ). As related total health-care costs double every decade, treating the consequences of obesity poses an expensive challenge for patients ( 5 ).

There are several possible mechanisms leading to obesity. Actually, the traditional view is usually that the main cause is the significantly more excess energy stored than the energy the body used. The excess energy is stored in fat cells, thereby developing the characteristic obesity pathology. The pathologic enlargement of fat cells will alter the nutrient signals responsible for obesity ( 6 ).However, the latest research showed that the food sources and quality of nutrients matter more than their quantities in the diet for weight control, and also for disease prevention ( 7 ). More and more etiologies or defects that lead to obesity can be identified under the background of struggle between nurture and nature, genetic and epigenetic, environmental and microenvironment. We are increasingly understanding how food cravings are upregulated in obesity individuals’ brains, how gut hormones, adipose tissue, or gut microbiota regulate appetite and satiety in the hypothalamus, as well as the roles of gut dysbiosis played in obesity development and how dysfunction of glucose and lipids metabolism causes secondary health problems ( 8 ). In addition, genetic factors are known to play critical roles in determining an individual’s predisposition to weight gain ( 9 ). Recent epigenetic studies have provided very useful tools for understanding the worldwide increase in obesity ( 10 ). Studies have discussed the relationships between genetics, epigenetics, and environment in obesity and explored the roles of epigenetic factors in metabolism regulation and obesity risk as well as its complications ( 11 ).

The field of obesity is rapidly evolving as an abundance of new scientific data continue to emerge. Herein, we discuss the epidemiology of obesity, covering the pathophysiology, pathogenesis, genetics, epigenetics, and environmental (macro and micro) causes that result in obesity. We end by summarizing possible management and prevention strategies.

Epidemiology of Obesity

BMI is used to define and diagnose obesity according to World Health Organization (WHO) guidelines ( 4 ). In adults, WHO defines ‘overweight’ as a BMI of 25.0 to 29.9 and ‘obese’ as a BMI ≥ 30.0. Obesity is further classified into three severity levels: class I (BMI 30.0-34.9), class II (BMI 35.0-39.9) and class III (BMI ≥ 40.0) ( 12 ).However, large individual differences exist in the percent body fat for the given BMI value, which can be attributed to sex, ethnicity and age ( 13 ).Excess fat deposition in the abdominal region is termed ‘abdominal obesity’ and is associated with greater health risks ( 14 ).The definition and measurement guidelines of abdominal obesity differed from WHO, IDF (International Diabetes Federation) to AHA (American Heart Association) ( 15 ). However, there is no international standard suitable for all countries or regions.

The prevalence of excessive weight gain has doubled worldwide since 1980, and about a third of the global population has been determined to be obese or overweight ( 16 ). Obesity rate has dramatically enhanced in both male and female, and across all ages, with proportionally higher prevalence in older persons and women ( 4 ). While this trend is present globally, absolute prevalence rates vary across regions, countries, and ethnicities. The prevalence of obesity also varies with socioeconomic status, with slower rates of BMI increase in high-​income and some middle-income countries. While obesity was once considered a problem of high-income countries, the incidence rates of obese or overweight children in high-​income countries, including the United States, Sweden, Denmark, Norway, France, Australia and Japan, have decreased or plateaued since the early 2000s ( 17 ).

In low- and middle-income countries, rates of overweight and obesity are rising especially in urban areas. In China, one study based on 12,543 participants monitored over 22 years revealed that the prevalence of age-adjusted obesity rose from 2.15% to 13.99% in both sexes, going from 2.78 to 13.22% in female and from 1.46 to 14.99% in male, respectively ( 18 , 19 ). The overweight rate of African children under 5 years old has increased by 24% since 2000. As of 2019, almost half of the Asian children under 5 years old were obese or overweight ( 20 ). WHO datasets from sub-Sarahan Africa reveal that prevalence of overweight and obese in adults and stunting, underweight, and wasting in children are inversely associated ( 21 ).

Pathogenesis of Obesity

The pathogenesis of obesity involves regulation of calorie utilization, appetite, and physical activity, but have complex interactions with availability of health-care systems, the role of socio-economic status, and underlying hereditary and environmental factors.

Food Intake and Energy Balance

The essential causes of obesity remain somewhat controversial. Current health recommendations to manage obesity are based on the underlying physiological property that fat accumulation is driven by an energy imbalance between consumed and expended calories. The obesity epidemic has been fueled in large part by increased energy from greater availability of highly rewarding and energy-dense food. Diet and various social, economic, and environmental factors related to food supply have a significant effect on patient’s ability to achieve the balance ( 22 ). In a 13-year follow-up study on 3,000 young, those who consumed much more fast-food were found to weigh an average of ~6kg more and have larger waist circumferences than those with the lowest fast-food-intake. They were also found to have higher incidences of negative weight-related health issues, such as elevated triglycerides and twice the odds of developing MetS ( 23 ). These issues are compounded in certain individuals that possess a genetic susceptibility to fat accumulation, which may be caused by significant interactions between homeostatic circuits and brain reward. Accumulation of lipid metabolites, inflammatory signaling, or other hypothalamic neuron impairing mechanisms may also lead to obesity, which might explain the biological defense of elevated body fat mass ( 24 ).

Obesogenic marketing to promote beverages or foods that are high in sugar and fat negatively modulates human behavior. Such advertisements may increase preference for energy-​dense foods and beverages ( 25 ). Analysis showed that African American programs had more food advertisements than other general market programs. More food advertisements were for meat, candy, soda, and fast food than for grains, pasta, cereals, vegetables, and fruits. Advertised products were designed to be cheap, have a long shelf-life, and taste ‘irresistible’. This applies particularly to high-fat, high-sugar junk foods that can stimulate the brain reward centers, the same part of the brain that’s stimulated by cocaine, heroin, and other addictive drugs, that is, these products are specifically engineered to be addictive ( 25 ). The brain reward offers a plausible mechanism to explain the elevated body fat mass, however, it seems that only certain individuals present this characteristic according to this theory.

For clinicians, a systematic evaluation of patient health factors affecting energy intake, metabolism, and expenditure is required for effective management of obesity. However, attempting to manage obesity through behavioral alterations aiming at addressing these three factors is more often than not unsuccessful. This suggests that our understanding of energy management and the interactions between intake, metabolism, and expenditure are not yet fully understood ( 26 ).

Family History and Lifestyle

Family history, lifestyle, and psychological factors all function in propensity for obesity. The likelihood of becoming obese can be affected by nature and nurture, enhanced by family genetics (propensity to accumulate fat) ( 27 ) or life style (poor dietary or exercise habits) ( 28 ). A child with one obese parent has a three-time risk to become obese as an adult, while when a child’s parents are both obese, this child has a 10-fold risk of future obesity. A cross-sectional observational study of 260 children (139 female, 121 male, aged 2.4 and 17.2 years) demonstrated that the family history of cardiometabolic diseases and obesity are critical risk factors for severity of obesity in childhood ( 29 ).

A prospective survey of 3148 school boys (aged six to ten years) in Ariana highlighted several child obesity risk factors, including parental obesity of parents, the snacks between meals especially after the dinner, lack of sleep (< 8 hours), and daily consumption of juice, sparkling drink, sweets, and sugary foods ( 30 ). Two studies of mother-child pairs in the United States found that the healthy lifestyle of mothers during the childhood and adolescence of their offspring was closely associated with a significantly reduced risk of obesity in their children ( 31 ). These results underscore the benefits of intervening at the family- or parental-level to reduce the risk of obesity in children ( 31 ).

However, parents are not the sole instigators of childhood obesity. For example, in the United States, physical education was used as a regular part of a public education curriculum ( 32 ). Starting 2011 physical education programs were curtailed such that 25 percent of students could achieve four out of five the national standards of at least 225 minutes weekly at the senior school levels and at least 150 minutes weekly at the primary level ( 33 ). Other factors that may have resulted in the decline of physical activity in children include increasing time spent on video game consoles and mobile devices at a reduction of time spent actively or outdoors. It is hard to argue against technological progress, but based on these studies, such innovations may be taking a toll on children’s health ( 34 ).

Microenvironment and Gut Microbiome

Our knowledge of the intestinal microbiome has grown substantially over recent years, as has our understanding of its intricate relationship to disease. For example, obesity is involved in an altered gut microenvironment that supports more diverse viral species than found in leaner hosts ( 35 ). This environment is more susceptible to the generation of pathogenic variants that can induce more serious disease ( 36 ). Increasing evidence shows that variations of gut microbiome cause alterations in host weight and metabolism. For example, compared with those with normal gut microbiota, germ-free male mice (without gut microflora) had 42% less total body fat, even while consuming 29% more food a day. However, after cecal microbe colonization, the total body fat of these mice increased 57%in, lean body mass decreased 7%, and daily food intake decreased 27% ( 35 ). A follow-up study suggested that these alterations resulted from decreased metabolic rates, with concomitant increased adipose tissue deposition, as capillary density in distal small intestinal villi increased 25% after microflora colonization. Similar results were also observed from female mice ( 37 ).

The human body contains around 3.8 × 10 13 microorganisms and the majority of them occupy the gastrointestinal tract. Over half of the microbial population are bacteria, followed by Archaea and Eukarya ( 38 ). The diversity of healthy gut microbiome allows for functional redundancy, in which multiple microbes can perform similar functions. Normally, gut microbiota have substantial beneficial roles in the host, including involvement in metabolism of carbohydrate and lipid, synthesis of vitamins and amino acids, epithelial cell proliferation, protection against pathogens, and hormone modulation. Gut bacteria can also break down indigestible molecules such as human milk oligosaccharides and plant polysaccharides ( 39 ). Imbalance of microbial populations (‘dysbiosis’) has been show to associate with a wide range of diseases including neurological disorders, inflammatory bowel disease, malnutrition, cancer, diabetes, and obesity ( 40 ). Recent research suggests that caloric restriction can beneficially reshape the gut microbiome and that antibiotic use can negatively harm gut microflora in ways that result in diabetes and obesity. Human studies support findings that microbiome alterations are associated with obesity; however, the exact mechanisms (i.e., ratios and amounts of microflora diversity) are still unknown ( 41 ).

Gut microbiota are central players in the host immune system. Disturbances in gut microflora can lead to inflammation of the intestinal lining ( 42 ). This response has been demonstrated to be mediated by TLRs (toll-like receptors), which identify and attack host microbes. For example, TLR4 recognizes the bacterial LPS (lipopolysaccharides) in the cell walls of Gram-negative bacteria while TLR5 recognizes bacterial flagellin. The body mass of TLR5-knockout mice increased 20% and their epididymal fat pad size increased 100% when compared to the wild-type controls ( 43 ). The dietary fiber and starch fermentation in lower gastrointestinal tract induced by microbiome can also produce SCFAs (Short-chain fatty acids), which can regulate production of gut hormone such as peptide YY (PYY) in the intestinal epithelium and GLP-1, GLP-2 (glucagon-like peptides), and the secretion of gastric inhibitory peptides by K cells ( 44 ). In obese patients, enzymes participated in or glucose signaling pathways are downregulated. It may be that alterations in specific microbial populations are more important than overall phylogenetic ratios, resulting in alterations in enzymes and SCFAs production, which further influence regulation of insulin and glucose, ultimately leading to development of obesity ( 41 ).

Genetic Factors and Causes

The studies from family and twin studies showed that around 40-70% of the obesity variation in human are resulted from genetic factors ( 45 ). While during the last 20 years, environmental alterations have increased obesity rates, the genetic factors play key roles in development of obesity ( 46 ). GWAS (Genome-wide association scans) approaches have identified over 400 genes associated with T2DM ( 47 , 48 ), however, these genes only predict 5% of obesity risk ( 49 ). The low predictive power may be due to the situation that gene-gene, gene-environment, and epigenetic interactions have not been thoroughly identified using the current methods based on population genetics ( 50 ). Many obesity -associated genes have been identified to be involved in energy homeostasis regulating pathways.

Genetic causes of obesity can be broadly classified as: 1) monogenic causes that result from a single gene mutation, primarily located in the leptin- melanocortin pathway. Many of the genes, such as AgRP (Agouti-related peptide), PYY (orexogenic), or MC4R (the melanocortin-4 receptor), were identified for monogenic obesity disrupt the regulatory system of appetite and weight, hormonal signals (ghrelin, leptin, insulin) are sensed by the receptors located in the arcuate nucleus of the hypothalamus ( 51 ). 2) Syndromic obesity were severe obesity results from neurodevelopmental abnormalities and other organ/system malformations. This may be caused by alterations in a single gene or a larger chromosomal region encompassing several genes ( 52 ). 3) Polygenic obesity is caused by cumulative contribution of many genes. Further, some people with obesity gain excess weight due to the multiple genes they have ( 53 ), and these genes make them to favor food and thereby have a higher caloric intake. The presence of these types of genes can cause increased caloric intake, increased hunger levels, reduced control overeating, reduced satiety, increased tendency to store body fat, and increased tendency to be sedentary ( 54 ).

Rare single-gene defects are associated with high level of hunger and can cause dramatic obesity in young children. Those individuals with severe obesity developed before two years old should consult obesity medicine specialists and consider to be involved in screening for MC4R Deficiency, leptin deficiency, and POMC deficiency ( 55 ). Leptin deficiency can cause diet-induced obesity and metabolic dysregulation. About 50% of female with polymorphism came up with binge eating ( 56 ). The MC4R polymorphism influences the release of ghrelin ( 57 ). The chromosome 2p22 (a region encompassing the POMC gene) has been identified as the site of gene(s) affecting obesity and obesity-related traits ( 58 ). These studies suggest that childhood obesity should be considered in the light of both environmental context and genetic heritage ( 59 ).

There are several genetic, neuroendocrine, and chromosomal precursors that can result in obesity. PWS (Prader-Willi Syndrome) is a neurodevelopmental disorder with hypothalamic dysfunction, due to the deficiency of imprinted genes ( 60 ). Endocrine disorders such as PCOS (Polycystic Ovary Syndrome) can also lead to increased body fat ( 61 ). Chromosomal defects can lead to obesity, including deletion of 16p11.2, 2q37 (brachydactyly mental retardation syndrome; BDMR), 1p36 (monosomy 1p36 syndrome), 9q34 (Kleefstra syndrome), 6q16 (PWS-like syndrome), 17p11.2 (Smith Magenis syndrome; SMS), and 11p13 (WAGR syndrome) ( 62 ). These conditions rely on the conventional current health recommendations that energy imbalance between calories consumed and expended is the key cause of obesity and present circumstances under which traditional weight management methods may not help.

Epigenetic Modification

We have been able to identify some of the genes that contribute to monogenic forms of obesity, but the human genome alterations on timescales that are too long for the genome to be a major player in the current obesity pandemic. Epigenetics, however, may offer a logical explanation for increasing obesity prevalence over the past few decades without necessitating a radical change in the genome ( 63 ). In multicellular organisms, the genetic code is homogenous throughout the body, but the expression of code can vary across cell types. Epigenetics studies showed that the heritable regulatory alterations in the genetic expression do not require alterations in the nucleotide sequence ( 64 ). Epigenetic modifications can be thought of as the differential packaging of the DNA that either allows or silences the expression of certain genes across tissues. Environmental and gut microbiota can influence the epigenetic programming of parental gametes, or programming in later stages of life ( 10 ).

The known epigenetic mechanisms include DNA methylation, histone modifications, and miRNA-mediated regulation. These can be passed from one generation to another meiotically or mitotically. There is evidence showing that the perinatal and embryo-fetal development period plays a critical role in human tissues and organs programming ( 65 ). DNA methylation appears to be the most important epigenetic mechanism for regulating gene expression. Alterations in DNA methylation can be a hallmark of many diseases such as cancers ( 66 ). LEP (Leptin) plays critical roles in adipose tissue regulation. The maternal metabolic status can affect DNA methylation of LEP profile at birth, affecting metabolic remodeling of obesity ( 67 ). The Adiponectin (ADIPOQ) epigenetic status also has relationship with obesity, and association has been reported between LDL-cholesterol levels and DNA methylation of both LEP and ADIPOQ ( 68 ). Paternal obesity has also been associated with inhibited methylation levels in IGF2 (insulin-like growth factor 2) regions, which promote the division and growth of various types of cells ( 69 ). Other genes investigated in the context of metabolism and obesity include: tumor necrosis factor (TNF), hypoxia-inducible factor 3a (HIF3A), neuropeptide Y (NPY), insulin receptor substrate 1 (IRS1), mitochondrial transcription factor A (TFAM), interleukin 6 (IL6), lymphocyte antigen 86 (LY86) and glucose transport 4 (GLUT4) ( 10 , 63 ).

Histones are proteins function in DNA packaging and modifications to histones are associated with epigenetic regulation of adipogenesis and obesity development ( 70 ). Five key regulatory genes in adipogenesis, CCAAT-enhancer-binding protein β (C/EBP β), pre-adipocyte factor-1 (Pref-1), adipocyte protein 2 (aP2), PPARγ, and C/EBPα, are modulated by histone modifications during adipocyte differentiation ( 71 ). The enzymes play roles in histone modification also function in obesity. They also regulate the expression of HDACs (histone deacetylases), which participated in the epigenetic control of gene expression involved in a large amount of environmental factors ( 72 ).

MicroRNAs (miRNAs) are 18 to 25 nucleotides long short noncoding RNA sequences that can regulate gene expression by gene silencing and post-transcriptional alterations. MicroRNAs function in a variety of biological processes, including adipocyte differentiation and proliferation, and are associated with low-grade inflammation and insulin resistance displayed in obese individuals ( 73 ). Increased levels of miRNAs including miR-486-3p, miR-142-3p, miR-486-5p, miR-423-5p and miR-130b were seen in children with high BMI values, among which 10 miRNAs exhibited significant alterations with increasing body weight ( 74 ). Zhao et al. identified miRNA as a signature for weight gain and showed that the individuals with a high-risk score for 8 of these miRNAs had over 3-fold higher odds of weight gain ( 75 ). Alterations in adipocyte-derived exosomal miRNAs is also seen following weight loss and decrease in insulin resistance after gastric bypass ( 76 ). miRNAs have been shown to play a key role in obesity and that the associated metabolic alterations can serve as biomarkers, or potentially therapeutic targets for intervention. Consideration of genetic and epigenetic causes of obesity provide valuable tools for the clinical treatment of obesity.

Therapeutics of Obesity

Lifestyle modifications.

Given the lack of specific pharmacological interventions, ‘lifestyle modification’ remains the cornerstone of obesity management ( 4 ). Individuals with obesity are suggested to lose at least 10% body weight via combination of diet, physical activity, and behavior therapy (or lifestyle modification) ( 77 ). Significant short-term weight loss can be achieved by consumption of portion-controlled diets ( 78 ). Long-term weight control can be achieved via high levels of physical activity and continued patient–practitioner contact. In many cases, lifestyle modification results in dramatic loss of body weight, leading to significant reduction of cardiovascular risk ( 79 ).

Since food choices are mainly determined by peoples’ surroundings, it is imperative that governments improve policies and environment to reduce the availability of unhealthful foods and make healthy foods more accessible. Policies should be changed to increase development of foods with reduced sugar, fat, and salt and decrease availability of obesogenic foods aimed at children ( 80 ). Policy makers and Practitioners must be made aware of the potential impact of food advertisements on human health and behavior and should encourage food manufacturers to create and promote weight-friendly foods. Nutrition educators should help teach how to evaluate food advertisements ( 81 ). Interventions aimed at motivating behavioral alterations (e.g., health promotion, nutrition education, incentives for healthy living, sugar-​sweetened beverage tax, and social marketing) and enforcing actions that reduce causes of obesity (e.g., policy changes, regulations and laws) are likely to have strong impacts on reducing the obesity crisis ( 82 ).

Anti-Obesity Medications

Pharmacotherapy is recommended for those whose BMI ≥30 (or a BMI ≥27 with comorbid conditions) and are unable to lose weight using lifestyle modification alone ( 83 ). The U.S. FDA (Food and Drug Administration) approved some new pharmacotherapy drugs for short-term obesity treatment ( Table 1 ) and since Lorcaserin was withdrawn, there are only four [Naltrexone-Bupropion (Contrave), Orlistat (Xenical, Alli), Liraglutide (Saxenda) and Phentermine-Topiramate (Qsymia)] approved in addition to Gelesis which is now the fifth, have been approved for long-term use ( 84 , 86 , 87 ). The FDA also approved the MC4R agonist-Setmelanotide for use in individuals with severe obesity due to either POMC, PCSK1 (proprotein convertase subtilisin/kexin type 1), or LEPR (leptin receptor) deficiency at the end of 2020 ( 85 ).

