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The challenges of assessing adiposity in a clinical setting

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Abstract

To tackle the burden of obesity-induced cardiometabolic disease, the scientific community relies on accurate and reproducible adiposity measurements in the clinic. These measurements guide our understanding of underlying biological mechanisms and clinical outcomes of human trials. However, measuring adiposity and adipose tissue distribution in a clinical setting can be challenging, and different measurement methods pose important limitations. BMI is a simple and high-throughput measurement, but it is associated relatively poorly with clinical outcomes when compared with waist-to-hip and sagittal abdominal diameter measurements. Body composition measurements by dual energy X-ray absorptiometry or MRI scans would be ideal due to their high accuracy, but are not high-throughput. Another important consideration is that adiposity measurements vary between men and women, between adults and children, and between people of different ethnic backgrounds. In this Perspective article, we discuss how these critical challenges can affect our interpretation of research data in the field of obesity and the design and implementation of clinical guidelines.

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Fig. 1: Adipocyte types and anatomical location of major white adipose tissue depots.
Fig. 2: Methods for measuring adiposity in the clinic.

References

  1. Abdelaal, M., le Roux, C. W. & Docherty, N. G. Morbidity and mortality associated with obesity. Ann. Transl. Med. 5, 161 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Neeland, I. J. et al. Dysfunctional adiposity and the risk of prediabetes and type 2 diabetes in obese adults. JAMA 308, 1150–1159 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Hagberg, C. E. & Spalding, K. L. White adipocyte dysfunction and obesity-associated pathologies in humans. Nat. Rev. Mol. Cell Biol. 25, 270–289 (2024).

    Article  CAS  PubMed  Google Scholar 

  4. Frank, A. P., de Souza Santos, R., Palmer, B. F. & Clegg, D. J. Determinants of body fat distribution in humans may provide insight about obesity-related health risks. J. Lipid Res. 60, 1710–1719 (2019).

    Article  CAS  PubMed  Google Scholar 

  5. AlZaim, I., de Rooij, L., Sheikh, B. N., Borgeson, E. & Kalucka, J. The evolving functions of the vasculature in regulating adipose tissue biology in health and obesity. Nat. Rev. Endocrinol. 19, 691–707 (2023).

    Article  PubMed  Google Scholar 

  6. Börgeson, E., Boucher, J. & Hagberg, C. E. Of mice and men: pinpointing species differences in adipose tissue biology. Front. Cell Dev. Biol. 10, 1003118 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Duren, D. L. et al. Body composition methods: comparisons and interpretation. J. Diabetes Sci. Technol. 2, 1139–1146 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Salmon-Gomez, L., Catalan, V., Fruhbeck, G. & Gomez-Ambrosi, J. Relevance of body composition in phenoty** the obesities. Rev. Endocr. Metab. Disord. 24, 809–823 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  9. NCD Risk Factor Collaboration (NCD-RisC) 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).

    Article  Google Scholar 

  10. Lister, N. B. et al. Child and adolescent obesity. Nat. Rev. Dis. Prim. 9, 24 (2023).

    Article  PubMed  Google Scholar 

  11. Jebeile, H., Kelly, A. S., O’Malley, G. & Baur, L. A. Obesity in children and adolescents: epidemiology, causes, assessment, and management. Lancet Diabetes Endocrinol. 10, 351–365 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Panera, N. et al. Genetics, epigenetics and transgenerational transmission of obesity in children. Front. Endocrinol. 13, 1006008 (2022).

    Article  Google Scholar 

  13. Santos, L. P., Santos, I. S., Matijasevich, A. & Barros, A. J. D. Changes in overall and regional body fatness from childhood to early adolescence. Sci. Rep. 9, 1888 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Jerome, M. L., Valcarce, V., Lach, L., Itriago, E. & Salas, A. A. Infant body composition: a comprehensive overview of assessment techniques, nutrition factors, and health outcomes. Nutr. Clin. Pract. 38, S7–S27 (2023).

