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Genetics and Epigenetics

Novel genomic variants related to visceral adiposity index (VAI) and body adiposity index (BAI) in Indian sib-pairs

International Journal of Obesity (2024)Cite this article

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Abstract

Background

Obesity is among the leading public health threats globally. Over the last few years, visceral adiposity index (VAI), and body adiposity index (BAI), derived from anthropometric, and biochemical measures, have gained importance as a measure of obesity. However, unlike other common indices like body mass index, and waist circumference, the genetic predisposition of VAI, and BAI under-examined.

Methods

2265 sib-pairs from Indian Migration Study were used for examining the association of genetic variants from the Cardio-Metabochip array with VAI, and BAI. Mixed linear regression models were run, and all inferences were based on the within-sib component of the Fulker’s association models. Gene-environment/lifestyle interaction analyses were also undertaken.

Results

rs6659428 at LOC400796 | SEC16B (β = 0.26, SE = 0.05), and rs7611535 at DRD3 | LOC645180 (β = 0.18, SE = 0.04) were associated with VAI at suggestive significance value of <8.21 × 10−6. For BAI, rs73300702 at JAZF1-AS1 (β = 0.27, SE = 0.06), was the top hit at p value < 8.21 × 10−6. Further, rs6659428 showed marginal effect modification with rural/urban location (β = 0.26, SE = 0.13, p value = 0.047), and rs73300702 with physical activity (β = −0.29,SE = 0.14, p value = 0.034).

Conclusion

We report three novel genetic loci for VAI, and BAI in Indians that are important indicators of adiposity. These findings need to be replicated and validated with larger samples from different ethnicities. Further, functional studies for understanding the biological mechanisms of these adiposity indices need to be undertaken to understand the underlying pathophysiology.

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Fig. 1: Manhattan plot for Body Adiposity Index (BAI).
Fig. 2: Manhattan plot for Visceral Adiposity Index (VAI).

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Data availability

The data analysed during the current study are not publicly available owing to ethical regulations but will be available from the corresponding author upon reasonable request.

References

  1. Hill JO, Wyatt HR, Peters JC. Energy balance and obesity. Circulation. 2012;126:126–32.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Boutari C, Mantzoros CS. A 2022 update on the epidemiology of obesity and a call to action: as its twin COVID-19 pandemic appears to be receding, the obesity and dysmetabolism pandemic continues to rage on. Metabolism. 2022;133:155217.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Anjana RM, Unnikrishnan R, Deepa M, Pradeepa R, Tandon N, Das AK, et al. Metabolic non-communicable disease health report of India: the ICMR-INDIAB national cross-sectional study (ICMR-INDIAB-17). Lancet Diabetes Endocrinol. 2023;11:474–89.

    Article  PubMed  Google Scholar 

  4. Singh GM, Danaei G, Farzadfar F, Stevens GA, Woodward M, Wormser D, et al. The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: a pooled analysis. PLoS ONE. 2013;8:e65174.

  5. Leutner M, Dervic E, Bellach L, Klimek P, Thurner S, Kautzky A. Obesity as pleiotropic risk state for metabolic and mental health throughout life. Transl Psychiatry. 2023;13:175.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Ssekamatte P, Sande OJ, van Crevel R, Biraro IA. Immunologic, metabolic and genetic impact of diabetes on tuberculosis susceptibility. Front Immunol. 2023;14:1122255.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Zhou M. The shifting income-obesity relationship: Conditioning effects from economic development and globalization. SSM - Popul Health. 2021;15:100849.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Maes HH, Neale MC, Eaves LJ. Genetic and environmental factors in relative body weight and human adiposity. Behav Genet. 1997;27:325–51.

    Article  CAS  PubMed  Google Scholar 

  9. Benmarhnia T, Deguen S, Kaufman JS, Smargiassi A. Review article: vulnerability to heat-related mortality: a systematic review, meta-analysis, and meta-regression analysis. Epidemiology. 2015;26:781–93.

    Article  PubMed  Google Scholar 

  10. Pouliot MC, Després JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, et al. Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol. 1994;73:460–8.

    Article  CAS  PubMed  Google Scholar 

  11. Gupta S, Kapoor S. Body adiposity index: its relevance and validity in assessing body fatness of adults. ISRN Obes. 2014;2014:1–5.

    Article  Google Scholar 

  12. Ferreira FG, Juvanhol LL, da Silva DCG, Longo GZ. Visceral adiposity index is a better predictor of unhealthy metabolic phenotype than traditional adiposity measures: results from a population-based study. Public Health Nutr. 2019;22:1545–54.

    Article  PubMed  Google Scholar 

  13. Amato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, et al. Visceral Adiposity Index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care. 2010;33:920–2.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Jiang K, Luan H, Pu X, Wang M, Yin J, Gong R. Association between visceral adiposity index and insulin resistance: a cross-sectional study based on US adults. Front Endocrinol. 2022;13:921067.