Table 1 Prescription medications approved for obesity treatment.

In addition, 11 different components have been identified from 54 families of the plants to have anti-obesity potential. These families include Celastraceae, Zingiberaceae, Theaceae, Magnoliaceae, and Solanaceae ( 88 ). Traditional Chinese medicine delivers unique solutions to treat obesity, such as regulating fat metabolism, enhancing hormone level, regulating intestinal microflora, among other pathways ( 89 ). These findings are helpful for selection of herbal medicine or traditional Chinese medicine for further research.

Bariatric Surgery

For individuals with a BMI > 40 or BMI > 35 with comorbidities who are unable to lose weight by lifestyle modifications or pharmacotherapy bariatric surgery or weight loss surgery is another option ( 83 ). Standard bariatric operations, including BPD (Bilio-pancreatic diversion), SG (sleeve gastrectomy), RYGB (Roux-en-Y gastric bypass), and AGB (adjustable gastric banding), benefits individuals׳ metabolic profiles to varying degrees ( 90 ). Studies reported that the benefits of bariatric surgery go beyond just losing weight. Bariatric surgery reduces chronic inflammation involved in obesity and alters biomarkers, the gut microbiota, and long-term remission for T2DM ( 91 – 93 ). Take RYGB for example, in human subjects, overall gut microbial richness increased after RYGB surgery ( 94 ). Further analysis revealed RYGB contributed to increase of expression of some specific white adipose tissue genes, upregulation of genes central to the transforming growth factor-β signaling pathway, and remarkable downregulation of genes involved in metabolic pathways and inflammatory responses ( 95 ). Decrease of serum leptin levels, which are associated with leaned BMI, typically results from bariatric surgery. Interestingly, those women who had a higher presurgical baseline leptin level were easier to remain the post-procedure weight loss, while those with a lower presurgical baseline level were easier to regain the weight. There is a correlation between the baseline leptin level and alterations in body mass, BMI, as well as total weight loss although the success degree of surgery cannot be predicted by a patient’s serum leptin level ( 96 ).

Fecal Microbiota Transplantation

FMT has attracted considerable research interest recently in the treatment of obesity ( 97 ). There are promising indications that FMT of microbes from healthy individuals into patients with obesity may be affected in weight loss and maintenance. In a groundbreaking key study, Ridaura et al. transplanted fecal slurries from human twins discordant for obesity into germ-free mice ( 98 ). The mice with obese individuals’ microbiota successfully developed obesity, while those with healthy individuals’ microbiota remained lean. The sequencing results of mice post-procedure stool samples showed that the human microbiomes were successfully infused, indicative of the transfer of functions related to the obese or lean microbial communities, respectively ( 98 ). Promising studies in humans are also being attempted: Vrieze et al. were able to improve microbial diversity and insulin sensitivity in obese, diabetic adult males after the transplantation with the taxa from lean donors ( 99 ). An increase was observed in butyrate-producing bacteria and Bacteroidetes, indicative of a shift toward a leaner phenotype related microbial community. While in early stages, FMT may be an option for replacing obesogenic microbial communities ( 100 ).

Summary and Conclusions

The global prevalence of obesity has nearly tripled since 1975 and continues to grow at an exponential rate. Obesity has become the number one lifestyle-related risk factor for premature death. As such, public health policies focused on reducing and treating obesity must be developed ( 17 ). The WHO “Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2020” defines strategies to prevent further increase in obesity prevalence, but progress so far has been slow ( 101 ). However, with the identification of the main obesity causes the modulating factors, the challenge remains is to translate them into effective actions.

Epigenetic modifications and interactions between our genes and environment have strong influences on human health and disease. Increasing evidence is revealing the involvement of epigenetics in obesity prevalence ( 9 ). Propensity for obesity can result from the effects of environmental factors, such as nutrition and lifestyle to the epigenetic remodeling of the early postnatal development, and parental gametes. Epigenetic marks could also significantly affect the obesity risk of the child and thus be transmitted trans-generationally ( 11 ). This epigenetic ‘memory’ may help explain our lack of evidence for genetic heritability in obesity and other diseases. A foundational knowledge of the mechanisms of epigenetic inheritance is of great importance for treating and preventing obesity. Exploration of epigenetic changes is a key for predicting disease trajectories and choosing effective treatment. The reversible characteristic of these modifications makes them ideal targets for epigenetic treatment, and promising “epigenetic drugs” for therapies of obesity are already in the marketplace or in various stages of development ( 102 ). These types of therapies include DNA methyltransferase inhibitors (DNMTis), protein arginine methyltransferase inhibitors (PRMTis), histone acetyltransferase inhibitors (HATi), histone deacetylase inhibitors (HDACi), sirtuin-activating compounds (STACs) and histone demethylating inhibitors (HDMis) ( 103 ).

Microbiome research holds much promise for treating pandemics such as obesity and diabetes. On-going developments in technology and bioinformatics of microbiology are increasingly allowing for the development of a microbiome-manipulating capsule to favor a healthy, lean, and insulin-sensitive profile, but this is still an area of active research ( 8 , 104 ). More targeted therapies will also become possible as we increase our understanding of microbial metabolites, allowing for clinal treatment of inflammation, weight gain and insulin resistance, and ultimately preventing the progression to obesity.

In conclusion, improved understanding of the various dimension of obesity, including propensity to regain lost weight, interindividual differences in pathogenesis, and response to therapy, is needed for developing effective as well as cost-effective interventions. The insights will in turn benefit the related health complications such as incidence of diabetes. More research is required to identify behavioral modification that are effective and available to people from diverse backgrounds. More studies were performed to develop more effective and safer medications to help obese people lose body weight and maintain a healthy weight for long term. Moreover, we must devote greater efforts and resources to the prevention of obesity in children as well as adults. Prevention is a key as treatment alone is not very effective and cannot well reverse the epidemic of obesity for long term.

Author Contributions

HL and XL conceived and wrote the manuscript. All authors contributed to the article and approved the submitted version.

This project was funded by grants from the Zhejiang Provincial Medical Science and Technology Program (2020KY166).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

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Keywords: obesity, epidemiology, pathophysiology, genetics, epigenetics, microenvironment

Citation: Lin X and Li H (2021) Obesity: Epidemiology, Pathophysiology, and Therapeutics. Front. Endocrinol. 12:706978. doi: 10.3389/fendo.2021.706978

Received: 08 May 2021; Accepted: 10 August 2021; Published: 06 September 2021.

Reviewed by:

Copyright © 2021 Lin and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Hong Li, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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• Henrietta E Graham , doctoral candidate 1 ,
• Elizabeth Sturgiss , NHMRC investigator 2 ,
• Victoria E Kettle , research associate 1 ,
• Kajal Gokal , senior research associate 1 ,
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• 1 Centre for Lifestyle Medicine and Behaviour (CLiMB), The School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough LE11 3TU, UK
• 2 School of Primary and Allied Health Care, Monash University, Melbourne, Australia
• 3 Department of Psychology, Addiction and Mental Health Group, University of Bath, Bath, UK
• Correspondence to: C D Madigan c.madigan{at}lboro.ac.uk (or @claire_wm and @lboroclimb on Twitter)
• Accepted 26 April 2022

Objective To examine the effectiveness of behavioural weight management interventions for adults with obesity delivered in primary care.

Design Systematic review and meta-analysis of randomised controlled trials.

Eligibility criteria for selection of studies Randomised controlled trials of behavioural weight management interventions for adults with a body mass index ≥25 delivered in primary care compared with no treatment, attention control, or minimal intervention and weight change at ≥12 months follow-up.

Data sources Trials from a previous systematic review were extracted and the search completed using the Cochrane Central Register of Controlled Trials, Medline, PubMed, and PsychINFO from 1 January 2018 to 19 August 2021.

Data extraction and synthesis Two reviewers independently identified eligible studies, extracted data, and assessed risk of bias using the Cochrane risk of bias tool. Meta-analyses were conducted with random effects models, and a pooled mean difference for both weight (kg) and waist circumference (cm) were calculated.

Main outcome measures Primary outcome was weight change from baseline to 12 months. Secondary outcome was weight change from baseline to ≥24 months. Change in waist circumference was assessed at 12 months.

Results 34 trials were included: 14 were additional, from a previous review. 27 trials (n=8000) were included in the primary outcome of weight change at 12 month follow-up. The mean difference between the intervention and comparator groups at 12 months was −2.3 kg (95% confidence interval −3.0 to −1.6 kg, I 2 =88%, P<0.001), favouring the intervention group. At ≥24 months (13 trials, n=5011) the mean difference in weight change was −1.8 kg (−2.8 to −0.8 kg, I 2 =88%, P<0.001) favouring the intervention. The mean difference in waist circumference (18 trials, n=5288) was −2.5 cm (−3.2 to −1.8 cm, I 2 =69%, P<0.001) in favour of the intervention at 12 months.

Conclusions Behavioural weight management interventions for adults with obesity delivered in primary care are effective for weight loss and could be offered to members of the public.

Systematic review registration PROSPERO CRD42021275529.

Introduction

Obesity is associated with an increased risk of diseases such as cancer, type 2 diabetes, and heart disease, leading to early mortality. 1 2 3 More recently, obesity is a risk factor for worse outcomes with covid-19. 4 5 Because of this increased risk, health agencies and governments worldwide are focused on finding effective ways to help people lose weight. 6

Primary care is an ideal setting for delivering weight management services, and international guidelines recommend that doctors should opportunistically screen and encourage patients to lose weight. 7 8 On average, most people consult a primary care doctor four times yearly, providing opportunities for weight management interventions. 9 10 A systematic review of randomised controlled trials by LeBlanc et al identified behavioural interventions that could potentially be delivered in primary care, or involved referral of patients by primary care professionals, were effective for weight loss at 12-18 months follow-up (−2.4 kg, 95% confidence interval −2.9 to−1.9 kg). 11 However, this review included trials with interventions that the review authors considered directly transferrable to primary care, but not all interventions involved primary care practitioners. The review included interventions that were entirely delivered by university research employees, meaning implementation of these interventions might differ if offered in primary care, as has been the case in other implementation research of weight management interventions, where effects were smaller. 12 As many similar trials have been published after this review, an updated review would be useful to guide health policy.

We examined the effectiveness of weight loss interventions delivered in primary care on measures of body composition (weight and waist circumference). We also identified characteristics of effective weight management programmes for policy makers to consider.

This systematic review was registered on PROSPERO and is reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. 13 14

Eligibility criteria

We considered studies to be eligible for inclusion if they were randomised controlled trials, comprised adult participants (≥18 years), and evaluated behavioural weight management interventions delivered in primary care that focused on weight loss. A primary care setting was broadly defined as the first point of contact with the healthcare system, providing accessible, continued, comprehensive, and coordinated care, focused on long term health. 15 Delivery in primary care was defined as the majority of the intervention being delivered by medical and non-medical clinicians within the primary care setting. Table 1 lists the inclusion and exclusion criteria.

Study inclusion and exclusion criteria

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We extracted studies from the systematic review by LeBlanc et al that met our inclusion criteria. 11 We also searched the exclusions in this review because the researchers excluded interventions specifically for diabetes management, low quality trials, and only included studies from an Organisation for Economic Co-operation and Development country, limiting the scope of the findings.

We searched for studies in the Cochrane Central Register of Controlled Trials, Medline, PubMed, and PsychINFO from 1 January 2018 to 19 August 2021 (see supplementary file 1). Reference lists of previous reviews 16 17 18 19 20 21 and included trials were hand searched.

Data extraction

Results were uploaded to Covidence, 22 a software platform used for screening, and duplicates removed. Two independent reviewers screened study titles, abstracts, and full texts. Disagreements were discussed and resolved by a third reviewer. All decisions were recorded in Covidence, and reviewers were blinded to each other’s decisions. Covidence calculates proportionate agreement as a measure of inter-rater reliability, and data are reported separately by title or abstract screening and full text screening. One reviewer extracted data on study characteristics (see supplementary table 1) and two authors independently extracted data on weight outcomes. We contacted the authors of four included trials (from the updated search) for further information. 23 24 25 26

Outcomes, summary measures, and synthesis of results

The primary outcome was weight change from baseline to 12 months. Secondary outcomes were weight change from baseline to ≥24 months and from baseline to last follow-up (to include as many trials as possible), and waist circumference from baseline to 12 months. Supplementary file 2 details the prespecified subgroup analysis that we were unable to complete. The prespecified subgroup analyses that could be completed were type of healthcare professional who delivered the intervention, country, intensity of the intervention, and risk of bias rating.

Healthcare professional delivering intervention —From the data we were able to compare subgroups by type of healthcare professional: nurses, 24 26 27 28 general practitioners, 23 29 30 31 and non-medical practitioners (eg, health coaches). 32 33 34 35 36 37 38 39 Some of the interventions delivered by non-medical practitioners were supported, but not predominantly delivered, by GPs. Other interventions were delivered by a combination of several different practitioners—for example, it was not possible to determine whether a nurse or dietitian delivered the intervention. In the subgroup analysis of practitioner delivery, we refer to this group as “other.”

Country —We explored the effectiveness of interventions by country. Only countries with three or more trials were included in subgroup analyses (United Kingdom, United States, and Spain).

Intensity of interventions —As the median number of contacts was 12, we categorised intervention groups according to whether ≤11 or ≥12 contacts were required.

Risk of bias rating —Studies were classified as being at low, unclear, and high risk of bias. Risk of bias was explored as a potential influence on the results.

Meta-analyses

Meta-analyses were conducted using Review Manager 5.4. 40 As we expected the treatment effects to differ because of the diversity of intervention components and comparator conditions, we used random effects models. A pooled mean difference was calculated for each analysis, and variance in heterogeneity between studies was compared using the I 2 and τ 2 statistics. We generated funnel plots to evaluate small study effects. If more than two intervention groups existed, we divided the number of participants in the comparator group by the number of intervention groups and analysed each individually. Nine trials were cluster randomised controlled trials. The trials had adjusted their results for clustering, or adjustment had been made in the previous systematic review by LeBlanc et al. 11 One trial did not report change in weight by group. 26 We calculated the mean weight change and standard deviation using a standard formula, which imputes a correlation for the baseline and follow-up weights. 41 42 In a non-prespecified analysis, we conducted univariate and multivariable metaregression (in Stata) using a random effects model to examine the association between number of sessions and type of interventionalist on study effect estimates.

Risk of bias

Two authors independently assessed the risk of bias using the Cochrane risk of bias tool v2. 43 For incomplete outcome data we defined a high risk of bias as ≥20% attrition. Disagreements were resolved by discussion or consultation with a third author.

Patient and public involvement

The study idea was discussed with patients and members of the public. They were not, however, included in discussions about the design or conduct of the study.

The search identified 11 609 unique study titles or abstracts after duplicates were removed ( fig 1 ). After screening, 97 full text articles were assessed for eligibility. The proportionate agreement ranged from 0.94 to 1.0 for screening of titles or abstracts and was 0.84 for full text screening. Fourteen new trials met the inclusion criteria. Twenty one studies from the review by LeBlanc et al met our eligibility criteria and one study from another systematic review was considered eligible and included. 44 Some studies had follow-up studies (ie, two publications) that were found in both the second and the first search; hence the total number of trials was 34 and not 36. Of the 34 trials, 27 (n=8000 participants) were included in the primary outcome meta-analysis of weight change from baseline to 12 months, 13 (n=5011) in the secondary outcome from baseline to ≥24 months, and 30 (n=8938) in the secondary outcome for weight change from baseline to last follow-up. Baseline weight was accounted for in 18 of these trials, but in 14 24 26 29 30 31 32 44 45 46 47 48 49 50 51 it was unclear or the trials did not consider baseline weight. Eighteen trials (n=5288) were included in the analysis of change in waist circumference at 12 months.

Studies included in systematic review of effectiveness of behavioural weight management interventions in primary care. *Studies were merged in Covidence if they were from same trial

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Study characteristics

Included trials (see supplementary table 1) were individual randomised controlled trials (n=25) 24 25 26 27 28 29 32 33 34 35 38 39 41 44 45 46 47 50 51 52 53 54 55 56 59 or cluster randomised controlled trials (n=9). 23 30 31 36 37 48 49 57 58 Most were conducted in the US (n=14), 29 30 31 32 33 34 35 36 37 45 48 51 54 55 UK (n=7), 27 28 38 41 47 57 58 and Spain (n=4). 25 44 46 49 The median number of participants was 276 (range 50-864).

Four trials included only women (average 65.9% of women). 31 48 51 59 The mean BMI at baseline was 35.2 (SD 4.2) and mean age was 48 (SD 9.7) years. The interventions lasted between one session (with participants subsequently following the programme unassisted for three months) and several sessions over three years (median 12 months). The follow-up period ranged from 12 months to three years (median 12 months). Most trials excluded participants who had lost weight in the past six months and were taking drugs that affected weight.

Meta-analysis

Overall, 27 trials were included in the primary meta-analysis of weight change from baseline to 12 months. Three trials could not be included in the primary analysis as data on weight were only available at two and three years and not 12 months follow-up, but we included these trials in the secondary analyses of last follow-up and ≥24 months follow-up. 26 44 50 Four trials could not be included in the meta-analysis as they did not present data in a way that could be synthesised (ie, measures of dispersion). 25 52 53 58 The mean difference was −2.3 kg (95% confidence interval −3.0 to −1.6 kg, I 2 =88%, τ 2 =3.38; P<0.001) in favour of the intervention group ( fig 2 ). We found no evidence of publication bias (see supplementary fig 1). Absolute weight change was −3.7 (SD 6.1) kg in the intervention group and −1.4 (SD 5.5) kg in the comparator group.

Mean difference in weight at 12 months by weight management programme in primary care (intervention) or no treatment, different content, or minimal intervention (control). SD=standard deviation

Supplementary file 2 provides a summary of the main subgroup analyses.

Weight change

The mean difference in weight change at the last follow-up was −1.9 kg (95% confidence interval −2.5 to −1.3 kg, I 2 =81%, τ 2 =2.15; P<0.001). Absolute weight change was −3.2 (SD 6.4) kg in the intervention group and −1.2 (SD 6.0) kg in the comparator group (see supplementary figs 2 and 3).

At the 24 month follow-up the mean difference in weight change was −1.8 kg (−2.8 to −0.8 kg, I 2 =88%, τ 2 =3.13; P<0.001) (see supplementary fig 4). As the weight change data did not differ between the last follow-up and ≥24 months, we used the weight data from the last follow-up in subgroup analyses.

In subgroup analyses of type of interventionalist, differences were significant (P=0.005) between non-medical practitioners, GPs, nurses, and other people who delivered interventions (see supplementary fig 2).

Participants who had ≥12 contacts during interventions lost significantly more weight than those with fewer contacts (see supplementary fig 6). The association remained after adjustment for type of interventionalist.

Waist circumference

The mean difference in waist circumference was −2.5 cm (95% confidence interval −3.2 to −1.8 cm, I 2 =69%, τ 2 =1.73; P<0.001) in favour of the intervention at 12 months ( fig 3 ). Absolute changes were −3.7 cm (SD 7.8 cm) in the intervention group and −1.3 cm (SD 7.3) in the comparator group.

Mean difference in waist circumference at 12 months. SD=standard deviation

Risk of bias was considered to be low in nine trials, 24 33 34 35 39 41 47 55 56 unclear in 12 trials, 25 27 28 29 32 45 46 50 51 52 54 59 and high in 13 trials 23 26 30 31 36 37 38 44 48 49 53 57 58 ( fig 4 ). No significant (P=0.65) differences were found in subgroup analyses according to level of risk of bias from baseline to 12 months (see supplementary fig 7).

