Obesity Phenotypes and Their Relationship with Visceral Adiposity and Adiponectin in an Indian Outpatient Cohort

Manidipa Mondal; Sandeep Garg; Sunita Aggarwal; Anubhuti Chitkara; Muskan Garg; Pujan Acharya; Radhika Garg

doi:10.55489/njmr.160220261270

Authors

Manidipa Mondal Department of General Medicine, Maulana Azad Medical College, New Delhi, India https://orcid.org/0009-0007-9634-8149
Sandeep Garg Department of General Medicine, Maulana Azad Medical College, New Delhi, India
Sunita Aggarwal Department of General Medicine, Maulana Azad Medical College, New Delhi, India
Anubhuti Chitkara Department of Biochemistry, Maulana Azad Medical College, New Delhi, India
Muskan Garg Department of General Medicine, Maulana Azad Medical College, New Delhi, India
Pujan Acharya Department of General Medicine, Maulana Azad Medical College, New Delhi, India https://orcid.org/0009-0006-0377-0563
Radhika Garg Hamdard Institute of Medical Sciences and Research, Hamdard Nagar, Delhi, India

DOI:

https://doi.org/10.55489/njmr.160220261270

Keywords:

Obesity phenotypes, Adiponectin, Metabolic Syndrome, Visceral fat, Bio impedance analysis

Abstract

Background: Obesity is a heterogeneous condition with diverse metabolic profiles and fat distribution patterns. Body mass index (BMI) alone may not adequately reflect adiposity and cardiometabolic risk. Different obesity phenotypes metabolically healthy obese (MHO), metabolically unhealthy obese (MUO), metabolically obese normal weight (MONW), normal weight obese (NWO), and lipodystrophy may have varying metabolic risks. This study aimed to characterize obesity phenotypes in a North Indian cohort and assess their anthropometric, metabolic, and biochemical profiles along with associated comorbidities.

Methods: A cross-sectional study was conducted in the Department of Medicine at a tertiary care hospital between January and October 2024. A total of 100 adults aged 18–70 years with BMI ≥23 kg/m² were included. Anthropometric measurements and body composition parameters were assessed using bioimpedance analysis. Biochemical investigations included fasting glucose, insulin, lipid profile, liver and thyroid function tests, and serum adiponectin levels. Participants were classified into five phenotypes: MUO, MHO, MONW, NWO, and lipodystrophy. Statistical analysis was performed using SPSS version 25.

Results: MUO was the most prevalent phenotype (46%), followed by MHO (29%), NWO (12%), MONW (8%), and lipodystrophy (5%). MUO individuals had significantly higher visceral fat, insulin resistance, adverse lipid profiles, and the lowest adiponectin levels (p <0.001). Metabolically unhealthy phenotypes showed higher prevalence of diabetes/prediabetes, hypertension, coronary artery disease, and sarcopenia (p <0.05).

Conclusion: Obesity phenotypes show significant metabolic heterogeneity not captured by BMI alone. Phenotype-based assessment may improve cardiometabolic risk stratification and guide targeted management in South Asian populations.

References

1. Apovian CM. Obesity: definition, comorbidities, causes, and burden. Am J Manag Care. 2016 Jun;22(7 Suppl):s176-85. PMID: 27356115.

2. Després JP, Moorjani S, Lupien PJ, Tremblay A, Nadeau A, Bouchard C. Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis. 1990 Jul-Aug;10(4):497-511. DOI: https://doi.org/10.1161/01.ATV.10.4.497 PMid:2196040

3. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004 Jan 10;363(9403):157-163. DOI: https://doi.org/10.1016/S0140-6736(03)15268-3 PMid:14726171

4. Pujia R, Tarsitano MG, Arturi F, De Lorenzo A, Lenzi A, Pujia A, et al. Advances in Phenotyping Obesity and in Its Dietary and Pharmacological Treatment: A Narrative Review. Frontiers in Nutrition [Internet]. 2022 Feb 15; 9:804719. DOI: https://doi.org/10.3389/fnut.2022.804719 PMid:35242796 PMCid:PMC8885626

5. Loos RJF, Yeo GSH. The genetics of obesity: from discovery to biology. Nat Rev Genet. 2022 Feb;23(2):120-133. DOI: https://doi.org/10.1038/s41576-021-00414-z. PMID: 34556834 PMCID: PMC8459824

6. Hruby A, Manson JE, Qi L, Malik VS, Rimm EB, Sun Q, et al. Determinants and Consequences of Obesity. Am J Public Health. 2016 Sep;106(9):1656-62. DOI: https://doi.org/10.2105/AJPH.2016.303326 PMid:27459460 PMCid:PMC4981805

7. Geetha L, Deepa M, Anjana RM, Mohan V. Prevalence and clinical profile of metabolic obesity and phenotypic obesity in Asian Indians. J Diabetes Sci Technol. 2011 Mar 1;5(2):439-446. DOI: https://doi.org/10.1177/193229681100500235 PMid:21527117 PMCid:PMC3125940