    Article  PubMed  Google Scholar 

  15. Larqué, E. et al. From conception to infancy – early risk factors for childhood obesity. Nat. Rev. Endocrinol. 15, 456–478 (2019).

    Article  PubMed  Google Scholar 

  16. Hills, A. P. et al. Body composition from birth to 2 years. Eur. J. Clin. Nutr. 76, 1165–1171 (2023).

    Google Scholar 

  17. Yousuf, E. I. et al. Growth and body composition trajectories in infants meeting the WHO growth standards study requirements. Pediatr. Res. 92, 1640–1647 (2022).

    Article  PubMed  Google Scholar 

  18. Lampl, M. & Thompson, A. L. Growth chart curves do not describe individual growth biology. Am. J. Hum. Biol. 19, 643–653 (2007).

    Article  PubMed  Google Scholar 

  19. Robertson, J. et al. Higher body mass index in adolescence predicts cardiomyopathy risk in midlife. Circulation 140, 117–125 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Marcus, M. D., Foster, G. D. & El Ghormli, L. Stability of relative weight category and cardiometabolic risk factors among moderately and severely obese middle school youth. Obesity 22, 1118–1125 (2014).

    Article  PubMed  Google Scholar 

  21. Felix, J. et al. Health related quality of life associated with extreme obesity in adolescents – results from the baseline evaluation of the YES-study. Health Qual. Life Outcomes 18, 58 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Chooi, Y. C., Ding, C. & Magkos, F. The epidemiology of obesity. Metabolism 92, 6–10 (2019).

    Article  CAS  PubMed  Google Scholar 

  23. Martos-Moreno, G. A. et al. Ethnicity strongly influences body fat distribution determining serum adipokine profile and metabolic derangement in childhood obesity. Front. Pediatr. 8, 551103 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Saltzman, E. & Karl, J. P. Nutrient deficiencies after gastric bypass surgery. Annu. Rev. Nutr. 33, 183–203 (2013).

    Article  CAS  PubMed  Google Scholar 

  25. Hoeltzel, G. D. et al. How safe is adolescent bariatric surgery? An analysis of short-term outcomes. J. Pediatr. Surg. 57, 1654–1659 (2022).

    Article  PubMed  Google Scholar 

  26. Malhotra, S. et al. Bariatric surgery in the treatment of adolescent obesity: current perspectives in the United States. Expert. Rev. Endocrinol. Metab. 16, 123–134 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Goedecke, J. H. & Mendham, A. E. Pathophysiology of type 2 diabetes in sub-Saharan Africans. Diabetologia 65, 1967–1980 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Cooper, L. B. et al. Multi-ethnic comparisons of diabetes in heart failure with reduced ejection fraction: insights from the HF-ACTION trial and the ASIAN-HF registry. Eur. J. Heart Fail. 20, 1281–1289 (2018).

    Article  PubMed  Google Scholar 

  29. Wright, A. K. et al. Age-, sex- and ethnicity-related differences in body weight, blood pressure, HbA1c and lipid levels at the diagnosis of type 2 diabetes relative to people without diabetes. Diabetologia 63, 1542–1553 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Yaghootkar, H., Whitcher, B., Bell, J. D. & Thomas, E. L. Ethnic differences in adiposity and diabetes risk – insights from genetic studies. J. Intern. Med. 288, 271–283 (2020).

    Article  CAS  PubMed  Google Scholar 

  31. Sun, C., Kovacs, P. & Guiu-Jurado, E. Genetics of body fat distribution: comparative analyses in populations with European, Asian and African ancestries. Genes 12, 841 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Volgman, A. S. et al. Atherosclerotic cardiovascular disease in South Asians in the United States: epidemiology, risk factors, and treatments: a scientific statement from the American Heart Association. Circulation 138, e1–e34 (2018).

    Article  PubMed  Google Scholar 

  33. Nono Nankam, P. A., Nguelefack, T. B., Goedecke, J. H. & Blüher, M. Contribution of adipose tissue oxidative stress to obesity-associated diabetes risk and ethnic differences: focus on women of African ancestry. Antioxidants 10, 622 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lean, M. E. et al. Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial. Lancet 391, 541–551 (2018).

