Abstract
Background/objectives
Adiponectin represents an important link between adipose tissue dysfunction and cardiometabolic risk in obesity; however, there is a lack of data on the effects of adiponectin-related genetic variations and gene-diet interactions on metabolic disorders in children. We aimed to investigate possible interactions between adiponectin-related genetic variants and habitual dietary patterns on metabolic health among children with normal weight versus overweight/obesity, and whether these effects in childhood longitudinally contribute to metabolic risk at follow-up.
Subjects/methods
In total, 3,317 Chinese children aged 6–18 at baseline and 339 participants at 10-year follow-up from the Beijing Child and Adolescent Metabolic Syndrome study cohort were included. Baseline lifestyle factors, plasma adiponectin levels, and six adiponectin-related genetic variants resulting from GWAS in East Asians (loci in/near ADIPOQ, CDH13, WDR11FGF, CMIP, and PEPD) were assessed for their associations with the metabolic disorders. Being metabolically unhealthy was defined by exhibiting any metabolic syndrome component.
Results
Among the six loci, ADIPOQ rs6773957 (OR 1.26, 95% CI:1.07–1.47, P = 0.004) and adiponectin receptor CDH13 rs4783244 (0.82, 0.69–0.96, P = 0.017) were correlated with metabolic risks independent of lifestyle factors in normal-weight children, but the associations were less obvious in those with overweight/obesity. A significant interaction between rs6773957 and diet (Pinteraction = 0.004) for metabolic health was observed in normal-weight children. The adiponectin-decreasing allele of rs6773957 was associated with greater metabolic risks in individuals with unfavorable diet patterns (P < 0.001), but not in those with healthy patterns (P > 0.1). A similar interaction effect was observed using longitudinal data (Pinteraction = 0.029).
Conclusions
These findings highlight a novel gene-diet interaction on the susceptibility to cardiometabolic disorders, which has a long-term impact from childhood onward, particularly in those with normal weight. Personalized dietary advice in these individuals may be recommended as an early possible therapeutic measure to improve metabolic health.
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Data availability
All datasets used in the current investigation are available from the corresponding author upon reasonable request.
Code availability
The code supporting the conclusions of this article is available upon a reasonable request from the authors.
References
Collaborators GBDO, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, et al. Health effects of overweight and obesity in 195 countries over 25 years. The. New England journal of medicine. 2017;377:13–27.
Phillips CM. Metabolically healthy obesity across the life course: epidemiology, determinants, and implications. Annals of the New York Academy of Sciences. 2017;1391:85–100.
Samocha-Bonet D, Dixit VD, Kahn CR, Leibel RL, Lin X, Nieuwdorp M, et al. Metabolically healthy and unhealthy obese–the 2013 Stock Conference report. Obes Rev. 2014;15:697–708.
Schulze MB. Metabolic health in normal-weight and obese individuals. Diabetologia. 2019;62:558–66.
Park JM, Park DH, Song Y, Kim JO, Choi JE, Kwon YJ, et al. Understanding the genetic architecture of the metabolically unhealthy normal weight and metabolically healthy obese phenotypes in a Korean population. Sci Rep. 2021;11:2279.
Stern JH, Rutkowski JM, Scherer PE. Adiponectin, leptin, and fatty acids in the maintenance of metabolic homeostasis through adipose tissue crosstalk. Cell Metab. 2016;23:770–84.
Asterholm IW, Scherer PE. Enhanced metabolic flexibility associated with elevated adiponectin levels. Am J Pathol. 2010;176:1364–76.
Heidemann C, Sun Q, van Dam RM, Meigs JB, Zhang C, Tworoger SS, et al. Total and high-molecular-weight adiponectin and resistin in relation to the risk for type 2 diabetes in women. Ann Intern Med. 2008;149:307–16.
Hivert MF, Sullivan LM, Fox CS, Nathan DM, D’Agostino RB Sr., Wilson PW, et al. Associations of adiponectin, resistin, and tumor necrosis factor-alpha with insulin resistance. The Journal of clinical endocrinology and metabolism. 2008;93:3165–72.
Weyer C, Funahashi T, Tanaka S, Hotta K, Matsuzawa Y, Pratley RE, et al. Hypoadiponectinemia in obesity and type 2 diabetes: close association with insulin resistance and hyperinsulinemia. J Clin Endocrinol Metab. 2001;86:1930–5.
Ahl S, Guenther M, Zhao S, James R, Marks J, Szabo A, et al. Adiponectin levels differentiate metabolically healthy vs unhealthy among obese and nonobese white individuals. J Clin Endocrinol Metab. 2015;100:4172–80.
Fu J, Li Y, Esangbedo IC, Li G, Feng D, Li L, et al. Circulating osteonectin and adipokine profiles in relation to metabolically healthy obesity in Chinese children: findings From BCAMS. Journal of the American Heart Association. 2018;7:e009169.
Chung CM, Lin TH, Chen JW, Leu HB, Yang HC, Ho HY, et al. A genome-wide association study reveals a quantitative trait locus of adiponectin on CDH13 that predicts cardiometabolic outcomes. Diabetes. 2011;60:2417–23.
