a case study in obesity

• Open access
• Published: 21 June 2021

The lived experience of people with obesity: study protocol for a systematic review and synthesis of qualitative studies

• Emma Farrell   ORCID: orcid.org/0000-0002-7780-9428 1 ,
• Marta Bustillo 2 ,
• Carel W. le Roux 3 ,
• Joe Nadglowski 4 ,
• Eva Hollmann 1 &
• Deirdre McGillicuddy 1  

Systematic Reviews volume  10 , Article number:  181 ( 2021 ) Cite this article

6547 Accesses

9 Altmetric

Metrics details

Obesity is a prevalent, complex, progressive and relapsing chronic disease characterised by abnormal or excessive body fat that impairs health and quality of life. It affects more than 650 million adults worldwide and is associated with a range of health complications. Qualitative research plays a key role in understanding patient experiences and the factors that facilitate or hinder the effectiveness of health interventions. This review aims to systematically locate, assess and synthesise qualitative studies in order to develop a more comprehensive understanding of the lived experience of people with obesity.

This is a protocol for a qualitative evidence synthesis of the lived experience of people with obesity. A defined search strategy will be employed in conducting a comprehensive literature search of the following databases: PubMed, Embase, PsycInfo, PsycArticles and Dimensions (from 2011 onwards). Qualitative studies focusing on the lived experience of adults with obesity (BMI >30) will be included. Two reviewers will independently screen all citations, abstracts and full-text articles and abstract data. The quality of included studies will be appraised using the critical appraisal skills programme (CASP) criteria. Thematic synthesis will be conducted on all of the included studies. Confidence in the review findings will be assessed using GRADE CERQual.

The findings from this synthesis will be used to inform the EU Innovative Medicines Initiative (IMI)-funded SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) study. The objective of SOPHIA is to optimise future obesity treatment and stimulate a new narrative, understanding and vocabulary around obesity as a set of complex and chronic diseases. The findings will also be useful to health care providers and policy makers who seek to understand the experience of those with obesity.

Systematic review registration

PROSPERO CRD42020214560 .

Peer Review reports

Obesity is a complex chronic disease in which abnormal or excess body fat (adiposity) impairs health and quality of life, increases the risk of long-term medical complications and reduces lifespan [ 1 ]. Operationally defined in epidemiological and population studies as a body mass index (BMI) greater than or equal to 30, obesity affects more than 650 million adults worldwide [ 2 ]. Its prevalence has almost tripled between 1975 and 2016, and, globally, there are now more people with obesity than people classified as underweight [ 2 ].

Obesity is caused by the complex interplay of multiple genetic, metabolic, behavioural and environmental factors, with the latter thought to be the proximate factor which enabled the substantial rise in the prevalence of obesity in recent decades [ 3 , 4 ]. This increased prevalence has resulted in obesity becoming a major public health issue with a resulting growth in health care and economic costs [ 5 , 6 ]. At a population level, health complications from excess body fat increase as BMI increases [ 7 ]. At the individual level, health complications occur due to a variety of factors such as distribution of adiposity, environment, genetic, biologic and socioeconomic factors [ 8 ]. These health complications include type 2 diabetes [ 9 ], gallbladder disease [ 10 ] and non-alcoholic fatty liver disease [ 11 ]. Excess body fat can also place an individual at increased cardiometabolic and cancer risk [ 12 , 13 , 14 ] with an estimated 20% of all cancers attributed to obesity [ 15 ].

Although first recognised as a disease by the American Medical Association in 2013 [ 16 ], the dominant cultural narrative continues to present obesity as a failure of willpower. People with obesity are positioned as personally responsible for their weight. This, combined with the moralisation of health behaviours and the widespread association between thinness, self-control and success, has resulted in those who fail to live up to this cultural ideal being subject to weight bias, stigma and discrimination [ 17 , 18 , 19 ]. Weight bias, stigma and discrimination have been found to contribute, independent of weight or BMI, to increased morbidity or mortality [ 20 ].

Thomas et al. [ 21 ] highlighted, more than a decade ago, the need to rethink how we approach obesity so as not to perpetuate damaging stereotypes at a societal level. Obesity research then, as now, largely focused on measurable outcomes and quantifiable terms such as body mass index [ 22 , 23 ]. Qualitative research approaches play a key role in understanding patient experiences, how factors facilitate or hinder the effectiveness of interventions and how the processes of interventions are perceived and implemented by users [ 24 ]. Studies adopting qualitative approaches have been shown to deliver a greater depth of understanding of complex and socially mediated diseases such as obesity [ 25 ]. In spite of an increasing recognition of the integral role of patient experience in health research [ 25 , 26 ], the voices of patients remain largely underrepresented in obesity research [ 27 , 28 ].

Systematic reviews and syntheses of qualitative studies are recognised as a useful contribution to evidence and policy development [ 29 ]. To the best of the authors’ knowledge, this will be the first systematic review and synthesis of qualitative studies focusing on the lived experience of people with obesity. While systematic reviews have been carried out on patient experiences of treatments such as behavioural management [ 30 ] and bariatric surgery [ 31 ], this review and synthesis will be the first to focus on the experience of living with obesity rather than patient experiences of particular treatments or interventions. This focus represents a growing awareness that ‘patients have a specific expertise and knowledge derived from lived experience’ and that understanding lived experience can help ‘make healthcare both effective and more efficient’ [ 32 ].

This paper outlines a protocol for the systematic review of qualitative studies based on the lived experience of people with obesity. The findings of this review will be synthesised in order to develop an overview of the lived experience of patients with obesity. It will look, in particular, at patient concerns around the risks of obesity and their aspirations for response to obesity treatment.

The review protocol has been registered within the PROSPERO database (registration number: CRD42020214560) and is being reported in accordance with the reporting guidance provided in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement [ 33 , 34 ] (see checklist in Additional file  1 ).

Information sources and search strategy

The primary source of literature will be a structured search of the following electronic databases (from January 2011 onwards—to encompass the increase in research focused on patient experience observed over the last 10 years): PubMed, Embase, PsycInfo, PsycArticles and Dimensions. There is no methodological agreement as to how many search terms or databases out to be searched as part of a ‘good’ qualitative synthesis (Toye et al. [ 35 ]). However, the breadth and depth of the search terms, the inclusion of clinical and personal language and the variety within the selected databases, which cover areas such as medicine, nursing, psychology and sociology, will position this qualitative synthesis as comprehensive. Grey literature will not be included in this study as its purpose is to conduct a comprehensive review of peer-reviewed primary research. The study’s patient advisory board will be consulted at each stage of the review process, and content experts and authors who are prolific in the field will be contacted. The literature searches will be designed and conducted by the review team which includes an experienced university librarian (MB) following the methodological guidance of chapter two of the JBI Manual for Evidence Synthesis [ 36 ]. The search will include a broad range of terms and keywords related to obesity and qualitative research. A full draft search strategy for PubMed is provided in Additional file  2 .

Eligibility criteria

Studies based on primary data generated with adults with obesity (operationally defined as BMI >30) and focusing on their lived experience will be eligible for inclusion in this synthesis (Table  1 ). The context can include any country and all three levels of care provision (primary, secondary and tertiary). Only peer-reviewed, English language, articles will be included. Studies adopting a qualitative design, such as phenomenology, grounded theory or ethnography, and employing qualitative methods of data collection and analysis, such as interviews, focus groups, life histories and thematic analysis, will be included. Publications with a specific focus, for example, patient’s experience of bariatric surgery, will be included, as well as studies adopting a more general view of the experience of obesity.

Screening and study selection process

Search results will be imported to Endnote X9, and duplicate entries will be removed. Covidence [ 38 ] will be used to screen references with two reviewers (EF and EH) removing entries that are clearly unrelated to the research question. Titles and abstracts will then be independently screened by two reviewers (EF and EH) according to the inclusion criteria (Table  1 ). Any disagreements will be resolved through a third reviewer (DMcG). This layer of screening will determine which publications will be eligible for independent full-text review by two reviewers (EF and EH) with disagreements again being resolved by a third reviewer (DMcG).

Data extraction

Data will be extracted independently by two researchers (EF and EH) and combined in table format using the following headings: author, year, title, country, research aims, participant characteristics, method of data collection, method of data analysis, author conclusions and qualitative themes. In the case of insufficient or unclear information in a potentially eligible article, the authors will be contacted by email to obtain or confirm data, and a timeframe of 3 weeks to reply will be offered before article exclusion.

Quality appraisal of included studies

This qualitative synthesis will facilitate the development of a conceptual understanding of obesity and will be used to inform the development of policy and practice. As such, it is important that the studies included are themselves of suitable quality. The methodological quality of all included studies will be assessed using the critical appraisal skills programme (CASP) checklist, and studies that are deemed of insufficient quality will be excluded. The CASP checklist for qualitative research comprises ten questions that cover three main issues: Are the results of the study under review valid? What are the results? Will the results help locally? Two reviewers (EF and EH) will independently evaluate each study using the checklist with a third and fourth reviewer (DMcG and MB) available for consultation in the event of disagreement.

Data synthesis

The data generated through the systematic review outlined above will be synthesised using thematic synthesis as described by Thomas and Harden [ 39 ]. Thematic synthesis enables researchers to stay ‘close’ to the data of primary studies, synthesise them in a transparent way and produce new concepts and hypotheses. This inductive approach is useful for drawing inference based on common themes from studies with different designs and perspectives. Thematic synthesis is made up of a three-step process. Step one consists of line by line coding of the findings of primary studies. The second step involves organising these ‘free codes’ into related areas to construct ‘descriptive’ themes. In step three, the descriptive themes that emerged will be iteratively examined and compared to ‘go beyond’ the descriptive themes and the content of the initial studies. This step will generate analytical themes that will provide new insights related to the topic under review.

Data will be coded using NVivo 12. In order to increase the confirmability of the analysis, studies will be reviewed independently by two reviewers (EF and EH) following the three-step process outlined above. This process will be overseen by a third reviewer (DMcG). In order to increase the credibility of the findings, an overview of the results will be brought to a panel of patient representatives for discussion. Direct quotations from participants in the primary studies will be italicised and indented to distinguish them from author interpretations.

Assessment of confidence in the review findings

Confidence in the evidence generated as a result of this qualitative synthesis will be assessed using the Grading of Recommendations Assessment, Development and Evaluation Confidence in Evidence from Reviews of Qualitative Research (GRADE CERQual) [ 40 ] approach. Four components contribute to the assessment of confidence in the evidence: methodological limitations, relevance, coherence and adequacy of data. The methodological limitations of included studies will be examined using the CASP tool. Relevance assesses the degree to which the evidence from the primary studies applies to the synthesis question while coherence assesses how well the findings are supported by the primary studies. Adequacy of data assesses how much data supports a finding and how rich this data is. Confidence in the evidence will be independently assessed by two reviewers (EF and EH), graded as high, moderate or low, and discussed collectively amongst the research team.

Reflexivity

For the purposes of transparency and reflexivity, it will be important to consider the findings of the qualitative synthesis and how these are reached, in the context of researchers’ worldviews and experiences (Larkin et al, 2019). Authors have backgrounds in health science (EF and EH), education (DMcG and EF), nursing (EH), sociology (DMcG), philosophy (EF) and information science (MB). Prior to conducting the qualitative synthesis, the authors will examine and discuss their preconceptions and beliefs surrounding the subject under study and consider the relevance of these preconceptions during each stage of analysis.

Dissemination of findings

Findings from the qualitative synthesis will be disseminated through publications in peer-reviewed journals, a comprehensive and in-depth project report and presentation at peer-reviewed academic conferences (such as EASO) within the field of obesity research. It is also envisaged that the qualitative synthesis will contribute to the shared value analysis to be undertaken with key stakeholders (including patients, clinicians, payers, policy makers, regulators and industry) within the broader study which seeks to create a new narrative around obesity diagnosis and treatment by foregrounding patient experiences and voice(s). This synthesis will be disseminated to the 29 project partners through oral presentations at management board meetings and at the general assembly. It will also be presented as an educational resource for clinicians to contribute to an improved understanding of patient experience of living with obesity.

Obesity is a complex chronic disease which increases the risk of long-term medical complications and a reduced quality of life. It affects a significant proportion of the world’s population and is a major public health concern. Obesity is the result of a complex interplay of multiple factors including genetic, metabolic, behavioural and environmental factors. In spite of this complexity, obesity is often construed in simple terms as a failure of willpower. People with obesity are subject to weight bias, stigma and discrimination which in themselves result in increased risk of mobility or mortality. Research in the area of obesity has tended towards measurable outcomes and quantitative variables that fail to capture the complexity associated with the experience of obesity. A need to rethink how we approach obesity has been identified—one that represents the voices and experiences of people living with obesity. This paper outlines a protocol for the systematic review of available literature on the lived experience of people with obesity and the synthesis of these findings in order to develop an understanding of patient experiences, their concerns regarding the risks associated with obesity and their aspirations for response to obesity treatment. Its main strengths will be the breadth of its search remit—focusing on the experiences of people with obesity rather than their experience of a particular treatment or intervention. It will also involve people living with obesity and its findings disseminated amongst the 29 international partners SOPHIA research consortium, in peer reviewed journals and at academic conferences. Just as the study’s broad remit is its strength, it is also a potential challenge as it is anticipated that searchers will generate many thousands of results owing to the breadth of the search terms. However, to the best of the authors’ knowledge, this will be the first systematic review and synthesis of its kind, and its findings will contribute to shaping the optimisation of future obesity understanding and treatment.

Availability of data and materials

Not applicable.

Abbreviations

Body mass index

Critical appraisal skills programme

Grading of Recommendations Assessment, Development and Evaluation Confidence in Evidence from Reviews of Qualitative Research

Innovative Medicines Initiative

Medical Subject Headings

Population, phenomenon of interest, context, study type

Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy

Wharton S, Lau DCW, Vallis M, Sharma AM, Biertho L, Campbell-Scherer D, et al. Obesity in adults: a clinical practice guideline. Can Med Assoc J. 2020;192(31):E875–91. https://doi.org/10.1503/cmaj.191707 .

Article   Google Scholar  

World Health Organisation. Fact sheet: obesity and overweight. Geneva: World Health Organisation; 2020.

Google Scholar  

Mechanick J, Hurley D, Garvey W. Adiposity-based chronic disease as a new diagnostic term: the American Association of Clinical Endocrinologists and American College Of Endocrinology position statement. Endocrine Pract. 2017;23(3):372–8. https://doi.org/10.4158/EP161688.PS .

Garvey W, Mechanick J. Proposal for a scientifically correct and medically actionable disease classification system (ICD) for obesity. Obesity. 2020;28(3):484–92. https://doi.org/10.1002/oby.22727 .

Article   PubMed   Google Scholar  

Biener A, Cawley J, Meyerhoefer C. The high and rising costs of obesity to the US health care system. J Gen Intern Med. 2017;32(Suppl 1):6–8. https://doi.org/10.1007/s11606-016-3968-8 .

Article   PubMed   PubMed Central   Google Scholar  

Department of Health and Social Care. Healthy lives, healthy people: a call to action on obesity in England. London: Department of Health and Social Care; 2011.

Di Angelantonio E, Bhupathiraju SN, Wormser D, Gao P, Kaptoge S, de Gonzalez AB, et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet. 2016;388(10046):776–86. https://doi.org/10.1016/S0140-6736(16)30175-1 .

Sharma AM. M, M, M & M: a mnemonic for assessing obesity. Obesity Reviews. 2010;11(11):808–9. https://doi.org/10.1111/j.1467-789X.2010.00766.x .

Article   PubMed   CAS   Google Scholar  

Asnawi A, Peeters A, de Courten M, Stoelwinder J. The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabetes Res Clin Pract 2010;89:309-19. Diab Res Clin Pract. 2010;89:309–19.

Dagfinn A, Teresa N, Lars JV. Body mass index, abdominal fatness and the risk of gallbladder disease. 2015;30(9):1009.

Longo M, Zatterale F, Naderi J, Parrillo L, Formisano P, Raciti GA, et al. Adipose tissue dysfunction as determinant of obesity-associated metabolic complications. Int J Mol Sci. 2019;20(9).

Fontaine KR, Redden DT, Wang C, Westfall AO, Allison DB. Years of life lost due to obesity. 2003;289(2):187-193.

Grover SA, Kaouache M, Rempel P, Joseph L, Dawes M, Lau DCW, et al. Years of life lost and healthy life-years lost from diabetes and cardiovascular disease in overweight and obese people: a modelling study. 2015;3(2):114-122.

Ackerman S, Blackburn O, Marchildon F, Cohen P. Insights into the link between obesity and cancer. Curr Obes Rep. 2017;6(2):195–203. https://doi.org/10.1007/s13679-017-0263-x .

Wolin K, Carson K, Colditz G. Obesity and cancer. Oncol. 2010;15(6):556–65. https://doi.org/10.1634/theoncologist.2009-0285 .

Resolution 420: Recognition of obesity as a disease [press release]. 05/16/13 2013.

Brownell KD. Personal responsibility and control over our bodies: when expectation exceeds reality. 1991;10(5):303-10.

Puhl RM, Latner JD, O'Brien K, Luedicke J, Danielsdottir S, Forhan M. A multinational examination of weight bias: predictors of anti-fat attitudes across four countries. 2015;39(7):1166-1173.

Browne NT. Weight bias, stigmatization, and bullying of obese youth. 2012;7(3):107-15.

Sutin AR, Stephan Y, Terracciano A. Weight discrimination and risk of mortality. 2015;26(11):1803-11.

Thomas SL, Hyde J, Karunaratne A, Herbert D, Komesaroff PA. Being “fat” in today’s world: a qualitative study of the lived experiences of people with obesity in Australia. 2008;11(4):321-30.

Ogden K, Barr J, Rossetto G, Mercer J. A “messy ball of wool”: a qualitative study of the dimensions of the lived experience of obesity. 2020;8(1):1-14.

Ueland V, Furnes B, Dysvik E, R¯rtveit K. Living with obesity-existential experiences. 2019;14(1):1-12.

Avenell A, Robertson C, Skea Z, Jacobsen E, Boyers D, Cooper D, et al. Bariatric surgery, lifestyle interventions and orlistat for severe obesity: the REBALANCE mixed-methods systematic review and economic evaluation. 2018;22(68).

The PLoS Medicine Editors. Qualitative research: understanding patients’ needs and experiences. Plos Med. 2007;4(8):1283–4.

Boulton M, Fitzpatrick R. Qualitative methods for assessing health care doi:10.1136/qshc.3.2.107. Qual Health Care. 1994;3:107–13.

Johnstone J, Herredsberg C, Lacy L, Bayles P, Dierking L, Houck A, et al. What I wish my doctor really knew: the voices of patients with obesity. Ann Fam Med. 2020;18(2):169–71. https://doi.org/10.1370/afm.2494 .

Brown I, Thompson J, Tod A, Jones G. Primary care support for tackling obesity: a qualitative study of the perceptions of obese patients. Br J Gen Pract. 2006;56(530):666–72.

PubMed   PubMed Central   Google Scholar  

Brown I, Gould J. Qualitative studies of obesity: a review of methodology. Health. 2013;5(8A3):69–80.

Garip G, Yardley L. A synthesis of qualitative research on overweight and obese people’s views and experiences of weight management. Clin Obes. 2011;1(2-3):10–126.

Coulman K, MacKichan F, Blazeby J, Owen-Smith A. Patient experiences of outcomes of bariatric surgery: a systematic review and qualitative synthesis. Obes Rev. 2017;18(5):547–59. https://doi.org/10.1111/obr.12518 .

European Patients’ Forum. “Patients’ Perceptions of Quality in Healthcare”: Report of a survey conducted by EPF in 2016 Brussels: European Patients’ Forum; 2017.

Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4(1):1. https://doi.org/10.1186/2046-4053-4-1 .

Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015;349(jan02 1):g7647. https://doi.org/10.1136/bmj.g7647 .

Toye F, et al. Meta-ethnography 25 years on: challenges and insights for synthesising a large number of qualitative studies. BMC Med Res Methodol. 2014;14(80).