    Article  PubMed  Google Scholar 

  35. Taheri, S. et al. Effect of intensive lifestyle intervention on bodyweight and glycaemia in early type 2 diabetes (DIADEM-I): an open-label, parallel-group, randomised controlled trial. Lancet Diabetes Endocrinol. 8, 477–489 (2020).

    Article  PubMed  Google Scholar 

  36. Sattar, N. et al. Dietary weight-management for type 2 diabetes remissions in South Asians: the South Asian diabetes remission randomised trial for proof-of-concept and feasibility (STANDby). Lancet Reg. Health Southeast. Asia 9, 100111 (2023).

    Article  PubMed  Google Scholar 

  37. Turner, B. E., Steinberg, J. R., Weeks, B. T., Rodriguez, F. & Cullen, M. R. Race/ethnicity reporting and representation in US clinical trials: a cohort study. Lancet Reg. Health Am. 11, 100252 (2022).

    PubMed  PubMed Central  Google Scholar 

  38. Blue, M. N. M., Tinsley, G. M., Ryan, E. D. & Smith-Ryan, A. E. Validity of body-composition methods across racial and ethnic populations. Adv. Nutr. 12, 1854–1862 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kautzky-Willer, A., Leutner, M. & Harreiter, J. Sex differences in type 2 diabetes. Diabetologia 66, 986–1002 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wong, N. D. & Sattar, N. Cardiovascular risk in diabetes mellitus: epidemiology, assessment and prevention. Nat. Rev. Cardiol. 20, 685–695 (2023).

    Article  PubMed  Google Scholar 

  41. Palmer, B. F. & Clegg, D. J. The sexual dimorphism of obesity. Mol. Cell Endocrinol. 402, 113–119 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Chang, E., Varghese, M. & Singer, K. Gender and sex differences in adipose tissue. Curr. Diab Rep. 18, 69 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Lumish, H. S., O’Reilly, M. & Reilly, M. P. Sex differences in genomic drivers of adipose distribution and related cardiometabolic disorders: opportunities for precision medicine. Arterioscler. Thromb. Vasc. Biol. 40, 45–60 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Abildgaard, J. et al. Changes in abdominal subcutaneous adipose tissue phenotype following menopause is associated with increased visceral fat mass. Sci. Rep. 11, 14750 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kakoly, N. S., Earnest, A., Teede, H. J., Moran, L. J. & Joham, A. E. The impact of obesity on the incidence of type 2 diabetes among women with polycystic ovary syndrome. Diabetes Care 42, 560–567 (2019).

    Article  PubMed  Google Scholar 

  46. Traish, A. M. Major cardiovascular disease risk in men with testosterone deficiency (hypogonadism): appraisal of short, medium and long-term testosterone therapy – a narrative review. Sex. Med. Rev. 11, 384–394 (2023).

    Article  PubMed  Google Scholar 

  47. Henninger, A. M., Eliasson, B., Jenndahl, L. E. & Hammarstedt, A. Adipocyte hypertrophy, inflammation and fibrosis characterize subcutaneous adipose tissue of healthy, non-obese subjects predisposed to type 2 diabetes. PLoS ONE 9, e105262 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Succurro, E. et al. Relative risk of cardiovascular disease is higher in women with type 2 diabetes, but not in those with prediabetes, as compared with men. Diabetes Care 43, 3070–3078 (2020).

    Article  PubMed  Google Scholar 

  49. Bancks, M. P. et al. Sex differences in cardiovascular risk factors before and after the development of type 2 diabetes and risk for incident cardiovascular disease. Diabetes Res. Clin. Pract. 166, 108334 (2020).