Dastani Z, Hivert MF, Timpson N, Perry JR, Yuan X, Scott RA, et al. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLoS Genet. 2012;8:e1002607.
Ling H, Waterworth DM, Stirnadel HA, Pollin TI, Barter PJ, Kesäniemi YA, et al. Genome-wide linkage and association analyses to identify genes influencing adiponectin levels: the GEMS Study. Obesity (Silver Spring). 2009;17:737–44.
Wu Y, Gao H, Li H, Tabara Y, Nakatochi M, Chiu YF, et al. A meta-analysis of genome-wide association studies for adiponectin levels in East Asians identifies a novel locus near WDR11-FGFR2. Hum Mol Genet. 2014;23:1108–19.
Gao H, Fall T, Fau-van Dam RM, van Dam Rm Fau-Flyvbjerg A, Flyvbjerg A, Fau-Zethelius B, et al. Evidence of a causal relationship between adiponectin levels and insulin sensitivity: a Mendelian randomization study. Diabetes. 2013;62:1338–44.
Ortega Moreno L, Copetti M, Fontana A, De Bonis C, Salvemini L, Trischitta V, et al. Evidence of a causal relationship between high serum adiponectin levels and increased cardiovascular mortality rate in patients with type 2 diabetes. Cardiovascular diabetology. 2016;15:17.
Borges MC, Barros AJD, Ferreira DLS, Casas JP, Horta BL, Kivimaki M, et al. Metabolic profiling of adiponectin levels in adults: mendelian randomization analysis. Circ Cardiovasc Genet. 2017;10:e001837.
Au Yeung SL, Schooling CM. Adiponectin and coronary artery disease risk: a bi-directional Mendelian randomization study. International journal of cardiology. 2018;268:222–6.
Yaghootkar H, Lamina C, Scott RA, Dastani Z, Hivert MF, Warren LL, et al. Mendelian randomization studies do not support a causal role for reduced circulating adiponectin levels in insulin resistance and type 2 diabetes. Diabetes. 2013;62:3589–98.
Borges MC, Lawlor DA, de Oliveira C, White J, Horta BL, Barros AJ. Role of adiponectin in coronary heart disease risk: a mendelian randomization study. Circ Res. 2016;119:491–9.
Ma W, Huang T, Zheng Y, Wang M, Bray GA, Sacks FM, et al. Weight-loss diets, adiponectin, and changes in cardiometabolic risk in the 2-year POUNDS lost trial. The Journal of clinical endocrinology and metabolism. 2016;101:2415–22.
Varady KA, Hellerstein MK. Do calorie restriction or alternate-day fasting regimens modulate adipose tissue physiology in a way that reduces chronic disease risk? Nutr Rev. 2008;66:333–42.
AlEssa HB, Malik VS, Yuan C, Willett WC, Huang T, Hu FB, et al. Dietary patterns and cardiometabolic and endocrine plasma biomarkers in US women. The American journal of clinical nutrition. 2017;105:432–41.
Li M, Fisette A, Zhao XY, Deng JY, Mi J, Cianflone K. Serum resistin correlates with central obesity but weakly with insulin resistance in Chinese children and adolescents. International journal of obesity. 2009;33:424–39.
Li G, Esangbedo IC, Xu L, Fu J, Li L, Feng D, et al. Childhood retinol-binding protein 4 (RBP4) levels predicting the 10-year risk of insulin resistance and metabolic syndrome: the BCAMS study. Cardiovascular diabetology. 2018;17:69.
Fu J, Han L, Zhao Y, Li G, Zhu Y, Li Y, et al. Vitamin D levels are associated with metabolic syndrome in adolescents and young adults: The BCAMS study. Clinical nutrition (Edinburgh, Scotland). 2019;38:2161–7.
Li G, Yin J, Fu J, Li L, Grant SFA, Li C, et al. FGF21 deficiency is associated with childhood obesity, insulin resistance and hypoadiponectinaemia: The BCAMS Study. Diabetes & metabolism. 2017;43:253–60.
Force GOCO. Body mass index reference norm for screening overweight and obesity in Chinese children and adolescents. China J Epidemiol. 2004;25:97–102.
Wa M, Jm T Puberty. In: F F, Jm T, editors. Human Growth. New York: Plenum Press; 1986. p. 171–210.
Li M, Wu C, Song A, K Z. Development and preliminary application of enzyme-linked immunosorbent assay for human net insulin in serum. Chin J Endocrinol Metab. 1997;13:214–17.
Li Q, Lu Y, Sun L, Yan J, Yan X, Fang L, et al. Plasma adiponectin levels in relation to prognosis in patients with angiographic coronary artery disease. Metabolism: clinical and experimental. 2012;61:1803–8.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412–9.
Li L, Yin J, Cheng H, Wang Y, Gao S, Li M, et al. Identification of genetic and environmental factors predicting metabolically healthy obesity in children: data from the BCAMS study. J Clin Endocrinol Metab. 2016;101:1816–25.
Li L, Fu J, Yu XT, Li G, Xu L, Yin J, et al. Sleep duration and cardiometabolic risk among Chinese school-aged children: do adipokines play a mediating role? Sleep. 2017;40:zsx042.