Risk of bias in included studies

Worldwide, governments are trying to find the most effective services to help people lose weight to improve the health of populations. We found weight management interventions delivered by primary care practitioners result in effective weight loss and reduction in waist circumference and these interventions should be considered part of the services offered to help people manage their weight. A greater number of contacts between patients and healthcare professionals led to more weight loss, and interventions should be designed to include at least 12 contacts (face-to-face or by telephone, or both). Evidence suggests that interventions delivered by non-medical practitioners were as effective as those delivered by GPs (both showed statistically significant weight loss). It is also possible that more contacts were made with non-medical interventionalists, which might partially explain this result, although the metaregression analysis suggested the effect remained after adjustment for type of interventionalist. Because most comparator groups had fewer contacts than intervention groups, it is not known whether the effects of the interventions are related to contact with interventionalists or to the content of the intervention itself.

Although we did not determine the costs of the programme, it is likely that interventions delivered by non-medical practitioners would be cheaper than GP and nurse led programmes. 41 Most of the interventions delivered by non-medical practitioners involved endorsement and supervision from GPs (ie, a recommendation or checking in to see how patients were progressing), and these should be considered when implementing these types of weight management interventions in primary care settings. Our findings suggest that a combination of practitioners would be most effective because GPs might not have the time for 12 consultations to support weight management.

Although the 2.3 kg greater weight loss in the intervention group may seem modest, just 2-5% in weight loss is associated with improvements in systolic blood pressure and glucose and triglyceride levels. 60 The confidence intervals suggest a potential range of weight loss and that these interventions might not provide as much benefit to those with a higher BMI. Patients might not find an average weight loss of 3.7 kg attractive, as many would prefer to lose more weight; explaining to patients the benefits of small weight losses to health would be important.

Strengths and limitations of this review

Our conclusions are based on a large sample of about 8000 participants, and 12 of these trials were published since 2018. It was occasionally difficult to distinguish who delivered the interventions and how they were implemented. We therefore made some assumptions at the screening stage about whether the interventionalists were primary care practitioners or if most of the interventions were delivered in primary care. These discussions were resolved by consensus. All included trials measured weight, and we excluded those that used self-reported data. Dropout rates are important in weight management interventions as those who do less well are less likely to be followed-up. We found that participants in trials with an attrition rate of 20% or more lost less weight and we are confident that those with high attrition rates have not inflated the results. Trials were mainly conducted in socially economic developed countries, so our findings might not be applicable to all countries. The meta-analyses showed statistically significant heterogeneity, and our prespecified subgroups analysis explained some, but not all, of the variance.

Comparison with other studies

The mean difference of −2.3 kg in favour of the intervention group at 12 months is similar to the findings in the review by LeBlanc et al, who reported a reduction of −2.4 kg in participants who received a weight management intervention in a range of settings, including primary care, universities, and the community. 11 61 This is important because the review by LeBlanc et al included interventions that were not exclusively conducted in primary care or by primary care practitioners. Trials conducted in university or hospital settings are not typically representative of primary care populations and are often more intensive than trials conducted in primary care as a result of less constraints on time. Thus, our review provides encouraging findings for the implementation of weight management interventions delivered in primary care. The findings are of a similar magnitude to those found in a trial by Ahern et al that tested primary care referral to a commercial programme, with a difference of −2.7 kg (95% confidence interval −3.9 to −1.5 kg) reported at 12 month follow-up. 62 The trial by Ahern et al also found a difference in waist circumference of −4.1 cm (95% confidence interval −5.5 to −2.3 cm) in favour of the intervention group at 12 months. Our finding was smaller at −2.5 cm (95% confidence interval −3.2 to −1.8 cm). Some evidence suggests clinical benefits from a reduction of 3 cm in waist circumference, particularly in decreased glucose levels, and the intervention groups showed a 3.7 cm absolute change in waist circumference. 63

Policy implications and conclusions

Weight management interventions delivered in primary care are effective and should be part of services offered to members of the public to help them manage weight. As about 39% of the world’s population is living with obesity, helping people to manage their weight is an enormous task. 64 Primary care offers good reach into the community as the first point of contact in the healthcare system and the remit to provide whole person care across the life course. 65 When developing weight management interventions, it is important to reflect on resource availability within primary care settings to ensure patients’ needs can be met within existing healthcare systems. 66

We did not examine the equity of interventions, but primary care interventions may offer an additional service and potentially help those who would not attend a programme delivered outside of primary care. Interventions should consist of 12 or more contacts, and these findings are based on a mixture of telephone and face-to-face sessions. Previous evidence suggests that GPs find it difficult to raise the issue of weight with patients and are pessimistic about the success of weight loss interventions. 67 Therefore, interventions should be implemented with appropriate training for primary care practitioners so that they feel confident about helping patients to manage their weight. 68

Unanswered questions and future research

A range of effective interventions are available in primary care settings to help people manage their weight, but we found substantial heterogeneity. It was beyond the scope of this systematic review to examine the specific components of the interventions that may be associated with greater weight loss, but this could be investigated by future research. We do not know whether these interventions are universally suitable and will decrease or increase health inequalities. As the data are most likely collected in trials, an individual patient meta-analysis is now needed to explore characteristics or factors that might explain the variance. Most of the interventions excluded people prescribed drugs that affect weight gain, such as antipsychotics, glucocorticoids, and some antidepressants. This population might benefit from help with managing their weight owing to the side effects of these drug classes on weight gain, although we do not know whether the weight management interventions we investigated would be effective in this population. 69

What is already known on this topic

Referral by primary care to behavioural weight management programmes is effective, but the effectiveness of weight management interventions delivered by primary care is not known

Systematic reviews have provided evidence for weight management interventions, but the latest review of primary care delivered interventions was published in 2014

Factors such as intensity and delivery mechanisms have not been investigated and could influence the effectiveness of weight management interventions delivered by primary care

What this study adds

Weight management interventions delivered by primary care are effective and can help patients to better manage their weight

At least 12 contacts (telephone or face to face) are needed to deliver weight management programmes in primary care

Some evidence suggests that weight loss after weight management interventions delivered by non-medical practitioners in primary care (often endorsed and supervised by doctors) is similar to that delivered by clinician led programmes

Ethics statements

Ethical approval.

Not required.

Data availability statement

Additional data are available in the supplementary files.

Contributors: CDM and AJD conceived the study, with support from ES. CDM conducted the search with support from HEG. CDM, AJD, ES, HEG, KG, GB, and VEK completed the screening and full text identification. CDM and VEK completed the risk of bias assessment. CDM extracted data for the primary outcome and study characteristics. HEJ, GB, and KG extracted primary outcome data. CDM completed the analysis in RevMan, and GMJT completed the metaregression analysis in Stata. CDM drafted the paper with AJD. All authors provided comments on the paper. CDM acts as guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: AJD is supported by a National Institute for Health and Care Research (NIHR) research professorship award. This research was supported by the NIHR Leicester Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. ES’s salary is supported by an investigator grant (National Health and Medical Research Council, Australia). GT is supported by a Cancer Research UK fellowship. The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: This research was supported by the National Institute for Health and Care Research Leicester Biomedical Research Centre; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work.

The lead author (CDM) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported, and that no important aspects of the study have been omitted.

Dissemination to participants and related patient and public communities: We plan to disseminate these research findings to a wider community through press releases, featuring on the Centre for Lifestyle Medicine and Behaviour website ( www.lboro.ac.uk/research/climb/ ) via our policy networks, through social media platforms, and presentation at conferences.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ .

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Significant gaps between science of obesity and the care patients receive

As research continues to produce evidence about the underlying causes of obesity and optimal strategies to treat and manage obesity have evolved, there are disparities in application of the latest scientific advances in the clinical care that people with obesity receive. Widespread adoption of current findings, consistency of care and expertise in obesity care varies by health care professional and institution. These findings are detailed in a new American Heart Association scientific statement, "Implementation of Obesity Science Into Clinical Practice," published today in the Association's flagship scientific journal Circulation .

"Obesity is undeniably a critical public health concern in the U.S. and around the world, affecting nearly all populations and straining our health care systems," said Deepika Laddu, Ph.D., FAHA, chair of the statement writing committee and a senior research scientist at Arbor Research Collaborative for Health in Ann Arbor, Michigan. "As a major risk factor for heart disease, obesity has significantly hindered progress in reducing heart disease rates. Despite advancements in understanding the complexities of obesity and newer treatment options, major gaps remain between obesity research and real-world implementation in clinical practice."

Studies show intensive lifestyle therapy is considerably more effective for weight loss than brief advice from a health care professional. However, general educational information is offered more frequently by health professionals rather than referrals to classes, programs or tangible resources for lifestyle changes. One study revealed that only 16% of health care professionals had working knowledge about evidence-based lifestyle treatments for obesity, including diet and nutrition, physical activity and intensive behavioral therapy referral. Other barriers to addressing weight loss are exacerbated by socioeconomic and racial or ethnic inequities. People of diverse races and ethnicities and people who are covered by Medicare or Medicaid are less likely to be referred to weight management programs or to have them covered by insurance.

The number of people living with obesity is increasing worldwide. For about 30 years, the prevalence of obesity in the United States and around the world has been escalating. Recent estimates indicate more than 40% of U.S. adults ages 20 and older are living with obesity, according to the U.S. Centers for Disease Control and Prevention.

Research has led experts to unlock the multifactorial causes of obesity, including sociological and physiological determinants of health. Treatments for obesity have also evolved with more strategies for lifestyle modifications, medication therapy and bariatric (weight-loss) surgery. However, each treatment approach comes with challenges.

"While significant strides have been made in advancing the science to help us understand obesity, there remains a considerable gap between what we know and what happens in the doctor's office," said Laddu. "Health care professionals and health care systems need to find better ways to put what we know about obesity into action so more people can get the right support and treatment. Adopting new technologies and telemedicine, making referrals to community-based weight management programs to encourage behavioral change, providing social support and increasing reach and access to treatments are just some of the promising methods we could implement to unlock successful, evidence-based obesity care."

Weight loss medications

Glucagon-like peptide-1 (GLP-1) agonists, such as high-dose semaglutide and tirzepatide, are the most recently FDA-approved medications for long-term weight management, and both are associated with an average weight loss of more than 10% at six months in clinical studies. However, despite half of adults in the U.S. meeting the BMI criteria for obesity and being eligible for these medications, a small proportion of this population is currently taking them. Until recently, the primary barriers to greater use of anti-obesity medications were lack of insurance coverage and high out-of-pocket costs for these medications.

Since the beginning of the Medicare (Part D) program in 2006, all medications taken for weight loss have been excluded from basic coverage. In March, the Centers for Medicare and Medicaid Services (CMS) determined that Medicare and Medicaid can cover the anti-obesity medication semaglutide when it is approved by the FDA for an additional use. That decision included high-dose semaglutide, which is FDA-approved for weight loss and to reduce the risk of cardiovascular death, heart attack and stroke in adults with cardiovascular disease and either obesity or overweight. State Medicaid programs, which provide health care coverage for people in low-income populations and who are disproportionately affected by obesity and heart disease, are required to cover nearly all FDA-approved anti-obesity medications for people meeting the health and body mass index (BMI) criteria. However, state health plans may require step therapy with other treatments or medications prior to approving use of GLP-1 medications.

"FDA approval and insurance coverage of the latest treatments, including GLP-1 medications, are integral to improving access to care and outcomes for people who need these therapies the most. This is especially true for high-risk, high-need patients for the prevention of adverse cardiovascular events. It is encouraging that these steps in increasing access may lead to reduced risk of CVD and improved outcomes for potentially millions of adults in the U.S.," said the scientific statement's Vice Chair Ian J. Neeland, M.D., FAHA, director of cardiovascular prevention, director of the Center for Integrated and Novel Approaches in Vascular-Metabolic Disease at University Hospitals Harrington Heart and Vascular Institute at Institute, and an associate professor of medicine at the Case Center for Diabetes, Obesity and Metabolism at Case Western Reserve University School of Medicine, both in Cleveland.

Weight loss surgery

In the decades since bariatric (weight loss) surgery was first introduced as an option for people with severe obesity, there have been advances in the expertise and safety of the procedures, as well as an increased understanding of the health benefits that often result after bariatric surgery. A comprehensive review of studies focused on weight loss surgeries showed that patients who underwent bariatric surgery had lower risks of cardiovascular disease and decreased risks for multiple other obesity-associated conditions, including Type 2 diabetes and high blood pressure. One challenge facing health care professionals is ensuring that the populations with the greatest needs have access to bariatric surgery in terms of costs, resources and social support.

The statement describes strategies that both address these challenges and improve how obesity-based research is incorporated into clinical care. The statement also identifies the need to develop solutions across populations in order to manage obesity at the community level. Potential improved public health policies and future research to expand patient care models and optimize the delivery and sustainability of equitable obesity-related care are suggested.

Specific approaches are highlighted in the statement to help bridge the gap between the science about obesity and clinical care, such as:

• To reach and successfully impact populations in need, health care professionals may consider how social determinants of health, including insurance type, household income, race and ethnicity, environment, health literacy, access to health-promoting resources and social supports all influence the likelihood of successful patient treatment.
• Education for health care professionals explaining the complex origins and clinical consequences of obesity is discussed. Such training should emphasize information about diagnosis, prevention and treatment of obesity. Despite the high prevalence of obesity around the world, there is a lack of education programs centered on obesity for medical professionals.
• Further evaluation of health policy changes that health care systems and insurance plans can implement and scale in order to make obesity treatment affordable for patients, especially those at high risk for adverse outcomes such as cardiovascular disease.
• A framework for delivering obesity care into clinical practice settings is reviewed, as well as efforts by some professional societies for developing interventions that make obesity treatment more accessible.

"The statement emphasizes the importance of a comprehensive approach across different levels of health care delivery and public policy, along with the adoption of feasible, evidence-based strategies in clinical settings," said Laddu. "It also underscores the need for future research and policy changes to improve current patient care models and ensure equitable access to obesity-related care for people in underrepresented groups."

The scientific statement also provides possible solutions for how to help people in their day-to-day lives, including interventions with digital technology and access through telemedicine. However, more research is needed in obesity science and treatment. Limited understanding of the cost-effectiveness of obesity prevention and the long-term health outcomes for established therapies has hindered the implementation of obesity science into clinical settings. Cross-collaborative obesity science research between stakeholders and health economists may serve as the bridge to developing and scaling cost-effective prevention programs.

Further research into Food Is Medicine approaches in health care, such as medically tailored meals and produce prescriptions, to prevent and treat cardiovascular disease and other diet-related diseases are also being explored in several settings including the Association's Health Care by Food TM initiative.

This scientific statement was prepared by the volunteer writing group on behalf of the American Heart Association Obesity Committee of the Council On Lifestyle and Cardiometabolic Health; the Council on Epidemiology and Prevention; the Council on Clinical Cardiology, the Council on Hypertension; the Council on the Kidney in Cardiovascular Disease; and the Council on Cardiovascular and Stroke Nursing. American Heart Association scientific statements promote greater awareness about cardiovascular diseases and stroke issues and help facilitate informed health care decisions. Scientific statements outline what is currently known about a topic and what areas need additional research. While scientific statements inform the development of guidelines, they do not make treatment recommendations. American Heart Association guidelines provide the Association's official clinical practice recommendations.

Additional co-authors and members of the writing group are Mercedes Carnethon, Ph.D., FAHA; Fatima C. Stanford, M.D., M.P.H., M.P.A., M.B.A., FAHA; Morgana Mongraw-Chaffin, Ph.D., FAHA; Bethany Barone Gibbs, Ph.D., FAHA; Chiadi E. Ndumele, M.D., Ph.D., FAHA; Chris T. Longenecker, M.D., FAHA; Misook L. Chung, Ph.D., R.N., FAHA; and Goutham Rao, M.D. 

• Diet and Weight Loss
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Materials provided by American Heart Association . Note: Content may be edited for style and length.

Journal Reference :

• Deepika Laddu, Ian J. Neeland, Mercedes Carnethon, Fatima C. Stanford, Morgana Mongraw-Chaffin, Bethany Barone Gibbs, Chiadi E. Ndumele, Chris T. Longenecker, Misook L. Chung, Goutham Rao. Implementation of Obesity Science Into Clinical Practice: A Scientific Statement From the American Heart Association . Circulation , 2024; DOI: 10.1161/CIR.0000000000001221

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Obesity and COVID-19

What to know.

Obesity is a complex chronic disease that puts people at higher risk for poor health outcomes. Obesity increases risk of severe illness from COVID-19. Individuals can help protect themselves by getting a COVID-19 vaccine. They can also engage in other healthy behaviors. These include eating a healthy diet, being active, and getting enough sleep.

Obesity worsens COVID-19 outcomes

Obesity is linked to impaired immune function. 1 2 Obesity also decreases lung capacity, which can make breathing more difficult. 3 For these reasons and others, adults and children with excess weight are at greater risk of severe illness from COVID-19.

During the early COVID-19 pandemic (March-November 2020), approximately 1 in 3 COVID-19 hospitalizations in adults were attributed to obesity (30.2%). 4

Risks of hospitalization, intensive care unit admission, invasive ventilation, and death increased as body mass index (BMI) increased. For example:

• Among adults with COVID-19, adults with BMI from 30 to 34.9 had a 1.4 times higher risk of needing invasive ventilation than adults with a healthy BMI from 18.5 to 24.9.
• Adults with BMI from 40 to 44.9 and BMI of 45 or higher were 1.7 times and 2.1 times more likely to need invasive ventilation than those with a healthy weight.
• The increased risk for hospitalization and death was particularly pronounced in those younger than 65. 5

In children

Children are less likely than adults to develop severe COVID-19 illness. However, children with obesity are at higher risk for COVID-19 complications than children with healthy weight.

In one study of pediatric patients with COVID-19, obesity was one of the strongest risk factors for severe COVID-19 illness. Children with obesity had a 3.1 times higher risk of hospitalization from COVID-19 than children with healthy weight. These children also had a 1.4 times higher risk of severe illness when hospitalized. 6

Obesity and COVID-19 affect some groups more than others

People from racial and ethnic minority groups have historically had fewer opportunities for good economic, physical, and emotional health. These inequities may have contributed to the increased risk of getting sick and dying from COVID-19 for some of these groups. Many of these same factors contribute to the higher level of obesity in some racial and ethnic minority groups.

In 2017-2020 , Hispanic and non-Hispanic Black adults had a higher prevalence of obesity. These groups were more likely to suffer worse outcomes from COVID-19.

Hispanic and non-Hispanic Black children also had a higher prevalence of obesity than non-Hispanic white children. In 2017–2020 , the overall prevalence of obesity among children was 19.7%. However, 26.2% of Hispanic children and 24.8% of non-Hispanic Black children had obesity.

Steps States, Partners, and Healthcare Providers Can Take‎

Steps you can take now.

Federal, state, and local partners are working on systemic changes to support healthier living. However, change takes time. Individuals can help protect themselves and their families from poor health outcomes of obesity and COVID-19 by:

Vaccinating against COVID-19

Vaccination helps protect against severe COVID-19 illness in both adults and children. COVID-19 vaccines help protect whole families and slow the spread of COVID-19 in communities.

Eating healthy foods

Eating a healthy diet with fruits and vegetables, lean protein, and fiber can help with losing weight and preventing weight gain. 7 Good nutrition also supports optimal immune function, 8 9 and prevents or supports management of heart disease, type 2 diabetes, and other chronic diseases. These diseases also increase risk of severe illness from COVID-19 .

Being active

Individuals who do not get enough physical activity are more likely to get very sick from COVID-19. Regular physical activity helps you feel better, sleep better, and reduce anxiety. Physical activity also prevents diseases such as heart disease and type 2 diabetes, thereby lowering the risk of severe COVID-19 illness. 10 Emerging research suggests it may also help boost immune function. 11 12

Getting enough sleep

Not getting enough sleep is linked to depression and chronic diseases such as heart disease and obesity. 13 These diseases increase the risk for severe COVID-19 illness.

Coping with stress

Stress may cause changes in sleep or eating patterns, more alcohol/tobacco use, or worsened chronic health problems .