    Article  Google Scholar 

  15. Qin Y, Qiao Y, Wang D, Li M, Yang Z, Li L, et al. Visceral adiposity index is positively associated with fasting plasma glucose: a cross-sectional study from National Health and Nutrition Examination Survey 2017–2020. BMC Public Health. 2023;23:313.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhang Y, He Q, Zhang W, Xiong Y, Shen S, Yang J, et al. Non-linear associations between visceral adiposity index and cardiovascular and cerebrovascular diseases: results from the NHANES (1999-2018). Front Cardiovasc Med. 2022;9:908020.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Silva N, Pinho C, Diniz A, Arruda I, Leão A, Rodrigues I. The applicability of the Visceral Adiposity Index (VAI) for predicting visceral fat. Rev bras Cineantropom Desempenho Hum. 2022;24:e83146.

    Article  Google Scholar 

  18. Bergman RN, Stefanovski D, Buchanan TA, Sumner AE, Reynolds JC, Sebring NG, et al. A better index of body adiposity. Obes (Silver Spring). 2011;19:1083–9.

    Article  Google Scholar 

  19. Cerqueira MS, Santos CAdos, Silva DAS, Amorim PRdosS, Marins JCB, Franceschini SdoCC. Validity of the body adiposity index in predicting body fat in adults: a systematic review. Adv Nutr. 2018;9:617–24.

    Article  PubMed  PubMed Central  Google Scholar 

  20. García AI, Niño-Silva LA, González-Ruíz K, Ramírez-Vélez R. Body adiposity index as marker of obesity and cardiovascular risk in adults from Bogotá, Colombia. Endocrinolía y Nutrición (Engl Ed). 2015;62:130–7.

    Article  Google Scholar 

  21. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316:889–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Loos RJ. The genetics of adiposity. Curr Opin Genet Dev. 2018;50:86–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Loos RJF, Yeo GSH. The genetics of obesity: from discovery to biology. Nat Rev Genet. 2022;23:120–33.

    Article  CAS  PubMed  Google Scholar 

  24. Teng MS, Wu S, Er LK, Hsu LA, Chou HH, Ko YL. LIPC variants as genetic determinants of adiposity status, visceral adiposity indicators, and triglyceride-glucose (TyG) index-related parameters mediated by serum triglyceride levels. Diabetol Metab Syndr. 2018;10:79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Saini S, Walia GK, Sachdeva MP, Gupta V. Genetics of obesity and its measures in India. J Genet. 2018;97:1047–71.

    Article  PubMed  Google Scholar 

  26. Lyngdoh T, Kinra S, Shlomo YB, Reddy S, Prabhakaran D, Smith GD, et al. Sib-recruitment for studying migration and its impact on obesity and diabetes. Emerg Themes Epidemiol. 2006;3:2.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Voight BF, Kang HM, Ding J, Palmer CD, Sidore C, Chines PS, et al. The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 2012;8:e1002793.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Izawa S, Okada M, Matsui H, Horita Y. A new direct method for measuring HDL-cholesterol which does not produce any biased values. J Med Pharm Sci. 1997;37:1385213881.

    Google Scholar 

  29. Purcell S, Cherny SS, Sham PC. Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics. 2003;19:149–50.

    Article  CAS  PubMed  Google Scholar 

  30. Fulker DW, Cherny SS, Sham PC, Hewitt JK. Combined linkage and association sib-pair analysis for quantitative traits. Am J Hum Genet. 1999;64:259–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Guo Y, Huang Y, Hou L, Ma J, Chen C, Ai H, et al. Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches. Genet Sel Evol. 2017;49:21.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Fu LW, Zhang MX, Gao LW, Mi J. [Association between SEC16B polymorphisms and body mass index variation or risk of obesity: a Meta-analysis]. Zhonghua Liu Xing Bing Xue Za Zhi. 2016;37:1288–95.

    CAS  PubMed  Google Scholar 

  33. Budnik A, Heesom KJ, Stephens DJ. Characterization of human Sec16B: indications of specialized, non-redundant functions. Sci Rep. 2011;1:77.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Yonekawa S, Furuno A, Baba T, Fujiki Y, Ogasawara Y, Yamamoto A, et al. Sec16B is involved in the endoplasmic reticulum export of the peroxisomal membrane biogenesis factor peroxin 16 (Pex16) in mammalian cells. Proc Natl Acad Sci USA. 2011;108:12746–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Shi R, Lu W, Tian Y, Wang B. Intestinal SEC16B modulates obesity by regulating chylomicron metabolism. Mol Metab. 2023;70:101693.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Klop B, Castro Cabezas M. Chylomicrons: A key biomarker and risk factor for cardiovascular disease and for the understanding of obesity. Curr Cardiovasc Risk Rep. 2012;6:27–34.

    Article  Google Scholar 

  37. del Casale A, Paolini M, Gentile G, Borro M, Zocchi C, Fiaschè F, et al. Dopamine DRD2 and DRD3 polymorphisms involvement in nicotine dependence in patients with treatment-resistant mental disorders. JPM. 2022;12:565.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Świtała K, Leońska-Duniec A. Physical activity and gene association with human obesity. BJHPA. 2021;13:99–111.