    Article  PubMed  Google Scholar 

  50. Wong, N. D. et al. Sex differences in coronary artery calcium and mortality from coronary heart disease, cardiovascular disease, and all causes in adults with diabetes: the Coronary Calcium Consortium. Diabetes Care 43, 2597–2606 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ohkuma, T., Iwase, M., Fujii, H. & Kitazono, T. Sex differences in cardiovascular risk, lifestyle, and psychological factors in patients with type 2 diabetes: the Fukuoka Diabetes Registry. Biol. Sex. Differ. 14, 32 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. World Health Organization. The SuRF report 2. Surveillance of chronic disease risk factors: Country-level data and comparable estimates. iris.who.int/bitstream/handle/10665/43190/9241593024_eng.pdf (2005).

  53. Tang, Y. et al. Age-related changes in body composition and bone mineral density and their relationship with the duration of diabetes and glycaemic control in type 2 diabetes. Diabetes Metab. Syndr. Obes. 13, 4699–4710 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Kuk, J. L., Saunders, T. J., Davidson, L. E. & Ross, R. Age-related changes in total and regional fat distribution. Ageing Res. Rev. 8, 339–348 (2009).

    Article  PubMed  Google Scholar 

  55. Kim, S. & Won, C. W. Sex-different changes of body composition in aging: a systemic review. Arch. Gerontol. Geriatr. 102, 104711 (2022).

    Article  PubMed  Google Scholar 

  56. Mott, J. W. et al. Relation between body fat and age in 4 ethnic groups. Am. J. Clin. Nutr. 69, 1007–1013 (1999).

    Article  CAS  PubMed  Google Scholar 

  57. Bray, G. A. Beyond BMI. Nutrients 15, 2254 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  58. de Morais, N. S. et al. Body fat is superior to body mass index in predicting cardiometabolic risk factors in adolescents. Int. J. Environ. Res. Public Health 20, 2074 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Nevill, A. M., Duncan, M. J. & Myers, T. BMI is dead; long live waist-circumference indices: but which index should we choose to predict cardio-metabolic risk? Nutr. Metab. Cardiovasc. Dis. 32, 1642–1650 (2022).

    Article  CAS  PubMed  Google Scholar 

  60. Assyov, Y., Gateva, A., Tsakova, A. & Kamenov, Z. A comparison of the clinical usefulness of neck circumference and waist circumference in individuals with severe obesity. Endocr. Res. 42, 6–14 (2017).

    Article  PubMed  Google Scholar 

  61. Yamanaka, A. B. et al. Determination of child waist circumference cut points for metabolic risk based on acanthosis nigricans, the Children’s Healthy Living Program. Prev. Chronic Dis. 18, E64 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Do, J. Y. & Kang, S. H. Comparison of various indices for predicting sarcopenia and its components in patients receiving peritoneal dialysis. Sci. Rep. 12, 14102 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Ben-Noun, L., Sohar, E. & Laor, A. Neck circumference as a simple screening measure for identifying overweight and obese patients. Obes. Res. 9, 470–477 (2001).

    Article  CAS  PubMed  Google Scholar 

  64. Mohseni-Takalloo, S., Mozaffari-Khosravi, H., Mohseni, H., Mirzaei, M. & Hosseinzadeh, M. Evaluating neck circumference as an independent predictor of metabolic syndrome and its components among adults: a population-based study. Cureus 15, e40379 (2023).

    PubMed  PubMed Central  Google Scholar 

  65. Preis, S. R. et al. Neck circumference as a novel measure of cardiometabolic risk: the Framingham Heart Study. J. Clin. Endocrinol. Metab. 95, 3701–3710 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Androutsos, O. et al. Neck circumference: a useful screening tool of cardiovascular risk in children. Pediatr. Obes. 7, 187–195 (2012).

    Article  CAS  PubMed  Google Scholar 

  67. Nafiu, O. O. et al. Neck circumference as a screening measure for identifying children with high body mass index. Pediatrics 126, e306–e310 (2010).

    Article  PubMed  Google Scholar 

  68. Kahn, H. S. Replacing the body mass index with the sagittal abdominal diameter (abdominal height). Obesity 31, 2720–2722 (2023).

