Unravelling obesity linked cardio-metabolic risk heterogeneity among young adults residing in middle India: a prevalence and comparative study across diverse phenotype

Shipra Das; Sanjay Kumar; Anil Baran Choudhury

doi:10.18203/2320-6012.ijrms20243696

Authors

Shipra Das Department of Physiology, Bharat Ratna Late Shri Atal Bihari Vajpayee Memorial Medical College, Pendri, Rajnandgaon, Chhattisgarh, India
Sanjay Kumar Department of Physiology, Sikkim Manipal Institute of Medical Sciences, 5th Mile Tadong, Gangtok, Sikkim, India
Anil Baran Choudhury Department of biochemistry, Bharat Ratna Late Shri Atal Bihari Vajpayee Memorial Medical College, Pendri, Rajnandgaon, Chhattisgarh, India

DOI:

https://doi.org/10.18203/2320-6012.ijrms20243696

Keywords:

Metabolically healthy normal weight, Metabolically healthy obese, Metabolically unhealthy normal weight, Metabolically unhealthy obese

Abstract

Background: Obesity is a complicated metabolic state with diverse phenotypic expressions; which linked with distinct cardio-metabolic risk profiles. Knowing prevalence of those phenotype and understanding cardio-metabolic risks across phenotypes is essential for developing effective public health care strategies. Objective of this study was to assess the prevalence and compare cardio-metabolic risk profiles among different phenotypes.

Methods: 403 young adult (aged 18-25 years) of both genders were included for this cross-sectional study. Participants were classified into obesity phenotypes based on body mass index (BMI) and metabolic health status. Under ethical consideration demographic, anthropometric measurements and cardio-metabolic risk factors were assessed by using standard protocol.

Results: Overall prevalence of metabolically unhealthy normal weight (MUNW), metabolically unhealthy obese (MUO) and metabolically healthy obese (MHO) phenotypes were 3.47%, 8.44% and 5.56% respectively, with slightly higher prevalence in males than females. MUO individuals exhibited the highest metabolic risk markers with the lowest HDL levels. MHO individuals demonstrated relatively better metabolic profiles. MUNW showed intermediate levels for most parameters.

Conclusions: The study highlights significant metabolic differences among obesity phenotypes, with MUO showing the highest risk and MHNW presenting the most favorable metabolic health. These findings emphasize the importance of distinguishing between different obesity phenotypes for effective management and intervention strategies.

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References

WHO. Obesity and overweight. Available from: https://www.who.int/news-room/fact-sheets/detail/ obesity-and-overweight. Accessed on 22 May 2020.

Okorodudu DO, Jumean MF, Montori VM, Romero-Corral A, Somers VK, Erwin PJ, et al. Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. Int J Obes. 2010;34(5):791-9.

Blüher M. Metabolically Healthy Obesity. Endocr Rev. 2020;41(3):405-20.

Mayoral LPC, Andrade GM, Mayoral EPC, Huerta TH, Canseco SP, Rodal Canales FJ, et al. Obesity subtypes, related biomarkers and heterogeneity. Indian J Med Res. 2020;151(1):11-21.

Fauchier G, Bisson A, Bodin A, Herbert J, Semaan C, Angoulvant D, et al. Metabolically healthy obesity and cardiovascular events: a nationwide cohort study. Diabetes Obes Metab. 2021;23(11):2492-501.

Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J, et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999-2004). Arch Intern Med. 2008;168(15):1617-24.

Blüher M. Are metabolically healthy obese individuals really healthy? Eur J Endocrinol. 2014;171(6):R209-19.

Huang PL. A comprehensive definition for metabolic syndrome. Dis Model Mech. 2009;2(5-6):231-7.

Madhu SV, Nitin K, Sambit D, Nishant R, Sanjay K. ESI clinical practice guidelines for the evaluation and management of obesity in India. Indian J Endocrinol Metab. 2022;26(4):295-318.

Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the Metabolic Syndrome. Circulation. 2009;120(16):1640-5.

Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. 2006;444(7121):840-6.

Prospective Studies Collaboration. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet. 2009;373(9669):1083-96.

Eckel RH, Alberti KG, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet. 2010;375(9710):181-3.

Tsatsoulis A, Paschou SA. Metabolically healthy obesity: criteria, epidemiology, controversies, and consequences. Curr Obes Rep. 2020;9(2):109-20.

Després JP, Lemieux I. Abdominal obesity and metabolic syndrome. Nature. 2006;444(7121):881-7.

Gallagher D, Kuznia P, Heshka S, Albu J, Heymsfield SB, Goodpaster B, et al. Adipose tissue in muscle: a novel depot similar in size to visceral adipose tissue. Am J Clin Nutr. 2005;81(4):903-10.

Blüher M. Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol. 2019;15(5):288-98.

Pischon T, Boeing H, Hoffmann K, Bergmann M, Schulze MB, Overvad K, et al. General and abdominal adiposity and risk of death in Europe. N Engl J Med. 2008;359(20):2105-20.

Lee SH, Han K, Yang HK, Kim HS, Cho JH, Kwon HS, et al. A novel criterion for identifying metabolically obese but normal weight individuals using the product of triglycerides and glucose. Nutr Diabetes. 2015;5(4):e149.

Xiong Q, Zhang Y, Li J, An Y, Yu S. Comparison of cardiovascular disease risk association with metabolic unhealthy obesity identified by body fat percentage and body mass index: Results from the 1999-2020 National Health and Nutrition Examination Survey. PLoS One. 2024;19(8):e0305592.

Kumanyika SK, Obarzanek E, Stettler N, Bell R, Field AE, Fortmann SP, et al. Population-based prevention of obesity: the need for comprehensive promotion of healthful eating, physical activity, and energy balance: a scientific statement from American Heart Association Council on Epidemiology and Prevention, Interdisciplinary Committee for Prevention (formerly the expert panel on population and prevention science). Circulation. 2008;118(4):428-64.

Dina C, Meyre D, Gallina S, Durand E, Körner A, Jacobson P, et al. Variation in FTO contributes to childhood obesity and severe adult obesity. Nat Genet. 2007;39(6):724-6.

Stefan N, Häring HU, Hu FB, Schulze MB. Metabolically healthy obesity: epidemiology, mechanisms, and clinical implications. Lancet Diabetes Endocrinol. 2013;1(2):152-62.