Lockwood C, Porrit K, Munn Z, Rittenmeyer L, Salmond S, Bjerrum M, et al. Chapter 2: Systematic reviews of qualitative evidence. In: Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis: JBI; 2020, doi: https://doi.org/10.46658/JBIMES-20-03 .

Methley AM, et al. PICO, PICOS and SPIDER: a comparison study of spcificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Services Res. 2014;14.

Covidence. Cochrane Community; 2020. Available from: https://www.covidence.org .

Thomas J, Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med Res Methodol. 2008;8(1):45. https://doi.org/10.1186/1471-2288-8-45 .

Lewin S, Booth A, Glenton C, Munthe-Kaas H, Rashidian A, Wainwright M, et al. Applying GRADE-CERQual to qualitative evidence synthesis findings: introduction to the series. Implement Sci. 2018;13(1):2. https://doi.org/10.1186/s13012-017-0688-3 .

Download references

Acknowledgements

Any amendments made to this protocol when conducting the study will be outlined in PROSPERO and reported in the final manuscript.

This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 875534. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and T1D Exchange, JDRF and Obesity Action Coalition. The funding body had no role in the design of the study and will not have a role in collection, analysis and interpretation of data or in writing the manuscript.

Author information

Authors and affiliations.

School of Education, University College Dublin, Belfield, Dublin 4, Ireland

Emma Farrell, Eva Hollmann & Deirdre McGillicuddy

University College Dublin Library, Dublin, Ireland

Marta Bustillo

Diabetes Complications Research Centre, University College Dublin, Dublin, Ireland

Carel W. le Roux

Obesity Action Coalition, Tampa, USA

Joe Nadglowski

You can also search for this author in PubMed   Google Scholar

Contributions

EF conceptualised and designed the protocol with input from DMcG and MB. EF drafted the initial manuscript. EF and MB defined the concepts and search items with input from DmcG, CleR and JN. MB and EF designed and executed the search strategy. DMcG, CleR, JN and EH provided critical insights and reviewed and revised the protocol. All authors have approved and contributed to the final written manuscript.

Corresponding author

Correspondence to Emma Farrell .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:..

PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist: recommended items to address in a systematic review protocol*.

Additional file 2: Table 1

. Search PubMed search string.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Farrell, E., Bustillo, M., le Roux, C.W. et al. The lived experience of people with obesity: study protocol for a systematic review and synthesis of qualitative studies. Syst Rev 10 , 181 (2021). https://doi.org/10.1186/s13643-021-01706-5

Download citation

Received : 28 October 2020

Accepted : 14 May 2021

Published : 21 June 2021

DOI : https://doi.org/10.1186/s13643-021-01706-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Lived experience
• Patient experience
• Obesity treatment
• Qualitative

Systematic Reviews

ISSN: 2046-4053

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

Case Examples

The role of psychologists and other behavioral health providers

Multicomponent behavioral treatment for obesity and overweight is often best provided by a team of healthcare professionals. A team may include a psychologist, physician, dietician, exercise specialist, nurse practitioner, or other professional. Which types of professionals should ideally be involved, and to what degree, depends on the needs and characteristics of the child or adolescent.

The following case examples focus on the role of psychologists (or other behavioral health providers), particularly at the early stages of treatment, rather than illustrating all aspects or stages of multicomponent behavioral treatment. These cases point to the need to consider such factors as the patient’s age, gender, socioeconomic status, ethnicity, and culture. Further, they demonstrate the relevance of psychosocial factors – such as the patient’s motivation, social support, family situation, and psychological symptoms (e.g., depression, anxiety, and executive function difficulties) – for understanding and addressing obesity and overweight.

These case examples were developed by Eleanor Mackey, PhD and Laura Kurzius, PhD of Children’s National Health System in Washington, DC. Each example describes an amalgamation of several patient presentations. None of these cases represents a specific patient.

Carmen, 6-year-old Latina girl

Carmen lived with her parents and grandmother. She was referred to a multidisciplinary weight management program due to concerns about her body mass index (BMI), which was at the 99th percentile for her age and gender.

Jason, 15-year-old white male

Jason lived with his mother and niece. He expressed a desire to be a healthier weight, but was having difficulty with managing his weight and had not been successful in a general weight management program.

Marcus, 11-year-old African-American boy

Marcus lived with his mother and two younger brothers and attended middle school. His body mass index (BMI) was at the 99th percentile for his age and gender. He had a very close relationship with his mother and did well in school, but he also needed additional support in completing tasks because of his diagnosis of ADHD.

June, 18-year-old biracial female

June, who lived with her parents, was a senior in high school with sporadic attendance. June was referred for psychotherapy and multicomponent behavioral weight treatment, with specific concerns focused on her difficulty making healthy eating choices and her low motivation to engage in physical activity.

CASE REPORT article

Clinical challenge: patient with severe obesity bmi 46 kg/m 2.

• Section of Endocrinology, Diabetes, Nutrition and Weight Management, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States

Obesity causes and exacerbates many disease processes and affects every organ system. Thus it is not surprising that clinical providers are often overwhelmed with the multitude of symptomatology upon initial presentation in patients with obesity. However, despite a “complicated medical history,” a systematic, organized approach in obesity medicine utilizes a personalized-tailored treatment strategy coupled with understanding of the disease state, presence of comorbidities, contraindications, side effects, and patient preferences. Here, we present the case of a young patient with Class 3b severe obesity, several obesity-related complications, and extensive psychological history. Through synergistic and additive treatments (behavioral/nutritional therapy combined with anti-obesity pharmacotherapy and concurrent enrollment in our bariatric surgery program), the patient was able to achieve significant −30.5% total body weight loss with improvement of metabolic parameters. Though these results are not typical of all patients, we must emphasize the need to encompass all available anti-obesity therapies (lifestyle, pharmacotherapy, medical devices, bariatric surgery in monotherapy or combination) in cases of refractory or severe obesity, as we do similarly for other disease modalities such as refractory hypertension or poorly controlled Type 2 diabetes that requires robust escalation in therapy.

Clinical Challenge

A 31 year old patient with a past medical history of Class 3 obesity BMI 46 kg/m 2 , Type 2 diabetes mellitus (A1c <5.7%, well controlled on metformin), polycystic ovarian syndrome, non-alcoholic steatosis of the liver, pulmonary and neurosarcoidosis on infliximab and methotrexate, and chronic worsening pain presents for weight management evaluation. She had a history of opioid use disorder due to the chronic pain, though in remission. She had been on several weight-promoting pain medications for symptom control, including gabapentin, duloxetine and nortriptyline. Contributing factors over the years to her weight gain also included her diagnosis of Bipolar Disorder with antipsychotic medication-induced weight gain (previously trialed aripiprazole, responded to lurasidone with decreasing efficiency, and now finally stable on paliperidone though weight gain promoting). Her highest adult weight was her current weight of 295 pounds with a lowest adult weight of 140 lbs. that pre-dated her Bipolar and sarcoidosis diagnoses several years ago. She had stable eating patterns, and often chose healthy meals such as hummus, vegetables, Greek salads, and lean meats, though had a weakness for sweet cravings. She engaged in structured gym exercise for 30 minutes three times per week despite the chronic pain. Recent stressors included her close aunt who had been diagnosed with cancer. She also suffered from insomnia and had been evaluated closely with sleep therapists and sleep hygiene specialists. Her polysomnogram was negative for sleep apnea.

What Would You Do Next?

A. Offer more aggressive intensive lifestyle therapy intervention

B. Trial of anti-obesity medication if option A above becomes ineffective

C. Metabolic and bariatric surgery only as anti-obesity medication would be contraindicated given her history of opioid use

D. Trial of anti-obesity medication for 3 months with concurrent referral to bariatric surgery

The patient depicted in the case has chronic, debilitating severe obesity classification with several inflammatory obesity-related comorbidities and other contributing etiology to her weight gain.

In regards to lifestyle intervention, the patient was started on a healthy low fat high fiber diet with increased consumption of vegetables, while minimizing intake of processed foods, added sugar, trans fats, and refined flours ( 1 ). Nutrient-dense whole foods prepared at home were encouraged. Acceptable macronutrient distribution range is 45–65% carbohydrates, 20–35% total fat of which <10% should be polyunsaturated fats, and 10–35% protein and amino acids 1 . However, obesity-related comorbidities such as type 2 diabetes mellitus, polycystic ovarian syndrome, and non-alcoholic steatosis of the liver suggesting features of insulin resistance need to be taken into consideration when implementing dietary modifications specific to this case. The patient's daily carbohydrate intake should be reduced to 40–50% to combat insulin resistance. Several studies have shown improvement in metabolic parameters and more rapid weight loss when a low carbohydrate diet was implemented initially in the first 3–6 months ( 2 , 3 ). At presentation, the patient's calculated daily protein intake was <20% of total daily intake and increasing her protein intake to 30% reduced her sweet cravings and increased satiety. In addition, she would benefit from at least 150 min per week of structured moderately intensive exercise as tolerated as recommended by The American College of Sports Medicine ( 4 ). Of note, the patient is also under significant stressors. Stress has been very strongly linked to hyperphagia, binging, and obesity ( 5 , 6 ). Stress management would also provide long-term strategies for emotional/stress eating should they arise. Her sleep has been adequately addressed by a specialist multidisciplinary team. Further, the patient was already under intense behavioral therapy given her underlying psychiatric illness. Early behavioral therapy intervention should be strongly considered in patients with adverse psychological factors, eating disorders and underlying psychiatric conditions that would otherwise impede their overall progress toward health goals. However, it may be difficult to promote more aggressive lifestyle intervention alone, especially in a patient with an advanced obesity disease staging who is already making strides to eat healthy and undergoing behavioral therapy.

Furthermore, the patient also meets criteria for initiation of anti-obesity pharmacotherapy (AOM): BMI >27 kg/m 2 plus the presence of one obesity-related comorbidity and/or BMI >30 kg/m 2 in conjunction with lifestyle intervention ( 7 , 8 ). Though the patient has a history of opioid use disorder, it is in remission and there is no active contraindication to AOM. The patient also does not have underlying heart disease, end-stage-renal disease, or acute angle glaucoma that would negate use of several AOM such as phentermine/topiramate, lorcaserin, and naltrexone/bupropion. Liraglutide 3.0 mg would be a first option given its double benefits in patients with severe obesity and diabetes ( 7 ) and other obesity-related comorbidities such as fatty liver ( 9 ) and polycystic ovarian disease ( 10 ). The medication is also generally well-tolerated and safe. Because anti-obesity medications can exert central effects in a patient with Bipolar Disorder, close monitoring and communication with the patient's psychiatrist would be critical. Because her BMI is already very elevated, clinically, both lifestyle changes and pharmacological treatment would be implemented together, rather than separately. Moreover, based on her current body mass index alone of 40 kg/m 2 , the patient meets National Institutes of Health consensus criteria for metabolic and bariatric surgery ( 11 ): BMI 35 kg/m 2 in the presence of at least one obesity-related comorbidity or BMI 40 kg/m 2 . Therefore, it would be prudent to discuss bariatric surgery in this patient given her disease severity.

The correct answer is D. The patient was actually started on AOM with concurrent referral to the institution's bariatric surgery program. Since the patient's insurance did not provide coverage for liraglutide 3.0 mg, she was alternatively prescribed a combination anti-obesity medication therapy (phentermine/topiramate) after discussion with her psychiatrist and other specialists. AOM were instrumental in improving the patient's overall hunger drive, cravings, and satiety. Despite being the best option for her at presentation, the patient was unwilling to undergo the bariatric procedure. Oftentimes, this may be the case in many patients until they consent to surgical intervention or have weight regain on non-surgical therapy. Future guidelines may need to be more definitive about earlier referral to bariatric surgery.

The patient continued AOM long-term, having lost 90 pounds over a 2 year time period ( Figure 1 ). Her BMI now is 28.7 kg/m 2 , weight 205 lbs. (reversed from Class 3 obesity, BMI 46 kg/m 2 , weight 295 lbs.) with improvement in quality of life and obesity-related comorbidities. Liver transaminases that were previously elevated in the context of fatty liver disease normalized along with return of regular menstrual cycles. In the process of losing weight with related attenuation in disease comorbidity and metabolic profile improvement, the patient's neurosarcoidosis continued to show remarkable recovery with stabilization of her mental health conditions and disability. Her specialists reported that this was the best she had been in many years. The patient lost −30.5% of her total body weight, which is typical weight loss achieved by metabolic and bariatric surgery means, through non-surgical intervention.

Figure 1 . Patient's weight graph derived from the electronic health record. The patient lost a total of 90 lbs. over a 2 year time period with adjunctive anti-obesity pharmacotherapy (phentermine/topiramate) in combination with behavioral and lifestyle intervention.

Though these results may not be usual for all patients, it is important to note that all treatment modalities (behavioral, lifestyle, pharmacological, and/or surgical whether as monotherapy or in combination) must be utilized for patients suffering with severe obesity and its devastating consequences on overall health and quality of life. Many of these patients present with complicated disease states and multiple comorbidities. Thus, important health targets include not only weight loss but treatment-enhanced double benefits leading to improvement of comorbidities.

Data Availability Statement

All datasets for this study were directly generated from the patient's electronic health record and are available upon request.

Informed Consent

Written informed consent to publish this case report was obtained from the patient.

Author Contributions

GS and CA contributed and edited the contents of this manuscript.

No external funding was provided for the creation of this manuscript.

Conflict of Interest

GS served as a consultant for Johnson and Johnson and advisor for Rhythm Pharmaceuticals. CA reports grants from Aspire Bariatrics, Myos, the Vela Foundation, the Dr. Robert C. and Veronica Atkins Foundation, Coherence Lab, Energesis, NIH, and PCORI, grants and personal fees from Orexigen, GI Dynamics, Takeda, personal fees from Nutrisystem, Zafgen, Sanofi-Aventis, NovoNordisk, Scientific Intake, Xeno Biosciences, Rhythm Pharmaceuticals, Eisai, EnteroMedics, Bariatrix Nutrition, and other from Science-Smart LLC, outside the submitted work.

Acknowledgments

We would like to thank the patient for permission to publish.

1. ^ http://www.nationalacademies.org/hmd/~/media/Files/ActivityFiles/Nutrition/DRI-Tables/8_MacronutrientSummary.pdf?la=en (accessed April 2, 2019).

1. Gardner CD, Trepanowski JF, Del Gobbo LC, Hauser ME, Rigdon J, Ioannidis JPA, et al. Effect of low-fat vs low-carbohydrate diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion: the DIETFITS randomized clinical trial. JAMA . (2018) 319:667–79. doi: 10.1001/jama.2018.0245

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Meng Y, Bai H, Wang S, Li Z, Wang Q, Chen L. Efficacy of low carbohydrate diet for type 2 diabetes mellitus management: a systematic review and meta-analysis of randomized controlled trials. Diabetes Res Clin Pract. (2017) 131:124–31. doi: 10.1016/j.diabres.2017.07.006

3. Jang EC, Jun DW, Lee SM, Cho YK, Ahn SB. Comparison of efficacy of low-carbohydrate and low-fat diet education programs in non-alcoholic fatty liver disease: A randomized controlled study. Hepatol Res. (2018) 48:E22–9. doi: 10.1111/hepr.12918

4. Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte MJ, Lee IM, et al. American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc. (2011) 43:1334–59. doi: 10.1249/MSS.0b013e318213fefb

5. Manna P, Jain SK. Obesity, oxidative stress, adipose tissue dysfunction, and the associated health risks: causes and therapeutic strategies. Metab Syndr Relat Disord. (2015) 13:423–44. doi: 10.1089/met.2015.0095

6. Razzoli M, Pearson C, Crow S, Bartolomucci A. Stress, overeating, and obesity: Insights from human studies and preclinical models. Neurosci Biobehav Rev. (2017) 76:154–62. doi: 10.1016/j.neubiorev.2017.01.026

7. Srivastava G, Apovian CM. Current pharmacotherapy for obesity. Nat Rev Endocrinol. (2018) 14:12–24. doi: 10.1038/nrendo.2017.122

8. Apovian CM, Aronne LJ, Bessesen DH, McDonnell ME, Murad MH, Pagotto U, et al. Pharmacological management of obesity: an endocrine Society clinical practice guideline. J Clin Endocrinol Metab. (2015) 100:342–62. doi: 10.1210/jc.2014-3415

9. Khoo J, Hsiang JC, Taneja R, Koo SH, Soon GH, Kam CJ, et al. Randomized trial comparing effects of weight loss by liraglutide with lifestyle modification in non-alcoholic fatty liver disease. Liver Int. (2019) 39:941–9. doi: 10.1111/liv.14065

10. Nylander M, Frossing S, Clausen HV, Kistorp C, Faber J, Skouby SO. Effects of liraglutide on ovarian dysfunction in polycystic ovary syndrome: a randomized clinical trial. Reprod Biomed Online. (2017) 35:121–7. doi: 10.1016/j.rbmo.2017.03.023

11. Clinical guidelines on the identification evaluation and treatment of overweight and obesity in adults–the evidence report. National Institutes of Health. Obes Res. (1998) 6(Suppl.2):51S–209S.

Google Scholar

Keywords: anti-obesity medications, weight loss drugs, combination therapy, bariatric surgery, lifestyle intervention

Citation: Srivastava G and Apovian CM (2019) Clinical Challenge: Patient With Severe Obesity BMI 46 kg/m 2 . Front. Endocrinol. 10:635. doi: 10.3389/fendo.2019.00635

Received: 30 April 2019; Accepted: 03 September 2019; Published: 02 October 2019.

Reviewed by:

Copyright © 2019 Srivastava and Apovian. 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: Gitanjali Srivastava, geet5sri@gmail.com

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.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Published: 23 September 2021

The genetics of obesity: from discovery to biology

• Ruth J. F. Loos   ORCID: orcid.org/0000-0002-8532-5087 1 , 2 , 3 , 4 &
• Giles S. H. Yeo   ORCID: orcid.org/0000-0001-8823-3615 5  

Nature Reviews Genetics volume  23 ,  pages 120–133 ( 2022 ) Cite this article

156k Accesses

447 Citations

484 Altmetric

Metrics details

• Disease genetics
• Endocrine system and metabolic diseases
• Genetic association study
• Genetic variation

The prevalence of obesity has tripled over the past four decades, imposing an enormous burden on people’s health. Polygenic (or common) obesity and rare, severe, early-onset monogenic obesity are often polarized as distinct diseases. However, gene discovery studies for both forms of obesity show that they have shared genetic and biological underpinnings, pointing to a key role for the brain in the control of body weight. Genome-wide association studies (GWAS) with increasing sample sizes and advances in sequencing technology are the main drivers behind a recent flurry of new discoveries. However, it is the post-GWAS, cross-disciplinary collaborations, which combine new omics technologies and analytical approaches, that have started to facilitate translation of genetic loci into meaningful biology and new avenues for treatment.

Similar content being viewed by others

A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes

Genome-wide discovery of genetic loci that uncouple excess adiposity from its comorbidities

Independent phenotypic plasticity axes define distinct obesity sub-types

Introduction.

Obesity is associated with premature mortality and is a serious public health threat that accounts for a large proportion of the worldwide non-communicable disease burden, including type 2 diabetes, cardiovascular disease, hypertension and certain cancers 1 , 2 . Mechanical issues resulting from substantially increased weight, such as osteoarthritis and sleep apnoea, also affect people’s quality of life 3 . The impact of obesity on communicable disease, in particular viral infection 4 , has recently been highlighted by the discovery that individuals with obesity are at increased risk of hospitalization and severe illness from COVID-19 (refs 5 , 6 , 7 ).