    Article  CAS  PubMed  Google Scholar 

  69. Yim, J. Y. et al. Sagittal abdominal diameter is a strong anthropometric measure of visceral adipose tissue in the Asian general population. Diabetes Care 33, 2665–2670 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Kahn, H. S. & Cheng, Y. J. Comparison of adiposity indicators associated with fasting-state insulinemia, triglyceridemia, and related risk biomarkers in a nationally representative, adult population. Diabetes Res. Clin. Pract. 136, 7–15 (2018).

    Article  CAS  PubMed  Google Scholar 

  71. Lewandowski, Z., Dychała, E., Pisula-Lewandowska, A. & Danel, D. P. Comparison of skinfold thickness measured by caliper and ultrasound scanner in normative weight women. Int. J. Environ. Res. Public Health 19, 16230 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Hume, P. & Marfell-Jones, M. The importance of accurate site location for skinfold measurement. J. Sports Sci. 26, 1333–1340 (2008).

    Article  PubMed  Google Scholar 

  73. Mei, Z. et al. Do skinfold measurements provide additional information to body mass index in the assessment of body fatness among children and adolescents? Pediatrics 119, e1306–e1313 (2007).

    Article  PubMed  Google Scholar 

  74. Majmudar, M. D. et al. Smartphone camera based assessment of adiposity: a validation study. NPJ Digit. Med. 5, 79 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Affuso, O. et al. A method for measuring human body composition using digital images. PLoS ONE 13, e0206430 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Medina-Inojosa, J., Somers, V. K., Ngwa, T., Hinshaw, L. & Lopez-Jimenez, F. Reliability of a 3D body scanner for anthropometric measurements of central obesity. Obes. Open. Access 2, https://doi.org/10.16966/2380-5528.122 (2016).

  77. Sager, R., Gusewell, S., Ruhli, F., Bender, N. & Staub, K. Multiple measures derived from 3D photonic body scans improve predictions of fat and muscle mass in young Swiss men. PLoS ONE 15, e0234552 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Jeon, S., Kim, M., Yoon, J., Lee, S. & Youm, S. Machine learning-based obesity classification considering 3D body scanner measurements. Sci. Rep. 13, 3299 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Kosilek, R. P. et al. Laser-based 3D body scanning reveals a higher prevalence of abdominal obesity than tape measurements: results from a population-based sample. Diagnostics 13, 2594 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Guarnieri Lopez, M., Matthes, K. L., Sob, C., Bender, N. & Staub, K. Associations between 3D surface scanner derived anthropometric measurements and body composition in a cross-sectional study. Eur. J. Clin. Nutr. 77, 972–981 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Garlie, T. N., Obusek, J. P., Corner, B. D. & Zambraski, E. J. Comparison of body fat estimates using 3D digital laser scans, direct manual anthropometry, and DXA in men. Am. J. Hum. Biol. 22, 695–701 (2010).

    Article  PubMed  Google Scholar 

  82. Cabre, H. E. et al. Validity of a 3-dimensional body scanner: comparison against a 4-compartment model and dual energy X-ray absorptiometry. Appl. Physiol. Nutr. Metab. 46, 644–650 (2021).

    Article  CAS  PubMed  Google Scholar 

  83. Harbin, M. M., Kasak, A., Ostrem, J. D. & Dengel, D. R. Validation of a three-dimensional body scanner for body composition measures. Eur. J. Clin. Nutr. 72, 1191–1194 (2018).

    Article  PubMed  Google Scholar 

  84. Dehghan, M. & Merchant, A. T. Is bioelectrical impedance accurate for use in large epidemiological studies? Nutr. J. 7, 26 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Mialich, M. S., Sicchieri, J. M. F. & Junior, A. A. J. Analysis of body composition: a critical review of the use of bioelectrical impedance analysis. Int. J. Clin. Nutr. 2, 1–10 (2014).

    Google Scholar 

  86. Jensky-Squires, N. E. et al. Validity and reliability of body composition analysers in children and adults. Br. J. Nutr. 100, 859–865 (2008).