• Tanaka SI, Isoda F, Ishihara Y, et al. T lymphopaenia in relation to body mass index and TNF-α in human obesity: Adequate weight reduction can be corrective. Clin Endocrinol. 2001;54(3):347-354.
• Alwarawrah Y, Kiernan K, MacIver NJ. Changes in Nutritional Status Impact Immune Cell Metabolism and Function. Front Immunol. 2018;16(9):1055.
• Simonnet A, Chetboun M, Poissy J, et al. High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation. Obesity. 2020;28(7):1195-1199.
• O'Hearn M, Liu J, Cudhea F, Micha R, Mozaffarian D. Coronavirus disease 2019 hospitalizations attributable to cardiometabolic conditions in the United States: A comparative risk assessment analysis. J Am Heart Assoc . 2021;10(5).
• Kompaniyets L, Goodman AB, Belay B, et al. Body Mass Index and risk for COVID-19-related hospitalization, intensive care unit admission, invasive mechanical ventilation, and death - United States, March-December 2020. MMWR Morb Mortal Wkly Rep . 2021;70(10):355-361.
• Kompaniyets L, Agathis NT, Nelson JM, et al. Underlying medical conditions associated with severe COVID-19 illness among children. JAMA Network Open. 2021;4(6):e2111182.
• Dietary Guidelines for Americans, 2020-2025 . 9th Edition. U.S. Department of Agriculture and U.S. Department of Health and Human Services; 2020.
• Childs CE, Calder PC, Miles EA. Diet and immune function. Nutrients . 2019;16;11(8):1933.
• Christ A, Lauterbach M, Latz E. Western diet and the immune system: an inflammatory connection. Immunity , 2019;51(5):794-811.
• Physical Activity Guidelines for Americans, 2nd edition. U.S. Department of Health and Human Services. U.S. Department of Health and Human Services; 2018.
• Nieman, DC, Wentz, LM. The compelling link between physical activity and the body's defense system. J Sport Health Sci . 2019;8(3), 201-217.
• Jones AW, Davison G. Exercise, Immunity, and Illness. Muscle and Exercise Physiology . 2019:317–44.
• Itani O, Jike M, Watanabe N, Kaneita Y. Short sleep duration and health outcomes: a systematic review, meta-analysis, and meta-regression. Sleep Med . 2017;32:246-256.
• Food assistance programs and food system guidance during COVID-19
• Policy resources to support social determinants of health
• Health Equity Resource Toolkit for State Practitioners Addressing Obesity Disparities
• COVID-19: Health Equity Considerations and Racial and Ethnic Minority Groups – What We Can Do
• Strategies to support healthy food systems , create activity-friendly environments , and prevent obesity .
• Healthy Eating for a Healthy Weight
• Physical Activity for a Healthy Weight
• Adult BMI Calculator
• CDC's Division of Nutrition, Physical Activity, and Obesity (DNPAO)

CDC's obesity prevention efforts focus on policy and environmental strategies to make healthy eating and active living accessible for everyone.

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Health care providers, public health.

• Systematic Review
• Open access
• Published: 13 May 2024

Sarcopenia and sarcopenic obesity among older adults in the nordic countries: a scoping review

• Fereshteh Baygi 1   na1 ,
• Sussi Friis Buhl 1   na1 ,
• Trine Thilsing 1 ,
• Jens Søndergaard 1 &
• Jesper Bo Nielsen 1  

BMC Geriatrics volume  24 , Article number:  421 ( 2024 ) Cite this article

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Metrics details

Sarcopenia and sarcopenic obesity (SO) are age-related syndromes that may compromise physical and mental health among older adults. The Nordic countries differ from other regions on prevalence of disease, life-style behavior, and life expectancy, which may impact prevalence of sarcopenia and SO. Therefore, the aim of this study is to review the available evidence and gaps within this field in the Nordic countries.

PubMed, Embase, and Web of science (WOS) were searched up to February 2023. In addition, grey literature and reference lists of included studies were searched. Two independent researcher assessed papers and extracted data.

Thirty-three studies out of 6,363 searched studies were included in this scoping review. Overall prevalence of sarcopenia varied from 0.9 to 58.5%. A wide prevalence range was still present for community-dwelling older adults when definition criteria and setting were considered. The prevalence of SO ranged from 4 to 11%, according to the only study on this field. Based on the included studies, potential risk factors for sarcopenia include malnutrition, low physical activity, specific diseases (e.g., diabetes), inflammation, polypharmacy, and aging, whereas increased levels of physical activity and improved dietary intake may reduce the risk of sarcopenia. The few available interventions for sarcopenia were mainly focused on resistance training with/without nutritional supplements (e.g., protein, vitamin D).

The findings of our study revealed inadequate research on SO but an increasing trend in the number of studies on sarcopenia. However, most of the included studies had descriptive cross-sectional design, small sample size, and applied different diagnostic criteria. Therefore, larger well-designed cohort studies that adhere to uniform recent guidelines are required to capture a full picture of these two age-related medical conditions in Nordic countries, and plan for prevention/treatment accordingly.

Peer Review reports

The number of older adults with age-related disorders is expected to increase worldwide [ 1 , 2 ]. Sarcopenia and sarcopenic obesity (SO) are both age-related syndromes that may compromise the physical and mental health of older adults and increase their need for health care services in old age [ 3 , 4 ], and this may challenge the sustainability of health care systems economically and by shortage of health care personnel [ 5 ].

Sarcopenia is characterized by low muscle mass in combination with low muscle strength [ 4 ]. SO is characterized by the co-existence of obesity (excessive adipose tissue) and sarcopenia [ 3 ]. Sarcopenia and SO are both associated with physical disability, risk of falls, morbidity, reduced quality of life and early mortality [ 4 , 6 , 7 , 8 , 9 ]. In SO the consequences of sarcopenia and obesity are combined and maximized [ 4 , 6 , 7 , 8 ].

Etiology of sarcopenia and SO is multifactorial and closely linked to multimorbidity [ 3 , 7 , 8 , 9 , 10 ]. Nevertheless, lifestyle and behavioral components particularly diet and physical activity, are important interrelated factors that potentially can be modified. Physical inactivity and sedentary behavior may accelerate age-related loss of muscle mass, reduce energy expenditure, and increase risk of obesity [ 3 , 11 ]. In addition, weight cycling (the fluctuations in weight following dieting and regain) and an unbalanced diet (particularly inadequate protein intake) may accelerate loss of muscle mass and increase severity of sarcopenia and SO in older adults [ 3 , 12 ]. International guideline for the treatment of sarcopenia emphasizes the importance of resistance training potentially in combination with nutritional supplementation to improve muscle mass and physical function [ 13 ]. Similar therapeutic approach is suggested for treatment of SO [ 14 ]. However, more research is needed to confirm optimal treatment of SO [ 14 ].

According to a recently published meta-analysis the global prevalence of sarcopenia ranged from 10 to 27% in populations of older adults ≥ 60 years [ 15 ]. Further the global prevalence of SO among older adults was 11% [ 8 ]. So, sarcopenia and SO are prevalent conditions, with multiple negative health outcomes and should be given special attention [ 16 ]. Despite the large burden on patients and health care systems, the awareness of the importance of skeletal muscle maintenance in obesity is low among clinicians and scientists [ 3 , 16 ].

A recent meta-analysis on publication trends revealed that despite an increase in global research on sarcopenia, the Nordic countries were only limitedly represented [ 6 ]. Nordic countries may differ from other regions on aspects associated with the prevalence and trajectory of sarcopenia and SO and challenge the representativeness of research findings from other parts of the world. These include a different prevalence pattern of noncommunicable diseases [ 17 ], different life-style behavior and life-style associated risk factors [ 15 , 18 ], and higher life expectancy [ 18 ].

The Nordic countries including Sweden, Finland, Iceland, Norway, Denmark, and three autonomous areas (Åland Islands, Greenland and Faroe Islands) share common elements of social and economic policies such as a comprehensive publicly financed health care system [ 18 , 19 ]. Additionally, these countries have a strong tradition of collaboration including a common vision of a socially sustainable region by promoting equal health and inclusive participation in society for older adults [ 20 ]. Therefore, more insight into the etiology, prevalence, and risk factors for sarcopenia and SO among older adults is a prerequisite for the development and implementation of effective strategies to prevent and treat these complex geriatric conditions in this geographic region. So, the aim of this study is to conduct a scoping review to systematically identify and map the available evidence while also addressing knowledge gaps and exploring the following research questions: (1) What are the prevalence of sarcopenia and SO in older adults living in the Nordic countries? (2) Which risk factors or contributing conditions are involved in the development of sarcopenia and SO in the Nordic Countries? (3) Which interventions to prevent or counteract negative health outcomes of sarcopenia and SO have been tested or implemented among older adults living in the Nordic countries?

Identification of relevant studies

The development and reporting of this review were done by following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [ 21 ].

The literature search was developed to target three main areas: Sarcopenia, sarcopenic obesity, and aging (See Appendix 1 for full search strategy). All studies published before the end of February 2023 were included in this scoping review. The optimal sensitivity of search was obtained by simultaneous search of the following databases: PubMed, Embase, and Web of science (WOS). Additionally, a detailed search for grey literature was performed in relevant databases (e.g., Research Portal Denmark, Libris, Oria, Research.fi). Besides, reference lists of the included studies were reviewed to identify eligible studies. Duplicates and non-peer reviewed evidence (e.g., PhD thesis) were excluded but if the latter contained published articles of relevance, these were included. If more than one publication on similar outcomes (e.g., prevalence) were based on a single study, just one publication was included. Data were extracted from large studies with combined data from several countries only when findings were presented separately for the Nordic countries.

Inclusion and exclusion criteria

The inclusion criteria were as follow : Broad selection criteria were used to be comprehensive: (1) studies with any outcome (e.g., prevalence, risk factors, etc.) to address our research questions on sarcopenia and SO, (2) studies on subjects with age ≥ 60 years in any type of settings (e.g., community, nursing homes, general practice, hospital, outpatients, homecare, etc.), (3) studies using any definition of sarcopenia and SO without restriction for criteria and cutoff values, (4) all type of study designs (e.g., randomized control trials, cohort studies, cross-sectional, etc.), (5) studies should be conducted in the Nordic countries The exclusion criteria are as follow : (1) studies without relevant outcome to sarcopenia or SO, (2) studies without sufficient information to determine eligibility.

Study selection and data extraction

Two independent researchers screened literature and conducted data extraction. Any discrepancies between them were resolved through discussion.

First, duplicates were removed by using EndNote 20.6 software, then titles and abstracts were screened to narrow down the list of potentially eligible studies. Finally, the full text review was done to examine in detail the studies that were not excluded in first step. For more clarification, the reasons for the exclusion were recorded (Fig.  1 ).

PRISMA diagram for searching resources

The following information was extracted: (1) study characteristics (e.g., first author’s name, country, year of publication), (2) characteristics of the target population (e.g., age, sex), (3) study design, setting, intervention duration and follow-up time (if applicable), measurements, tools, criteria, and results.

Study selection

A combined total of 6,358 studies were identified through the initial electronic database and grey literature searches. An additional five articles were identified through other sources (citation searching). After removing duplication, 3,464 articles remained. A total of 3107 articles were excluded based on screening titles and abstracts. Out of the remaining 357 studies, 324 were excluded after the full-text review. Finally, 33 studies met our inclusion criteria and were included in this current scoping review [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ] (Fig.  1 ).

Study characteristics

Table  1 summarized characteristics of the included studies.

The number of documents showed an increasing trend between 2020 and 2021. A peak in the number of publications was observed in 2021 (24.2% of all documents). All the studies were conducted across four (Denmark, Norway, Sweden, and Finland) out of the five Nordic countries and three autonomous areas. The highest contribution in this field was made by Sweden ( n  = 12).

Most studies were conducted in community-dwelling settings [ 22 , 23 , 24 , 28 , 30 , 31 , 35 , 36 , 38 , 39 , 40 , 42 , 45 , 46 , 47 , 48 , 49 , 54 ]. Seven studies included patients with acute diseases (hospital-setting) [ 26 , 27 , 33 , 37 , 50 , 51 , 52 ], while four studies included patients with chronic conditions (out-patient setting) [ 25 , 32 , 41 , 44 ], and one study including nursing-home residents [ 34 ]. In terms of study design, most of the studies were observation studies with a cross-sectional or longitudinal design ( 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 33 , 34 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ), while three studies [ 32 , 35 , 46 ] applied interventions. It appears, however, that one study [ 32 ] out of the above three interventions is sub-project conducted within the framework of larger intervention program. Sample size ranged from 49 in a cross-sectional case control study [ 52 ] to 3334 in a cohort study [ 30 ].

Five studies were among males only [ 22 , 24 , 36 , 45 , 53 ] and three studies included females only [ 38 , 47 , 54 ]. The rest of the studies had a mixed sample. Top subject area was sarcopenia (31 out of the 33 included studies), and on this subject, publications were categorized into the following research areas (with some studies addressing more areas): prevalence [ 22 , 23 , 24 , 25 , 26 , 27 , 29 , 30 , 33 , 35 , 36 , 37 , 40 , 42 , 44 , 45 , 47 , 49 , 50 , 51 , 52 , 53 , 54 ], risk factors [ 24 , 27 , 28 , 30 , 31 , 34 , 38 , 40 , 42 , 44 , 47 , 49 , 50 , 51 ], and effectiveness of interventions on sarcopenia or indicator of sarcopenia [ 32 , 35 , 46 ].

In most studies sarcopenia was defined according to the criteria set by the European Working Group on Sarcopenia in Older People in the updated version from 2019 (EWGSOP2) ( n  = 15) or the original version from 2010 (EWGSOP) ( n  = 14). However, in some studies multiple criteria such as EWGSOP, EWGSOP2, and National Institutes of Health Sarcopenia Project definition (FNIH) were applied [ 27 , 39 , 43 ], and in other studies alternative criteria were used [ 26 , 33 , 35 , 45 , 57 ].

Different assessment methods of muscle mass including Dual energy X-ray absorptiometry (DXA) [ 22 , 24 , 25 , 27 , 29 , 30 , 32 , 33 , 38 , 39 , 40 , 41 , 45 , 46 , 47 , 52 , 53 , 54 ], Bioelectrical Impedance Analysis (BIA) [ 28 , 31 , 34 , 44 , 48 , 49 ], Bioimpedance Spectroscopy (BIS) [ 35 , 42 , 43 ], Computed Tomography (CT) [ 33 ], and Computed Tomography Angiogram (CTA) [ 26 ] were used in the included studies.

SO were defined by the co-existence of sarcopenia with obesity. Studies on SO used the EWGSOP2 criteria [ 39 ], or the EWGSOP2 criteria for hand grip strength only (probable sarcopenia) [ 23 ] in combination with obesity estimated from BMI cut points [ 23 , 39 ], waist circumference [ 23 , 39 ], and fat mass percentage [ 39 ]. Lastly, one study used measures of body composition measures that reflect adiposity as estimates of SO [ 48 ].

Four studies reported the prevalence of “probable sarcopenia” [ 23 , 30 , 36 , 45 ], while two studies reported the prevalence of sarcopenia and comorbidities (e.g., osteopenia, pre-frailty, malnutrition) [ 33 , 40 ].

Narrative synthesis

Due to the heterogeneity of the studies in definition of sarcopenia, settings, and sample size, the overall reported prevalence was variable and ranged from 0.9% [ 54 ] to 58.5% [ 26 ]. However, according to the most commonly used criteria (EWGSOP2) the highest (46%) and lowest (1%) prevalence of sarcopenia was reported in Sweden among inpatients in geriatric care [ 27 ], and community-dwelling older adults [ 30 ], respectively.

Prevalence of sarcopenia according to population and definition criteria is illustrated in Table  2 . Higher prevalence rates of sarcopenia were found in females compared to males among community-dwelling older adults [ 49 ] and in older adults acutely admitted to hospital [ 51 ]. Further, acutely admitted female patients also presented with more severe sarcopenia compared to male patients [ 51 ].

Frequency of sarcopenia was higher (9.1–40.0%) in patients with diabetes (with and without complications of charcot osteoarthropathy), compared to age-matched healthy adults [ 52 ].

The prevalence of “probable sarcopenia” ranged between 20.4% (reduced muscle strength only) and 38.1% (fulfilling one of the following criteria: reduced muscle strength, reduced muscle mass, or low physical function) in Finnish community-dwelling adults [ 23 , 36 ], while longitudinal studies on Swedish community-dwelling old (70 years) and very old adults (≥ 85 years) the prevalence of “probable sarcopenia” (reduced muscle strength only) ranged from 1.8 to 73%, respectively [ 30 , 45 ]. Lastly, in a Swedish study among nursing home residents the prevalence of probable sarcopenia was 44% (evaluated by an impaired chair stand test) [ 34 ].

Prevalence of Osteosarcopenia (sarcopenia and osteoporosis) was 1.5% [ 36 ], and the prevalence of co-occurrence of all three following conditions: pre-frail, malnutrition, and sarcopenia was 7% [ 34 ].

We only identified two studies with prevalence of SO [ 39 ] and probable SO [ 23 ]. The prevalence of SO in a Swedish population was 4% and 11% in females and males, respectively, while the prevalence of probable SO among Finnish community-dwelling ranged between 5.8% and 12.6%, depending on the criteria to define the obesity (e.g., BMI, waist circumference, etc.) [ 23 ].

Several studies investigated aspects of etiology and risk factors for sarcopenia [ 24 , 27 , 28 , 30 , 31 , 34 , 36 , 38 , 40 , 42 , 43 , 44 , 47 , 49 , 50 , 51 ] and one study focused on SO [ 49 ]. Higher physical activity was associated with a decreased likelihood of sarcopenia [ 30 ]. In addition, adhering to world health organization (WHO) guidlines for physical activity and the Nordic nutritional recommendations for protein intake was positively associated with greater physical function and lower fat mass in older female community-dwellers [ 38 ]. In older adults who are physically active, eating a healthy diet (based on the frequency of intake of favorable food like fish, fruits, vegetables, and whole grains versus unfavorable foods like red/processed meats, desserts/sweets/sugar-sweetened beverages, and fried potatoes) was associated with lower risk of sarcopenia [ 28 ]. Further, among older adults who already meet the physical activity guidelines, additional engagement in muscle-strengthening activities was associated with a lower sarcopenia risk score and improved muscle mass and chair rise time [ 31 ].

Associations between sarcopenia, risk of sarcopenia and malnutrition or nutritional status was identified in geriatric patients [ 27 , 51 ], older patients with hip fracture [ 50 ], nursing home residents [ 34 ] and in community-dwelling older adults [ 49 ]. Moreover, the importance of nutritional intake was investigated in the following studies [ 24 , 36 , 47 ]. A study among community-dwelling men revealed an inverse association between total energy intake, protein intake (total, plant, and fish protein), intake of dietary fibers, fat (total and unsaturated), and vitamin D with sarcopenia status [ 36 ]. In a cohort of 71-year-old men a dietary pattern characterized by high consumption of fruit, vegetables, poultry, rice and pasta was associated with lower prevalence of sarcopenia after 16 years [ 24 ]. A longitudinal Finnish study on sarcopenia indices among postmenopausal older women, showed that lower adherence to the Mediterranean (focuses on high consumption of olive oil) or Baltic Sea (focuses on the dietary fat quality and low-fat milk intake) diets resulted in higher loss of lean mass over a 3-year period [ 47 ]. Further, a higher adherence to the Baltic Sea diet was associated with greater lean mass and better physical function, and higher adherence to the Mediterranean diet was associated with greater muscle quality [ 47 ].

In a study of patients with hip fracture age, polypharmacy, and low albumin levels was associated with sarcopenia [ 50 ]. Exocrine pancreatic insufficiency was an independent risk factor for sarcopenia [ 44 ]. This study also revealed that sarcopenia was associated with reduced quality of life, physical function, and increased risk of hospitalization [ 44 ]. In a longitudinal study of community-dwelling adults (+ 75 years) at risk of sarcopenia, high physical function, muscle strength, muscle mass and low BMI predicted better physical function and reduced need for care after four years [ 42 ]. Furthermore, in community-dwelling adults with sarcopenia, muscle mass, muscle strength and physical function are independent predictors of all-cause mortality. As a result, they have been proposed by researchers as targets for the prevention of sarcopenia-related over-mortality [ 43 ]. Lastly, community-dwelling older adults with sarcopenia had lower bone mineral density compared to those without sarcopenia and they were more likely to develop osteoporosis (Osteosarcopenia) [ 40 ].