On the basis of the latest data from the NCD Risk Factor Collaboration, in 2016 almost 2 billion adults (39% of the world’s adult population) were estimated to be overweight (defined by a body mass index (BMI) of ≥25 kg m − 2 ), 671 million (12% of the world’s adult population) of whom had obesity (BMI ≥30 kg m − 2 ) — a tripling in the prevalence of obesity since 1975 (ref. 8 ) (Fig.  1 ). Although the rate of increase in obesity seems to be declining in most high-income countries, it continues to rise in many low-income and middle-income countries and prevalence remains high globally 8 . If current trends continue, it is expected that 1 billion adults (nearly 20% of the world population) will have obesity by 2025. Particularly alarming is the global rise in obesity among children and adolescents; more than 7% had obesity in 2016 compared with less than 1% in 1975 (ref. 8 ).

The prevalence of obesity has risen steadily over the past four decades in children, adolescents (not shown) and adults worldwide. a | Prevalence of obesity (body mass index (BMI) ≥30 kg m −2 ) in women and men ≥20 years of age, from 1975 to 2016. b | Prevalence of obesity (weight ≥2 s.d. above the median of the WHO growth reference) in 5-year-old girls and boys from 1975 to 2016. Geographical regions are represented by different colours. Graphs are reproduced from the NCD Risk Factor Collaboration (NCD RisC) website and are generated from data published in ref. 8 .

Although changes in the environment have undoubtedly driven the rapid increase in prevalence, obesity results from an interaction between environmental and innate biological factors. Crucially, there is a strong genetic component underlying the large interindividual variation in body weight that determines people’s response to this ‘obesogenic’ environment . Twin, family and adoption studies have estimated the heritability of obesity to be between 40% and 70% 9 , 10 . As a consequence, genetic approaches can be leveraged to characterize the underlying physiological and molecular mechanisms that control body weight.

Classically, we have considered obesity in two broad categories (Fig.  2 ): so-called monogenic obesity , which is inherited in a Mendelian pattern, is typically rare, early-onset and severe and involves either small or large chromosomal deletions or single-gene defects; and polygenic obesity (also known as common obesity), which is the result of hundreds of polymorphisms that each have a small effect. Polygenic obesity follows a pattern of heritability that is similar to other complex traits and diseases. Although often considered to be two distinct forms, gene discovery studies of monogenic and polygenic obesity have converged on what seems to be broadly similar underlying biology. Specifically, the central nervous system (CNS) and neuronal pathways that control the hedonic aspects of food intake have emerged as the major drivers of body weight for both monogenic and polygenic obesity. Furthermore, early evidence shows that the expression of mutations causing monogenic obesity may — at least in part — be influenced by the individual’s polygenic susceptibility to obesity 11 .

Key features of monogenic and polygenic forms of obesity .

In this Review, we summarize more than 20 years of genetic studies that have characterized the molecules and mechanisms that control body weight, specifically focusing on overall obesity and adiposity, rather than fat distribution or central adiposity. Although most of the current insights into the underlying biology have been derived from monogenic forms of obesity, recent years have witnessed several successful variant-to-function translations for polygenic forms of obesity. We also explore how the ubiquity of whole-exome sequencing (WES) and genome sequencing has begun to blur the line that used to demarcate the monogenic causes of obesity from common polygenic obesity. Syndromic forms of obesity, such as Bardet–Biedl, Prader–Willi, among many others 12 , are not reviewed here. Although obesity is often a dominant feature of these syndromes, the underlying genetic defects are often chromosomal abnormalities and typically encompass multiple genes, making it difficult to decipher the precise mechanisms directly related to body-weight regulation. Finally, as we enter the post-genomic era, we consider the prospects of genotype-informed treatments and the possibility of leveraging genetics to predict and hence prevent obesity.

Gene discovery approaches

The approaches used to identify genes linked to obesity depend on the form of obesity and genotyping technology available at the time. Early gene discovery studies for monogenic forms of obesity had a case-focused design: patients with severe obesity, together with their affected and unaffected family members, were examined for potential gene-disrupting causal mutations via Sanger sequencing. By contrast, genetic variation associated with common forms of obesity have been identified in large-scale population studies, either using a case–control design or continuous traits such as BMI. Gene discovery for both forms of obesity was initially hypothesis driven; that is, restricted to a set of candidate genes that evidence suggests have a role in body-weight regulation. Over the past two decades, however, advances in high-throughput genome-wide genotyping and sequencing technologies, combined with a detailed knowledge of the human genetic architecture, have enabled the interrogation of genetic variants across the whole genome for their role in body-weight regulation using a hypothesis-generating approach.

Gene discovery for monogenic obesity

Many of the candidate genes and pathways linked to body-weight regulation were initially identified in mice, such as the obese ( ob ) 13 and diabetes ( db ) 14 mouse lines, in which severe hyperphagia and obesity spontaneously emerged. Using reverse genetics , the ob gene was shown to encode leptin, a hormone produced from fat, and it was demonstrated that leptin deficiency resulting from a mutation in the ob gene caused the severe obesity seen in the ob/ob mouse 15 (Fig.  3 ). Shortly after the cloning of ob , the db gene was cloned and identified as encoding the leptin receptor (LEPR) 16 . Reverse genetics was also used to reveal that the complex obesity phenotype of Agouti ‘lethal yellow’ mice is caused by a rearrangement in the promoter sequence of the agouti gene that results in ectopic and constitutive expression of the agouti peptide 17 , 18 , which antagonizes the melanocortin 1 and 4 receptors (MC1R and MC4R) 19 , 20 . This finding linked the melanocortin pathway to body-weight regulation, thereby unveiling a whole raft of new candidate genes for obesity.

Genes identified for monogenic obesity in a given year are shown on the left. Discoveries made for polygenic obesity are shown on the right, including a cumulative count of newly discovered loci per year and by ancestry. Although candidate gene and genome-wide linkage studies became available in the late 1990s, findings were limited, and these study designs are not as frequently used as genome-wide association studies.

Once the genes for leptin and its receptor were identified, they became candidate genes for human obesity, and in 1997 the first humans with congenital leptin deficiency were identified 21 . This discovery was rapidly followed by the report of humans with mutations in the gene encoding the leptin receptor ( LEPR ) 22 , as well as in genes encoding multiple components of the melanocortin pathway, including PCSK1 (ref. 23 ), MC4R 24 , 25 , 26 and POMC 27 , 28 , 29 , all of which were found to result in severe early-onset obesity (Table  1 ).

Advances in high-throughput DNA sequencing led to candidate gene screening being replaced by WES, an unbiased approach that allows all coding sequences to be screened for mutations. However, it rapidly became clear that, whereas candidate gene studies yielded few mutations, WES identified too many potential obesity-associated variants such that the noise often masked the true causative mutations. However, with improved algorithms to predict the pathogenicity of mutations, as well as a rapidly expanding toolkit of functional assays, it has become easier to filter the likely pathogenic mutations. Several success stories have been reported in which WES has identified novel pathways and genes linked to obesity, such as the class 3 semaphorins (SEMA3A–G), which have been shown to direct the development of certain hypothalamic neurons, including those expressing pro-opiomelanocortin (POMC) 30 (see ‘Other neuronal circuits and molecules linked to severe obesity’).

Most monogenic obesity mutations have been identified in cohorts of patients with severe and early-onset (<10 years old) obesity. Additionally, as monogenic obesity often demonstrates a recessive inheritance pattern 31 , consanguinity in populations has further increased the chance of identifying mutations, owing to greater chances of homozygosity of deleterious mutations 32 . For example, studies have reported that mutations in the genes encoding leptin, LEPR and MC4R explain 30% of cases of severe obesity in children from a consanguineous Pakistani population 33 , and single-gene defects more broadly account for nearly 50% 34 .

Gene discovery for polygenic obesity

The discovery of genes that influence polygenic obesity, which is common in the general population, started off slowly with candidate gene studies and genome-wide linkage studies . The candidate gene approach was first applied in the mid-1990s and aimed to validate genes identified through human and animal models of extreme obesity for a role in common obesity (Fig.  3 ). Common variants in such candidate genes were tested for association with obesity risk, BMI or other body composition traits. Over the subsequent 15 years, hundreds of genes were studied as candidates, but variants in only six ( ADRB3 (ref. 35 ), BDNF 36 , CNR1 (ref. 37 ), MC4R 38 , PCSK1 (ref. 39 ) and PPARG 40 ) showed reproducible association with obesity outcomes. The genome-wide linkage approach made its entrance into the field towards the end of the 1990s (Fig.  3 ). Genome-wide linkage studies rely on the relatedness of individuals and test whether certain chromosomal regions co-segregate with a disease or trait across generations. Even though more than 80 genome-wide linkage studies identified >300 chromosomal loci with suggestive evidence of linkage with obesity traits, few loci were replicated and none was successfully fine-mapped to pinpoint the causal gene or genes 41 . Ultimately, candidate gene and genome-wide linkage studies, constrained by small sample sizes, sparse coverage of genetic variation across the genome and lack of replication, only had a marginal impact on the progression of gene discovery for common obesity outcomes.

However, the pace of gene discovery for common diseases accelerated with the advent of genome-wide association studies (GWAS) (Fig.  3 ). The first GWAS for obesity traits were published in 2007 and identified a cluster of common variants in the first intron of the FTO locus that was convincingly associated with BMI 42 , 43 . Many more GWAS followed and, to date, nearly 60 GWAS have identified more than 1,100 independent loci associated with a range of obesity traits 44 (Supplementary Tables 1 , 2 ).

As sample sizes increase with each consecutive GWAS, the statistical power to identify more loci also increases, in particular for loci that are less common and/or have smaller effects. For example, the first GWAS were relatively small ( n = ~5,000) and identified only the FTO locus 42 , 43 . The BMI-increasing allele of FTO is common, particularly in populations of European ancestry (minor allele frequency (MAF) 40–45%), and has a relatively large effect on BMI (0.35 kg m −2 per allele; equivalent to 1 kg for a person who is 1.7 m tall). Ten years and numerous GWAS later, the most recent GWAS for BMI included nearly 800,000 individuals, identified more than 750 loci, with MAFs as small as 1.6% and per-allele effects as low as 0.04 kg m −2 per allele (equivalent to 120 g for a person who is 1.7 m tall) 45 . Combined, these genome-wide significant loci explained 6% of variation in BMI 45 . Large-scale international collaborations have been formed, such as the Genetic Investigation for Anthropometric Traits (GIANT) consortium , that combine summary statistics of individual GWAS to generate data sets comprising hundreds of thousands of individuals. Furthermore, many GWAS efforts have maximized sample size by focusing on BMI as the primary obesity outcome, an inexpensive and easy-to-obtain measurement that is readily available in most studies. As such, the vast majority of loci have been identified first in GWAS of BMI, but their effects typically transfer to other overall adiposity outcomes.

Even though BMI is widely used, it is considered a crude proxy of overall adiposity because it does not distinguish between lean and fat mass 46 . Therefore, GWAS have been performed for more refined obesity traits, such as body fat percentage 47 , 48 , fat-free mass 49 , imaging-derived adipose tissue 50 , circulating leptin levels 51 and LEPR levels 52 . In addition, two GWAS have focused on persistent healthy thinness, assuming that genes that determine resistance to weight gain may also inform obesity prevention and weight loss maintenance 53 , 54 . Although GWAS of more refined and alternative obesity outcomes are generally much smaller than those for BMI, the phenotypes are often a more accurate representation of body-weight regulation and, as such, the loci identified tend to more often point to relevant biological pathways that underlie obesity.

Almost all GWAS loci for obesity outcomes were first identified in adults. Most of these loci also associate with obesity and/or BMI in children and adolescents, highlighting the fact that the genetic underpinning of obesity is relatively constant across the course of life 55 , 56 , 57 . Similarly to gene discovery for other common diseases, the obesity genetics field has suffered from a strong bias in population representation, with the vast majority of GWAS being performed in populations that are exclusively or predominantly of European ancestry. Nevertheless, some loci have first been discovered in populations of Asian 58 , African 59 , 60 , Hispanic or other ancestry 61 , despite their much smaller sample sizes. Broadly, loci identified in one ancestry demonstrate good transferability (that is, directionally consistent associations) across other ancestries, even though effect sizes and allele frequencies may differ. The modest-to-high genetic correlations across ancestries observed for BMI ( r  = 0.78) are consistent with good transferability 62 , but also suggest that ancestry-specific loci remain to be discovered. Besides increasing the sample sizes of GWAS in populations of non-European ancestry, demographic, evolutionary and/or genomic features of specific populations (such as founder, consanguineous or isolated populations) have been leveraged for gene discovery, identifying genetic variants with large effects that are common in the discovery population, such as CREBRF , first identified in Samoan populations, and ADCY3 , first identified in the Greenlandic population, but rare or nonexistent in most others 63 , 64 , 65 , 66 . CREBRF has been shown to play a role in cellular energy storage and use, and may be implicated in cellular and organismal adaptation to nutritional stress 65 . ADCY3 colocalizes with MC4R at the primary cilia of a subset of hypothalamic neurons that have been implicated in body-weight regulation 67 .

GWAS have typically focused on biallelic, common genetic variation (MAF >5%), but have also been used to screen for the role of copy number variants (CNVs) in obesity. So far, only a few CNVs have been identified that have a convincing association with BMI, such as the 1p31.1 45-kb deletion near NEGR1 (ref. 68 ), which encodes a cell-adhesion molecule expressed in the brain 69 ; the 16p12.3 21-kb deletion upstream of GPRC5B 70 , which may modulate insulin secretion 71 ; the 10q11.22 CNV in PPYR1 (also known as NPY4R ) 72 , which encodes a potent anti-obesity agent known to inhibit food intake 73 ; and the 1p21.1 multi-allele CNV encompassing AMY1A 74 , which produces salivary α-amylase, a key enzyme in starch digestion 75 .

To determine the role of other types of variation in obesity, alternative genome-wide screens have been performed. For example, the impact of low-frequency and rare protein-coding variants has been tested using exome sequencing and exome array data 76 , 77 , 78 , 79 . It was speculated that low-frequency (MAF 1–5%) and rare (MAF <1%) variants would have larger effects than common variants, and thus be easier to detect. Nevertheless, even large-scale studies identified only a few robust associations for rare coding variants. For example, exome-wide screening based on array data from more than 400,000 individuals identified p.Tyr35Ter (rs13447324) in MC4R ; p.Arg190Gln (rs139215588) and p.Glu288Gly (rs143430880) in GIPR , which stimulates insulin secretion and mediates fat deposition 80 ; p.Arg95Ter (rs114285050) in GRP151 , which modulates habenular function that controls addiction vulnerability 81 ; and p.Arg769Ter (rs533623778) in PKHD1L1 , which has been involved in cancer development 77 , 78 . A recent study that leveraged WES data for more than 600,000 individuals identified 16 genes for which the burden of rare nonsynonymous variants was associated with BMI, including five brain-expressed G protein-coupled receptors ( CALCR , MC4R , GIPR , GPR151 and GPR75 ) 79 .

As obesity is a complex, multifactorial condition, some GWAS have integrated demographic factors (such as sex and age 82 ) and environmental factors (such as physical activity 83 , diet 84 or smoking 85 ) into their analyses. Despite sample sizes of more than 200,000 individuals, these genome-wide gene-by-environment (G×E) interaction analyses remain challenging and so far only 12 loci have been identified, the effects of which on obesity are attenuated or exacerbated by non-genetic factors. Nevertheless, the G×E interaction between the FTO locus and a healthy lifestyle has been robustly replicated. Specifically, increased physical activity or a healthy diet can attenuate the effect of the FTO locus on obesity risk by 30–40% 86 , 87 .

The increasing availability of large-scale cohorts and biobanks, such as the UK Biobank , the Million Veterans Project , All of Us , Biobank Japan and 23andMe , combined with ongoing work by the GIANT consortium, will boost sample sizes further to easily exceed 4 million participants in meta-analyses, expediting the discovery of many more obesity-associated loci. However, translation of GWAS-identified loci into new biological insights remains a major challenge.

From genes to biology

Despite the difficulties in validating causative mutations and variants, genetic studies into both rare and common obesity over the past two decades have revealed two surprisingly cogent, overarching biological messages: first, the leptin–melanocortin pathway is a key appetitive control circuit 31 , 88 (Fig.  4 ); and second, genes that are either enriched or exclusively expressed within the brain and CNS have a central role in obesity 89 .

Pro-opiomelanocortin (POMC)-expressing neurons and agouti-related protein (AGRP)-expressing neurons within the arcuate nucleus of the hypothalamus (ARC) act to sense circulating leptin (LEP) levels, which reflect fat mass. These neurons signal to melanocortin 4 receptor (MC4R)-expressing neurons in the paraventricular nucleus of the hypothalamus (PVN), which controls appetite, thus linking long-term energy stores to feeding behaviour. Binding of class 3 semaphorins (SEMA3) to their receptors NRP and PLXNA influences the projection of POMC neurons to the PVN. Binding of brain-derived neurotrophic factor (BDNF) to its receptor neurotrophic receptor tyrosine kinase 2 (NTRK2) is thought to be an effector of leptin-mediated synaptic plasticity of neurons, including those in the ARC and PVN. The transcription factor SIM1 is crucial for the proper development of the PVN. +, agonist; −, antagonist; LEPR, leptin receptor; MRAP2, melanocortin receptor accessory protein 2; MSH, melanocyte-stimulating hormone; SH2B1, SH2B adaptor protein 1.

The leptin–melanocortin pathway and MC4R

Leptin is a key hormone secreted by adipocytes, which circulates at levels in proportion to fat mass 90 . Leptin also responds to acute changes in energy state, as its levels decrease with food deprivation and are restored during re-feeding. Administration of leptin to fasted mice abrogates many of the neuroendocrine consequences of starvation, suggesting that the normal biological role of leptin is to initiate the starvation response 91 . Leptin signals through the LEPR, which exists in several different isoforms. However, obesity-related effects of leptin are predominantly mediated by a long isoform that contains an intracellular domain (LEPRb), which is expressed in various regions of the CNS 90 .

Within the arcuate nucleus (ARC) of the hypothalamus, LEPRb is found on two populations of neurons at the heart of the melanocortin pathway, one of which expresses POMC and the other agouti-related protein (AGRP) 92 (Fig.  4 ). POMC is post-translationally processed by prohormone convertases to produce several biologically active moieties, including β-lipotrophin and β-endorphin, and, crucially, the melanocortin peptides adrenocorticotrophin (ACTH) and α-, β- and γ-melanocyte-stimulating hormone (MSH) 93 . The ARC POMC neurons project to MC4R neurons within the paraventricular nucleus (PVN) where melanocortin peptides signal to decrease food intake 92 . By contrast, AGRP acts as an endogenous antagonist of MC4R to increase food intake 92 , 94 . MC3R is another centrally expressed receptor that binds to both melanocortin peptides and AGRP; however, as mice with targeted deletions in the gene are not obese but instead have altered fat to lean mass ratio, MC3R is less likely to be related to food intake and more likely to be involved in nutrient partitioning 95 , 96 .

We can state with confidence that the fine balance of melanocortinergic agonism and AGRP antagonism of MC4R, in response to peripheral nutritional cues such as leptin, plays a central part in influencing appetitive drive 92 . The genetic evidence clearly supports this contention, with mutations in most genes of the melanocortin pathway resulting in hyperphagia and severe obesity in both humans and mice 31 , 88 . In fact, the vast majority of single-gene disruptions causing severe early-onset obesity in humans fall within this pathway, including LEPR , POMC , AGRP , MCR4R , PCSK1 (ref. 23 ), SH2B1 (ref. 97 ), PHIP 98 , MRAP2 (ref. 99 ) and SIM1 (ref. 100 ) (Fig.  4 ; Table  1 ). Mutations in MC4R in particular, are the most common single-gene defect leading to hyperphagia and obesity. Pathogenic mutations in MC4R are found in up to 5% of cases of severe childhood obesity 101 and up to 0.3% of the general population 101 , 102 . Of note, the degree of receptor dysfunction, as measured by in vitro assays, can predict the amount of food eaten at a test meal by an individual harbouring that particular mutation 101 . Thus MC4R does not act in a binary on/off manner, but as a rheostat; put simply, the melanocortin pathway is a ‘tunable’ system. In addition to regulating food intake, it also regulates food preference, with individuals who carry mutations in MC4R showing a preference for food with higher fat content 103 .