8. Ling CH, de Craen AJ, Slagboom PE, Gunn DA, Stokkel MP, Westendorp RG, Maier AB. Accuracy of direct segmental multi-frequency bioimpedance analysis in the assessment of total body and segmental body composition in middle-aged adult population. Clin Nutr. 2011 Oct;30(5):610-615. DOI: https://doi.org/10.1016/j.clnu.2011.04.001 PMid:21555168

9. De Lorenzo A, Martinoli R, Vaia F, Di Renzo L. Normal weight obese (NWO) women: an evaluation of a candidate new syndrome. Nutr Metab Cardiovasc Dis. 2006 Dec;16(8):513-523. DOI: https://doi.org/10.1016/j.numecd.2005.10.010 PMid:17126766

10. NCEP. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Final Report. Circulation. 2002 Dec 17;106(25):3143-421. DOI: https://doi.org/10.1161/circ.106.25.3143 PMid:12485966

11. Després JP, Lemieux I, Bergeron J, Pibarot P, Mathieu P, Larose E, et al. Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. Arterioscler Thromb Vasc Biol. 2008 Jun;28(6):1039-49. DOI: https://doi.org/10.1161/ATVBAHA.107.159228 PMid:18356555

12. Shah RV, Murthy VL, Abbasi SA, Blankstein R, Kwong RY, Goldfine AB, et al. Visceral adiposity and the risk of metabolic syndrome across body mass index: the MESA Study. JACC Cardiovasc Imaging. 2014 Dec;7(12):1221-1235. DOI: https://doi.org/10.1016/j.jcmg.2014.07.017 PMid:25440591 PMCid:PMC4268163

13. Virtue S, Vidal-Puig A. Adipose tissue expandability, lipotoxicity and the Metabolic Syndrome--an allostatic perspective. Biochim Biophys Acta. 2010 Mar;1801(3):338-349.DOI: https://doi.org/10.1016/j.bbalip.2009.12.006 PMid:20056169

14. Mann JP, Savage DB. What lipodystrophies teach us about the metabolic syndrome. J Clin Invest. 2019 Aug 5;129(10):4009-4021. DOI: https://doi.org/10.1172/JCI129190 PMid:31380809 PMCid:PMC6763226

15. Misra A, Garg A. Clinical features and metabolic derangements in acquired generalized lipodystrophy: case reports and review of the literature. Medicine (Baltimore). 2003 Mar;82(2):129-46. DOI: https://doi.org/10.1097/00005792-200303000-00007 PMid:12640189

16. Phillips CM, Perry IJ. Does Inflammation Determine Metabolic Health Status in Obese and Nonobese Adults? The Journal of Clinical Endocrinology & Metabolism. 2013 Oct;98(10): E1610-9. DOI: https://doi.org/10.1210/jc.2013-2038 PMid:23979951 PMCid:PMC10358162

17. Wei Y, Wang R, Wang J, Han X, Wang F, Zhang Z, et al. Transitions in Metabolic Health Status and Obesity Over Time and Risk of Diabetes: The Dongfeng-Tongji Cohort Study. J Clin Endocrinol Metab. 2023 Jul 14;108(8):2024-2032. DOI: https://doi.org/10.1210/clinem/dgad047 PMid:36718514

18. Gao M, Lv J, Yu C, Guo Y, Bian Z, Yang R, et al. Metabolically healthy obesity, transition to unhealthy metabolic status, and vascular disease in Chinese adults: A cohort study. PLoS Med. 2020 Oct 30;17(10):e1003351. DOI: https://doi.org/10.1371/journal.pmed.1003351 PMid:33125374 PMCid:PMC7598496

19. Yamamoto Y, Hirose H, Saito I, Tomita M, Taniyama M, Matsubara K, et al. Correlation of the adipocyte-derived protein adiponectin with insulin resistance index and serum high-density lipoprotein-cholesterol, independent of body mass index, in the Japanese population. Clin Sci (Lond). 2002 Aug;103(2):137-142. DOI: https://doi.org/10.1042/cs1030137 PMid:12149104

20. Shetty GK, Economides PA, Horton ES, Mantzoros CS, Veves A. Circulating adiponectin and resistin levels in relation to metabolic factors, inflammatory markers, and vascular reactivity in diabetic patients and subjects at risk for diabetes. Diabetes Care. 2004 Oct;27(10):2450-2457. DOI: https://doi.org/10.2337/diacare.27.10.2450 PMid:15451915

21. Kanwal F, Neuschwander-Tetri BA, Rohit Loomba, Rinella ME. Metabolic dysfunction-associated steatotic liver disease: Update and impact of new nomenclature on the American Association for the Study of Liver Diseases practice guidance on nonalcoholic fatty liver disease. Hepatology. 2023 Nov 9;79(5):1212-1219. DOI: https://doi.org/10.1097/HEP.0000000000000670 PMid:38445559