    Article  CAS  PubMed  Google Scholar 

  87. Siedler, M. R. et al. Assessing the reliability and cross-sectional and longitudinal validity of fifteen bioelectrical impedance analysis devices. Br. J. Nutr. 130, 827–840 (2023).

    Article  CAS  PubMed  Google Scholar 

  88. Brunani, A. et al. Body composition assessment using bioelectrical impedance analysis (BIA) in a wide cohort of patients affected with mild to severe obesity. Clin. Nutr. 40, 3973–3981 (2021).

    Article  PubMed  Google Scholar 

  89. Ritz, P., Salle, A., Audran, M. & Rohmer, V. Comparison of different methods to assess body composition of weight loss in obese and diabetic patients. Diabetes Res. Clin. Pract. 77, 405–411 (2007).

    Article  CAS  PubMed  Google Scholar 

  90. Kreissl, A., Jorda, A., Truschner, K., Skacel, G. & Greber-Platzer, S. Clinically relevant body composition methods for obese pediatric patients. BMC Pediatr. 19, 84 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Fields, D. A., Goran, M. I. & McCrory, M. A. Body-composition assessment via air-displacement plethysmography in adults and children: a review. Am. J. Clin. Nutr. 75, 453–467 (2002).

    Article  CAS  PubMed  Google Scholar 

  92. Francis, K. T. Body-composition assessment using underwater weighing techniques. Phys. Ther. 70, 657–662 (1990).

    Article  CAS  PubMed  Google Scholar 

  93. Chaves, L. et al. Assessment of body composition by whole-body densitometry: what radiologists should know. Radiol. Bras. 55, 305–311 (2022).

    PubMed  PubMed Central  Google Scholar 

  94. Damilakis, J., Adams, J. E., Guglielmi, G. & Link, T. M. Radiation exposure in X-ray-based imaging techniques used in osteoporosis. Eur. Radiol. 20, 2707–2714 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Borga, M. et al. Advanced body composition assessment: from body mass index to body composition profiling. J. Investig. Med. 66, 1–9 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Heymsfield, S., Bell, J. D. & Heber, D. in Precision Nutrition: The Science and Promise of Personalized Nutrition and Health Ch. 7 (eds Heber, D., Li, Z. & Ordovas, J.) 143–152 (Academic Press, 2024).

  97. Lee, S. B. et al. Automated segmentation of whole-body CT images for body composition analysis in pediatric patients using a deep neural network. Eur. Radiol. 32, 8463–8472 (2022).

    Article  PubMed  Google Scholar 

  98. Weston, A. D. et al. Automated abdominal segmentation of CT scans for body composition analysis using deep learning. Radiology 290, 669–679 (2019).

    Article  PubMed  Google Scholar 

  99. Yoo, H. J. et al. Deep learning-based fully automated body composition analysis of thigh CT: comparison with DXA measurement. Eur. Radiol. 32, 7601–7611 (2022).

    Article  CAS  PubMed  Google Scholar 

  100. Duan, K. et al. Effect of glucagon-like peptide-1 receptor agonists on fat distribution in patients with type 2 diabetes: a systematic review and meta-analysis. J. Diabetes Invest. 13, 1149–1160 (2022).

    Article  CAS  Google Scholar 

  101. Wang, X., Wu, N., Sun, C., **, D. & Lu, H. Effects of SGLT-2 inhibitors on adipose tissue distribution in patients with type 2 diabetes mellitus: a systematic review and meta-analysis of randomized controlled trials. Diabetol. Metab. Syndr. 15, 113 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Verweij, L. M., Terwee, C. B., Proper, K. I., Hulshof, C. T. & van Mechelen, W. Measurement error of waist circumference: gaps in knowledge. Public. Health Nutr. 16, 281–288 (2013).

    Article  PubMed  Google Scholar 

  103. Pamoukdjian, F. et al. Obesity survival paradox in cancer patients: results from the Physical Frailty in Older Adult Cancer Patients (PF-EC) study. Clin. Nutr. 38, 2806–2812 (2019).