The importance of the melanocortin pathway in regulating feeding behaviour is highlighted by the identification of naturally occurring mutations in pathway genes in a wide range of different species where the appropriate selection pressure has been present (Table  1 ). For example, studies have found that 20–25% of Labrador retrievers, which are known to be more food-motivated than other dog breeds, carry a 14-bp deletion in POMC that disrupts the β-MSH and β-endorphin coding sequences and is associated with greater food motivation and increased body weight 104 . Also, certain breeds of pig have been shown to carry MC4R missense mutations that are associated with fatness, growth and food intake traits 105 . MC4R mutations even contribute to the adaptation and survival of blind Mexican cavefish to the nutrient-poor conditions of their ecosystem 106 .

Other neuronal circuits and molecules linked to severe obesity

It is now clear that in addition to engaging classical neuropeptide–receptor systems within the brain, leptin also rapidly modifies synaptic connections between neurons 107 , and that this structural plasticity is crucial to its downstream functions. One of the ways in which this plasticity is thought to be achieved is via brain-derived neurotrophic factor (BDNF) signalling to its receptor TrkB. BDNF is widely expressed in the CNS where it plays an important part in neuronal development 108 , 109 . In the hippocampus, BDNF contributes to synaptic plasticity and long-term potentiation associated with memory and learning 110 . However, evidence has emerged that implicates BDNF and TrkB in the regulation of mammalian eating behaviour and energy balance 111 . BDNF is downregulated by nutritional deprivation and upregulated by leptin within the ventromedial nucleus (VMN) of the hypothalamus 112 , although this regulation is probably indirect, as very few VMN BDNF neurons express the LEPR 113 (Fig.  4 ) and some evidence indicates that it acts at least in part downstream of melanocortin signalling 112 . Crucially, genetic disruption of BDNF 114 , 115 and TrkB 112 , 116 in both humans and mice results in hyperphagia and severe obesity.

Another group of neuronal proteins important in the development of neuronal circuitry and linked to energy balance are the class 3 semaphorins (SEMA3A–G). A study in humans found that 40 rare loss-of-function variants in SEMA3A–G and their receptors (PLXNA1–4, NRP1 and NRP2) were significantly enriched in 982 individuals with severe obesity compared with 4,449 controls 30 . Disruption of several of these genes in zebrafish caused increased somatic growth and/or adiposity, and experiments with mouse hypothalamic explants suggest that SEMA3 signalling via NRP2 receptors drives the development of POMC projections from the ARC to the PVN 30 . However, given that these results are from a single study, more data are required to confirm the exact role of class 3 semaphorins in energy homeostasis.

Insights from genetic loci linked to common obesity

Unlike candidate gene studies, GWAS make no a priori assumptions about the underlying biology that links genetic variants to a disease of interest. While this agnostic approach allows for new biological insights, the vast majority of GWAS-identified variants map to the non-coding parts of genes or to regions between genes. As such, they do not directly disrupt the protein-coding regions, but instead overlap with regulatory elements that influence expression of genes in close proximity or even over long distances.

However, even if the causative genes are unknown, pathway, tissue and functional enrichment analyses based on the genes located in the GWAS loci can provide insights into potential mechanisms. Since the very first GWAS for BMI 68 , 117 , such analyses have pointed to the CNS being a key player in body-weight regulation, consistent with insights from human and animal models of extreme obesity. Recent analyses that include the latest BMI-associated loci, combined with updated multi-omics databases and advanced computational tools, have further refined these observations. In addition to the hypothalamus and pituitary gland (which are both known appetite regulation sites), other brain areas have been highlighted, including the hippocampus and the limbic system (which are involved in learning, cognition and emotion) and the insula and the substantia nigra (which are related to addiction and reward) 58 , 89 , 118 , 119 . The enrichment of immune-related cells (such as lymphocytes and B cells) and adipose tissue was found to be weaker 58 .

Although enrichment analyses provide preliminary insights into the broad biology represented by genes in the GWAS loci, determining which genes, variants and/or underlying mechanisms are causal has proved an arduous task. For example, the FTO locus, which was identified more than a decade ago and harbours six genes, is the most extensively studied GWAS-identified obesity locus (Fig.  5 ). Despite its highly significant and widely replicated association with obesity 120 , the causal variants and/or genes in the FTO locus have not yet been pinpointed with convincing evidence, and the mechanisms by which the locus affects body weight have not been fully elucidated. Early functional follow-up analyses suggested that FTO itself might be responsible, as Fto deficiency in mice results in a lean phenotype, whereas Fto overexpression is associated with increased body weight 121 , 122 . Studies in mice have suggested that FTO plays a role in cellular nutrient sensing 123 , 124 . Other studies found evidence that FTO influences brain regions that affect appetite, reward processing and incentive motivation by regulating ghrelin levels in humans 125 or by controlling dopaminergic signalling in mice 126 , 127 . In addition, variants in the FTO locus were shown to alter a regulatory element that controls the transcription of Rpgrip1l in mice, a ciliary gene located immediately upstream of Fto 128 , 129 , 130 . Mice with reduced Rpgrip1l activity exhibit hyperphagic obesity, possibly mediated through diminished leptin signalling 128 , 129 , 130 . In recent years, studies in human and animal models have shown that variants in the FTO locus directly interact with the promoter of Irx3 , a gene located 0.5 Mb downstream of FTO . Irx3 -deficient mice were found to exhibit weight loss and increased metabolic rate with browning of white adipose tissue, without changes in physical activity or appetite 131 , 132 . Further in-depth functional characterization showed that rs1421085 in the FTO locus disrupts a conserved binding motif for the transcriptional repressor ARID5B, which leads to a doubling of IRX3 and IRX5 expression during early adipocyte differentiation 132 . The authors argue that increased expression of these genes results in a developmental shift from energy-dissipating beige adipocytes to energy-storing white adipocytes, a fivefold reduction in mitochondrial thermogenesis and increased lipid storage 132 . However, given that multiple studies have shown that the FTO locus is robustly associated with food intake, with no evidence to date linking it to changes in energy expenditure, the relevance of this observation to the actual observed human phenotype still needs to be explored 133 . A recent study reports that the FTO locus affects gene expression in multiple tissues, including adipose tissue and brain, and, more broadly, that the genetic architecture of disease-associated loci may involve extensive pleiotropy and allelic heterogeneity across tissues 134 .

FTO contains nine exons (depicted by blue rectangles) and the body mass index (BMI)-associated SNP identified in genome-wide association studies (depicted by a red ×) maps to intron 1. IRX3 and RPGRIP1L have both been proposed to be the causal genes for obesity within the locus and to act on body weight through distinct mechanisms. HFD, high-fat diet.

Besides the FTO locus, functional follow-up analyses have been performed for only a few obesity-associated GWAS loci. For example, early studies identified a cluster of variants just downstream of TMEM18 (refs 68 , 117 ). TMEM18 encodes a poorly characterized transmembrane protein that is highly conserved across species and widely expressed across tissues, including in several regions of the brain 135 , 136 . Tmem18 deficiency in mice results in a higher body weight owing to increased food intake, whereas Tmem18 overexpression reduces food intake and limits weight gain 136 . A knockdown experiment in Drosophila melanogaster suggests that TMEM18 affects carbohydrate and lipid levels by disrupting insulin and glucagon signalling 137 .

Two other GWAS loci for which functional analyses have been performed are located just upstream of CADM1 (ref. 82 ) and in CADM2 (ref. 70 ), genes that encode cell-adhesion proteins of the immunoglobulin superfamily and mediate synaptic assembly in the CNS 138 . The BMI-increasing alleles at each locus are associated with increased expression of CADM1 and CADM2 in the hypothalamus 139 , 140 . Deficiency of either Cadm1 or Cadm2 in mice results in a lower body weight and increased insulin sensitivity, glucose tolerance and energy expenditure without any change in food intake 139 , 140 . Conversely, increased neuronal expression of either Cadm1 or Cadm2 is associated with elevated body weight 139 , 140 . Furthermore, CADM1 is expressed in POMC neurons and Cadm1 deficiency leads to an increase in the number of excitatory synapses, suggestive of an increased synaptic plasticity 140 . Cadm2 -deficient mice exhibit increased locomotor activity and higher core body temperature 139 .

Another GWAS locus, just upstream of NEGR1 , harbours two deletions associated with increased obesity risk 68 , 117 , 141 . These deletions do not overlap with the coding sequence of NEGR1 , but encompass a conserved transcription factor-binding site for NKX6.1 , a potent transcriptional repressor 68 , 141 . Loss of binding of NKX6.1 leads to higher NEGR1 expression 141 , which is consistent with the observation that BMI-increasing alleles (that is, deletions) at this locus are associated with higher NEGR1 expression in the brain. Similar to CADM1 and CADM2, NEGR1 is a cell-adhesion molecule of the immunoglobulin superfamily that is expressed in several regions of the brain and has been shown to have a role in brain connectivity 69 , 142 , a process believed to be important in obesity 143 . NEGR1 deficiency in mice was shown to result in lower body weight, mainly due to reduced lean mass, mediated by lower food intake 144 . However, two other functional studies, one in mice and one in rats, found that knockdown of Negr1 expression resulted in the opposite phenotype — increased body weight and food intake 145 , 146 . While NEGR1 deficiency in mice was found to impair core behaviours, so far, findings and proposed mechanisms are not fully aligned 69 , 147 , 148 , 149 .

Taken together, functional follow-up analyses for these loci are slowly expanding our understanding of the pathophysiology that drives weight gain. However, many more obesity-associated loci are waiting to be translated into new biological insights. A major hurdle in translating GWAS loci into plausible candidate genes and appropriate paradigms for functional research is the annotation of the associated variants in a locus. Defining the regulatory function of the non-coding variants, identifying their putative effector transcripts and determining their tissues of action remains an ongoing challenge. The advent of high-throughput genome-scale technologies for mapping regulatory elements, combined with comprehensive multi-omics databases, advanced computational tools and the latest genetic engineering and molecular phenotyping approaches, is poised to speed up the translation of GWAS loci into meaningful biology 150 .

Converging results from monogenic and polygenic forms of obesity

Gene discovery is often dichotomized by allele frequency and disease prevalence; that is, mutations are sought for monogenic forms of obesity and common variants for polygenic obesity (Fig.  2 ). However, it is increasingly recognized that monogenic and polygenic forms of obesity are not discrete entities. Instead, they lie on a spectrum and share — at least in part — the same biology. As GWAS have continued to discover more obesity-associated loci, an increasing number of these loci harbour genes that were first identified for extreme and early-onset obesity in humans or animal models, including MC4R 151 , 152 , BDNF 117 , SH2B1 (refs 68 , 117 ), POMC 70 , LEP 51 , 153 , LEPR 52 , 154 , NPY 155 , SIM1 (ref. 155 ), NTRK2 (ref. 58 ), PCSK1 (ref. 154 ) and KSR2 (ref. 77 ). In fact, most of these genes encode components of the leptin–melanocortin and BDNF–TrkB signalling pathways (Table  1 ). Thus, whereas genetic disruption of components of these pathways results in severe obesity, genetic variants in or near these same genes that have more subtle effects on their expression will influence where an individual might sit in the normal distribution of BMI.

Although most genes have been first identified for extreme forms of obesity, a locus harbouring ADCY3 was first identified in GWAS for common obesity 77 , and ADCY3 was subsequently confirmed as having a role in extreme obesity 63 , 64 . ADCY3 encodes an adenylate cyclase that catalyses the synthesis of cAMP, an important second messenger in signalling pathways. There is some evidence that ADCY3 (adenylate cyclase) colocalizes with MC4R at the primary cilia of PVN neurons 67 and that cilia are required specifically on MC4R-expressing neurons for the control of energy homeostasis 156 . In mice, disruption of Adcy3 or Mc4r in the cilia of these neurons impairs melanocortin signalling, resulting in hyperphagia and obesity 67 .

As more GWAS loci are reported, we expect that findings across different lines of obesity research will continue to converge, providing accumulating evidence for new biology.

From genes to clinical care

Genetic insights from gene discovery efforts are increasingly being used in the context of precision medicine in ways that directly affect health. Knowing a patient’s genotype may enable a more precise diagnosis of the type of obesity, which in turn allows the prescription of personalized treatment or prevention strategies. Furthermore, knowing an individual’s genetic susceptibility to obesity early in life may help to more accurately predict those most at risk of gaining weight in the future.

Use of genotype information in treatment of obesity

When a disease is caused by a single mutation and the environmental contribution is limited, as is the case for some forms of extreme and early-onset obesity, a genetic test can be instrumental in correctly diagnosing patients. Although no standard genetic testing panel is currently available for extreme and early-onset obesity, some clinics, research centres and pharmaceutical companies sequence well-known candidate genes to identify the functional mutation that may be the cause of a patient’s excess body weight. Such a genetic diagnosis can lessen the feelings of guilt and blame for the patient, and alleviate social stigma and discrimination. Importantly, a genetic diagnosis can inform disease prognosis and, in some cases, it will determine treatment. To date, there are two treatments for obesity that are tailored to patient genotype.

The prototype of genotype-informed treatment for obesity is the administration of recombinant human leptin in patients who are leptin-deficient owing to mutations in the LEP gene 157 , 158 . Although congenital leptin deficiency is exceptionally rare (only 63 cases have been reported to date 28 ), leptin replacement therapy has been remarkably beneficial for these patients by substantially reducing food intake, body weight and fat mass, and normalizing endocrine function 157 , 158 . It has literally transformed their lives.

The second genotype-informed treatment for obesity is setmelanotide, a selective MC4R agonist that was recently approved by the FDA for rare monogenic obesity conditions including LEPR, PCSK1 and POMC deficiency 159 . Setmelanotide acts as a substitute for the absent MSH in patients with POMC deficiency owing to mutations in POMC or PCSK1 , and in patients with LEPR deficiency owing to mutations in LEPR , which is essential for POMC function 160 , 161 , 162 . Daily subcutaneous injection of setmelanotide results in substantial weight loss and in reduction of hunger 160 , 161 , 162 . After a 1-year treatment with setmelanotide in phase III trials, patients with POMC deficiency lost on average 25.6% of their initial weight, with 80% of patients achieving at least a 10% weight loss 162 . The adverse effects of setmelanotide treatment are minor, and include hyperpigmentation, nausea and/or vomiting, penile erection and injection site reactions. Weight loss in patients with LEPR deficiency was less pronounced; on average, they lost 12.5% of their initial weight, with only 45% of patients achieving at least a 10% weight loss 162 . The difference in weight loss between the two patient groups may be because POMC deficiency directly affects the production of MC4R ligands (α-MSH and β-MSH), whereas LEPR deficiency affects signalling upstream of POMC 162 . As such, setmelanotide may be able to completely restore MC4R signalling in POMC deficiency, but only partially in LEPR deficiency. Even though the average weight loss in POMC-deficient patients was twice that in LEPR-deficient patients, the reduction in hunger was substantially larger in LEPR-deficient patients (−43.7%) than in POMC-deficient patients (−27.1%) 162 . The reasons for the discrepancy between weight loss and reduction in hunger remain to be studied in greater depth. It has been estimated that in the USA, >12,800 individuals carry mutations in the melanocortin pathway for whom setmelanotide may be more effective for weight loss than any other treatment 163 . Although 12,800 carriers represent only a fraction (0.004%) of the adult population in the USA, and not all of these mutation carriers are overweight or obese, for the patients for whom setmelanotide is effective, it may end a lifelong battle to lose weight 163 . In patients without genetic defects, neither setmelanotide nor leptin administration have, to date, demonstrated a substantial effect on weight loss 164 , 165 .

These two genotype-informed treatments show how insight into the underlying biological mechanisms can guide the development of molecules and medications that restore impaired pathways, at least in monogenic forms of obesity caused by deficiency of one protein. Nevertheless, there remain substantial obstacles in the transition from conventional to precision medicine for monogenic obesity, which would require the adoption of systematic WES for individuals suspected to be carriers of deleterious mutations, and eventually even standardized screening at birth. We are clearly a long way from such a scenario at present.

Use of genotype information in prediction of obesity

As more variants are being discovered for common obesity, there is a growing expectation that genetic information will soon be used to identify individuals at risk of obesity. Knowing a person’s genetic susceptibility would allow for a more accurate prediction of who is at risk of gaining weight and give an opportunity to intervene earlier to prevent obesity more effectively. Genetic susceptibility to complex disease, including obesity, is assessed using a polygenic score (PGS). PGSs to assess obesity susceptibility are based on GWAS for BMI (PGS BMI ), the latest of which includes data on more than 2 million variants and explains 8.4% of the variation in BMI 166 . The average BMI of individuals with a high PGS BMI (top decile) is 2.9 kg m −2 (equivalent to 8 kg in body weight) higher and their odds of severe obesity (BMI ≥40 kg m −2 ) is 4.2-fold higher than those with a lower PGS BMI (lowest nine deciles) 166 .

Despite these strong associations with BMI and obesity, the predictive performance of the PGS BMI is weak, which is unsurprising given its limited explained variance. For example, using the same PGS BMI and data from the UK Biobank, we estimate that the area under the receiver operating characteristic curve (AUC ROC ) is only 0.64 to predict obesity. This means that the probability that an individual with obesity has a higher PGS BMI than an individual without obesity is 0.64. However, for a PGS to have clinical utility, the AUC ROC needs to be much higher (>0.80). In addition, we calculated the extent to which a PGS BMI ≥90th percentile correctly classifies individuals with obesity (Fig.  6 ). We found that such a predictive test (PGS BMI ≥90th percentile) has a positive predictive value of 0.43, meaning that of those who were predicted to develop obesity, only 43% actually developed obesity. Its sensitivity is 0.19, which means that of the individuals who developed obesity, only 19% had been correctly classified by the PGS BMI . Given that the current treatment options for obesity are low risk, or even generally beneficial, the high false-positive rate is less concerning than the low sensitivity, as some at-risk individuals may miss the opportunity for early prevention.

The outcome is illustrated for a polygenic score (PGS) that assumes that individuals with a score in the highest decile (≥90th percentile (pct)) will develop obesity, has a positive predictive value of 0.4 and a sensitivity of 0.19. Of ten individuals with a high score classified by the PGS as ‘with obesity’, four will be classified correctly but the other six will be misclassified and will not develop obesity — a positive predictive value of 0.4. Likewise, 17 of the 90 individuals with a score <90th pct who are predicted to not develop obesity, will develop obesity. Thus, only four of the 21 individuals who developed obesity were correctly classified by the PGS — a sensitivity of 0.19. Misclassified individuals are indicated by the red boxes, individuals correctly classified as ‘with obesity’ are indicated by a blue box. Adapted with permission from ref. 170 , Elsevier.

Thus, the current PGS BMI has a high rate of misclassification and does not reliably predict who is at risk of developing obesity and who is not. The predictive ability of PGSs are expected to improve as GWAS increase in sample size and algorithms to calculate the scores become more refined. Nevertheless, given the importance of socio-demographic, lifestyle and clinical risk factors in the aetiology of obesity, it is unlikely that a PGS BMI will ever be able to accurately predict obesity on its own. Instead, effective prediction models will have to include genetic and non-genetic factors, including a broad spectrum of demographic, environmental, clinical and possibly molecular markers, as well.

Conclusions and future perspectives

What initially began as two apparently distinct approaches, one studying rare Mendelian causes of extreme obesity, and the other exploring complex polygenic influences of population body-weight distribution, have eventually converged on the central role of the brain in regulating body weight. In particular, both approaches have highlighted the roles of the leptin–melanocortin pathway and TrkB–BDNF signalling. Perhaps it seems obvious now, but it was by no means certain that, just because genetic disruption of a pathway resulted in a severe phenotype, polymorphisms within that same pathway would produce a more subtle and nuanced result.

The GWAS approach is hypothesis-free, with the promise to reveal new genes that point to new biology and pathways. However, for the vast majority of the >1,000 GWAS-identified loci, we do not know which genes are causal, what cells, tissues and organs they act in to affect body weight, and we do not understand the underlying mechanisms. The translation from variant to function is a well-known challenge 167 , but with increasing availability of new omics data, high-throughput technologies and advanced analytical approaches, there is an unprecedented opportunity to speed up the translation of hundreds of GWAS loci.