Gabriel S, Ziaugra L, Tabbaa D. SNP genotyping using the Sequenom MassARRAY iPLEX platform. Curr protoc in human genet. 2009; Chapter 2:Unit 2.12.
Fu J, Hou C, Li L, Feng D, Li G, Li M, et al. Vitamin D modifies the associations between circulating betatrophin and cardiometabolic risk factors among youths at risk for metabolic syndrome. Cardiovascular diabetology. 2016;15:142.
Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120:1640–5.
Goto A, Noda M, Goto M, Yasuda K, Mizoue T, Yamaji T, et al. Plasma adiponectin levels, ADIPOQ variants, and incidence of type 2 diabetes: A nested case-control study. Diabetes research and clinical practice. 2017;127:254–64.
Qi L, Li T, Rimm E, Zhang C, Rifai N, Hunter D, et al. The +276 polymorphism of the APM1 gene, plasma adiponectin concentration, and cardiovascular risk in diabetic men. Diabetes. 2005;54:1607–10.
Patel S, Flyvbjerg A, Kozakova M, Frystyk J, Ibrahim IM, Petrie JR, et al. Variation in the ADIPOQ gene promoter is associated with carotid intima media thickness independent of plasma adiponectin levels in healthy subjects. European heart journal. 2008;29:386–93.
Chen J-M, Férec C, Cooper DN. A systematic analysis of disease-associated variants in the 3′ regulatory regions of human protein-coding genes I: general principles and overview. Human Genetics. 2006;120:1–21.
Xie X, Lu J, Kulbokas EJ, Golub TR, Mootha V, Lindblad-Toh K, et al. Systematic discovery of regulatory motifs in human promoters and 3’ UTRs by comparison of several mammals. Nature. 2005;434:338–45.
Vishvanath L, Gupta RK. Contribution of adipogenesis to healthy adipose tissue expansion in obesity. J Clin Invest. 2019;129:4022–31.
Johannsen DL, Tchoukalova Y, Tam CS, Covington JD, Xie W, Schwarz JM, et al. Effect of 8 weeks of overfeeding on ectopic fat deposition and insulin sensitivity: testing the “adipose tissue expandability” hypothesis. Diabetes Care. 2014;37:2789–97.
Gao H, Kim YM, Chen P, Igase M, Kawamoto R, Kim MK, et al. Genetic variation in CDH13 is associated with lower plasma adiponectin levels but greater adiponectin sensitivity in East Asian populations. Diabetes. 2013;62:4277–83.
Denzel MS, Scimia MC, Zumstein PM, Walsh K, Ruiz-Lozano P, Ranscht B. T-cadherin is critical for adiponectin-mediated cardioprotection in mice. The Journal of clinical investigation. 2010;120:4342–52.
Keaton JM, Gao C, Guan M, Hellwege JN, Palmer ND, Pankow JS, et al. Genome-wide interaction with the insulin secretion locus MTNR1B reveals CMIP as a novel type 2 diabetes susceptibility gene in African Americans. Genet Epidemiol. 2018;42:559–70.
Dhurandhar NV, Schoeller D, Brown AW, Heymsfield SB, Thomas D, Sørensen TI, et al. Energy balance measurement: when something is not better than nothing. International journal of obesity. 2015;39:1109–13.
Acknowledgements
The authors thank Dr. Jie Mi, the professor of Capital Institute of Pediatrics in Beijing, and other BCAMS study members and participants for their continuing participation in this research effort.
Funding
This work was supported by grants from the National Key Research Program of China (2016YFC1304801), National Natural Science Foundation of China (81970732), the Capital’s Funds for Health Improvement and Research (2020-2Z-40117), Beijing Natural Science Foundation (7172169), key program of Beijing Municipal Science & Technology Commission (D111100000611001, D111100000611002), Beijing Science & Technology Star Program (2004A027), Novo Nordisk Union Diabetes Research Talent Fund (2011A002), National Key Program of Clinical Science (WBYZ2011-873), the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2018PT32001), and AMS Innovation Fund for Medical Sciences (CIFMS 2021-1-I2M-016).
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GL contributed to the data analysis and drafted the manuscript; LZ performed data analyses and edited the manuscript; LH, YW, BL, DW, YL, and QZ contributed to data collection; YZ contributed to the data interpretation; QL, JRS and SMW contributed to the data interpretation and edited/ revised the manuscript; ML contributed to the concept, design of the study, analyzed the data and revised the manuscript. SG was responsible for the concept, design, and data collection in the BCAMS follow-up study, and contributed to the acquisition and interpretation of the data, and revised the manuscript.
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The study was approved by the local ethics committee and is following the declaration of Helsinki on ethical principles for medical research involving human participants. Written informed consent was obtained from all patients before participation in this study.
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Li, G., Zhong, L., Han, L. et al. Genetic variations in adiponectin levels and dietary patterns on metabolic health among children with normal weight versus obesity: the BCAMS study. Int J Obes 46, 325–332 (2022). https://doi.org/10.1038/s41366-021-01004-z
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DOI: https://doi.org/10.1038/s41366-021-01004-z
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