    Article  PubMed  Google Scholar 

  104. Keller, K., Munzel, T. & Ostad, M. A. Sex-specific differences in mortality and the obesity paradox of patients with myocardial infarction ages >70 y. Nutrition 46, 124–130 (2018).

    Article  PubMed  Google Scholar 

  105. de Miguel-Diez, J. et al. Obesity survival paradox in patients hospitalized with community-acquired pneumonia. Assessing sex-differences in a population-based cohort study. Eur. J. Intern. Med. 98, 98–104 (2022).

    Article  PubMed  Google Scholar 

  106. Butt, J. H. et al. Anthropometric measures and adverse outcomes in heart failure with reduced ejection fraction: revisiting the obesity paradox. Eur. Heart J. 44, 1136–1153 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Kvist, H., Chowdhury, B., Grangård, U., Tylén, U. & Sjöström, L. Total and visceral adipose-tissue volumes derived from measurements with computed tomography in adult men and women: predictive equations. Am. J. Clin. Nutr. 48, 1351–1361 (1988).

    Article  CAS  PubMed  Google Scholar 

  108. National Health and Nutrition Examination Survey (NHANES). Anthropometry Procedures Manual. wwwn.cdc.gov/nchs/data/nhanes/2019-2020/manuals/2020-Anthropometry-Procedures-Manual-508.pdf (CDC, 2020).

  109. National Health and Nutrition Examination Survey (NHANES). Anthropometry Procedures Manual. wwwn.cdc.gov/nchs/data/nhanes/2013-2014/manuals/2013_anthropometry.pdf (CDC, 2013)

  110. Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  111. Tabesh, M. R. et al. Nutrition, physical activity, and prescription of supplements in pre- and post-bariatric surgery patients: an updated comprehensive practical guideline. Obes. Surg. 33, 2557–2572 (2023).

    Article  PubMed  Google Scholar 

  112. Guerrero-Juarez, C. F. & Plikus, M. V. Emerging nonmetabolic functions of skin fat. Nat. Rev. Endocrinol. 14, 163–173 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Kahn, C. R., Wang, G. & Lee, K. Y. Altered adipose tissue and adipocyte function in the pathogenesis of metabolic syndrome. J. Clin. Invest. 129, 3990–4000 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Rodríguez, A., Becerril, S., Hernández-Pardos, A. W. & Frühbeck, G. Adipose tissue depot differences in adipokines and effects on skeletal and cardiac muscle. Curr. Opin. Pharmacol. 52, 1–8 (2020).

    Article  PubMed  Google Scholar 

  115. Bruder-Nascimento, T., Kress, G, C. & Belin de Chantemele, E. J. Recent advances in understanding lipodystrophy: a focus on lipodystrophy-associated cardiovascular disease and potential effects of leptin therapy on cardiovascular function. F1000Res. 8, 1756 (2019).

    Article  CAS  Google Scholar 

  116. Cesaro, A. et al. Visceral adipose tissue and residual cardiovascular risk: a pathological link and new therapeutic options. Front. Cardiovasc. Med. 10, 1187735 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Lee, Y. et al. The effect of omentectomy added to bariatric surgery on metabolic outcomes: a systematic review and meta-analysis of randomized controlled trials. Surg. Obes. Relat. Dis. 14, 1766–1782 (2018).

    Article  PubMed  Google Scholar 

  118. Sotak, M. et al. Healthy subcutaneous and omental adipose tissue is associated with high expression of extracellular matrix components. Int. J. Mol. Sci. 23, 520 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Tchernof, A. & Despres, J. P. Pathophysiology of human visceral obesity: an update. Physiol. Rev. 93, 359–404 (2013).

    Article  CAS  PubMed  Google Scholar 

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E.B. researched data for the article. All authors contributed substantially to discussion of the content. All authors wrote the article. All authors reviewed and/or edited the manuscript before submission.

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Börgeson, E., Tavajoh, S., Lange, S. et al. The challenges of assessing adiposity in a clinical setting. Nat Rev Endocrinol (2024). https://doi.org/10.1038/s41574-024-01012-9

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