Sample size remains a major driver for gene discovery. In an ongoing collaboration that combines data from more than 3 million individuals of diverse ancestry from the GIANT consortium, the UK Biobank and 23andMe, the number of BMI-associated GWAS loci is set to double. Also, a recent WES effort of more than 640,000 individuals has demonstrated that rare mutations are discoverable when sample sizes are sufficiently large 79 . However, alternative study designs, a focus on more refined phenotypes or a focus on population subgroups (that is, more homogeneous groups of individuals with similar outcomes) could further add to gene discovery.

Translation of only a few dozen of the GWAS-identified loci could tremendously improve our insights into the biology of obesity and possibly reveal new therapeutic targets. It would also take us a little closer to the ‘holy grail’ — the ability to move away from a failed ‘one-size-fits-all’ strategy, and towards true precision medicine for obesity, metabolic disease and other diet-related illnesses.

GBD 2015 Obesity Collaborators. Health effects of overweight and obesity in 195 countries over 25 years. N. Engl. J. Med. 377 , 13–27 (2017).

Article   Google Scholar  

Must, A. et al. The disease burden associated with overweight and obesity. JAMA 282 , 1523–1529 (1999).

Article   CAS   PubMed   Google Scholar  

Fontaine, K. R. & Barofsky, I. Obesity and health-related quality of life. Obes. Rev. 2 , 173–182 (2001).

Bhattacharya, I., Ghayor, C., Perez Dominguez, A. & Weber, F. E. From influenza virus to novel corona virus (SARS-CoV-2)—the contribution of obesity. Front. Endocrinol. 11 , 556962 (2020).

Petrilli, C. M. et al. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ 369 , m1966 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Cummings, M. J. et al. Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study. Lancet 395 , 1763–1770 (2020).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Zhao, X. et al. Obesity increases the severity and mortality of influenza and COVID-19: a systematic review and meta-analysis. Front. Endocrinol. 11 , 595109 (2020).

Abarca-Gómez, L. et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet 390 , 2627–2642 (2017).

Maes, H. H., Neale, M. C. & Eaves, L. J. Genetic and environmental factors in relative body weight and human obesity. Behav. Genet. 27 , 325–351 (1997).

Elks, C. E. et al. Variability in the heritability of body mass index: a systematic review and meta-regression. Front. Endocrinol. 3 , 29 (2012). This paper reports a large-scale meta-analysis of heritability data of twin and family studies .

Chami, N., Preuss, M., Walker, R. W., Moscati, A. & Loos, R. J. F. The role of polygenic susceptibility to obesity among carriers of pathogenic mutations in MC4R in the UK Biobank population. PLoS Med. 17 , e1003196 (2020). This study shows that the effect of MC4R mutations on BMI and obesity risk is attenuated by the polygenic background .

Kaur, Y., de Souza, R. J., Gibson, W. T. & Meyre, D. A systematic review of genetic syndromes with obesity. Obes. Rev. 18 , 603–634 (2017).

Ingalls, A. M., Dickie, M. M. & Snell, G. D. Obese, a new mutation in the house mouse. J. Hered. 41 , 317–318 (1950).

Hummel, K. P., Dickie, M. M. & Coleman, D. L. Diabetes, a new mutation in the mouse. Science 153 , 1127–1128 (1966).

Zhang, Y. et al. Positional cloning of the mouse obese gene and its human homologue. Nature 372 , 425–432 (1994). This paper reports the cloning of leptin, which provided the first molecular evidence that feeding and regulation of body weight had a hormonal basis .

Article   CAS   Google Scholar  

Chen, H. et al. Evidence that the diabetes gene encodes the leptin receptor: identification of a mutation in the leptin receptor gene in db / db mice. Cell 84 , 491–495 (1996). This paper reports the cloning of the LEPR and the localization of its signalling version, which highlights the role of the brain in the control of appetitive behaviour .

Bultman, S. J., Michaud, E. J. & Woychik, R. P. Molecular characterization of the mouse agouti locus. Cell 71 , 1195–1204 (1992).

Miller, M. W. et al. Cloning of the mouse agouti gene predicts a secreted protein ubiquitously expressed in mice carrying the lethal yellow mutation. Genes Dev. 7 , 454–467 (1993).

Lu, D. et al. Agouti protein is an antagonist of the melanocyte-stimulating-hormone receptor. Nature 371 , 799–802 (1994).

Fan, W., Boston, B. A., Kesterson, R. A., Hruby, V. J. & Cone, R. D. Role of melanocortinergic neurons in feeding and the agouti obesity syndrome. Nature 385 , 165–168 (1997).

Montague, C. T. et al. Congenital leptin deficiency is associated with severe early-onset obesity in humans. Nature 387 , 903–908 (1997). This work demonstrates that leptin signalling is also relevant in the human context, and provides early evidence, together with mutations in PCSK1 , of a monogenic cause of severe human obesity .

Clement, K. et al. A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature 392 , 398–401 (1998). This work confirms the observations from mice about leptin signalling to the brain .

Jackson, R. S. et al. Obesity and impaired prohormone processing associated with mutations in the human prohormone convertase 1 gene. Nat. Genet. 16 , 303–306 (1997). This study, together with mutations in the leptin gene, provides early evidence of a monogenic cause of severe human obesity, and also highlights the role of the nascent melanocortin pathway in body-weight regulation .

Huszar, D. et al. Targeted disruption of the melanocortin-4 receptor results in obesity in mice. Cell 88 , 131–141 (1997).

Yeo, G. S. et al. A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nat. Genet. 20 , 111–112 (1998).

Vaisse, C., Clement, K., Guy-Grand, B. & Froguel, P. A frameshift mutation in MC4R is associated with a dominant form of obesity. Nat. Genet. 20 , 113–114 (1998). This paper, together with Yeo et al. (1998), shows that heterozygous mutations in MC4R result in severe human obesity, establishing the role of the central melanocortin pathway in regulating human appetitive behaviour .

Krude, H. et al. Severe early-onset obesity, adrenal insufficiency and red hair pigmentation caused by POMC mutations in humans. Nat. Genet. 19 , 155–157 (1998). This paper provides direct evidence that melanocortin peptides play a key role in the regulation of energy homeostasis .

Yaswen, L., Diehl, N., Brennan, M. B. & Hochgeschwender, U. Obesity in the mouse model of pro-opiomelanocortin deficiency responds to peripheral melanocortin. Nat. Med. 5 , 1066–1070 (1999).

Challis, B. G. et al. Mice lacking pro-opiomelanocortin are sensitive to high-fat feeding but respond normally to the acute anorectic effects of peptide-YY(3-36). Proc. Natl Acad. Sci. USA 101 , 4695–4700 (2004).

van der Klaauw, A. A. et al. Human semaphorin 3 variants link melanocortin circuit development and energy balance. Cell 176 , 729–742.e18 (2019).

Farooqi, S. & O’Rahilly, S. Genetics of obesity in humans. Endocr. Rev. 27 , 710–718 (2006).

Saeed, S., Arslan, M. & Froguel, P. Genetics of obesity in consanguineous populations: toward precision medicine and the discovery of novel obesity genes. Obesity 26 , 474–484 (2018).

Article   PubMed   Google Scholar  

Saeed, S. et al. Genetic variants in LEP, LEPR, and MC4R explain 30% of severe obesity in children from a consanguineous population. Obesity 23 , 1687–1695 (2015).

Saeed, S. et al. Genetic causes of severe childhood obesity: a remarkably high prevalence in an inbred population of Pakistan. Diabetes 69 , 1424–1438 (2020).

Kurokawa, N. et al. The ADRB3 Trp64Arg variant and BMI: a meta-analysis of 44 833 individuals. Int. J. Obes. 32 , 1240–1249 (2008).

Shugart, Y. Y. et al. Two British women studies replicated the association between the Val66Met polymorphism in the brain-derived neurotrophic factor (BDNF) and BMI. Eur. J. Hum. Genet. 17 , 1050–1055 (2009).

Benzinou, M. et al. Endocannabinoid receptor 1 gene variations increase risk for obesity and modulate body mass index in European populations. Hum. Mol. Genet. 17 , 1916–1921 (2008).

Wang, D. et al. Association of the MC4R V103I polymorphism with obesity: a Chinese case–control study and meta-analysis in 55,195 individuals. Obesity 18 , 573–579 (2010).

Nead, K. T. et al. Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: a systematic review and meta-analysis with evidence from up to 331 175 individuals. Hum. Mol. Genet. 24 , 3582–3594 (2015).

Tonjes, A., Scholz, M., Loeffler, M. & Stumvoll, M. Association of Pro12Ala polymorphism in peroxisome proliferator-activated receptor gamma with pre-diabetic phenotypes: meta-analysis of 57 studies on nondiabetic individuals. Diabetes Care 29 , 2489–2497 (2006).

Rankinen, T. et al. The human obesity gene map: the 2005 update. Obesity 14 , 529–644 (2006).

Frayling, T. M. et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316 , 889–894 (2007). This paper describes the first GWAS (for type 2 diabetes) to identify a locus ( FTO ) robustly associated with BMI .

Scuteri, A. et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 3 , e115 (2007).

Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47 , D1005–D1012 (2019).

Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in approximately 700 000 individuals of European ancestry. Hum. Mol. Genet. 27 , 3641–3649 (2018).

Javed, A. et al. Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: a systematic review and meta-analysis. Pediatr. Obes. 10 , 234–244 (2015).

Kilpelainen, T. O. et al. Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nat. Genet. 43 , 753–760 (2011).

Lu, Y. et al. New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nat. Commun. 7 , 10495 (2016).

Zillikens, M. C. et al. Large meta-analysis of genome-wide association studies identifies five loci for lean body mass. Nat. Commun. 8 , 80 (2017).

Chu, A. Y. et al. Multiethnic genome-wide meta-analysis of ectopic fat depots identifies loci associated with adipocyte development and differentiation. Nat. Genet. 49 , 125–130 (2017).

Kilpelainen, T. O. et al. Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels. Nat. Commun. 7 , 10494 (2016).

Sun, Q. et al. Genome-wide association study identifies polymorphisms in LEPR as determinants of plasma soluble leptin receptor levels. Hum. Mol. Genet. 19 , 1846–1855 (2010).

Riveros-McKay, F. et al. Genetic architecture of human thinness compared to severe obesity. PLoS Genet. 15 , e1007603 (2019).

Orthofer, M. et al. Identification of ALK in thinness. Cell 181 , 1246–1262.e22 (2020).

Bradfield, J. P. et al. A trans-ancestral meta-analysis of genome-wide association studies reveals loci associated with childhood obesity. Hum. Mol. Genet. 28 , 3327–3338 (2019).

Felix, J. F. et al. Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index. Hum. Mol. Genet. 25 , 389–403 (2016).

Vogelezang, S. et al. Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits. PLoS Genet. 16 , e1008718 (2020).

Akiyama, M. et al. Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nat. Genet. 49 , 1458–1467 (2017).

Ng, M. C. Y. et al. Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium. PLoS Genet. 13 , e1006719 (2017).

Gurdasani, D. et al. Uganda genome resource enables insights into population history and genomic discovery in Africa. Cell 179 , 984–1002.e36 (2019).

Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570 , 514–518 (2019).

Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51 , 584–591 (2019).

Grarup, N. et al. Loss-of-function variants in ADCY3 increase risk of obesity and type 2 diabetes. Nat. Genet. 50 , 172–174 (2018).

Saeed, S. et al. Loss-of-function mutations in ADCY3 cause monogenic severe obesity. Nat. Genet. 50 , 175–179 (2018).

Minster, R. L. et al. A thrifty variant in CREBRF strongly influences body mass index in Samoans. Nat. Genet. 48 , 1049–1054 (2016).

Andersen, M. K. et al. The derived allele of a novel intergenic variant at chromosome 11 associates with lower body mass index and a favorable metabolic phenotype in Greenlanders. PLoS Genet. 16 , e1008544 (2020).

Siljee, J. E. et al. Subcellular localization of MC4R with ADCY3 at neuronal primary cilia underlies a common pathway for genetic predisposition to obesity. Nat. Genet. 50 , 180–185 (2018).

Willer, C. J. et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat. Genet. 41 , 25–34 (2009).

Singh, K. et al. Neural cell adhesion molecule Negr1 deficiency in mouse results in structural brain endophenotypes and behavioral deviations related to psychiatric disorders. Sci. Rep. 9 , 5457 (2019).

Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42 , 937–948 (2010).

Soni, A., Amisten, S., Rorsman, P. & Salehi, A. GPRC5B a putative glutamate-receptor candidate is negative modulator of insulin secretion. Biochem. Biophys. Res. Commun. 441 , 643–648 (2013).

Jarick, I. et al. Novel common copy number variation for early onset extreme obesity on chromosome 11q11 identified by a genome-wide analysis. Hum. Mol. Genet. 20 , 840–852 (2011).

Sainsbury, A. et al. Synergistic effects of Y2 and Y4 receptors on adiposity and bone mass revealed in double knockout mice. Mol. Cell Biol. 23 , 5225–5233 (2003).

Falchi, M. et al. Low copy number of the salivary amylase gene predisposes to obesity. Nat. Genet. 46 , 492–497 (2014).

Meisler, M. H. & Ting, C. N. The remarkable evolutionary history of the human amylase genes. Crit. Rev. Oral Biol. Med. 4 , 503–509 (1993).

Hendricks, A. E. et al. Rare variant analysis of human and rodent obesity genes in individuals with severe childhood obesity. Sci. Rep. 7 , 4394 (2017).

Turcot, V. et al. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat. Genet. 50 , 26–41 (2018). This large-scale exome-wide discovery study reports the use of array data to identify rare coding variations associated with BMI .

Emdin, C. A. et al. Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease. Nat. Commun. 9 , 1613 (2018).

Akbari, P. et al. Sequencing of 640,000 exomes identifies GPR75 variants associated with protection from obesity. Science 373 , eabf8683 (2021). This large-scale exome-wide discovery study reports the use of WES data to identify mutations associated with BMI .

Baggio, L. L. & Drucker, D. J. Biology of incretins: GLP-1 and GIP. Gastroenterology 132 , 2131–2157 (2007).

Antolin-Fontes, B. et al. The habenular G-protein-coupled receptor 151 regulates synaptic plasticity and nicotine intake. Proc. Natl Acad. Sci. USA 117 , 5502–5509 (2020).

Winkler, T. W. et al. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. PLoS Genet. 11 , e1005378 (2015).

Graff, M. et al. Genome-wide physical activity interactions in adiposity — a meta-analysis of 200,452 adults. PLoS Genet. 13 , e1006528 (2017).

Smith, C. E. et al. Genome-wide interactions with dairy intake for body mass index in adults of European descent. Mol. Nutr. Food Res. 62 , 1700347 (2018).

Justice, A. E. et al. Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits. Nat. Commun. 8 , 14977 (2017).

Qi, Q. et al. Fried food consumption, genetic risk, and body mass index: gene–diet interaction analysis in three US cohort studies. BMJ 348 , g1610 (2014).

Kilpelainen, T. O. et al. Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of 218,166 adults and 19,268 children. PLoS Med. 8 , e1001116 (2011).

Yeo, G. S. H. Genetics of obesity: can an old dog teach us new tricks? Diabetologia 60 , 778–783 (2017).

Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518 , 197–206 (2015). This large-scale GWAS for BMI shows that BMI-associated loci frequently localize in or near genes that act in the brain .

Friedman, J. M. & Halaas, J. L. Leptin and the regulation of body weight in mammals. Nature 395 , 763–770 (1998).

Ahima, R. S. et al. Role of leptin in the neuroendocrine response to fasting. Nature 382 , 250–252 (1996).

Cowley, M. A. et al. Integration of NPY, AGRP, and melanocortin signals in the hypothalamic paraventricular nucleus: evidence of a cellular basis for the adipostat. Neuron 24 , 155–163 (1999).

Bertagna, X. Proopiomelanocortin-derived peptides. Endocrinol. Metab. Clin. North Am. 23 , 467–485 (1994).

Ollmann, M. M. et al. Antagonism of central melanocortin receptors in vitro and in vivo by agouti-related protein. Science 278 , 135–138 (1997).

Butler, A. A. et al. A unique metabolic syndrome causes obesity in the melanocortin-3 receptor-deficient mouse. Endocrinology 141 , 3518–3521 (2000).

Chen, A. S. et al. Inactivation of the mouse melanocortin-3 receptor results in increased fat mass and reduced lean body mass. Nat. Genet. 26 , 97–102 (2000).

Doche, M. E. et al. Human SH2B1 mutations are associated with maladaptive behaviors and obesity. J. Clin. Invest. 122 , 4732–4736 (2012).

Marenne, G. et al. Exome sequencing identifies genes and gene sets contributing to severe childhood obesity, linking PHIP variants to repressed POMC transcription. Cell Metab. 31 , 1107–1119.e12 (2020).

Asai, M. et al. Loss of function of the melanocortin 2 receptor accessory protein 2 is associated with mammalian obesity. Science 341 , 275–278 (2013).

Michaud, J. L. et al. Sim1 haploinsufficiency causes hyperphagia, obesity and reduction of the paraventricular nucleus of the hypothalamus. Hum. Mol. Genet. 10 , 1465–1473 (2001).

Farooqi, I. S. et al. Clinical spectrum of obesity and mutations in the melanocortin 4 receptor gene. N. Engl. J. Med. 348 , 1085–1095 (2003).

Wade, K. H. et al. Loss-of-function mutations in the melanocortin 4 receptor in a UK birth cohort. Nat. Med. 27 , 1088–1096 (2021). This paper establishes the frequency of loss-of-function mutations in MC4R to be 0.3%, much more common than previously appreciated .

van der Klaauw, A. et al. Role of melanocortin signalling in the preference for dietary macronutrients in human beings. Lancet 385 , S12 (2015).

Raffan, E. et al. A deletion in the canine POMC gene is associated with weight and appetite in obesity-prone Labrador retriever dogs. Cell Metab. 23 , 893–900 (2016).

Kim, K. S., Larsen, N., Short, T., Plastow, G. & Rothschild, M. F. A missense variant of the porcine melanocortin-4 receptor (MC4R) gene is associated with fatness, growth, and feed intake traits. Mamm. Genome 11 , 131–135 (2000).

Aspiras, A. C., Rohner, N., Martineau, B., Borowsky, R. L. & Tabin, C. J. Melanocortin 4 receptor mutations contribute to the adaptation of cavefish to nutrient-poor conditions. Proc. Natl Acad. Sci. USA 112 , 9668–9673 (2015).

Pinto, S. et al. Rapid rewiring of arcuate nucleus feeding circuits by leptin. Science 304 , 110–115 (2004).

Davies, A. M. The role of neurotrophins in the developing nervous system. J. Neurobiol. 25 , 1334–1348 (1994).

McAllister, A. K., Katz, L. C. & Lo, D. C. Opposing roles for endogenous BDNF and NT-3 in regulating cortical dendritic growth. Neuron 18 , 767–778 (1997).

Korte, M. et al. Hippocampal long-term potentiation is impaired in mice lacking brain-derived neurotrophic factor. Proc. Natl Acad. Sci. USA 92 , 8856–8860 (1995).

Kernie, S. G., Liebl, D. J. & Parada, L. F. BDNF regulates eating behavior and locomotor activity in mice. EMBO J. 19 , 1290–1300 (2000).

Xu, B. et al. Brain-derived neurotrophic factor regulates energy balance downstream of melanocortin-4 receptor. Nat. Neurosci. 6 , 736–742 (2003).

Xu, B. & Xie, X. Neurotrophic factor control of satiety and body weight. Nat. Rev. Neurosci. 17 , 282–292 (2016).

Rios, M. et al. Conditional deletion of brain-derived neurotrophic factor in the postnatal brain leads to obesity and hyperactivity. Mol. Endocrinol. 15 , 1748–1757 (2001).