Regarding SO risk factors, a longitudinal study among community-dwelling older adults in Finland found that SO (operationalized by measures of adiposity) were associated with poorer physical function after ten years [ 48 ].

Our literature search identified three randomized controlled trials investigating the effectiveness of interventions to prevent or counteract sarcopenia in older adults of Norway, Finland, and Sweden, respectively [ 32 , 35 , 46 ]. The Norwegian study [ 32 ] was a double-blinded randomized controlled trial (RCT). The study included those who were at risk of developing sarcopenia, including patients with chronic obstructive pulmonary disease (COPD) or individuals who showed diagnostic indications of sarcopenia. Participants received either vitamin D 3 or placebo supplementation for 28 weeks. Additionally, resistance training sessions were provided to all participants from weeks 14 to 27. Vitamin D supplementation did not significantly affect response to resistance training in older adults at risk of sarcopenia with or without COPD [ 32 ].

Furthermore, a RCT among pre-sarcopenic Swedish older adults investigated the effectiveness of three weekly sessions of instructor-led progressive resistance training in combination with a non-mandatory daily nutritional supplement (175 kcal, 19 g protein) compared to control group. The 10 weeks intervention resulted in significant between group improvements of physical function and a significant improvement in body composition in the intervention group [ 46 ].

Another intervention study revealed that a 12-month intervention with two daily nutritional supplements (each containing 20 g whey protein) did not attenuate the deterioration of physical function and muscle mass in sarcopenic older community-dwelling adults compared to isocaloric placebo supplements or no supplementation. All participants were given instructions on home-based exercises, importance of dietary protein and vitamin D supplementation [ 35 ].

Based on our broad literature search 33 studies were identified that concerned sarcopenia and SO and met the inclusion criteria. However, research on SO was very limited with only three studies identified. Narrative synthesis of the included studies revealed that the most reported classification tool for sarcopenia in Nordic countries was the EWGSOP2. Moreover, some studies estimated sarcopenia using EWGSOP. The overall prevalence of sarcopenia in Nordic countries according to EWGSOP2 ranged between 1% and 46% [ 25 , 28 ]. The prevalence of SO, however, was reported only in one study in Sweden (4–11%) [ 39 ]. Even though the previous systematic reviews and meta-analysis have reported the prevalence of sarcopenia and SO in different regions and settings (e.g., community-dwelling, nursing home, etc.) [ 8 , 15 , 55 , 56 ], this current scoping review is to the best of our knowledge the first study that provides an overview of research on sarcopenia and SO in the Nordic countries.

Based on our findings from 24 studies, there were large variability in prevalence of sarcopenia in studies conducted in the Nordic countries. We think that the wide variation in estimated prevalence of sarcopenia in our scoping review might be due to a different definition/diagnostic criterion (e.g., EWGSOP, EWGSOP2, FNIH), methodology to measure muscle mass (DXA, BIA, CT), and heterogeneity in characteristics of the study population (e.g., setting, age, medical conditions, co-occurrence of multiple risk factors). A previous study on prevalence of sarcopenia in Swedish older people showed significant differences between prevalence of sarcopenia based on EWGSOP2 and EWGSOP1 [ 29 ]. Therefore, researchers stressed that prevalence is more dependent on cut-offs than on the operational definition [ 29 , 57 ]. Further, we know that various international sarcopenia working groups have issued expert consensus and such diagnostic criteria are being updated [ 4 , 58 ]. Since the revision of criteria focuses primarily on the adjustment of cut-off values, the main reason for differences in prevalence even when using an updated version of one diagnosis criteria is modification in cut-off values. For instance, if the cut-off value for gait speed was increased by 0.2 m/s, the prevalence of sarcopenia may increase by 8.5% [ 57 ]. Meaning that even a small change in cut-off value can have a big impact on how sarcopenia is diagnosed. Besides when we take definition criteria into account (Table  2 ), the prevalence of sarcopenia is still variable in the population of community-dwelling adults for instance. We believe it is basically because studies have applied different assessment tools and tests to identify older adults with low muscle mass and muscle strength, although using the same definition criteria (Table  1 ). Previous studies have illustrated that choice of methodology to assess muscle strength (e.g., hand grip strength, chair rise) [ 59 ] and muscle mass (e.g., DXA, BIA, anthropometry) [ 60 , 61 , 62 ] in older adults may impact findings and this variability may explain some of the variability in our findings. So, adherence to the latest uniform diagnostic criteria for future studies is recommended to simplify the comparison of findings within the same country, across countries, and regions. Moreover, we suggest that medical community particularly GPs to come to an agreement on assessment methods for muscle mass and muscle strength and the use of one set of definition criteria for sarcopenia.

In previous meta-analyses [ 15 ], sub-group analyses based on region and classification tool, revealed that the prevalence of sarcopenia was higher in European studies using EWGSOP (12%) compared to rest of the studies using Asian Working Group for Sarcopenia (AWGS), FNIH, and EWGSOP (3%) [ 15 ]. In our scoping review, we also found a high prevalence of sarcopenia in Nordic countries. Longevity and life expectancy is higher in the Nordic countries compared to estimates for rest of the world [ 18 ], which means that in this region many people reach old age, and consequently they are more likely to be diagnosed with sarcopenia as an age-related disorder. Therefore, the authors of this current scoping review emphasis the importance of preventive strategies targeted major risk factors and effective interventions to limit the consequences of sarcopenia in the Nordic populations. Besides, we think that the health care system in the Nordic countries should be better equipped with the necessary healthcare resources for both a timely diagnosis and dealing with this major age-related issue in the years to come. However, due to the limitations regarding the timely diagnosis, we highly recommend a comprehensive approach including establishment of support services, implement educational programs, offer training for health care professionals, and engage the community.

Many countries have conducted research on SO [ 7 , 39 , 63 , 64 , 65 ]. Based on our findings, however, among the Nordic countries only Sweden and Finland have investigated the prevalence of probable SO and SO [ 23 , 29 ]. Besides, we only found one study investigating the association between body adiposity and physical function over time [ 54 ]. We did not find any literature on risk factors or interventions among older adults with SO in this region. Therefore, we call on medical and research community in Nordic countries to attach importance to screening of SO in elderly people to capture a full picture of this public health risk to aging society and allocate healthcare resources accordingly.

In terms of risk factors for sarcopenia, our study revealed that malnutrition, low levels of physical activity, specific diseases (e.g., diabetes, osteoporosis), inflammation, polypharmacy (multiple medicines), BMI, and ageing are potential risk factor for sarcopenia in populations of the Nordic region. However, evidence on risk factors derived mainly from cross-sectional associations [ 27 , 28 , 30 , 31 , 34 , 40 , 44 , 49 , 50 , 51 ], and only to a limited extend from longitudinal studies [ 24 , 38 , 43 , 47 ]. Therefore, the associations between risk factors and sarcopenia should be interpreted with caution due to the possibility of reverse causality and confounding affecting the results. Moreover, our findings on risk factors mainly came from community-dwelling older adults, and only to a limited extend hospital and nursing home settings. We think that risk factors may vary depending on population characteristics (e.g., age, sex, health condition) and setting (e.g., hospital, nursing home, community). Therefore, we encourage researchers of the Nordic countries to perform well-designed prospective cohort studies in different settings to enhance the possibility to establish causal inference as well as understanding degree and direction of changes over time.

A recently published meta-analyses revealed a higher risk of having polypharmacy in Europe among individuals with sarcopenia compared to people without this condition [ 66 ]. A nationwide register-based study in Swedish population also showed that the prevalence of polypharmacy has increased in Sweden over the last decade [ 67 ]. Sarcopenia itself is associated with morbidity (identified by specific disease or inflammatory markers) and different health-related outcomes (e.g., disability) [ 7 ]; therefore, future research should investigate whether polypharmacy is a major factor to sarcopenia development [ 66 ]. Although we lack information on polypharmacy in Nordic countries other than Sweden, we encourage researchers in this region to examine the above research gap in their future studies.

According to previous studies physiological changes in ageing include systemic low-grade inflammation which results in insulin resistance, affect protein metabolism and leads to increased muscle wasting [ 68 ]. Acute and chronic disease may increase the inflammatory response and accelerate age-related loss of muscle mass and increase risk of sarcopenia [ 68 , 69 ]. Hence, we think that special attention should be made by health care professionals particularly GPs to older adults with acute or chronic conditions to limit the risk of sarcopenia.

Literature from the Nordic countries also indicated that higher levels of physical activity and different dietary patterns (e.g., higher protein intake, fruit, vegetables, fibers) were associated with reduced risk of sarcopenia or improvement in indicators of sarcopenia. There was a large heterogeneity in the studied aspect which makes direct comparison of studies difficult. Nevertheless, according to findings from a recent systematic review of meta-analyses on sarcopenia the identified risk factors are in alignment with previously identified risk factors globally [ 70 ]. Other potential lifestyle-related risk factors suggested from the above meta-analysis included smoking and extreme sleep duration. However, we did not identify studies investigating these health behaviors in the Nordic populations. Therefore, high-quality cohort studies are needed to deeply understand such associations with the risk of sarcopenia.

In this current review, we only found three intervention studies in Nordic countries. However, two of them were sub-projects of big intervention programs, meaning that such studies were not designed explicitly for the prevention/treatment of sarcopenia. Therefore, explicit intervention studies on sarcopenia in this region is recommended.

We believe that on a global level, research on sarcopenia will carry on with nutrition, exercise, and understanding of molecular mechanisms. Furthermore, examining the link between sarcopenia and other medical conditions/diseases would be the next step [ 6 ]. In the Nordic countries, however, already performed studies have a basic and descriptive design, so that, well-designed research and advanced analyses are lacking. Hence, we recommend conducting large well-designed and adequately powered studies to (a) explore the scale of this age-related health issue on country and regional level, (b) investigate the patterns of physical activity and sedentary behavior to understand if this should be a target in older adults with SO and sarcopenia, (c) determine whether elderly populations are suffering from nutritional deficiency or are at risk of malnutrition. The latest can support further studies to assess the impact of combined physical activity and dietary intake, which are still lacking globally [ 6 ].

A previous systematic review on therapeutic strategies for SO revealed that exercise-based interventions (e.g., resistance training) reduced total adiposity and consequently improved body composition. However, evidence of other therapeutic strategies (e.g., nutritional supplementation) was limited due to scarcity of data and lack of unique definition for SO [ 69 ]. Therefore, authors suggested that more research should be done to clarify optimal treatment options for various age-groups and not only for older adults [ 14 ].

In our scoping review, the included studies, did not provide a status of either SO or the prevention/treatment methods in this region. We believe that SO is practically neglected in clinical practice and research as well, and this is mainly because it is difficult to separate it from general obesity. The consequence of lacking knowledge in this research area is that when older adults with SO are recommended weight loss- a frequently used strategy for management of general obesity- this may accelerate the loss of muscle mass and increase the severity of the sarcopenia [ 3 ]. Consequently, we think that this issue may have adverse effects both on patients (e.g., decreasing quality of their life) and on the health care system (e.g., increasing the health care demands) of this region. Therefore, we encourage researchers to perform cohort studies to understand the epidemiology and etiological basis of SO, which are poorly understood even on a global scale [ 8 ]. We think that the consensus definition on SO from the European Society for Clinical Nutrition and Metabolism (ESPEN) and European Association for the Study of Obesity (EASO) which was published in 2022 [ 3 ], can positively affect the ability to define studies on prevalence and prevention of SO. Besides, we recommend conducting further research to find the optimal treatment for SO and reduce its adverse consequences both at individual and society levels. Additionally, we think that the concepts of sarcopenia and SO might be somehow unfamiliar to health care personnel. Therefore, it is highly recommended that more information be provided to bring their attention to the significance of prevention, timely diagnosis, and treatment of these two aging disorders.

Strengths and limitations of the study

This is the first study providing an overview of available evidence on sarcopenia and SO among older adults in the Nordic countries. These countries have important similarities in welfare sectors and on a population level and we believe that our findings will be a significant benefit for researchers and health care providers to understand the knowledge gaps and plan for future studies in this geographical region. However, the current scoping review has limitations. This review was limited to studies among individuals more than 60 years old which may limit the overview of available research in this field, as well as understanding risk factors, confounders for prevention, and the potential for early detection of these two diseases in younger age population. The included cross-sectional studies in our review cannot provide information on causality of the associations.

Sarcopenia and SO are generally prevalent syndromes among older adults in Nordic countries, even though the prevalence of them varies according to the criteria for definition, population, and setting. Research among older adults with SO was very limited in this region. Besides, studies on risk factors were primarily cross-sectional and only few intervention studies were identified. Therefore, we encourage researchers performing well-designed studies (e.g., prospective cohorts) to understand the epidemiology and etiological basis of these two age-related disorders. For the next step, implementation of interventions targeting risk factors (e.g., combined physical activity and dietary intake) and evaluating of their impact on prevention or treatment of sarcopenia and SO is recommended. Furthermore, for the comprehensive advancement of muscle health in older adults, we recommend implementing interventions directed at health care personnel and encouraging more collaboration among clinicians, professional societies, researchers, and policy makers to ensure comprehensive and effective approach to health care initiatives.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Abbreviations

sarcopenic obesity

Web of science

Preferred Reporting Items for Systematic Reviews and Meta-analyses

European Working Group on Sarcopenia in Older People in the updated version from 2019

National Institutes of Health Sarcopenia Project definition

Dual energy X-ray absorptiometry

Bioelectrical Impedance Analysis

Bioimpedance Spectroscopy

Computed Tomography

Computed Tomography Angiogram

World Health Organization

General Practitioner

Randomized Controlled Trial

Chronic Obstructive Pulmonary Disease

European Association for the Study of Obesity

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Research Unit of General Practice, Department of Public Health, University of Southern Denmark, Odense, Denmark

Fereshteh Baygi, Sussi Friis Buhl, Trine Thilsing, Jens Søndergaard & Jesper Bo Nielsen

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FB conceived and designed the review, participated in literature review, data extraction, interpretation of the results and wrote the manuscript. SFB designed the review, participated in literature review, data extraction, and revised the manuscript. TT, JBN and JS contributed to the conception of the study and revised the manuscript critically. All the authors approved the final manuscript.

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Baygi, F., Buhl, S.F., Thilsing, T. et al. Sarcopenia and sarcopenic obesity among older adults in the nordic countries: a scoping review. BMC Geriatr 24 , 421 (2024). https://doi.org/10.1186/s12877-024-04970-x

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Childhood and Adolescent Obesity in the United States: A Public Health Concern

Adekunle sanyaolu.

1 Federal Ministry of Health, Abuja, Nigeria

Chuku Okorie

2 Essex County College, Newark, NJ, USA

3 Saint James School of Medicine, Anguilla, British West Indies

Jennifer Locke

Saif rehman.

Childhood and adolescent obesity have reached epidemic levels in the United States. Currently, about 17% of US children are presenting with obesity. Obesity can affect all aspects of the children including their psychological as well as cardiovascular health; also, their overall physical health is affected. The association between obesity and other conditions makes it a public health concern for children and adolescents. Due to the increase in the prevalence of obesity among children, a variety of research studies have been conducted to discover what associations and risk factors increase the probability that a child will present with obesity. While a complete picture of all the risk factors associated with obesity remains elusive, the combination of diet, exercise, physiological factors, and psychological factors is important in the control and prevention of childhood obesity; thus, all researchers agree that prevention is the key strategy for controlling the current problem. Primary prevention methods are aimed at educating the child and family, as well as encouraging appropriate diet and exercise from a young age through adulthood, while secondary prevention is targeted at lessening the effect of childhood obesity to prevent the child from continuing the unhealthy habits and obesity into adulthood. A combination of both primary and secondary prevention is necessary to achieve the best results. This review article highlights the health implications including physiological and psychological factors comorbidities, as well as the epidemiology, risk factors, prevention, and control of childhood and adolescent obesity in the United States.

Introduction

Childhood and adolescent obesity have reached epidemic levels in the United States, affecting the lives of millions of people. In the past 3 decades, the prevalence of childhood obesity has more than doubled in children and tripled in adolescents. 1 The latest data from the National Health and Nutrition Examination Survey show that the prevalence of obesity among US children and adolescents was 18.5% in 2015-2016. Overall, the prevalence of obesity among adolescents (12-19 years; 20.6%) and school-aged children (6-11 years; 18.4%) was higher than among preschool-aged children (2-5 years; 13.9%). School-aged boys (20.4%) had a higher prevalence of obesity than preschool-aged boys (14.3%). Adolescent girls (20.9%) had a higher prevalence of obesity than preschool-aged girls (13.5%; Figure 1 ). 1 Moreover, the rates of obesity have been steadily rising from 1999-2000 through 2015-2016 ( Figure 2 ). 1 According to Ahmad et al, 80% of adolescents aged 10 to 14 years, 25% of children younger than the age of 5 years, and 50% of children aged 6 to 9 years with obesity are at risk of remaining adults with obesity. 2

Prevalence of obesity among children and adolescents aged 2 to 19 years, by sex and age: the United States, 2015-2016.

Trends in obesity prevalence among children and adolescents aged 2 to 19 years: the United States, 1999-2000 through 2015-2016.

Obesity can affect all aspects of children and adolescents including but not limited to their psychological health and cardiovascular health and also their overall physical health. 3 The association between obesity and morbid outcomes makes it a public health concern for children and adolescents. 4 Obesity has an enormous impact on both physical and psychological health. Consequently, it is associated with several comorbidity conditions such as hypertension, hyperlipidemia, diabetes, sleep apnea, poor self-esteem, and even serious forms of depression. 5 In addition, children with obesity who were followed-up to adulthood were much more likely to suffer from cardiovascular and digestive diseases. 3 The increase in body fat also exposes the children to increase in the risk of numerous forms of cancers, such as breast, colon, esophageal, kidney, and pancreatic cancers. 6

Due to its public health significance, the increasing trend in childhood obesity needs to be closely monitored. 7 However, these trends have proved to be challenging to quantify and compare. While there are many factors and areas to consider when discussing obesity in children and adolescents, there are a few trends that are evident in recent studies. For example, the prevalence of obesity varies among ethnic groups, age, sex, education levels, and socioeconomic status. A report published by the National Center for Health Statistics using data from the National Health and Nutrition Examination Survey provides the most recent national estimates from 2015 to 2016 on obesity prevalence by sex, age, race, and overall estimates from 1999-2000 through 2015-2016. 1 Prevalence of obesity among non-Hispanic black (22.0%) and Hispanic (25.8%) children and adolescents aged 2 to 19 years was higher than among both non-Hispanic white (14.1%) and non-Hispanic Asian (11.0%) children and adolescents. There were no significant differences in the prevalence of obesity between non-Hispanic white and non-Hispanic Asian children and adolescents or between non-Hispanic black and Hispanic children and adolescents. The pattern among girls was similar to the pattern in all children and adolescents. The prevalence of obesity was 25.1% in non-Hispanic black, 23.6% in Hispanic, 13.5% in non-Hispanic white, and 10.1% in non-Hispanic Asian girls. The pattern among boys was similar to the pattern in all children and adolescents except that Hispanic boys (28.0%) had a higher prevalence of obesity than non-Hispanic black boys (19.0%; Figure 3 ). 1 This review article is aimed at studying the health implications including physical and psychological factors and comorbidities, as well as the epidemiology, risk factors, prevention, and control of childhood and adolescent obesity in the United States.

Prevalence of obesity among children and adolescents aged 2 to 19 years, by sex and race and Hispanic origin: the United States, 2015-2016.

Methodology

We performed a literature search using online electronic databases (PubMed, MedlinePlus, Mendeley, Google Scholar, Research Gate, Global Health, and Scopus) using the keywords “childhood,” “adolescents,” “obesity,” “BMI,” and “overweight.” Articles were retrieved and selected based on relevance to the research question.

Ethical Approval and Informed Consent

Ethics approval and informed consent were not required for this narrative review.