    Article  Google Scholar 

  39. Volkow ND, Wang GJ, Baler RD. Reward, dopamine and the control of food intake: implications for obesity. Trends Cogn Sci. 2011;15:37–46.

    Article  CAS  PubMed  Google Scholar 

  40. McQuade JA, Benoit SC, Xu M, Woods SC, Seeley RJ. High-fat diet induced adiposity in mice with targeted disruption of the dopamine-3 receptor gene. Behav Brain Res. 2004;151:313–9.

    Article  CAS  PubMed  Google Scholar 

  41. Bakay M, Pandey R, Hakonarson H. Genes involved in type 1 diabetes: an update. Genes. 2013;4:499–521.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Chen W, Zhong Y, Yuan Y, Zhu M, Hu W, Liu N, et al. New insights into the suppression of inflammation and lipid accumulation by JAZF1. Genes Dis. 2023;10:2457–69.

    Article  CAS  PubMed  Google Scholar 

  43. Jeong J, Jang S, Park S, Kwon W, Kim SY, Jang S, et al. JAZF1 heterozygous knockout mice show altered adipose development and metabolism. Cell Biosci. 2021;11:161.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Tao W, Moore R, Meng Y, Yeasky TM, Smith ER, Xu XX. Disabled-2 determines commitment of a pre-adipocyte population in juvenile mice. Sci Rep. 2016;6:35947.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The Indian Migration Study was funded by the Wellcome Trust (grant number GR070797MF) awarded to Prof. Shah Ebrahim, LSHTM, UK. The genotyping of the study participants was funded by the Wellcome Trust enhancement grant (grant number 083541/Z/07/A) awarded to Prof. Shah Ebrahim, LSHTM, UK. The present work was supported by the grant awarded by the Wellcome Trust/DBT India Alliance (grant no. IA/CPHE/16/1/502649) to Dr. Gagandeep Kaur Walia. We also extend our gratitude to the Indian Migration Study team for providing access to relevant data needed. We would also like to acknowledge the contribution of Dr. Sheetal Gandotra, IGIB-CSIR, India for her expert suggestions that helped in conducting, and interpreting the study findings.

Author information

Authors and Affiliations

  1. Indian Institute of Public Health-Delhi, Public Health Foundation of India, Delhi, India

    Tripti Agarwal

  2. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India

    Tripti Agarwal & Gagandeep Kaur Walia

  3. Indian Council of Medical Research, New Delhi, India

    Tanica Lyngdoh

  4. All India Institute of Medical Sciences, New Delhi, India

    Rajesh Khadgawat

  5. Department of Population Health Sciences, University of Leicester, Leicester, UK

    Frank Dudbridge

  6. Department of Non-Communicable Disease Epidemiology, London School of Hygiene, and Tropical Medicine, London, UK

    Sanjay Kinra & Shah Ebrahim

  7. MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK

    Caroline Relton & George Davey Smith

  8. Centre for Chronic Disease Control, New Delhi, India

    Dorairaj Prabhakaran & Gagandeep Kaur Walia

  9. Genomic Research in Complex diseases (GRC Group), CSIR-Centre for Cellular, and Molecular Biology, Hyderabad, India

    Giriraj Ratan Chandak

  10. Department of Anthropology, University of Delhi, New Delhi, India

    Vipin Gupta

  11. Public Health Foundation of India, Delhi, India

    Gagandeep Kaur Walia

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Contributions

Tripti Agarwal—Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing—original draft ; Tanica Lyngdoh—Investigation, Methodology, Supervision, Writing—review & editing; Rajesh Khadgawat—Methodology, Supervision, Writing—review & editing; Frank Dudbridge—Formal analysis, Methodology, Resources, Software, Validation, Writing—review & editing; Sanjay Kinra—Conceptualization, Funding acquisition, Data curation, Resources, Writing—review & editing; Caroline L. Relton—Conceptualization, Funding acquisition, Supervision, Writing—review & editing; George Davey Smith—Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Writing—review & editing; Shah Ebrahim—Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing—review & editing; Dorairaj Prabhakaran—Conceptualization, Funding acquisition, Resources, Supervision, Writing—review & editing; Giriraj Ratan Chandak—Conceptualization, Funding acquisition, Data curation, Formal analysis, Investigation, Methodology, Resources, Supervision, Writing—review & editing; Vipin Gupta—Conceptualization, Funding acquisition, Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Supervision, Writing—review & editing; Gagandeep Kaur Walia—Conceptualization, Funding acquisition, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing—original draft, Writing—review & editing

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Correspondence to Vipin Gupta or Gagandeep Kaur Walia.

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Agarwal, T., Lyngdoh, T., Khadgawat, R. et al. Novel genomic variants related to visceral adiposity index (VAI) and body adiposity index (BAI) in Indian sib-pairs. Int J Obes (2024). https://doi.org/10.1038/s41366-024-01570-y

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