Gray, J. et al. Hyperphagia, severe obesity, impaired cognitive function, and hyperactivity associated with functional loss of one copy of the brain-derived neurotrophic factor (BDNF) gene. Diabetes 55 , 3366–3371 (2006).

Yeo, G. S. et al. A de novo mutation affecting human TrkB associated with severe obesity and developmental delay. Nat. Neurosci. 7 , 1187–1189 (2004).

Thorleifsson, G. et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat. Genet. 41 , 18–24 (2009).

Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50 , 621–629 (2018).

Ndiaye, F. K. et al. The expression of genes in top obesity-associated loci is enriched in insula and substantia nigra brain regions involved in addiction and reward. Int. J. Obes. 44 , 539–543 (2020).

Loos, R. J. & Yeo, G. S. The bigger picture of FTO: the first GWAS-identified obesity gene. Nat. Rev. Endocrinol. 10 , 51–61 (2014).

Church, C. et al. Overexpression of Fto leads to increased food intake and results in obesity. Nat. Genet. 42 , 1086–1092 (2010).

Fischer, J. et al. Inactivation of the Fto gene protects from obesity. Nature 458 , 894–898 (2009).

Cheung, M. K., Gulati, P., O’Rahilly, S. & Yeo, G. S. FTO expression is regulated by availability of essential amino acids. Int. J. Obes. 37 , 744–747 (2013).

Gulati, P. et al. Role for the obesity-related FTO gene in the cellular sensing of amino acids. Proc. Natl Acad. Sci. USA 110 , 2557–2562 (2013).

Karra, E. et al. A link between FTO, ghrelin, and impaired brain food-cue responsivity. J. Clin. Invest. 123 , 3539–3551 (2013).

Hess, M. E. et al. The fat mass and obesity associated gene (Fto) regulates activity of the dopaminergic midbrain circuitry. Nat. Neurosci. 16 , 1042–1048 (2013).

Ruud, J. et al. The fat mass and obesity-associated protein (FTO) regulates locomotor responses to novelty via D2R medium spiny neurons. Cell Rep. 27 , 3182–3198.e9 (2019).

Stratigopoulos, G., LeDuc, C. A., Cremona, M. L., Chung, W. K. & Leibel, R. L. Cut-like homeobox 1 (CUX1) regulates expression of the fat mass and obesity-associated and retinitis pigmentosa GTPase regulator-interacting protein-1-like (RPGRIP1L) genes and coordinates leptin receptor signaling. J. Biol. Chem. 286 , 2155–2170 (2011).

Stratigopoulos, G. et al. Hypomorphism for RPGRIP1L, a ciliary gene vicinal to the FTO locus, causes increased adiposity in mice. Cell Metab. 19 , 767–779 (2014).

Stratigopoulos, G. et al. Hypomorphism of Fto and Rpgrip1l causes obesity in mice. J. Clin. Invest. 126 , 1897–1910 (2016).

Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507 , 371–375 (2014).

Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373 , 895–907 (2015).

O’Rahilly, S., Coll, A. P. & Yeo, G. S. FTO obesity variant and adipocyte browning in humans. N. Engl. J. Med. 374 , 191 (2016).

PubMed   Google Scholar  

Sobreira, D. R. et al. Extensive pleiotropism and allelic heterogeneity mediate metabolic effects of IRX3 and IRX5. Science 372 , 1085–1091 (2021).

Almen, M. S. et al. The obesity gene, TMEM18, is of ancient origin, found in majority of neuronal cells in all major brain regions and associated with obesity in severely obese children. BMC Med. Genet. 11 , 58 (2010).

Larder, R. et al. Obesity-associated gene TMEM18 has a role in the central control of appetite and body weight regulation. Proc. Natl Acad. Sci. USA 114 , 9421–9426 (2017). Aside from the efforts at characterizing the FTO locus, this paper establishes a plausible role for yet another GWAS-identified candidate gene, TMEM18 , in energy homeostasis .

Wiemerslage, L. et al. The Drosophila ortholog of TMEM18 regulates insulin and glucagon-like signaling. J. Endocrinol. 229 , 233–243 (2016).

Biederer, T. et al. SynCAM, a synaptic adhesion molecule that drives synapse assembly. Science 297 , 1525–1531 (2002).

Yan, X. et al. Cadm2 regulates body weight and energy homeostasis in mice. Mol. Metab. 8 , 180–188 (2018).

Rathjen, T. et al. Regulation of body weight and energy homeostasis by neuronal cell adhesion molecule 1. Nat. Neurosci. 20 , 1096–1103 (2017).

Wheeler, E. et al. Genome-wide SNP and CNV analysis identifies common and low-frequency variants associated with severe early-onset obesity. Nat. Genet. 45 , 513–517 (2013).

Schafer, M., Brauer, A. U., Savaskan, N. E., Rathjen, F. G. & Brummendorf, T. Neurotractin/kilon promotes neurite outgrowth and is expressed on reactive astrocytes after entorhinal cortex lesion. Mol. Cell. Neurosci. 29 , 580–590 (2005).

Matikainen-Ankney, B. A. & Kravitz, A. V. Persistent effects of obesity: a neuroplasticity hypothesis. Ann. N. Y. Acad. Sci. 1428 , 221–239 (2018).

Lee, A. W. et al. Functional inactivation of the genome-wide association study obesity gene neuronal growth regulator 1 in mice causes a body mass phenotype. PLoS ONE 7 , e41537 (2012).

Joo, Y., Kim, H., Lee, S. & Lee, S. Neuronal growth regulator 1-deficient mice show increased adiposity and decreased muscle mass. Int. J. Obes. 43 , 1769–1782 (2019).

Boender, A. J., van Gestel, M. A., Garner, K. M., Luijendijk, M. C. & Adan, R. A. The obesity-associated gene Negr1 regulates aspects of energy balance in rat hypothalamic areas. Physiol. Rep. 2 , e12083 (2014).

Singh, K. et al. Neuronal growth and behavioral alterations in mice deficient for the psychiatric disease-associated negr1 gene. Front. Mol. Neurosci. 11 , 30 (2018).

Szczurkowska, J. et al. NEGR1 and FGFR2 cooperatively regulate cortical development and core behaviours related to autism disorders in mice. Brain 141 , 2772–2794 (2018).

PubMed   PubMed Central   Google Scholar  

Noh, K. et al. Negr1 controls adult hippocampal neurogenesis and affective behaviors. Mol. Psychiatry 24 , 1189–1205 (2019).

Cano-Gamez, E. & Trynka, G. From GWAS to function: using functional genomics to identify the mechanisms underlying complex diseases. Front. Genet. 11 , 424 (2020).

Loos, R. J. et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat. Genet. 40 , 768–775 (2008).

Chambers, J. C. et al. Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat. Genet. 40 , 716–718 (2008).

Yaghootkar, H. et al. Genetic studies of leptin concentrations implicate leptin in the regulation of early adiposity. Diabetes 69 , 2806–2818 (2020).

Kichaev, G. et al. Leveraging polygenic functional enrichment to improve GWAS power. Am. J. Hum. Genet. 104 , 65–75 (2019).

Pulit, S. L. et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum. Mol. Genet. 28 , 166–174 (2019).

Wang, Y. et al. Melanocortin 4 receptor signals at the neuronal primary cilium to control food intake and body weight. J. Clin. Invest. 131 , e142064 (2021).

Article   CAS   PubMed Central   Google Scholar  

Farooqi, I. S. et al. Effects of recombinant leptin therapy in a child with congenital leptin deficiency. N. Engl. J. Med. 341 , 879–884 (1999). This paper is the first description of the treatment of leptin deficiency using leptin replacement therapy .

Farooqi, I. S. & O’Rahilly, S. 20 years of leptin: human disorders of leptin action. J. Endocrinol. 223 , T63–T70 (2014).

Yeo, G. S. H. et al. The melanocortin pathway and energy homeostasis: from discovery to obesity therapy. Mol. Metab. 48 , 101206 (2021).

Kuhnen, P. et al. Proopiomelanocortin deficiency treated with a melanocortin-4 receptor agonist. N. Engl. J. Med. 375 , 240–246 (2016). This paper is the first to demonstrate that the melanocortin pathway is a relevant and, so far, safe therapeutic target for the treatment of at least rare genetic causes of obesity .

Clement, K. et al. MC4R agonism promotes durable weight loss in patients with leptin receptor deficiency. Nat. Med. 24 , 551–555 (2018).

Clement, K. et al. Efficacy and safety of setmelanotide, an MC4R agonist, in individuals with severe obesity due to LEPR or POMC deficiency: single-arm, open-label, multicentre, phase 3 trials. Lancet Diabetes Endocrinol. 8 , 960–970 (2020).

Ayers, K. L. et al. Melanocortin 4 receptor pathway dysfunction in obesity: patient stratification aimed at MC4R agonist treatment. J. Clin. Endocrinol. Metab. 103 , 2601–2612 (2018).

Heymsfield, S. B. et al. Recombinant leptin for weight loss in obese and lean adults: a randomized, controlled, dose-escalation trial. JAMA 282 , 1568–1575 (1999).

Collet, T. H. et al. Evaluation of a melanocortin-4 receptor (MC4R) agonist (setmelanotide) in MC4R deficiency. Mol. Metab. 6 , 1321–1329 (2017).

Khera, A. V. et al. Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell 177 , 587–596.e9 (2019). This study describes a comprehensive PGS and its association with BMI throughout the life course .

Loos, R. J. F. 15 years of genome-wide association studies and no signs of slowing down. Nat. Commun. 11 , 5900 (2020).

Pearce, L. R. et al. KSR2 mutations are associated with obesity, insulin resistance, and impaired cellular fuel oxidation. Cell 155 , 765–777 (2013).

Greenfield, J. R. et al. Modulation of blood pressure by central melanocortinergic pathways. N. Engl. J. Med. 360 , 44–52 (2009).

Torkamani, A. & Topol, E. Polygenic risk scores expand to obesity. Cell 177 , 518–520 (2019).

Download references

Acknowledgements

R.J.F.L. is supported by funding from Novo Nordisk Foundation (NNF Laureate Award) and the US National Institutes of Health (R01DK110113; R01DK107786; R01HL142302; R01 DK124097). G.S.H.Y. is supported by the Medical Research Council (MRC Metabolic Diseases Unit (MC_UU_00014/1)). The authors thank M. Guindo Martinez for her help with creating data for Fig. 3 and Supplementary Tables 1 and 2.

Author information

Authors and affiliations.

Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark

Ruth J. F. Loos

Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA

MRC Metabolic Diseases Unit, University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK

Giles S. H. Yeo

You can also search for this author in PubMed   Google Scholar

Contributions

The authors contributed equally to all aspects of the article.

Corresponding authors

Correspondence to Ruth J. F. Loos or Giles S. H. Yeo .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Peer review information.

Nature Reviews Genetics thanks D. Meyre, T. Fall and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

23andMe: https://research.23andme.com/collaborate/

All of Us: https://allofus.nih.gov/

Biobank Japan: http://jenger.riken.jp/en/

Genetic Investigation for ANthropometric Traits (GIANT) consortium: https://portals.broadinstitute.org/collaboration/giant/index.php/Main_Page

Million Veterans Project: https://www.research.va.gov/mvp/

NCD Risk Factor Collaboration (NCD RisC): https://www.ncdrisc.org/data-visualisations.html

UK Biobank: https://www.ukbiobank.ac.uk/

Supplementary information

Supplementary information.

An environment that promotes weight gain.

A severe, early-onset form of obesity, caused by a single-gene mutation, with little or no influence of the environment.

A common multifactorial form of obesity, resulting from an interaction between the obesogenic environment and hundreds of genetic variants.

An approach used to understand the function of a gene by analysing the consequences of genetically manipulating specific sequences within the gene.

A hypothesis-driven approach to study the effect of a given gene (chosen based on the current understanding of its biology and pathophysiology) on susceptibility to the phenotype under study.

A method that relies on the relatedness of study participants to test whether certain chromosomal regions co-segregate with a disease or trait across generations.

(GWAS). A hypothesis-generating approach that screens whole genomes for associations between genetic variants and a phenotype of interest at much higher resolution than is possible for genome-wide linkage studies, and is thus better able to narrow down the associated locus.

(PGS). A measure used to assess an individual’s genetic susceptibility to disease, calculated by summing the number of disease-increasing alleles, weighted by each variant’s effect size observed in a genome-wide association study.

(AUC ROC ). A metric used to assess the ability of a predictor to discriminate between individuals with and without a disease. The AUC ranges from 0.50 (equal to tossing a coin) to 1.0 (perfect prediction).

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Loos, R.J.F., Yeo, G.S.H. The genetics of obesity: from discovery to biology. Nat Rev Genet 23 , 120–133 (2022). https://doi.org/10.1038/s41576-021-00414-z

Download citation

Accepted : 17 August 2021

Published : 23 September 2021

Issue Date : February 2022

DOI : https://doi.org/10.1038/s41576-021-00414-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Genetic effects of sequence-conserved enhancer-like elements on human complex traits.

• Wing Hung Wong

Genome Biology (2024)

A low-calorie meal replacement improves body composition and metabolic parameters in shift workers with overweight and obesity: a randomized, controlled, parallel group trial

• Piumika Sooriyaarachchi
• Ranil Jayawardena
• Neil A. King

Nutrition & Metabolism (2024)

Hyperphagia and impulsivity: use of self-administered Dykens’ and in-house impulsivity questionnaires to characterize eating behaviors in children with severe and early-onset obesity

• Lara Arnouk
• Hélène Chantereau
• Béatrice Dubern

Orphanet Journal of Rare Diseases (2024)

Associations between maternal pre-pregnancy BMI and infant striatal mean diffusivity

• Aylin Rosberg
• Harri Merisaari
• Jetro J. Tuulari

BMC Medicine (2024)

Identifying latent genetic interactions in genome-wide association studies using multiple traits

• Andrew J. Bass
• Shijia Bian
• Michael P. Epstein

Genome Medicine (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Childhood obesity treatment: case studies

Resources Policy Dossiers Childhood Obesity Treatment Childhood obesity treatment: case studies

• Sugar-Sweetened Beverage Tax
• Digital Marketing
• School-based interventions
• Community-level interventions
• Pregnancy & Obesity
• Childhood Obesity Treatment
• Front-of-pack nutrition labelling
• Obesity & COVID-19
• Physical Activity
• Food Systems
• Weight Stigma

Family-Based Interventions in the Prevention and Management of Childhood Overweight and Obesity: An International Review of Best Practices, and a Review of current Irish Interventions

The aim of this review is "to identify current family-based practice internationally for the prevention and treatment of childhood overweight and obesity and to examine current Irish Programmes so that best practice recommendations can be drawn up."

Cost-effectiveness of intensive inpatient treatment for severely obese children and adolescents in the Netherlands; a randomized controlled trials (HELIOS)

This paper presents "the design of a randomized controlled trial comparing the cost-effectiveness of two itnensive one-year inpatient treatments to each other and to usual are for severely obese children and adolescents." 

Family-based behavioural treatment of childhood obesity in a UK National Health Service setting: randomized controlled trial

The objective of this randomised controlled trial was "to examine the acceptability and effectiveness of 'family-based behavioural treatment' (FBBT) for childhood obesity in an ethnically and social diverse sample of families in a UK National Health Service (NHS) setting."  

Reducing childhood obesity in Poland by effective policies

The purpose of this report was "to faciliate the development of an action plan and implementation of the strategy dimensions around childhood obesity by providing evidence-based policy options adapted to the national context."  https://www.who.int/europe/publications/i/item/WHO-EURO-2017-2977-42735-59610

The Malaysian Childhood Obesity Treatment Trial (MASCOT)

The primary aim of the study is "to describe a behavioural family-centred, group-based treatment programme for childhood obesity in Malaysia - the MASCOT." 

Process evaluation of an up-scaled community-based child obesity treatment program: NSW Go4Fun®

This paper "describes the up-scaling of Go4Fun in New South Wales and the characteristics of the population it has reached and retained since inception in 2009,including characteristics of children who completed and did not copmlete the programme."  

Randomized Controlled Trial of the MEND Program: A Family-Based Community Intervention for Childhood Obesity

The objective of this study was to evaluate the effectiveness of the Mind, Exercise, Nutrition, Do it (MEND) Programme. 

Assessing the short-term outcomes of a community-based intervention for overweight and obese children: the MEND 5-7 programme

The aim of this study was "to report outcomes from the UK service level delivery of MEND 5-7." 

Effectiveness of a Multi-Component Intervention for Overweight and Obese Children (Nereu Program): A Randomized Controlled Trial

The objective of this study was "to evaluate the effectiveness of the Nereu Program in improving anthropometric parameters, physical activity and sedentary behaviours, and dietary intake." 

Laparoscopic Roux-en-Y gastric bypass in adolescents with severe obesity: a prospective five-year Swedish nationwide study (AMOS)

The objective of this study was "to report outcomes over 5 years in adolescents follow Roux-en-Y gastric bypass (RYGB) or conservative treatment in a Swedish nationwide prospective non-randomised controlled study, with an additional matched adult comparison group undergoing RYGB."

Mapping the health system response to childhood obesity in the WHO European Region. An overview and country perspectives

This project aimed "to assess the response of health care delivery systems in 19 countries in the WHO European Region to the childhood obesity epidemic." 

An Integrated Clinic-Community Partnership for Child Obesity Treatment: A Randomized Pilot Trial  

This study aims “to describe the implementation of an integrated clinic-community partnership for child obesity treatment and [...] to evaluate the effectiveness of integrated treatment on child BMI and health outcomes” in a lower-income area. Enrolled children were between 5 and 11 years of age, over the 95th percentile for BMI, and referred to clinic by their paediatrician.

Adapting Pediatric Obesity Treatment Delivery for Low-Income Families: A Public–Private Partnership

The aim of this study was to “evaluate the feasibility of delivering a paediatric weight management intervention adapted for low-income families.” 

Challenges and results of a school-based intervention to manage excess weight among school children in Tunisia 2012-2014

This study intended to “demonstrate the feasibility and effectiveness of a school-based weight management program based on healthy lifestyle promotion for obese and overweight adolescents in Sousse, Tunisia.” 

The Effect of a Multidisciplinary Lifestyle Intervention on Obesity Status, Body Composition, Physical Fitness, and Cardiometabolic Risk Markers in Children and Adolescents with Obesity

The aim of this study was to develop a “moderate-intensity multidisciplinary lifestyle intervention program” to treat obesity in the “real world” and evaluate its effectiveness through anthropometric measures. 

The GReat-Child™ Trial: A Quasi-Experimental Intervention on Whole Grains with Healthy Balanced Diet to Manage Childhood Obesity in Kuala Lumpur, Malaysia

Scientists designed the GReat-Child™ trial to determine if increasing whole grain consumption could effectively impact health parameters in Malaysian children. 

Impact of readiness to change behaviour on the effects of a multidisciplinary intervention in obese Brazilian children and adolescents

This study examined how the success of a multifaceted obesity treatment was related to a child’s willingness to alter their lifestyle using Stages of Readiness for Behavior Change (SRBC). 

Sacbe, a Comprehensive Intervention to Decrease Body Mass Index in Children with Adiposity: A Pilot Study

The aim of this study was to “to achieve a higher percentage of success in lowering the BMI z-score in children with adiposity and their parents through a pilot program "Sacbe" based on HLS, sensitive to the sociocultural context previously explored and with the active participation of parents.” 

A Novel Home-Based Intervention for Child and Adolescent Obesity: The Results of the Whānau Pakari Randomized Controlled Trial

The aim of this study was to “report 12‐month outcomes from a multidisciplinary child obesity intervention program, targeting high‐risk groups” in New Zealand. 

Systematic reviews

Cost studies

Government guidelines & recommendations

Share this page.

Training & Events

SCOPE E-Learning

We offer the only internationally recognised course on obesity management. Read more here.

Global Obesity Observatory

We offer various statistics, maps and key data around the topic of obesity. You can find all that and more here.

Policy & Advocacy

Our Policy Priorities

We have developed five key areas of policy that are a priority to us. Want to know more? Check them out here!