Definition of Childhood Obesity

Defining obesity requires a suitable measurement of body fat and an appropriate cutoff range. 8 Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared, rounded to 1 decimal place. Obesity in children and adolescents was defined as a BMI of greater than or equal to the age- and sex-specific 95th percentile and overweight with a BMI between the 85th and 95th percentiles of the 2000 Centers for Disease Control and Prevention (CDC) growth charts. 9

However, the use of the BMI percentile according to the age/sex of the CDC growth charts for very high BMIs can result in estimates that differ substantially from those that are observed, 10 , 11 and this constrains the maximum BMI that is attainable at given sex and age. 12 , 13 These limitations have resulted in the classification of severe obesity as a BMI ≥120% of the 95th percentile rather than a percentile greater than the 95th percentile. 11 , 14 A BMI of 120% of the 95th percentile corresponds to a BMI of ~35 among 16 to 18 year olds.

Physiology of Energy Regulation and Obesity

Obesity is a chronic multifactorial disease, characterized by an excessive accumulation of adipose tissue, commonly as a result of excessive food intake and/or low energy expenditure. Obesity can be triggered by genetic, psychological, lifestyle, nutritional, environmental, and hormonal factors. 15

Obesity is found in individuals that are susceptible genetically and involves the biological defense of an elevated body fat mass, the mechanism of which could be explained in part by interactions between brain reward and homeostatic circuits, inflammatory signaling, accumulation of lipid metabolites, or other mechanisms that impair hypothalamic neurons. 16

Normal energy regulation physiology is under tight neurohormonal control. The neurohormonal control is performed in the central nervous system through neuroendocrine connections, in which circulating peripheral hormones, such as leptin and insulin, provide signals to specialized neurons of the hypothalamus reflecting body fat stores and induces appropriate responses to maintain the stability of these stores. The hypothalamic region is where the center of the regulation of hunger and satiety is located. Some of them target the activity of endogenous peptides, such as ghrelin, pancreatic polypeptide, 17 peptide YY, and neuropeptide Y, 18 as well as their receptors.

The physiology of energy regulation may result in obesity in susceptible people when it goes awry from genetic and environmental modulators. There is strong evidence of the majority of obesity cases that are associated with central resistance to both leptin and insulin actions. 19 , 20 The environmental modulators equally play critical roles in obesity. Changes in the circadian clock are associated with temporal alterations in feeding behavior and increased weight gain. 21 Stress interferes with cognitive processes such as executive function and self-regulation. Second, stress can affect behavior by inducing overeating and consumption of foods that are high in calories, fat, or sugar; by decreasing physical activity; and by shortening sleep. Third, stress triggers physiological changes in the hypothalamic-pituitary-adrenal axis, reward processing in the brain, and possibly the gut microbiome. Finally, stress can stimulate the production of biochemical hormones and peptides such as leptin, ghrelin, and neuropeptide Y. 17

The lateral hypothalamus (LH) plays a fundamental role in regulating feeding and reward-related behaviors; however, the contributions of neuronal subpopulations in the LH are yet to be identified thoroughly. 22 The LH has also been associated with other aspects of body weight regulation, such as physical activity and thermogenesis. 23 The LH contains a heterogeneous assembly of neuronal cell populations, in which γ-aminobutyric acid (GABA) neurons predominate. 23 LH GABA neurons are known to mediate multiple behaviors important for body weight regulation, thus altering energy expenditure. 23

Etiology and Risk Factors

Excess body fat is a major health concern in childhood and adolescent populations. The dramatic increase in childhood obesity foreshadows the serious health consequences of their adult life. As obesity begins from childhood and spans through adult life, it becomes increasingly more difficult to treat successfully. Being able to identify the risk factors and potential causes of childhood obesity is one of the best strategies for preventing the epidemic. 24

According to the Morbidity and Mortality Weekly Report released in 2011, there is an acceptance that there is no single cause of childhood obesity and that energy imbalance is just a part of the numerous factors. 25 Many children have a discrepancy between what is taken in and what is expended. 26 For example, children with obesity consume approximately 1000 calories more than what is necessary for their body to function healthily and to be able to participate in regular physical activities. Over 10 years, there will be an excess of 57 pounds of unnecessary weight. With excessive caloric intake, as well as sedentary lifestyles, childhood obesity will continue to rise if no changes are implemented. Adding daily physical activity, better sleep patterns, as well as dietary changes can help decrease the number of excess calories and help with obesity-related problems in the future.

Also, during childhood, excess fat accumulates when the increase in caloric intake exceeds the total energy expenditure. 26 Furthermore, children living in the United States today compared with children living in the 1900s are participating in more than 6 hours per day activities on social media. This includes but is not limited to traditional television, video gaming, and blogging/Facebook activities. An additional economic rationalization for the increase in childhood obesity is technology. In other words, Americans can now eat more in less time.

In a study, Cutler et al found that an increase in consumption of food tends to be related to technology innovation in food production and transportation. Technology has thus made it increasingly possible for firms to mass prepare food and ship to consumers for ready consumption, thereby taking advantage of scale economies in food preparation. The result of this change has been a significant reduction in the time costs for food production. These lower time costs have led to increased food consumption and, ultimately, increased weights. 27 Eliminating the time cost of food preparation disproportionately increases consumption for hyperbolic discounters because time delay is a particularly important mechanism for discouraging those individuals from consuming. 27 Society today prefers immediate satisfaction with regard to food and convenience over the long-term goals of living a long, healthy life. The availability of high-caloric, less-expensive food coupled with the extensive advertisement and easy accessibility of these foods has contributed immensely to the rising trend of obesity. 28 For example, there have been reductions in the price of McDonalds and Coca-Cola (5.44% and 34.89%, respectively) between 1990 and 2007, while there was about a 17% increase in the price of fruits and vegetables between 1997 and 2003. 29

Likewise, only 16% of children walk or bike to school today as compared with 42% in the late 1960s. However, the distance, convenience, weather, scanty sidewalks, and anxiety about crimes against children could all contribute to this difference. Furthermore, with elementary, middle, and high school combined, only 13.8% of these schools provide adequate daily physical education classes for at least 4 hours a week. 30

Some other potential risk factors have been reported through research studies that involve issues that affect the child in utero and childhood. Table 1 represents potential risk factors and confounders of childhood obesity. 31

Potential Risk Factors of Childhood Obesity.

Abbreviations: BMI, body mass index; SES, socioeconomic status.

Catalano et al argues that maternal BMI before conception, independent of maternal glucose status or birth weight, is a strong predictor of childhood obesity. 32 Infants at the highest quarter for weight at 8 and 18 months are more likely to become children with obesity at age 7, than children in the lower quarters. Certain behaviors have been linked to childhood obesity and overweight; these are a lack of physical activity and unhealthy eating patterns (eating more food away from home, drinking more sugar-sweetened drinks, and snacking more frequently), resulting in excess energy intake. 22 , 31 In addition, when one parent presents with obesity, there is an increased potential for the child to become obese over the years. Naturally, the risk is higher for the children when both parents present with obesity. Furthermore, a study that followed children over time observed that children who got less sleep <10.5 hours at age 3 were 45% more likely to be children with obesity at the age of 7, than children who got greater than 12 hours of sleep during their first 3 years of life. 33 , 34

While all the above-mentioned factors are informative, there is still the need for further research concerning childhood and adolescent obesity and obesity in general. Risk factors for obesity in childhood are still somewhat uncertain, and evidence-based research for preventative strategies is lacking. Moreover, effective action to prevent the childhood obesity epidemic requires evidence-based on early life risk factors, and this evidence, unfortunately, is still incomplete. Furthermore, a research study has attempted to capture the complete picture of childhood obesity early life course risk factors. In the study, they identified that parental BMI and gestational weight gain among other factors should be considered in prevention programs. 35

Health Effects of Childhood Obesity

Childhood obesity is known to have a significant impact on both physical and psychological health. Sahoo et al stated that “childhood obesity can profoundly affect children’s physical health, social and emotional well-being, as well as self-esteem.” They associated poor academic performance and a lower quality of life experienced by the child with childhood obesity. They also stated that “metabolic, cardiovascular, orthopedic, neurological, hepatic, pulmonary, and menstrual disorders among others are consequences of childhood obesity.” 36 There are many health consequences of childhood obesity, and three of the more common ones are sleep apnea, diabetes, and cardiovascular diseases. 36

Psychological Consequences of Obesity

Several studies related to childhood and adolescent obesity have focused primarily on physiological consequences. Other studies have been conducted regarding the association between psychiatric disorders and obesity; these have resulted in conflict due to obesity being found to be an insignificant factor for psychopathology. However, a comparative study by Britz et al found that high rates of mood, anxiety, somatoform, and eating disorders were detected among children with obesity. The study also observed that most psychiatric disorders began after the onset of obesity. In this large population-based study, it was found that a staggering 60% of females and 35% of males reported that they have engaged in binge eating and expressed a lack of control over their diet. 37

Goldfield et al conducted a study among 1400 adolescents with obesity, overweight, and normal weight in grades 7 to 12. Their BMIs, as determined by the International Obesity Task Force, were the criteria used to define each group. Each participant completed a questionnaire on body images, eating behaviors, and moods. Adolescents with obesity reported significantly higher body dissatisfaction, social isolation, depression symptoms, anhedonia, and negative self-esteem than those of normal weight. 38 There is widespread stigmatization of people with obesity that causes harm rather than the intention to motivate people to lose weight. Stigma contributes to behaviors such as binge eating, social isolation, avoidance of health care services, decreased physical activity, and increased weight gain, which worsens obesity and creates additional barriers to healthy behavior change. 39 Weight-based bullying in youth is considered a common, serious problem in many countries. 40 In a study conducted by O’Brien et al, to test whether the association between weight stigma experiences and disordered eating behaviors, that is, emotional eating, uncontrolled eating, and loss-of-control eating, are mediated by weight bias internalization and psychological distress among 634 undergraduate university students, and results of statistical analyses showed that weight stigma was significantly associated with all measures of disordered eating, and with weight bias internalization and psychological distress. 41

Asthma and Obesity

There is mounting evidence that childhood obesity is a risk factor for the development of asthma. 42 A research study was conducted by Belamarich et al to investigate 1322 children aged 4 to 9 years with asthma. Obesity, as defined by the CDC, is the BMI, with weight and height being greater than the 95th percentile. This was the criteria used to identify the 249 children with obesity, while the BMI between the 5th and 95th percentile identified the children who were not obese. After a baseline assessment was done, the 9-month study found that the children with obesity had a higher number of days of wheezing over 2 weeks (4.0 vs 3.4) and as well had more unscheduled emergency hospital visits (39% vs 31%). 42

Obesity directly correlates with the severity of asthma, as well as poor response to corticosteroids. 43 In fact, children with obesity who also have a history of asthma are more challenging to control and linked to worse quality of life. 44 A prospective trial found that weight loss in patients with obesity and a history of asthma can significantly aid them to control the asthma attacks. 43

Chronic Inflammation and Childhood Obesity

Lumeng and Saltiel reported that obesity in children affects multiple organ systems and predisposes them to diseases. The effect of obesity on the tissue can manifest in the development of insulin-resistant type 2 diabetes, the risk of cancer, and pulmonary diseases. 45

The inflammatory response to obesity triggers pathogens, systematic increases in circulatory inflammatory cytokines, and acute-phase reactants (eg, C-reactive proteins), which inflames the tissues. This is often caused by the activation of tissue leukocytes. Chronic inflammation in children with obesity can induce meta-inflammation that is unique when compared with other inflammatory paradigms (eg, infection, autoimmune diseases). 45 Researchers have reported that children with obesity are at risk of lifelong meta-inflammation. In these children, the inflammatory markers are elevated as early as in the third year of life. 45 , 46 This has been linked to heart disease later in life. 19 The long-term consequences of such findings can cause cumulative vascular damage that correlates with the increased weight status. 47

The short-term and long-term effects of obesity on the health of children is a significant concern because of the negative psychological and health consequences. 46 The potential negative psychological outcomes are depressive symptoms, poor body image, low self-esteem, a risk for eating disorders, and behavior and learning problems. Additional negative health consequences include insulin resistance, type 2 diabetes, asthma, hypertension, high total, and low-density lipoprotein cholesterol and triglyceride levels in the blood, low high-density lipoprotein cholesterol levels in the blood, sleep apnea, early puberty, orthopedic problems, and nonalcoholic steatohepatitis 46 , 47 ( Figure 4 ). Children with obesity are more likely to become adults with obesity, thus increasing their risk for several diseases before they even reach their teen years. 48

Comorbidities and potential health consequences of childhood obesity. 47

Prevention and Control

There are two primary components to the prevention and control of childhood obesity.

The first is to educate parents on proper nutritional requirements for their children and the second is to implement the learned information. Educating parents on proper nutrition and dietary caloric intake requirements for their children is at the forefront for the prevention of obesity; however, the way the information is disseminated may affect the usefulness of the information. For example, one of the main limitations to the education of parents about childhood obesity is that typically written information is used as the conduit to health information and disease prevention. 49 The Growing Right Onto Wellness (GROW) trial used a systematic assessment of patient education material that was used for the prevention of childhood obesity in the low health literate population. 49 Results suggest that the average readability is of grade 6 level (SMOG [Simple Measure of Gobbledygook] Index 5.63 ± 0.76 and Fry graph 6.0 ± 0.85) and that adjustment of education material must be done for low health literate populations to adequately comprehend educational material and maintain motivation on the prevention of childhood obesity. 49 A similar study was conducted to further support this improvement when using color-coordinated diagrams to help parents visualize instead of trying to comprehend with numbers and words. It proved to be successful as parents were able to see where they were going wrong and make the necessary changes in their children’s diet. 49

Similarly, the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development conducted a study on 744 adolescents and parents, and analyzed data to determine if parental (maternal and paternal, individually) reactions to children’s behavior was related to childhood obesity. 50 The study concluded that informing parents that their attitude toward their children’s behaviors will play a prominent role in preventing childhood obesity. 50 Parental education on nutrition, health, and the involvement of politicians, physicians, and school personnel are key for the prevention of childhood obesity. However, community and educational institutions have begun legislating and incorporating programs such as providing healthy foods at schools and also health information sessions directed toward young individuals, aimed at preventing childhood obesity in the United States and Canada. 51

Another effective prevention measure against childhood obesity is the awareness of parents on the meal and snack portion sizes. In a systematic review conducted on the effects of portion size manipulation with children and portion education/training interventions on dietary intake with parents, it was determined that the ability of adults to accurately estimate portion size improved following education/training. 52 Education of parents and children on diet requirements has its limitations in that the information must be easy to understand and be easily accessible in order to be practical. Making the available education materials easier to understand from just tables and numbers to more relatable aspects such as colors or figures, parents were able to visualize the changes they need to make whether it is with regard to portion sizes or even seeing how much childhood obesity is present in their family. Although much of the literature provided to parents is targeted to help those with lower numeracy skills, many parents benefited from the information being comparative from right/wrong and good/bad with regard to dieting. 49

The study recommended that proper educational materials, including useful and understandable literature, be used to control meal portion sizes and to help parents identify when children are at risk of obesity. Similarly, healthy eating practices should be taught by schools as a mandatory and essential method in the prevention of childhood obesity. 52

The implementation of healthy eating practices and adequate exercise regimes are essential in the prevention and control of childhood obesity. For example, information from systematic reviews, randomized controlled trials, and well-designed observational studies indicate that evidence-based prevention and control of childhood obesity can be accomplished with the collaboration of community/school, primary health care, and home-based/family-based interventions that involve both physical activity and dietary component. 53 In particular, the control of children with obesity is of significant value, as is the prevention of obesity. Two randomized control trials of 182 families were conducted from November 2005 to September 2007, and they studied the efficacy of US pediatric obesity treatment guidelines in children aged 4 to 9 years with a standardized BMI (ZBMI) greater than the 85 percentile. 54 Briefly, Trial 1 studied the impact on ZBMI by reducing snack foods and sugar-sweetened beverages and increasing fruits, vegetables, and low-fat dairy. 54 Trial 2 studied the impact on ZBMI by decreasing sugar-sweetened beverages and increasing physical activity and increasing low-fat milk consumption and reducing television watching. In Trial 1, the resulting ZBMI reduced within 6 months, and this was maintained through to the 12th month (ΔZBMI 0-12 months = −0.12 ± 0.22). 53 In Trial 2, the resulting ZBMI reduced within 6 months and continued to improve till the 12 months (ΔZBMI 0-12 months = −0.16 ± 0.31). 50

A similar cluster-randomized trial in England studied the effects of the reduction of carbonated beverages on the number of children with obesity in 29 classes (644 children). 51 Results indicate that a decrease of 0.6 glasses of carbonated drinks (250 mL) over three days per week decreased the number of children with obesity by 0.2%, while the control group increased by 7.5% (mean difference = 7.7%, 2.2% to 13.1%) at 12 months. However, diet control is only one component of the control and prevention of childhood obesity, while adequate exercise is another. 55

A systematic review and meta-analyses of the impact of diet and exercise programs (single or combined) was done on their effects on metabolic risk reduction in the pediatric population. 56 Analyses indicated that the addition of exercise to dietary intervention led to greater improvements in the levels of high-density lipoprotein cholesterol (3.86 mg/dL; 95% confidence interval [CI] = 2.70 to 4.63), fasting glucose (−2.16 mg/dL; 95% CI = −3.78 to −0.72), and fasting insulin (−2.75 µIU/mL; 95% CI = −4.50 to −1.00) over 6 months. 56 Diet and exercise are both important factors in the control and prevention of childhood obesity. It is our recommendation that parents and community (teachers and doctors) should be involved in identifying children at risk based on their BMI and participate in implementing practices such as good diet control through the reduction of sugary drinks, fatty foods, and also encouraging safe exercise programs to prevent and control childhood obesity in the society. 56

While all of the previous data express the more obvious prevention methods with regard to childhood obesity, it is imperative to note that ensuring that the whole family is involved in the intervention will yield the greatest results. 2 All current studies indicate that families must be included in childhood treatment of obesity. However, for the success of the child’s weight loss program, it is vital that the parents understand that the causes of obesity are often a mixture of four factors: genetic causes, parental habits, overeating, and poor exercise habits. Thus, instilling some responsibility on the parents and informing them that controlled food preparation, diet control, and family participation in physical activities will all assist in the treatment and control of obesity in their children. 2

Childhood obesity has increased significantly in recent decades and has quickly become a public health crisis in the United States and all over the world. Its increase in prevalence has provoked widespread research efforts to identify the factors that contributed to these changes. 57 Obesity starts with an imbalance between caloric intake and caloric expenditure. 58 Children with obesity are at greater risk of adult obesity; therefore, if we can educate and improve the health habits of families even before they start having children, this can help reduce the increasing rate of childhood obesity in the United States. Parents and caregivers with proper education on the causes and consequences of childhood obesity can help prevent childhood obesity by providing healthy meals and snacks, daily physical activity, and nutrition education to their family members. 59 Families need to take the approach of not adapting to their family being on a diet but more of a healthy lifestyle. A family’s home environment can influence children at a young age; therefore, making changes starting in the household early can educate and influence them to grow up healthy. Although prevention programs may be more expensive in the short term, the long-term benefits acquired through prevention are much more likely to save an even greater amount of health care costs. Not only will the children have a better childhood and self-esteem, but prevention programs can also decrease the incidence of cardiovascular diseases, diabetes, stroke, and possibly cancers in adulthood. 60 The overall need to decrease the obesity rate will help children and their families in the generations to come by building a healthy lifestyle and environment. In order to tackle the climbing obesity rate, overall health and lifestyle needs to be a priority as they balance one with the other. 49 While effective interventions to thwart childhood obesity still remain elusive, the sustainability of the interventions already in place will enable children and their families to adopt these important health behaviors as lifelong practices and improve their health. 58

Treatment of Obesity and the Physiology of Energy Regulation

As discussed previously, a variety of mechanisms participate in weight regulation and the development of obesity in children, including genetics, developmental influences (“metabolic programming” or epigenetics), individual and family health behaviors, and environmental factors. Among these potential mechanisms, only environmental factors are potentially modifiable during childhood and adolescence.