• Our Members
• Member Benefits
• Membership Application Form
• Partnerships
• Global Obesity Forum
• Patient Portal
• Finance Committee
• Annual Report and Financials
• Prevalence of Obesity
• Causes of Obesity
• Obesity Classification
• Prevention of Obesity
• Obesity as a disease
• Commercial determinants of obesity
• Childhood Obesity
• Obesity in Universal Health Coverage
• The ROOTS of Obesity
• World Obesity Day
• Healthy Venues
• Reinventing the Food System: A Report
• The Spotlight Project
• SCOPE Examination
• Guide to SCOPE Certification
• SCOPE Fellows
• Leadership Programme
• Accreditation
• SCOPE Schools
• SCOPE Pricing
• SCOPE Sessions
• SCOPE Accredited Events
• Event Archive
• International Congress on Obesity

Sign up for notifications

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Severe obesity, emotions and eating habits: a case-control study

1 University of Helsinki, Helsinki, Finland

H. Naukkarinen

2 Carea Hospital District, Kymenlaakso Psychiatric Hospital, University of Helsinki, Töölönkatu 26 C 55, 00260 Helsinki, Finland

Associated Data

The data will not be shared. The data and materials are only in possession of the author.

Obesity has a multifaceted etiology that involves genetic, biological and behavioral factors, body growth, eating habits, energy expenditure and the function of adipose tissue. The present study aimed to expand upon knowledge about the relationships among obesity, emotions and eating habits in severely obese individuals using a case-control method.

The subject group consisted of 112 individuals (81 females and 31 males) receiving a permanent disability pension primarily for obesity. The control subjects were randomly selected from the same area and were receiving a disability pension for a different primary illness. The controls were matched with the subjects by the place of residence, sex, age, the time since the pension was granted and occupation. Psychiatric interviews were conducted on all participants. The results were analyzed using the chi-squared test (χ 2 -test) and the percent distribution. The subject and control groups were compared using the t-test for paired variables. Conditional logistic regression analysis was also conducted.

The emotional state of eating was significantly associated with quarrels and feelings of loneliness. The subjects suffered from night eating syndrome, which was associated with an increased risk of early retirement. Binge eating syndrome was observed more frequently in the study group. The subjects reported feeling increased hunger compared with the controls. A significant percentage of the subjects had a body mass index of ≥ 40. No differences in eating habits were observed between the groups.

This study provides information on the relationship between emotions and eating habits in obesity, which is a rarely studied topic. We believe that our study provides a novel and necessary overview of the associations among severe obesity, emotions and eating habits.

Obesity has a multifaceted etiology that includes genetic, biological and behavioral factors, such as body growth, eating habits, energy expenditure and the function of adipose tissue. Recently, there has been growing research interest in obesity. Studies have explored the psychological significance of food in individuals who struggle with weight. Furthermore, the possibility of an emotional disorder secondary to obesity should always be evaluated separately.

Biological aspects of eating habits

Many peripheral hormones control appetite and food intake. Hunger and satiety hormonal signals are released from the adipose tissue, pancreas and gastrointestinal tract and travel to the brain [ 1 , 2 ]. Leptin is synthesized by adipose tissue and decreases the impact of appetite-stimulating neuropeptides [ 3 ]. Insulin is a pancreatic hormone that maintains glucose homeostasis. Like leptin, insulin is capable of modifying the dopaminergic pathway and can influence eating behaviors [ 4 , 5 ]. Ghrelin is produced by the stomach and increases food intake [ 6 ]. Peptide tyrosine-tyrosine (PYY) decreases appetite and increases satiety [ 7 , 8 ]. Glucagon-like peptide decreases food intake [ 9 ]. Cholecystokinin (CCK) helps to control appetite, ingestive behavior and gastric emptying [ 10 ]. Neuroimaging studies, which have assessed appetite and body weight regulation, have found modifications in dopaminergic function in response to eating or food cues [ 11 , 12 ].

Psychological aspects of eating habits

Hudson and Williams [ 13 ] found that eating in obese individuals was more frequently associated with the emotions of anger, boredom and depression compared with normal-weight individuals. Eating alone was also much more common among the obese individuals, and they often tried to conceal their eating [ 13 ].

According to Hamburger [ 14 ], hyperphagia may involve eating to relieve emotional tension, such as that caused by unspecified anxiety or feelings of rejection. Masheb and Grilo [ 15 ] examined emotional overeating in overweight patients with binge eating disorder (BED) and found significant correlations between the emotional overeating items and total score and binge frequency, eating disorder features and depressive symptomatology. Further, Yannakoulia et al. [ 16 ] have found that the dietary patterns differ between anxious men and women, after adjusting for potential confounders.

According to Nedeltcheva et al. [ 17 ], recurrent bedtime restriction can modify the amount, composition and distribution of human food intake. Sleeping short hours in an obesity-promoting environment may facilitate the excessive consumption of energy from snacks but not from actual meals.

According to Ostrovsky et al., obesity in males and females is associated with binge eating, social anxiety and emotional eating [ 18 ]. The findings of Dalton et al. provide additional support that the trait of binge eating represents a hedonic subgroup of obesity. The authors have emphasized the importance of food composition and have determined that gluttons fail to recognize when they are full [ 19 ]. Dalle et al. have investigated personality features that influence eating habits, the development of obesity and the likelihood of treatment success for obesity and have identified particular personality traits (binge eating and night eating) that are associated with obesity in women [ 20 ]. One study has shown that obese individuals with BED represent a specific subgroup of obesity with increased food-related impulsivity. In addition, the authors found increased reward sensitivity in obese individuals, which was more pronounced among those with BED [ 21 ]. Lent et al. have identified similarities between addictive personality and poor eating habits [ 22 ]. Further, a lack of self-discipline has been demonstrated to be highly associated with BED and obesity [ 23 ].

Considering the background information presented above, this study examined the relationships among severe obesity, emotions and eating habits; these associations have not been fully investigated to date. In this case-control study, we assessed emotions and eating habits in a group of severely obese individuals.

The subject and control groups

The subjects consisted of 152 individuals living in southern Finland who were receiving a permanent disability pension primarily for obesity. Among these individuals, 19 had been granted a temporary pension and were excluded from the sample. In addition, individuals who had died or who no longer received a pension were also excluded from the sample. The subject group consisted of 112 patients (81 females and 31 males). The control subjects were randomly selected from the same area and were receiving a disability pension for a different primary illness. The controls were matched with the subjects by place of residence and sex. In addition, all steps were taken to match the controls based on age, the time since the pension was granted and occupation. The occupations of the controls were either the same as the subjects or unknown.

The male and the female controls were selected separately. Three controls for each female subject and five for each male subject were selected from among the potential controls. For the interview, attempts were made to have at least two controls for each female subject and three for each male subject. In total, 510 participants (112 subjects and 398 controls) were enrolled in this study. More males than females refused to participate in this study. In addition, more matched controls than subjects refused to participate. The numbers of females and males are provided in Table  1 . The data were collected in cooperation with the Social Insurance Institution of Finland. Three letters of invitation to participate were sent to each subject and control. The letters were discreetly worded and emphasized the confidentiality of the study. Most of the individuals who refused to participate indicated their reasons for refusal in writing, and this information was made available to the authors. The interviews were blinded. The interviewer did not know whether the participant was in the subject or control group until all of the research material was compiled and the codes were revealed.

Basic characteristics of study participants

The participants were interviewed by the author of this manuscript using an interview form that was partially filled out during the interview and then completed after. The pilot study ( n  = 30) was conducted at the neurological ward of Hesperia Hospital in Helsinki, Finland. This ward contained neurological patients with commonly observed diseases. Adjustments were made to the interview form following the pilot study based on how well the participants understood the form and the amount of time needed to fill it out.

Body mass index (BMI) was calculated using the following formula: body weight (kg) divided by the square of body height. According to the World Health Organization (WHO) guidelines, the weights were classified as follows: overweight, BMI 25 ≤ 29; obese, BMI 30 ≤ 34; severely obese, BMI 35 ≤ 4; and morbidly obese, BMI > 40.

The Sickness Insurance Act and the National Pensions Act provide insurance against disability for all residents of Finland. The National Pensions Scheme offers basic pension insurance to all Finnish citizens. Age, professional skills, and other factors are also important for evaluating disability. Individual differences in working capacity should be recognized, with consideration of the applicants’ ages.

Further, standard occupational classifications from the Social Insurance Institution (1982) were used in this study.

The study protocol was approved by the Ethics Committee of Hesperia/Aurora Hospital (a community psychiatric hospital in Helsinki) and Lapinlahti Hospital (a psychiatric clinic at Helsinki University)/Psychiatric Centrum of Helsinki University. Informed consent was obtained from the participants, and the ethical principles of the Declaration of Helsinki were followed throughout the study.

Statistical methods

The results were analyzed using the χ 2 -test, t-test and conditional logistic linear regression analysis. Because the subjects were matched, the means were calculated for both the subjects and controls, and then the data were analyzed using the t-test for paired variables. Paired variables that were statistically significant were further analyzed by conditional logistic regression analysis. For the results that remained significant, the risk ratio (RR) and the upper and lower limits of the confidence interval were calculated. Statistical analysis was performed using Statistical Package for Social Sciences software (SPSS), version 11.01 (Windows, Chicago, IL, USA). Logistic linear regression analysis was performed using GLIM program [ 24 ]. For continuous variables, the results were analyzed using the paired t-test. Conditional logistic regression is a straightforward analysis provided that the data are grouped separately for each individual. A major advantage of this technique is that it is easy to perform and has inherent flexibility when all data for each individual are included in analysis.

The observations in each matched set included one case and 0-5 controls. These observations were each considered a count in logistic regression analysis, and the model included a Poisson error distribution and logarithmic link function; therefore, the model was a special form of a log-linear model. The linear predictor in the systematic part of the model for each observation is a linear function of the observed exposure variables for each individual plus a constant (set) term, which may vary from matched set to matched set. This model for analysis of case-control data is termed a conditional logistic regression model.

In statistical analysis, for cases in which none of the controls completely matched the subject, the next most closely matched control was used to avoid decreasing the size of the subject group. Use of this matched control approach resulted in exclusion of some of the subjects who had agreed to participate in the study during statistical analysis because no matched control was available. Because several specific variables were absent in some cases, the number of observations available for comparisons was further diminished [ 25 ].

Group differences were considered highly significant, significant, and almost significant when the probabilities (p) of error in rejecting the null hypothesis were p  < 0.001, p  < 0.01, and p  < 0.05, respectively.

Study participation refusal

In total, 37 individuals refused to participate (9 males and 28 females) in the study. One male subject could not be contacted after initial inclusion in the study, and one female subject dropped out of the study before the psychological test was administered. The mean ages of the refused male and female participants in the study group were 59 (standard deviation (SD), 3.61) and 61 (SD, 4.46) years, respectively. A total of 31 participants had primary school education, and 34 had no vocational education. The individuals who refused to participate had the same education level, age and sex distribution as the participating individuals (Table  1 ). More matched controls than subjects refused to participate in this study. Table  1 shows the complete information for the participants in this study.

Table  1 illustrates the background characteristics of the study participants.

The mean weight of the subjects ( n  = 75) was 106.2 kg (SD = 18 kg), and that of the controls was 72.3 kg (SD = 14.3 kg). Matching of the subjects and controls was successful. The χ 2 -test revealed that there were no significant differences in age, marital status, basic education level or occupation between the subjects and controls. At the time of the personal interview, 40% of the female subjects and none of the female controls had a BMI of over 40 kg/m 2 , and 33% of the female subjects had a BMI of 35-40 kg/m 2 . Among the men, 36% of the subjects and none of the controls had a BMI exceeding 40 kg/m 2 , and 41% of the subjects and 2% of the controls had a BMI of 35-40 kg/m 2 . In addition, 6% of the female subjects and none of the male subjects had a BMI of 25-30 kg/m 2 .

Among the subjects, 91% (68 subjects) had received a secondary somatic diagnosis from the Social Insurance Institution. The most common secondary diagnosis was “diseases of the musculoskeletal system and connective tissue”, which had been diagnosed in 38% of the case subjects. Among the controls, “disease pertaining to the cardiovascular organs” was the primary diagnosis (20% of the controls). All of the controls had been diagnosed with a primary illness other than obesity (Table  1 and Fig.  1 ).

Body mass index distribution for the participants in the study group

Table  2 shows the influence of the emotional state on eating. The results showed that 14% of the subjects and 23% of the controls reported eating following a quarrel. The paired t-test showed that this difference was significant ( p  = 0.007), and logistic regression analysis revealed a risk ratio of 45 and confidence interval of 14-145; in addition, the χ 2 -test revealed that this difference was highly significant ( p  = 0.001). Among the subjects, 3% reported eating when they were angry, and this behavior was not observed among the controls. Similar results were obtained with the paired t-test ( p  = 0.159) and χ 2 -test ( p  = 0.086), which showed non-significant differences between the groups. In addition, 4% of the subjects and 1% of the controls reported eating when they felt displeasure. Similar results were obtained with both the paired t-test (p = 0.083) and χ 2 -test ( p  = 0.154), which showed non-significant differences between the groups. Furthermore, 15% of the subjects and 7% of the controls reported eating when feeling pleasure; the paired t-test ( p  = 0.073) and χ 2 -test ( p  = 0.054) showed that this difference was almost significant. Among the controls, 1% reported eating when excited; the paired t-test ( p  = 0.182) and χ 2 -test ( p  = 0.557) again revealed non-significant differences between the groups. Moreover, 11% of the subjects and 3% of the controls reported eating when feeling lonely; this difference was also almost significant according to the paired t-test ( p  = 0.197) and χ 2 -test ( p  = 0.023). Finally, 18% of the subjects and 12% of the controls said that their eating was associated with some non-specific emotional state (paired t-test; p  = 0.211; and χ 2 -test; p  = 0.311).

Influences of different emotional states on eating

We also investigated the prevalence of binge eating and found that 8% of the subjects and 2% of the controls had experienced periods of binge eating, although this difference was not significant ( p  = 0.060). Furthermore, 36% of the subjects and 11% of the controls reported having night eating syndrome (NES). Logistic regression analysis revealed that the subjects with NES had a significantly higher risk of early retirement (being placed on pension early) (RR: 4.5, confidence interval: 2.5-8.1, p  = 0.000).

In addition, 15% of the subjects and 3% of the controls reported being constantly hungry, while 20% of the subjects and 24% of the controls said that they were often hungry (paired t-test, p  = 0.039). Logistic regression analysis revealed that these differences were almost significant, and the χ2-test indicated significant differences ( p  = 0.008).

The respondents were also asked when they felt hungry, and 34% of the subjects and 27% of the controls reported being hungriest in the evening, suggesting that most of their eating took place in the evening. Furthermore, 10% of the subjects and 3% of the controls said that they were the hungriest at night, whereas 10% of the subjects and 15% of the controls reported being hungry in the morning. The differences in feelings of hunger approached statistical significance between the subjects and controls in the paired t-test ( p  = 0.021), and the χ 2 -test indicated significant differences ( p  = 0.004).

To determine the respondents’ eating habits, they were asked which foods they liked and disliked. The results showed that 54% and 52% of the subjects and controls, respectively, reported eating all types of food. Only 9% of both the subjects and controls reported liking vegetables. None of the subjects and 1.8% of the controls reported eating fruits and berries. None of the subjects liked sausage, although 30% liked meat, compared with 31% of the controls (χ 2 -test, p  = 0.856). The eating habit findings were similar between the males and females.

Overall, 40% of the subjects and 41% of the controls reported liking sweets (χ 2 -test, p  = 0.887).

Table  3 shows family attitudes toward food and eating during the respondents’ formative years. The results showed that 6.7% of the subjects and 6.6% of the controls were taught that eating was very important during the formative years. In addition, 2.7% of the subjects stated that everything on the plate had to be eaten during the formative years. Moreover, 5.3% of the subjects and 5.1% of the controls only provided the different courses they ate during a meal. The subjects reported that their mothers had prepared their meals (χ 2 -test; p  = 0.85).

Family attitudes toward food and eating during the respondents’ adolescence

The respondents were further asked who cooks in their present family, and 75% of the subjects and 72% of the controls reported that they did the cooking themselves, whereas 22.7% of the subjects and 23.6% of the controls stated that their spouse made dinner. Finally, 96% of both the subjects and controls reported eating mostly at home.

To obtain information about the development of obesity, the participants were asked about their own perceptions of why they were obese. The results showed that 17% of the subjects and 3% of the controls indicated that metabolic factors were the reason for their obesity. Additionally, 42% of the participants thought that overeating caused their obesity, and 18% believed that they were overweight due to lack of exercise. Furthermore, 5% of the subjects felt that they were not excessively overweight, whereas 26% of the controls reported similar feelings (χ 2 -test, p  = 0.0007). Most notably, 6% of the males and 4.7% of the females in the subject group believed they did not have obesity. The χ 2 -test showed that this difference approached significance among the men ( p  = 0.022); however, the χ 2 -test revealed a significant difference between the subject and control groups ( p  = 0.004) (Table  4 ).

The reasons given by the participants for their obesity

Among the participants, 16% reported feeling angry when they attempted to lose weight, 10% reported feeling tired, 7% stated that they thought only of eating, 11% reported feeling good, 8% reported feeling weak and 4% reported feeling stressed. In addition, 3% of the subjects stated that trying to lose weight made them feel depressed, which was not reported by any of the controls.

The results of this study demonstrated that the emotional state was significantly connected to eating in association with quarrels and loneliness. In addition, feelings of anger and pleasure were also related to eating habits. BED was more common in the subject group than in the control group in this study. Logistic regression analysis revealed that the subjects with NES had a significantly higher risk of early retirement because of obesity.

A significant difference was observed between the subject and control groups in the feeling of hunger, with the subject group experiencing increased hunger. Further, the subjects were hungrier more often during the evening and night compared with the controls.

We found minor differences between the subject and control groups in their responses to questions about foods that they liked or disliked. Surprisingly, there was no significant difference in the preference for sweets between the subject and control groups.

In this study, we also investigated eating habits during the formative years. The majority of the subjects reported that everything on their plate had to be eaten. In their present family, many of the participants reported eating mostly at home and that they did the cooking themselves. These findings were similar between the subject and control groups.

When the participants were asked about their own perceptions of their obesity status, few of the subjects felt that they were not excessively overweight, whereas one-quarter of the controls reported having similar feelings. This finding was statistically significant.

Bruch [ 26 ] has reported that the feeling of hunger is not innate and that it is somewhat acquired by learning. In overeating disorders, the feeling of hunger is abnormally enhanced, prompting the urge to eat. The feeling of hunger gets mixed with other signals of discomfort and emotional tension. Individuals eat when they are disappointed, and they use their love of eating to compensate for these feelings. Bruch has also discussed “reactive obesity”, which affects individuals who eat when they are feeling tension, anxiety or loneliness. According to Hamburger (14), overeating tends to be associated with very strong emotional feelings; individuals eat when they are emotional disturbed.

Obesity is associated with uncontrolled hunger, anger, anxiety, boredom and fatigue. Varsha et al. [ 27 ] have also demonstrated that obese individuals have poor control of eating; they eat when they have stress, anxiety and boredom. Hudson and Williams [ 13 ] reported similar findings. According to Rosenthal and Wehr [ 28 ], who studied “seasonal affective disorder,” vegetative symptoms increase hunger and weight gain.

In this study of severe obesity, emotions and eating habits, we also found a connection between eating habits and emotions.

We found that loneliness was the emotion most strongly associated with eating. Brownell and Wadden have found that many individuals use food to escape and that they may use food as a substitute for relationships. Many obese individuals report that food is their best friend, and they look forward to times when they can be alone with food [ 29 ].