Unfortunately, despite intensive lifestyle modifications and support for healthy practices within the children’s environment, some children will continue to struggle with extreme excess weight and associated comorbidities. 61 , 62 Therefore, a combination of pharmacotherapy and lifestyle modification can be considered. 61 Overweight children should not be treated with medications unless significant, severe comorbidities persist despite lifestyle modification. The use of pharmacotherapy should also be considered in overweight children with a strong family history of type 2 diabetes or cardiovascular risk factors. Constant bidirectional communication between the brain and the gastrointestinal tract, as well as the brain and other relevant tissues (ie, adipose tissue, pancreas, and liver), ensures that the brain constantly perceives and responds accordingly to the energy status/needs of the body. This elegant biological system is subject to disruption by a toxic obesogenic environment, leading to syndromes such as leptin and insulin resistance, and ultimately further exposing individuals who are obese to further weight gain and type 2 diabetes mellitus. Currently, the only Food and Drug Administration–approved prescription drug indicated for the treatment of pediatric obesity is orlistat (Xenical; Genentech USA, Inc, South San Francisco, CA). 63 Orlistat works by inhibiting gastric and pancreatic lipases, the enzymes that break down triglycerides in the intestine. Moreover, imaging studies in humans are beginning to examine the influence that higher- order/hedonic brain regions have on homeostatic areas, as well as their responsiveness to homeostatic peripheral signals. With a greater understanding of these mechanisms, the field moves closer to understanding and eventually treating the casualties of obesity.

The number of children with obesity in the United States has increased substantially over the years; due to its public health significance, the increasing trends need to be closely monitored. While a complete picture of all the risk factors associated with obesity remains elusive, many of the studies agreed that prevention is the key strategy for controlling the current problem. Since the combination of diet, exercise, and physiological and psychological factors are all important factors in the control and prevention of childhood obesity, primary prevention methods should be aimed at educating the child and family and encouraging appropriate diet and exercise from a young age through adulthood while secondary prevention should be targeted at lessening the effect of childhood obesity by preventing the child from continuing unhealthy habits and obesity into adulthood. A combination of primary and secondary prevention is necessary to achieve the best results. Thus, a combined implementation of both types of preventions can significantly help lower the current prevalence of childhood and adolescent obesity in the United States. Failure to take appropriate actions could lead to serious public health consequences.

Author Contributions: AS: Contributed to conception and design; drafted manuscript; gave final approval; agrees to be accountable for all aspects of work ensuring integrity and accuracy.

XQ: Contributed to the acquisition, analysis, and interpretation.

JL: Contributed to the acquisition, analysis, and interpretation.

SR: Contributed to the acquisition, analysis, and interpretation.

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

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• Published: 13 May 2024

Long-term weight loss effects of semaglutide in obesity without diabetes in the SELECT trial

• Donna H. Ryan 1 ,
• Ildiko Lingvay   ORCID: orcid.org/0000-0001-7006-7401 2 ,
• John Deanfield 3 ,
• Steven E. Kahn 4 ,
• Eric Barros   ORCID: orcid.org/0000-0001-6613-4181 5 ,
• Bartolome Burguera 6 ,
• Helen M. Colhoun   ORCID: orcid.org/0000-0002-8345-3288 7 ,
• Cintia Cercato   ORCID: orcid.org/0000-0002-6181-4951 8 ,
• Dror Dicker 9 ,
• Deborah B. Horn 10 ,
• G. Kees Hovingh 5 ,
• Ole Kleist Jeppesen 5 ,
• Alexander Kokkinos 11 ,
• A. Michael Lincoff   ORCID: orcid.org/0000-0001-8175-2121 12 ,
• Sebastian M. Meyhöfer 13 ,
• Tugce Kalayci Oral 5 ,
• Jorge Plutzky   ORCID: orcid.org/0000-0002-7194-9876 14 ,
• André P. van Beek   ORCID: orcid.org/0000-0002-0335-8177 15 ,
• John P. H. Wilding   ORCID: orcid.org/0000-0003-2839-8404 16 &
• Robert F. Kushner 17  

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In the SELECT cardiovascular outcomes trial, semaglutide showed a 20% reduction in major adverse cardiovascular events in 17,604 adults with preexisting cardiovascular disease, overweight or obesity, without diabetes. Here in this prespecified analysis, we examined effects of semaglutide on weight and anthropometric outcomes, safety and tolerability by baseline body mass index (BMI). In patients treated with semaglutide, weight loss continued over 65 weeks and was sustained for up to 4 years. At 208 weeks, semaglutide was associated with mean reduction in weight (−10.2%), waist circumference (−7.7 cm) and waist-to-height ratio (−6.9%) versus placebo (−1.5%, −1.3 cm and −1.0%, respectively; P  < 0.0001 for all comparisons versus placebo). Clinically meaningful weight loss occurred in both sexes and all races, body sizes and regions. Semaglutide was associated with fewer serious adverse events. For each BMI category (<30, 30 to <35, 35 to <40 and ≥40 kg m − 2 ) there were lower rates (events per 100 years of observation) of serious adverse events with semaglutide (43.23, 43.54, 51.07 and 47.06 for semaglutide and 50.48, 49.66, 52.73 and 60.85 for placebo). Semaglutide was associated with increased rates of trial product discontinuation. Discontinuations increased as BMI class decreased. In SELECT, at 208 weeks, semaglutide produced clinically significant weight loss and improvements in anthropometric measurements versus placebo. Weight loss was sustained over 4 years. ClinicalTrials.gov identifier: NCT03574597 .

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What is the pipeline for future medications for obesity?

The worldwide obesity prevalence, defined by body mass index (BMI) ≥30 kg m − 2 , has nearly tripled since 1975 (ref. 1 ). BMI is a good surveillance measure for population changes over time, given its strong correlation with body fat amount on a population level, but it may not accurately indicate the amount or location of body fat at the individual level 2 . In fact, the World Health Organization defines clinical obesity as ‘abnormal or excessive fat accumulation that may impair health’ 1 . Excess abnormal body fat, especially visceral adiposity and ectopic fat, is a driver of cardiovascular (CV) disease (CVD) 3 , 4 , 5 , and contributes to the global chronic disease burden of diabetes, chronic kidney disease, cancer and other chronic conditions 6 , 7 .

Remediating the adverse health effects of excess abnormal body fat through weight loss is a priority in addressing the global chronic disease burden. Improvements in CV risk factors, glycemia and quality-of-life measures including personal well-being and physical functioning generally begin with modest weight loss of 5%, whereas greater weight loss is associated with more improvement in these measures 8 , 9 , 10 . Producing and sustaining durable and clinically significant weight loss with lifestyle intervention alone has been challenging 11 . However, weight-management medications that modify appetite can make attaining and sustaining clinically meaningful weight loss of ≥10% more likely 12 . Recently, weight-management medications, particularly those comprising glucagon-like peptide-1 receptor agonists, that help people achieve greater and more sustainable weight loss have been developed 13 . Once-weekly subcutaneous semaglutide 2.4 mg, a glucagon-like peptide-1 receptor agonist, is approved for chronic weight management 14 , 15 , 16 and at doses of up to 2.0 mg is approved for type 2 diabetes treatment 17 , 18 , 19 . In patients with type 2 diabetes and high CV risk, semaglutide at doses of 0.5 mg and 1.0 mg has been shown to significantly lower the risk of CV events 20 . The SELECT trial (Semaglutide Effects on Heart Disease and Stroke in Patients with Overweight or Obesity) studied patients with established CVD and overweight or obesity but without diabetes. In SELECT, semaglutide was associated with a 20% reduction in major adverse CV events (hazard ratio 0.80, 95% confidence interval (CI) 0.72 to 0.90; P  < 0.001) 21 . Data derived from the SELECT trial offer the opportunity to evaluate the weight loss efficacy, in a geographically and racially diverse population, of semaglutide compared with placebo over 208 weeks when both are given in addition to standard-of-care recommendations for secondary CVD prevention (but without a focus on targeting weight loss). Furthermore, the data allow examination of changes in anthropometric measures such as BMI, waist circumference (WC) and waist-to-height ratio (WHtR) as surrogates for body fat amount and location 22 , 23 . The diverse population can also be evaluated for changes in sex- and race-specific ‘cutoff points’ for BMI and WC, which have been identified as anthropometric measures that predict cardiometabolic risk 8 , 22 , 23 .

This prespecified analysis of the SELECT trial investigated weight loss and changes in anthropometric indices in patients with established CVD and overweight or obesity without diabetes, who met inclusion and exclusion criteria, within a range of baseline categories for glycemia, renal function and body anthropometric measures.

Study population

The SELECT study enrolled 17,604 patients (72.3% male) from 41 countries between October 2018 and March 2021, with a mean (s.d.) age of 61.6 (8.9) years and BMI of 33.3 (5.0) kg m − 2 (ref. 21 ). The baseline characteristics of the population have been reported 24 . Supplementary Table 1 outlines SELECT patients according to baseline BMI categories. Of note, in the lower BMI categories (<30 kg m − 2 (overweight) and 30 to <35 kg m − 2 (class I obesity)), the proportion of Asian individuals was higher (14.5% and 7.4%, respectively) compared with the proportion of Asian individuals in the higher BMI categories (BMI 35 to <40 kg m − 2 (class II obesity; 3.8%) and ≥40 kg m − 2 (class III obesity; 2.2%), respectively). As the BMI categories increased, the proportion of women was higher: in the class III BMI category, 45.5% were female, compared with 20.8%, 25.7% and 33.0% in the overweight, class I and class II categories, respectively. Lower BMI categories were associated with a higher proportion of patients with normoglycemia and glycated hemoglobin <5.7%. Although the proportions of patients with high cholesterol and history of smoking were similar across BMI categories, the proportion of patients with high-sensitivity C-reactive protein ≥2.0 mg dl −1 increased as the BMI category increased. A high-sensitivity C-reactive protein >2.0 mg dl −1 was present in 36.4% of patients in the overweight BMI category, with a progressive increase to 43.3%, 57.3% and 72.0% for patients in the class I, II and III obesity categories, respectively.

Weight and anthropometric outcomes

Percentage weight loss.

The average percentage weight-loss trajectories with semaglutide and placebo over 4 years of observation are shown in Fig. 1a (ref. 21 ). For those in the semaglutide group, the weight-loss trajectory continued to week 65 and then was sustained for the study period through week 208 (−10.2% for the semaglutide group, −1.5% for the placebo group; treatment difference −8.7%; 95% CI −9.42 to −7.88; P  < 0.0001). To estimate the treatment effect while on medication, we performed a first on-treatment analysis (observation period until the first time being off treatment for >35 days). At week 208, mean weight loss in the semaglutide group analyzed as first on-treatment was −11.7% compared with −1.5% for the placebo group (Fig. 1b ; treatment difference −10.2%; 95% CI −11.0 to −9.42; P  < 0.0001).

a , b , Observed data from the in-trial period ( a ) and first on-treatment ( b ). The symbols are the observed means, and error bars are ±s.e.m. Numbers shown below each panel represent the number of patients contributing to the means. Analysis of covariance with treatment and baseline values was used to estimate the treatment difference. Exact P values are 1.323762 × 10 −94 and 9.80035 × 10 −100 for a and b , respectively. P values are two-sided and are not adjusted for multiplicity. ETD, estimated treatment difference; sema, semaglutide.

Categorical weight loss and individual body weight change

Among in-trial (intention-to-treat principle) patients at week 104, weight loss of ≥5%, ≥10%, ≥15%, ≥20% and ≥25% was achieved by 67.8%, 44.2%, 22.9%, 11.0% and 4.9%, respectively, of those treated with semaglutide compared with 21.3%, 6.9%, 1.7%, 0.6% and 0.1% of those receiving placebo (Fig. 2a ). Individual weight changes at 104 weeks for the in-trial populations for semaglutide and placebo are depicted in Fig. 2b and Fig. 2c , respectively. These waterfall plots show the variation in weight-loss response that occurs with semaglutide and placebo and show that weight loss is more prominent with semaglutide than placebo.

a , Categorical weight loss from baseline at week 104 for semaglutide and placebo. Data from the in-trial period. Bars depict the proportion (%) of patients receiving semaglutide or placebo who achieved ≥5%, ≥10%, ≥15%, ≥20% and ≥25% weight loss. b , c , Percentage change in body weight for individual patients from baseline to week 104 for semaglutide ( b ) and placebo ( c ). Each patient’s percentage change in body weight is plotted as a single bar.

Change in WC

WC change from baseline to 104 weeks has been reported previously in the primary outcome paper 21 . The trajectory of WC change mirrored that of the change in body weight. At week 208, average reduction in WC was −7.7 cm with semaglutide versus −1.3 cm with placebo, with a treatment difference of −6.4 cm (95% CI −7.18 to −5.61; P  < 0.0001) 21 .

WC cutoff points

We analyzed achievement of sex- and race-specific cutoff points for WC by BMI <35 kg m − 2 or ≥35 kg m − 2 , because for BMI >35 kg m − 2 , WC is more difficult technically and, thus, less accurate as a risk predictor 4 , 25 , 26 . Within the SELECT population with BMI <35 kg m − 2 at baseline, 15.0% and 14.3% of the semaglutide and placebo groups, respectively, were below the sex- and race-specific WC cutoff points. At week 104, 41.2% fell below the sex- and race-specific cutoff points for the semaglutide group, compared with only 18.0% for the placebo group (Fig. 3 ).

WC cutoff points; Asian women <80 cm, non-Asian women <88 cm, Asian men <88 cm, non-Asian men <102 cm.

Waist-to-height ratio

At baseline, mean WHtR was 0.66 for the study population. The lowest tertile of the SELECT population at baseline had a mean WHtR <0.62, which is higher than the cutoff point of 0.5 used to indicate increased cardiometabolic risk 27 , suggesting that the trial population had high WCs. At week 208, in the group randomized to semaglutide, there was a relative reduction of 6.9% in WHtR compared with 1.0% in placebo (treatment difference −5.87% points; 95% CI −6.56 to −5.17; P  < 0.0001).

BMI category change

At week 104, 52.4% of patients treated with semaglutide achieved improvement in BMI category compared with 15.7% of those receiving placebo. Proportions of patients in the BMI categories at baseline and week 104 are shown in Fig. 4 , which depicts in-trial patients receiving semaglutide and placebo. The BMI category change reflects the superior weight loss with semaglutide, which resulted in fewer patients being in the higher BMI categories after 104 weeks. In the semaglutide group, 12.0% of patients achieved a BMI <25 kg m − 2 , which is considered the healthy BMI category, compared with 1.2% for placebo; per study inclusion criteria, no patients were in this category at baseline. The proportion of patients with obesity (BMI ≥30 kg m − 2 ) fell from 71.0% to 43.3% in the semaglutide group versus 71.9% to 67.9% in the placebo group.

In the semaglutide group, 12.0% of patients achieved normal weight status at week 104 (from 0% at baseline), compared with 1.2% (from 0% at baseline) for placebo. BMI classes: healthy (BMI <25 kg m − 2 ), overweight (25 to <30 kg m − 2 ), class I obesity (30 to <35 kg m − 2 ), class II obesity (35 to <40 kg m − 2 ) and class III obesity (BMI ≥40 kg m − 2 ).

Weight and anthropometric outcomes by subgroups

The forest plot illustrated in Fig. 5 displays mean body weight percentage change from baseline to week 104 for semaglutide relative to placebo in prespecified subgroups. Similar relationships are depicted for WC changes in prespecified subgroups shown in Extended Data Fig. 1 . The effect of semaglutide (versus placebo) on mean percentage body weight loss as well as reduction in WC was found to be heterogeneous across several population subgroups. Women had a greater difference in mean weight loss with semaglutide versus placebo (−11.1% (95% CI −11.56 to −10.66) versus −7.5% in men (95% CI −7.78 to −7.23); P  < 0.0001). There was a linear relationship between age category and degree of mean weight loss, with younger age being associated with progressively greater mean weight loss, but the actual mean difference by age group is small. Similarly, BMI category had small, although statistically significant, associations. Those with WHtR less than the median experienced slightly lower mean body weight change than those above the median, with estimated treatment differences −8.04% (95% CI −8.37 to −7.70) and −8.99% (95% CI −9.33 to −8.65), respectively ( P  < 0.0001). Patients from Asia and of Asian race experienced slightly lower mean weight loss (estimated treatment difference with semaglutide for Asian race −7.27% (95% CI −8.09 to −6.46; P  = 0.0147) and for Asia −7.30 (95% CI −7.97 to −6.62; P  = 0.0016)). There was no difference in weight loss with semaglutide associated with ethnicity (estimated treatment difference for Hispanic −8.53% (95% CI −9.28 to −7.76) or non-Hispanic −8.52% (95% CI −8.77 to 8.26); P  = 0.9769), glycemic status (estimated treatment difference for prediabetes −8.53% (95% CI −8.83 to −8.24) or normoglycemia −8.48% (95% CI −8.88 to −8.07; P  = 0.8188) or renal function (estimated treatment difference for estimated glomerular filtration rate (eGFR) <60 or ≥60 ml min −1  1.73 m − 2 being −8.50% (95% CI −9.23 to −7.76) and −8.52% (95% CI −8.77 to −8.26), respectively ( P  = 0.9519)).

Data from the in-trial period. N  = 17,604. P values represent test of no interaction effect. P values are two-sided and are not adjusted for multiplicity. The dots show estimated treatment differences, and the error bars show 95% CIs. Details of the statistical models are available in Methods . ETD, estimated treatment difference; HbA1c, glycated hemoglobin; MI, myocardial infarction; PAD, peripheral artery disease; sema, semaglutide.

Safety and tolerability according to baseline BMI category

We reported in the primary outcome of the SELECT trial that adverse events (AEs) leading to permanent discontinuation of the trial product occurred in 1,461 patients (16.6%) in the semaglutide group and 718 patients (8.2%) in the placebo group ( P  < 0.001) 21 . For this analysis, we evaluated the cumulative incidence of AEs leading to trial product discontinuation by treatment assignment and by BMI category (Fig. 6 ). For this analysis, with death modeled as a competing risk, we tracked the proportion of in-trial patients for whom drug was withdrawn or interrupted for the first time (Fig. 6 , left) or cumulative discontinuations (Fig. 6 , right). Both panels of Fig. 6 depict a graded increase in the proportion discontinuing semaglutide, but not placebo. For lower BMI classes, discontinuation rates are higher in the semaglutide group but not the placebo group.

Data are in-trial from the full analysis set. sema, semaglutide.

We reported in the primary SELECT analysis that serious adverse events (SAEs) were reported by 2,941 patients (33.4%) in the semaglutide arm and by 3,204 patients (36.4%) in the placebo arm ( P  < 0.001) 21 . For this study, we analyzed SAE rates by person-years of treatment exposure for BMI classes (<30 kg m − 2 , 30 to <35 kg m − 2 , 35 to <40 kg m − 2 , and ≥40 kg m − 2 ) and provide these data in Supplementary Table 2 . We also provide an analysis of the most common categories of SAEs. Semaglutide was associated with lower SAEs, primarily driven by CV event and infections. Within each obesity class (<30 kg m − 2 , 30 to <35 kg m − 2 , 35 to <40 kg m − 2 , and ≥40 kg m − 2 ), there were fewer SAEs in the group receiving semaglutide compared with placebo. Rates (events per 100 years of observation) of SAEs were 43.23, 43.54, 51.07 and 47.06 for semaglutide and 50.48, 49.66, 52.73 and 60.85 for placebo, with no evidence of heterogeneity. There was no detectable difference in hepatobiliary or gastrointestinal SAEs comparing semaglutide with placebo in any of the four BMI classes we evaluated.

The analyses of weight effects of the SELECT study presented here reveal that patients assigned to once-weekly subcutaneous semaglutide 2.4 mg lost significantly more weight than those receiving placebo. The weight-loss trajectory with semaglutide occurred over 65 weeks and was sustained up to 4 years. Likewise, there were similar improvements in the semaglutide group for anthropometrics (WC and WHtR). The weight loss was associated with a greater proportion of patients receiving semaglutide achieving improvement in BMI category, healthy BMI (<25 kg m − 2 ) and falling below the WC cutoff point above which increased cardiometabolic risk for the sex and race is greater 22 , 23 . Furthermore, both sexes, all races, all body sizes and those from all geographic regions were able to achieve clinically meaningful weight loss. There was no evidence of increased SAEs based on BMI categories, although lower BMI category was associated with increased rates of trial product discontinuation, probably reflecting exposure to a higher level of drug in lower BMI categories. These data, representing the longest clinical trial of the effects of semaglutide versus placebo on weight, establish the safety and durability of semaglutide effects on weight loss and maintenance in a geographically and racially diverse population of adult men and women with overweight and obesity but not diabetes. The implications of weight loss of this degree in such a diverse population suggests that it may be possible to impact the public health burden of the multiple morbidities associated with obesity. Although our trial focused on CV events, many chronic diseases would benefit from effective weight management 28 .