Gearhardt et al. studied the eating habits of patients with BED and found that nearly half of the patients had a food addiction. In addition, they detected significant associations between negative affect and emotional dysregulation, eating disorders, psychopathology and low self-esteem in the BED patients [ 30 ]. The number of binge eaters in the present study was lower compared with previous studies [ 31 ] [ 32 ] [ 33 ]. In addition, the prevalence of NES in the present study was higher than that reported by Stunkard et al. [ 34 ]. Marcus et al., who investigated obesity in nurses, found that the severity of binge eating was increased in younger individuals and in individuals with higher levels of obesity. In addition, the severity of binge eating has been shown to be related to dietary restraint [ 35 ]. According to Napolitano et al. [ 36 ], NES is a subcategory of obesity that overlaps with binge eating. In addition, Pawlow et al. have found that stress and anxiety play roles in NES and have suggested that practicing relaxation techniques may be an important component of treatment of this condition [ 37 ]. Further, our findings are in line with those of Masheb and Grilo [ 15 ]; however, we could not directly compare the findings of that study with our results because that group studied BED patients, only some of whom were overweight.

We found that the obese individuals in the subject group experienced and reported feeling hunger more often than the individuals in the control group; this difference approached statistical significance. Our findings are in contrast with those of Varsha et al., who have found that although obese patients report having enormous appetites, they are able to consume a large amount of food before they feel full. Further, they have found that individuals with obesity rarely report feelings of hunger [ 27 ].

Konttinen has investigated uncontrolled and emotional eating among Finnish men and has shown that individuals who are motivated to lose weight eat less [ 38 ]. In addition, Konttinen has found that emotional eating and depressive symptoms are correlated with increased weight in both males and females. Furthermore, emotional eating has been shown to be related to eating sweets in both genders, and depressive symptoms and non-emotional eating have been demonstrated to be related to reduced consumption of fruits and vegetables. These findings support the associations of emotional eating and depressive symptoms with eating unhealthy food [ 39 ]. In our study, the same amount of subjects and controls reported liking to eat sweets. We did not find any difference in the consumption of fruits and vegetables between the groups.

Eating habits are culturally dependent and are learned as a child. In addition to the quality of nutrition, more attention should be paid to the emotional reasons for eating, as suggested by Brownell and Wadden [ 29 ]. In this study, we assessed childhood eating habits and found minor differences between the study and control groups.

The clear advantage of this study is its use of a non-selective sample of individuals with severe obesity. Unlike most studies of obesity, the subjects were not recruited from a group of dieters. This study concentrated on a group of individuals receiving a disability pension for obesity. All of the subjects were individually interviewed by an experienced psychiatrist. The interview was conducted such that the interviewer did not know whether the individual was in the subject or control group. The subject and control groups were successfully matched. The occupational and social statuses were nearly identical between the two groups. Both the subjects and controls were receiving a pension for the same duration of time, which minimized influences of the subjects’ living situations. The fact that the controls were selected by random sampling using data from the Social Insurance Institution of Finland adds further value to our findings. This study was conducted by psychiatrists; although additional benefits would have been achieved by performing analyses with the expertise of a dietician, this was not possible in this study.

We believe that our study provides a novel and necessary overview of severe obesity, emotions and eating habits. We hope that this overview will provide insights that will help to revise and update the current knowledge on obesity. Our finding of a connection between emotions and obesity confirms the importance our study. We believe that this study provides encouraging possibilities for research on the potential health effects of severe obesity and it’s development.

Acknowledgements

No funding was obtained in this study.

Availability of data and materials

Authors’ contributions.

MK M.D., DPH and HN Adjunct Professor, M.D., Ph.D. has given final approval of the version to be published in BMC Obesity.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Ethics approval and consent to participate, abbreviations.

Follow us on :

Take a look at the Recent articles

A case study: obesity and the metabolic syndrome. a three-pronged program, targeting education, close follow-up and a dietary supplement, significantly decrease body weight and body fat, grethe s birketvedt.

Medical Center for Obesity and Research, Baerum, Oslo, Norway

E-mail : [email protected]

Carl Fredrik Schou

Teres Colosseum, Oslo, Norway

Erling Thom

ETC Research and Development, Oslo, Norway

DOI: 10.15761/IOD.1000143

A 38-year old woman with a body fat content of 52.2% and a BMI of 48.2 kg/m 2 was seeking medical treatment in an outpatient obesity clinic in Oslo, Norway. She suffered from a severe abdominal hernia and was not qualified for surgery of this condition until her BMI was under 30 kg/m 2 .  Additionally, she was severely challenged in terms of mobility as she was born with cerebral palsy and required either a wheel chair or crutches to get around. Over the years she had sought several treatment options to control her obesity but with no success. She did not qualify for bariatric surgery and was severely depressed when she came to the clinic. After examination and diagnosis, the decision was made to begin a multi-pronged treatment using a natural dietary supplement, combined with the customized educational program called “The Body in the Brain”, and a close medical follow-up with regular appointments to the outpatient clinic.  After twenty-three months of treatment, the woman had lost 38 kg of bodyweight and had normalized fat percentage for her age and gender. In conclusion, it is possible to successfully treat severe obesity and return a patient to a normal body fat percentage with the combination of a natural dietary supplement, a designed diet composition and a weight loss behavioral program.

obesity, weight loss, metabolic syndrome

Introduction

Obesity and the metabolic syndrome are linked together [1]. When an individual gets severely obese, insulin resistance, hypertension and increased abdominal circumference follow as a natural cause due to the excess fat in the body. Obesity and the metabolic syndrome has been extensively researched and today clinical evidence implicates intra-abdominal adiposity as a powerful driving force for elevated cardio metabolic risk [2]. This association appears to arise directly, via secretion of adipokines, and indirectly, through promotion of insulin resistance.

The most important therapeutic intervention effective in subjects with the metabolic syndrome should focus on weight reduction and regular daily physical activities. Health experts agree that making lifestyle changes, including following a healthy eating pattern, reducing caloric intake, and engaging in physical activity, are the basis for achieving long-term weight loss [3,4]. However, weight-loss and weight-management regimens have frequently been ineffective. Therefore, effective medical interventions to manage weight gain and slow or prevent progression to obesity are needed. Control of diet and exercise are cornerstones of the management of excess weight. A number of nutritional approaches and diets with different proportions of lipids, proteins and carbohydrates have been prescribed for weight loss. Initial guidance on weight loss was earlier years a restriction in saturated fats that unfortunately did not necessarily result in weight loss. Recently, a shift towards a reduction in refined carbohydrates has been a new approach to lose weight.  

Several studies have indicated that fiber-rich foods and fiber supplements have moderate weight reducing effects, and may also improve the lipid profile in overweight and obese individuals [5,6]. There are hundreds of weight loss products sold over the counter today. Typically, these OTC supplements have not been clinically tested, can have significant unwanted side effects and not yield successful results in helping people to lose weight.

The natural product, used in this case study is supplement that consists of a unique combination of three natural ingredients: white kidney bean extract, locust bean gum extract and green tea extract that affect weight loss with little to no side effects.  The white kidney bean extract is phaseolus vulgaris, a bean extract containing phaseolamin. Phaseolamin is a glycoprotein found mainly in white and red kidney beans and is an effective alfa-amylase inhibitor [7]. The extract of locust bean gum, is a seed-coat extract that decreases ghrelin [8], the hunger hormone and make you feel faster satiated and will postpone the hunger sensation after a meal. Locust bean gum has also shown lipid lowering effects in several studies [9]. The third ingredient is a green tea extract [10-12], Camellia sinensis with anti-inflammatory and antioxidant properties with a small increase in the energy expenditure.

Aim of study

The aim of this study was to investigate whether a dietary supplement with white kidney bean extract, locust bean gum extract and green tea extract in combination with a program with lifestyle changes would enhance weight loss and fat loss and improve the metabolic parameters in a severe obese patient with the metabolic syndrome.

A 38 year old woman with a history of obesity, diabetes type 2 and hypertension was seeking treatment in an out-patient clinic in Oslo, Norway for medical weight loss management. She was well aware of the link between obesity, diabetes and cardiovascular disease and felt this appointment she had asked for was her last chance in getting help with her health problems.

She had been normal weight as a child and adolescent, but do to a dependency of crutches and a wheelchair she had gradually put on weight in her twenties. She was married with two young children and she increased in weight after each child birth. She suffered a severe abdominal hernia that stressed her, but she had been refused surgery due to her heavy weight.

She had in her childhood and teens always been of normal weight, active and healthy in spite of her physical disabilities. When she got married, she gradually gained weight and the weight culminated after her second child was born. She had developed diabetes type 2 and hypertension after her children were born, and was medicated with antihypertensive and antidiabetics. Her primary care physician had not really been interested in her weight and had several times suggested higher doses of medications or insulin injections. The patient was not interested in insulin injections as she was afraid of gaining more weight.

Our patient had been sedentary the last 5 years due to the abdominal hernia. She had tried many weight loss efforts on her own, had started working with a personal trainer and had weekly sessions with a physical therapist. Her diet had been high in fat and calories although she was very well educated in nutritious food. However, she admitted to overeating, and periods of binging. She drunk about 2.5 liter of diet soda a day including diet juice. She was very conscious about eating habits when it came to her two kids, and they were both healthy and in normal weight. She had a university education and was well informed of her health situation. But she was under much stress in her daily life and struggled daily to get help from health authorities.

Her initial anthropometric measurements included a weight of 125kg with a height of 1.61m, a body mass index (BMI) of 48.2kg/m 2 which classified her as morbidly obese. Her fat % was 52.2% with 65 kg fat mass measured by bioelectrical impedance analysis (BIA)[13] (Tanita Body Composition Analyzer BC-418) for analyzing the composition of the body, such as weight, lean body mass (LBM), total body water(TBW), fat free mass (FFM) and basal metabolic rate (BMR). Her HbA1c had the last 2 months ranged from 11.7% till 8.8% and her hypertension was 160/95 mm Hg.

Informed consent

The patient has signed and approved the consent form.

On the first visit to our clinic, the patient was advised of which food items of simple carbohydrates she should try to avoid in her daily diet. She was given restrictions in caloric content and a diet plan, specifically designed for her health situation with emphasis on her hypertension and diabetes type 2. She was also advised to drink water with a slice of lime instead of diet sodas and diet juice. One of her main goals was to be able to not require medications for control of her hypertension that would then improve her diabetes type 2 and simultaneously decrease her weight. It was extremely important for the treating physician to give her food compositions that targeted the ability to relieve stress in the gut-brain axis.

Her resting metabolic rate (RMR) was measured to 1828 kcal and the physician designed a diet in the range of 1200kcal to 1600 kcal. In that way, she at least could have a deficit of about 400 kcal a day taking into account her limited physical activity level.  In a two week period this regimen would theoretically allow her approximately a 0.5 kg loss in weight. Due to her decrease in simple carbohydrates she was advised to check her blood sugar 3 times per day and write the recordings down until next meeting. She was instructed on how to decrease her diabetes medication based on her blood sugar levels.

The weight management program at our clinic was continuing with bi-weekly visits by the patient for the next six weeks, and then monthly visits after that time. Furthermore, the patient  was advised after six weeks to additionally take one capsule of the dietary supplement twenty minutes before each of the main meals, breakfast, lunch and dinner. 

On a monthly basis, her weight and body fat percentage were recorded with BIA at the doctor visits. Moreover, she was given 1 hour consultation with behavioral modification with advise to lifestyle changes according to a program entitled the “Body in the Brain”, a recently published book in Norway, targeting education on how the brain and the body work together in hormonal harmony when the right diet is introduced for the right person. The patient was allowed to eat whatever she wanted in the diet plan restricted to 1200-1600 kcal, excluded from the carbohydrate list were white breads and pasta, cookies, cake, candy, sugar-sweetened sodas and drinks as well as diet sodas and diet juice. She followed the educational program related to the “Body in the Brain”[14] where she each month was given new insight into how the body and the brain worked together in a hormonally balanced way. She was also gradually introduced to healthier foods, e.g., food that was rich in tryptophan, an essential amino acid that target serotonin in the brain and indirectly impact insulin levels.  In her diet plan was a list of tryptophan rich food such as e.g.salmon, chicken, cod, tuna, apricots, broccoli, sprouts, whole grain, skimmed milk and almonds, food that was known as comfort food or mood food. The list was extended each visit and the food the patient did not like was replaced with other food items.

In her first two weeks of treatment she lost only one pound, but she reported that her blood sugar had not spiked as much as prior times after she had tried to avoid sugar and other simple carbohydrates. She admitted it was difficult to avoid these foods as she always had had a sweet tooth.  On her second visit she was educated in how the body relates to the brain in a hormonal way when certain food items are ingested. She was introduced to the amino acid tryptophan and how the tryptophan rich food would create more harmony in the gut-brain axis, increase serotonin levels and decrease cortisol and thereby improve insulin sensitivity. The education went on for 22 months and at each visit the biochemistry of food were addressed. How the food she ingested had an impact on her body and brain was a favorite topic of the visits to come.

Over the next four weeks she had lost only 1.2 kg. The visit two weeks later showed a decrease of an additional 0.7 kg, however the fat percentage in her body had not changed. Until this time, the fat lost was attributable to pure lean body mass. She was then introduced to the patented supplement consisting of Green tea extract, White kidney bean extract and Locust bean gum extract, a supplement that was sold over the counter in Norway, approved by the Norwegian Medicines Agency and also recently the ingredients were approved by the FDA in the US. She gradually lost weight each month with a simultaneous loss in fat percentage. 12 months later she had lost 21 kg of which 85% was loss in fat mass. She became less depressed, her energy level had improved, and she was still very motivated for further weight loss.

By the end of the 23 month treatment period she had lost 38 kg and the fat percentage in the body had decreased to 31.9% which was within normal limits for her age. Her blood sugar was under control. However, she was still on antidiabetics, however, her blood sugar and HbA1c was within normal limits and her hypertension was well regulated. Six months later, she was accepted for the surgery of her abdominal hernia as her fat mass was within normal range in spite of a BMI>30kg/m 2 .

The patented diet supplement with white kidney bean extract, locust bean gum and green tea extract in combination with an education program (The Body in The Brain) consisting of twenty-six outpatient clinic sessions,  resulted in a very significant weight loss, improvement in fat percentage, hypertension and blood sugar levels in an obese  woman following this program. In terms of the weight loss observed in this patient, fat was more than 75% of the total weight lost indicating a qualitative weight reduction where less than one quart of the weight lost was lean body mass[15].  The patient lost 25% more body fat of her weight lost than would predicted with lifestyle changes alone. The special designed diet program was modified accordingly in subsequent visits due to changes in the BMR. Her caloric intake was never changed to lesser than her BMR. The reason why her energy level increased and her mood improved, can very well be caused by the change in diet.,At each meal, she ate primarily foods rich in tryptophan combined with complex carbohydrates and thereby increased her serotonin levels. Several studies have shown that increased serotonin levels are related to mood elevations [16,17]. However, her improved mood and higher energy in this patient, may also be caused by the fat lost relieving the stress in the gut-brain axis. 

The amount of fat mass lost of weight lost was far more than reported in earlier studies. This is in accordance with earlier unpublished pilot studies with the diet supplement used in this case report. We believe that adding this specific supplement to this combined treatment enhanced fat loss and thereby normalized parameters associated with the metabolic syndrome. Earlier studies have shown that in severe obese individuals it is almost impossible to reach normal fat mass with lifestyle changes and behavioral modification alone. We believe that our natural supplement had both carbohydrate and lipid lowering effects on fat metabolism and also increased the fat expenditure. Moreover, we believe that the education program, The Body in the Brain  used in this three-pronged program, enhanced the weight loss. The patient understood the mechanisms in her body related to the food she ate, which increased her motivation for weight loss and prevented weight gain again as in earlier reports. Moreover, an encouraging physician at each visit may also be important for the patient to reach her goals. We cannot neglect the fact that obese patients are very sensitive to the knowledge of the physician and the way she is being encouraged on her road to weight loss.

A program like this can be a valuable method in the treatment of obesity in the future.

A three-pronged treatment paradigm that includes close physician follow-up, a well designed education program, and the addition of a dietary supplement consisting of an extract of white kidney bean, an extract of locust bean gum and an extract of green tea extract gave a substantial weight loss and a loss in fat mass towards a normal fat percentage in a severe obese person with the metabolic syndrome.

• Després JP, Lemieux I (2006) Abdominal obesity and metabolic syndrome.  Nature 444: 881-887. [ Crossref ]
• Scheen AJ (2004) Management of the metabolic syndrome.  Minerva Endocrinol 29: 31-45. [ Crossref ]
• Pittler MH, Ernst E (2004) Dietary supplements for body-weight reduction: a systematic review.  Am J ClinNutr 79: 529-536. [ Crossref ]
• Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, et al. (2013) AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation : 1- 63.
• Birketvedt GS, Aaseth J, Florholmen JR, Ryttig K (2000) Long-term effect of fibre supplement and reduced energy intake on body weight and blood lipids in overweight subjects.  ActaMedica (Hradec Kralove) 43: 129-132. [ Crossref ]
• Birketvedt GS, Langbakk B, Florholmen J (2004)A Dietary Supplement with Bean Extract Decreases Body Weight, Body Fat, Waist Circumference and Blood Pressure in Overweight and Obese Subjects. Current Topics in Nutraceutical Research 3:137-142.
• Preuss HG (2009) Bean amylase inhibitor and other carbohydrate absorption blockers: effects on diabesity and general health.  J Am CollNutr 28: 266-276. [ Crossref ]
• Gruendel S, Garcia AL, Otto B, Mueller C, Steiniger J, et al. (2006) Carob pulp preparation rich in insoluble dietary fiber and polyphenols enhances lipid oxidation and lowers postprandial acylated ghrelin in humans. J Nutr 136:1533-1538. [ Crossref ]
• Zavoral JH, Hannan P, Fields DJ, Hanson MN, Frantz ID, et al. (1983) Thehypolipidemic effect of locust bean gum food products in familial hypercholesterolemic adults and children.  Am J ClinNutr 38: 285-294. [ Crossref ]
• Toda (1989) Green tea extract has been shown to decrease weight in overweight subjects. Nippon SaikingakuZasshi 44: 669-672.
• Dulloo AG, Duret C, Rohrer D, Girardier L, Mensi N, et al. (1999) Efficacy of a green tea extract rich in catechin polyphenols and caffeine in increasing 24-h energy expenditure and fat oxidation in humans.  Am J ClinNutr 70: 1040-1045. [ Crossref ]
• Wolfram S (2007) Effects of green tea and EGCG on cardiovascular and metabolic health.  J Am CollNutr 26: 373S-388S. [ Crossref ]
• Böhm A, Heitmann BL (2013) The use of bioelectrical impedance analysis for body composition in epidemiological studies.  Eur J ClinNutr 67 Suppl 1: S79-85. [ Crossref ]
• Birketvedt GS (2008) The Body in the Brain. Editor XLibriz, Norway. 280 p Norwegian language. In press for English translation.
• Heymsfield SB, Gonzalez MC, Shen W, Redman L, Thomas D (2014) Weight loss composition is one-fourth fat-free mass: a critical review and critique of this widely cited rule.  Obes Rev 15: 310-321. [ Crossref ]
• Benton D, Donohoe RT (1999) The effects of nutrients on mood.  Public Health Nutr 2: 403-409. [ Crossref ]
• Ottley C (2000) Food and mood.  Nurs Stand 15: 46-52. [ Crossref ]

Editorial Information

Editor-in-chief.

Sharma S Prabhakar Texas Tech University Health Sciences Center

Article Type

Publication history.

Received: January12, 2016 Accepted: February08, 2016 Published: February 11, 2016

©2016Birketvedt GS.This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Birketvedt GS, Schou CF, Thom E(2016) A case study: Obesity and the metabolic syndrome. A threepronged program, targeting education, close follow-up and a dietary supplement, significantly decrease body weight and body fat. Integr ObesityDiabetes. 2:doi: 10.15761/IOD.1000143

Corresponding author

Medical Center for Obesity and Research, Baerum, Oslo, Norway.

Journal Home

• Article Info
• Author Info
• Figures & Data
• Privacy Policy
• Terms & conditions

© 2018 Copyright OAT. All rights reserved.