There were variations in the weight-loss response. Individual changes in body weight with semaglutide and placebo were striking; still, 67.8% achieved 5% or more weight loss and 44.2% achieved 10% weight loss with semaglutide at 2 years, compared with 21.3% and 6.9%, respectively, for those receiving placebo. Our first on-treatment analysis demonstrated that those on-drug lost more weight than those in-trial, confirming the effect of drug exposure. With semaglutide, lower BMI was associated with less percentage weight loss, and women lost more weight on average than men (−11.1% versus −7.5% treatment difference from placebo); however, in all cases, clinically meaningful mean weight loss was achieved. Although Asian patients lost less weight on average than patients of other races (−7.3% more than placebo), Asian patients were more likely to be in the lowest BMI category (<30 kg m − 2 ), which is known to be associated with less weight loss, as discussed below. Clinically meaningful weight loss was evident in the semaglutide group within a broad range of baseline categories for glycemia and body anthropometrics. Interestingly, at 2 years, a significant proportion of the semaglutide-treated group fell below the sex- and race-specific WC cutoff points, especially in those with BMI <35 kg m − 2 , and a notable proportion (12.0%) fell below the BMI cutoff point of 25 kg m − 2 , which is deemed a healthy BMI in those without unintentional weight loss. As more robust weight loss is possible with newer medications, achieving and maintaining these cutoff point targets may become important benchmarks for tracking responses.

The overall safety profile did not reveal any new signals from prior studies, and there were no BMI category-related associations with AE reporting. The analysis did reveal that tolerability may differ among specific BMI classes, since more discontinuations occurred with semaglutide among lower BMI classes. Potential contributors may include a possibility of higher drug exposure in lower BMI classes, although other explanations, including differences in motivation and cultural mores regarding body size, cannot be excluded.

Is the weight loss in SELECT less than expected based on prior studies with the drug? In STEP 1, a large phase 3 study of once-weekly subcutaneous semaglutide 2.4 mg in individuals without diabetes but with BMI >30 kg m − 2 or 27 kg m − 2 with at least one obesity-related comorbidity, the mean weight loss was −14.9% at week 68, compared with −2.4% with placebo 14 . Several reasons may explain the observation that the mean treatment difference was −12.5% in STEP 1 and −8.7% in SELECT. First, SELECT was designed as a CV outcomes trial and not a weight-loss trial, and weight loss was only a supportive secondary endpoint in the trial design. Patients in STEP 1 were desirous of weight loss as a reason for study participation and received structured lifestyle intervention (which included a −500 kcal per day diet with 150 min per week of physical activity). In the SELECT trial, patients did not enroll for the specific purpose of weight loss and received standard of care covering management of CV risk factors, including medical treatment and healthy lifestyle counseling, but without a specific focus on weight loss. Second, the respective study populations were quite different, with STEP 1 including a younger, healthier population with more women (73.1% of the semaglutide arm in STEP 1 versus 27.7% in SELECT) and higher mean BMI (37.8 kg m − 2 versus 33.3 kg m − 2 , respectively) 14 , 21 . Third, major differences existed between the respective trial protocols. Patients in the semaglutide treatment arm of STEP 1 were more likely to be exposed to the medication at the full dose of 2.4 mg than those in SELECT. In SELECT, investigators were allowed to slow, decrease or pause treatment. By 104 weeks, approximately 77% of SELECT patients on dose were receiving the target semaglutide 2.4 mg weekly dose, which is lower than the corresponding proportion of patients in STEP 1 (89.6% were receiving the target dose at week 68) 14 , 21 . Indeed, in our first on-treatment analysis at week 208, weight loss was greater (−11.7% for semaglutide) compared with the in-trial analysis (−10.2% for semaglutide). Taken together, all these issues make less weight loss an expected finding in SELECT, compared with STEP 1.

The SELECT study has some limitations. First, SELECT was not a primary prevention trial, and the data should not be extrapolated to all individuals with overweight and obesity to prevent major adverse CV events. Although the data set is rich in numbers and diversity, it does not have the numbers of individuals in racial subgroups that may have revealed potential differential effects. SELECT also did not include individuals who have excess abnormal body fat but a BMI <27 kg m − 2 . Not all individuals with increased CV risk have BMI ≥27 kg m − 2 . Thus, the study did not include Asian patients who qualify for treatment with obesity medications at lower BMI and WC cutoff points according to guidelines in their countries 29 . We observed that Asian patients were less likely to be in the higher BMI categories of SELECT and that the population of those with BMI <30 kg m − 2 had a higher percentage of Asian race. Asian individuals would probably benefit from weight loss and medication approaches undertaken at lower BMI levels in the secondary prevention of CVD. Future studies should evaluate CV risk reduction in Asian individuals with high CV risk and BMI <27 kg m − 2 . Another limitation is the lack of information on body composition, beyond the anthropometric measures we used. It would be meaningful to have quantitation of fat mass, lean mass and muscle mass, especially given the wide range of body size in the SELECT population.

An interesting observation from this SELECT weight loss data is that when BMI is ≤30 kg m − 2 , weight loss on a percentage basis is less than that observed across higher classes of BMI severity. Furthermore, as BMI exceeds 30 kg m − 2 , weight loss amounts are more similar for class I, II and III obesity. This was also observed in Look AHEAD, a lifestyle intervention study for weight loss 30 . The proportion (percentage) of weight loss seems to be less, on average, in the BMI <30 kg m − 2 category relative to higher BMI categories, despite their receiving of the same treatment and even potentially higher exposure to the drug for weight loss 30 . Weight loss cannot continue indefinitely. There is a plateau of weight that occurs after weight loss with all treatments for weight management. This plateau has been termed the ‘set point’ or ‘settling point’, a body weight that is in harmony with the genetic and environmental determinants of body weight and adiposity 31 . Perhaps persons with BMI <30 kg m − 2 are closer to their settling point and have less weight to lose to reach it. Furthermore, the cardiometabolic benefits of weight loss are driven by reduction in the abnormal ectopic and visceral depots of fat, not by reduction of subcutaneous fat stores in the hips and thighs. The phenotype of cardiometabolic disease but lower BMI (<30 kg m − 2 ) may be one where reduction of excess abnormal and dysfunctional body fat does not require as much body mass reduction to achieve health improvement. We suspect this may be the case and suggest further studies to explore this aspect of weight-loss physiology.

In conclusion, this analysis of the SELECT study supports the broad use of once-weekly subcutaneous semaglutide 2.4 mg as an aid to CV event reduction in individuals with overweight or obesity without diabetes but with preexisting CVD. Semaglutide 2.4 mg safely and effectively produced clinically significant weight loss in all subgroups based on age, sex, race, glycemia, renal function and anthropometric categories. Furthermore, the weight loss was sustained over 4 years during the trial.

Trial design and participants

The current work complies with all relevant ethical regulations and reports a prespecified analysis of the randomized, double-blind, placebo-controlled SELECT trial ( NCT03574597 ), details of which have been reported in papers describing study design and rationale 32 , baseline characteristics 24 and the primary outcome 21 . SELECT evaluated once-weekly subcutaneous semaglutide 2.4 mg versus placebo to reduce the risk of major adverse cardiac events (a composite endpoint comprising CV death, nonfatal myocardial infarction or nonfatal stroke) in individuals with established CVD and overweight or obesity, without diabetes. The protocol for SELECT was approved by national and institutional regulatory and ethical authorities in each participating country. All patients provided written informed consent before beginning any trial-specific activity. Eligible patients were aged ≥45 years, with a BMI of ≥27 kg m − 2 and established CVD defined as at least one of the following: prior myocardial infarction, prior ischemic or hemorrhagic stroke, or symptomatic peripheral artery disease. Additional inclusion and exclusion criteria can be found elsewhere 32 .

Human participants research

The trial protocol was designed by the trial sponsor, Novo Nordisk, and the academic Steering Committee. A global expert panel of physician leaders in participating countries advised on regional operational issues. National and institutional regulatory and ethical authorities approved the protocol, and all patients provided written informed consent.

Study intervention and patient management

Patients were randomly assigned in a double-blind manner and 1:1 ratio to receive once-weekly subcutaneous semaglutide 2.4 mg or placebo. The starting dose was 0.24 mg once weekly, with dose increases every 4 weeks (to doses of 0.5, 1.0, 1.7 and 2.4 mg per week) until the target dose of 2.4 mg was reached after 16 weeks. Patients who were unable to tolerate dose escalation due to AEs could be managed by extension of dose-escalation intervals, treatment pauses or maintenance at doses below the 2.4 mg per week target dose. Investigators were allowed to reduce the dose of study product if tolerability issues arose. Investigators were provided with guidelines for, and encouraged to follow, evidence-based recommendations for medical treatment and lifestyle counseling to optimize management of underlying CVD as part of the standard of care. The lifestyle counseling was not targeted at weight loss. Additional intervention descriptions are available 32 .

Sex, race, body weight, height and WC measurements

Sex and race were self-reported. Body weight was measured without shoes and only wearing light clothing; it was measured on a digital scale and recorded in kilograms or pounds (one decimal with a precision of 0.1 kg or lb), with preference for using the same scale throughout the trial. The scale was calibrated yearly as a minimum unless the manufacturer certified that calibration of the weight scales was valid for the lifetime of the scale. Height was measured without shoes in centimeters or inches (one decimal with a precision of 0.1 cm or inches). At screening, BMI was calculated by the electronic case report form. WC was defined as the abdominal circumference located midway between the lower rib margin and the iliac crest. Measures were obtained in a standing position with a nonstretchable measuring tape and to the nearest centimeter or inch. The patient was asked to breathe normally. The tape touched the skin but did not compress soft tissue, and twists in the tape were avoided.

The following endpoints relevant to this paper were assessed at randomization (week 0) to years 2, 3 and 4: change in body weight (%); proportion achieving weight loss ≥5%, ≥10%, ≥15% and ≥20%; change in WC (cm); and percentage change in WHtR (cm cm −1 ). Improvement in BMI category (defined as being in a lower BMI class) was assessed at week 104 compared with baseline according to BMI classes: healthy (BMI <25 kg m − 2 ), overweight (25 to <30 kg m − 2 ), class I obesity (30 to <35 kg m − 2 ), class II obesity (35 to <40 kg m − 2 ) and class III obesity (≥40 kg m − 2 ). The proportions of individuals with BMI <35 or ≥35 kg m − 2 who achieved sex- and race-specific cutoff points for WC (indicating increased metabolic risk) were evaluated at week 104. The WC cutoff points were as follows: Asian women <80 cm, non-Asian women <88 cm, Asian men <88 cm and non-Asian men <102 cm.

Overall, 97.1% of the semaglutide group and 96.8% of the placebo group completed the trial. During the study, 30.6% of those assigned to semaglutide did not complete drug treatment, compared with 27.0% for placebo.

Statistical analysis

The statistical analyses for the in-trial period were based on the intention-to-treat principle and included all randomized patients irrespective of adherence to semaglutide or placebo or changes to background medications. Continuous endpoints were analyzed using an analysis of covariance model with treatment as a fixed factor and baseline value of the endpoint as a covariate. Missing data at the landmark visit, for example, week 104, were imputed using a multiple imputation model and done separately for each treatment arm and included baseline value as a covariate and fit to patients having an observed data point (irrespective of adherence to randomized treatment) at week 104. The fit model is used to impute values for all patients with missing data at week 104 to create 500 complete data sets. Rubin’s rules were used to combine the results. Estimated means are provided with s.e.m., and estimated treatment differences are provided with 95% CI. Binary endpoints were analyzed using logistic regression with treatment and baseline value as a covariate, where missing data were imputed by first using multiple imputation as described above and then categorizing the imputed data according to the endpoint, for example, body weight percentage change at week 104 of <0%. Subgroup analyses for continuous and binary endpoints also included the subgroup and interaction between treatment and subgroup as fixed factors. Because some patients in both arms continued to be followed but were off treatment, we also analyzed weight loss by first on-treatment group (observation period until first time being off treatment for >35 days) to assess a more realistic picture of weight loss in those adhering to treatment. CIs were not adjusted for multiplicity and should therefore not be used to infer definitive treatment effects. All statistical analyses were performed with SAS software, version 9.4 TS1M5 (SAS Institute).

Reporting summary

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

Data availability

Data will be shared with bona fide researchers who submit a research proposal approved by the independent review board. Individual patient data will be shared in data sets in a deidentified and anonymized format. Information about data access request proposals can be found at https://www.novonordisk-trials.com/ .

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Acknowledgements

Editorial support was provided by Richard Ogilvy-Stewart of Apollo, OPEN Health Communications, and funded by Novo Nordisk A/S, in accordance with Good Publication Practice guidelines ( www.ismpp.org/gpp-2022 ).

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Donna H. Ryan

Department of Internal Medicine/Endocrinology and Peter O’ Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA

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Contributions

D.H.R., I.L. and S.E.K. contributed to the study design. D.B.H., I.L., D.D., A.K., S.M.M., A.P.v.B., C.C. and J.P.H.W. were study investigators. D.B.H., I.L., D.D., A.K., S.M.M., A.P.v.B., C.C. and J.P.H.W. enrolled patients. D.H.R. was responsible for data analysis and manuscript preparation. All authors contributed to data interpretation, review, revisions and final approval of the manuscript.

Corresponding author

Correspondence to Donna H. Ryan .

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Competing interests.

D.H.R. declares having received consulting honoraria from Altimmune, Amgen, Biohaven, Boehringer Ingelheim, Calibrate, Carmot Therapeutics, CinRx, Eli Lilly, Epitomee, Gila Therapeutics, IFA Celtics, Novo Nordisk, Pfizer, Rhythm, Scientific Intake, Wondr Health and Zealand Pharma; she declares she received stock options from Calibrate, Epitomee, Scientific Intake and Xeno Bioscience. I.L. declares having received research funding (paid to institution) from Novo Nordisk, Sanofi, Mylan and Boehringer Ingelheim. I.L. received advisory/consulting fees and/or other support from Altimmune, AstraZeneca, Bayer, Biomea, Boehringer Ingelheim, Carmot Therapeutics, Cytoki Pharma, Eli Lilly, Intercept, Janssen/Johnson & Johnson, Mannkind, Mediflix, Merck, Metsera, Novo Nordisk, Pharmaventures, Pfizer, Regeneron, Sanofi, Shionogi, Structure Therapeutics, Target RWE, Terns Pharmaceuticals, The Comm Group, Valeritas, WebMD and Zealand Pharma. J.D. declares having received consulting honoraria from Amgen, Boehringer Ingelheim, Merck, Pfizer, Aegerion, Novartis, Sanofi, Takeda, Novo Nordisk and Bayer, and research grants from British Heart Foundation, MRC (UK), NIHR, PHE, MSD, Pfizer, Aegerion, Colgate and Roche. S.E.K. declares having received consulting honoraria from ANI Pharmaceuticals, Boehringer Ingelheim, Eli Lilly, Merck, Novo Nordisk and Oramed, and stock options from AltPep. B.B. declares having received honoraria related to participation on this trial and has no financial conflicts related to this publication. H.M.C. declares being a stockholder and serving on an advisory panel for Bayer; receiving research grants from Chief Scientist Office, Diabetes UK, European Commission, IQVIA, Juvenile Diabetes Research Foundation and Medical Research Council; serving on an advisory board and speaker’s bureau for Novo Nordisk; and holding stock in Roche Pharmaceuticals. C.C. declares having received consulting honoraria from Novo Nordisk, Eli Lilly, Merck, Brace Pharma and Eurofarma. D.D. declares having received consulting honoraria from Novo Nordisk, Eli Lilly, Boehringer Ingelheim and AstraZeneca, and received research grants through his affiliation from Novo Nordisk, Eli Lilly, Boehringer Ingelheim and Rhythm. D.B.H. declares having received research grants through her academic affiliation from Novo Nordisk and Eli Lilly, and advisory/consulting honoraria from Novo Nordisk, Eli Lilly and Gelesis. A.K. declares having received research grants through his affiliation from Novo Nordisk and Pharmaserve Lilly, and consulting honoraria from Pharmaserve Lilly, Sanofi-Aventis, Novo Nordisk, MSD, AstraZeneca, ELPEN Pharma, Boehringer Ingelheim, Galenica Pharma, Epsilon Health and WinMedica. A.M.L. declares having received honoraria from Novo Nordisk, Eli Lilly, Akebia Therapeutics, Ardelyx, Becton Dickinson, Endologix, FibroGen, GSK, Medtronic, Neovasc, Provention Bio, ReCor, BrainStorm Cell Therapeutics, Alnylam and Intarcia for consulting activities, and research funding to his institution from AbbVie, Esperion, AstraZeneca, CSL Behring, Novartis and Eli Lilly. S.M.M. declares having received consulting honoraria from Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Daichii-Sankyo, esanum, Gilead, Ipsen, Eli Lilly, Novartis, Novo Nordisk, Sandoz and Sanofi; he declares he received research grants from AstraZeneca, Eli Lilly and Novo Nordisk. J.P. declares having received consulting honoraria from Altimmune, Amgen, Esperion, Merck, MJH Life Sciences, Novartis and Novo Nordisk; he has received a grant, paid to his institution, from Boehringer Ingelheim and holds the position of Director, Preventive Cardiology, at Brigham and Women’s Hospital. A.P.v.B. is contracted via the University of Groningen (no personal payment) to undertake consultancy for Novo Nordisk, Eli Lilly and Boehringer Ingelheim. J.P.H.W. is contracted via the University of Liverpool (no personal payment) to undertake consultancy for Altimmune, AstraZeneca, Boehringer Ingelheim, Cytoki, Eli Lilly, Napp, Novo Nordisk, Menarini, Pfizer, Rhythm Pharmaceuticals, Sanofi, Saniona, Tern Pharmaceuticals, Shionogi and Ysopia. J.P.H.W. also declares personal honoraria/lecture fees from AstraZeneca, Boehringer Ingelheim, Medscape, Napp, Menarini, Novo Nordisk and Rhythm. R.F.K. declares having received consulting honoraria from Novo Nordisk, Weight Watchers, Eli Lilly, Boehringer Ingelheim, Pfizer, Structure and Altimmune. E.B., G.K.H., O.K.J. and T.K.O. are employees of Novo Nordisk A/S.

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Extended data

Extended data fig. 1 effect of semaglutide treatment or placebo on waist circumference from baseline to week 104 by subgroups..

Data from the in-trial period. N  = 17,604. P values represent test of no interaction effect. P values are two-sided and not adjusted for multiplicity. The dots show estimated treatment differences and the error bars show 95% confidence intervals. Details of the statistical models are available in Methods . BMI, body mass index; CI, confidence interval; CV, cardiovascular; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; ETD, estimated treatment difference; HbA1c, glycated hemoglobin; MI, myocardial infarction; PAD, peripheral artery disease; sema, semaglutide.

Supplementary information

Reporting summary, supplementary tables 1 and 2.

Supplementary Table 1. Baseline characteristics by BMI class. Data are represented as number and percentage of patients. Renal function categories were based on the eGFR as per Chronic Kidney Disease Epidemiology Collaboration. Albuminuria categories were based on UACR. Smoking was defined as smoking at least one cigarette or equivalent daily. The category ‘Other’ for CV inclusion criteria includes patients where it is unknown if the patient fulfilled only one or several criteria and patients who were randomized in error and did not fulfill any criteria. Supplementary Table 2. SAEs according to baseline BMI category. P value: two-sided P value from Fisher’s exact test for test of no difference.

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Ryan, D.H., Lingvay, I., Deanfield, J. et al. Long-term weight loss effects of semaglutide in obesity without diabetes in the SELECT trial. Nat Med (2024). https://doi.org/10.1038/s41591-024-02996-7

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Received : 01 March 2024

Accepted : 12 April 2024

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DOI : https://doi.org/10.1038/s41591-024-02996-7

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