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Health issues and nutrition in the elderly

Mendelian randomization on the association of obesity with vitamin D: Guangzhou Biobank Cohort Study

European Journal of Clinical Nutrition volume 77, pages 195–201 (2023)Cite this article

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Abstract

Background

Mendelian randomization (MR) analyses from the West provide evidence that obesity causes lower 25-hydroxyvitamin D [25(OH)D]. As Asian populations are prone to metabolic disorders at a lower body mass index (BMI), whether the association remains in Asian is unclear. We studied whether obesity causes vitamin D deficiency using MR analysis in Chinese.

Methods

We used data from the Guangzhou Biobank Cohort Study. A genetic score including seven BMI-related single-nucleotide polymorphisms (n = 15,249) was used as the instrumental variable (IV) for BMI. Two-stage least square regression and conventional multivariable linear regression in 2,036 participants with vitamin D data were used to analyze association of BMI with vitamin D.

Results

Proportion of variation explained by the genetic score was 0.7% and the first stage F-statistic for MR analysis was 103. MR analyses showed that each 1 kg/m2 higher BMI was associated with lower 25(OH)D by −2.35 (95% confidence interval (CI) −4.68 to −0.02) nmol/L. In conventional multivariable linear regression, higher BMI was also associated with lower 25(OH)D (β = −0.26 nmol/L per 1 kg/m2 increase in BMI, 95% CI −0.46 to −0.06). Sensitivity analyses using two-sample IV analysis and leave-one-out method showed similar results.

Conclusion

We have first shown by MR and conventional multivariable linear regression that higher BMI causes vitamin D deficiency in Chinese. Our findings highlight the importance of weight control and suggest that vitamin D supplementation may be needed in individuals with overweight or obesity.

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Fig. 1: Schematic diagram of Mendelian randomization study design and assumptions.
Fig. 2: Two-sample Mendelian randomization of body mass index and vitamin D in the Guangzhou Biobank Cohort Study.

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Data availability

Data that support findings are restricted to researchers who have permission from the Guangzhou Biobank Cohort Study, and so are not publicly available.

References

  1. Collaboration NCDRF. 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. 2017;390:2627–42.

    Article  Google Scholar 

  2. Xu L, Lam TH. Stage of obesity epidemic model: Learning from tobacco control and advocacy for a framework convention on obesity control. J Diabetes. 2018;10:564–71.

    Article  Google Scholar 

  3. Bluher M. Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol. 2019;15:288–98.

    Article  Google Scholar 

  4. Pereira-Santos M, Costa PR, Assis AM, Santos CA, Santos DB. Obesity and vitamin D deficiency: a systematic review and meta-analysis. Obes Rev. 2015;16:341–9.

    Article  CAS  Google Scholar 

  5. Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Smith GD. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27:1133–63.

    Article  Google Scholar 

  6. Vimaleswaran KS, Berry DJ, Lu C, Tikkanen E, Pilz S, Hiraki LT, et al. Causal Relationship between Obesity and Vitamin D Status: Bi-Directional Mendelian Randomization Analysis of Multiple Cohorts. Plos Med. 2013;10:e1001383.

    Article  Google Scholar 

  7. Afzal S, Brondum-Jacobsen P, Bojesen SE, Nordestgaard BG. Vitamin D concentration, obesity, and risk of diabetes: a mendelian randomisation study. Lancet Diabetes Endocrinol. 2014;2:298–306.

    Article  CAS  Google Scholar 

  8. Palaniswamy S, Gill D, De Silva NM, Lowry E, Jokelainen J, Karhu T, et al. Could vitamin D reduce obesity-associated inflammation? Observational and Mendelian randomization study. Am J Clin Nutr. 2020;111:1036–47.

    Article  Google Scholar 

  9. Consultation WHOE. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–63.

    Article  Google Scholar 

  10. van Schoor N, Lips P. Global Overview of Vitamin D Status. Endocrinol Metab Clin North Am. 2017;46:845–70.

    Article  Google Scholar 

  11. Jiang C, Thomas GN, Lam TH, Schooling CM, Zhang W, Lao X, et al. Cohort profile: The Guangzhou Biobank Cohort Study, a Guangzhou-Hong Kong-Birmingham collaboration. Int J Epidemiol. 2006;35:844–52.

    Article  Google Scholar 

  12. Xu L, Jiang CQ, Cheng KK, Au Yeung SL, Zhang WS, Lam TH, et al. Alcohol Use and Gamma-Glutamyltransferase Using a Mendelian Randomization Design in the Guangzhou Biobank Cohort Study. PLoS ONE. 2015;10:e0137790.

    Article  Google Scholar 

  13. Xu L, Jiang CQ, Lam TH, Zhang WS, Zhu F, Jin YL, et al. Mendelian randomization estimates of alanine aminotransferase with cardiovascular disease: Guangzhou Biobank Cohort study. Hum Mol Genet. 2017;26:430–7.

    CAS  Google Scholar 

  14. Zhao J, Jiang CQ, Lam TH, Liu B, Cheng KK, Xu L, et al. Genetically predicted testosterone and cardiovascular risk factors in men: a Mendelian randomization analysis in the Guangzhou Biobank Cohort Study. Int J Epidemiol. 2014;43:140–8.

    Article  Google Scholar 

  15. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42:937–U53.

    Article  CAS  Google Scholar 

  16. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Felix R, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 2015;518:197–U401.

    Article  CAS  Google Scholar 

  17. Wang K, Li WD, Zhang CK, Wang ZH, Glessner JT, Grant SFA, et al. A Genome-Wide Association Study on Obesity and Obesity-Related Traits. Plos ONE. 2011;6:e18939.

    Article  CAS  Google Scholar 

  18. Thorleifsson G, Walters GB, Gudbjartsson DF, Steinthorsdottir V, Sulem P, Helgadottir A, et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet. 2009;41:18–24.

    Article  CAS  Google Scholar 

  19. Wen WQ, Zheng W, Okada Y, Takeuchi F, Tabara Y, Hwang JY, et al. Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index. Hum Mol Genet. 2014;23:5492–504.

    Article  CAS  Google Scholar 

  20. Jiang CQ, Chan YH, Xu L, Jin YL, Zhu T, Zhang WS, et al. Smoking and serum vitamin D in older Chinese people: cross-sectional analysis based on the Guangzhou Biobank Cohort Study. Bmj Open. 2016;6:e010946.

    Article  CAS  Google Scholar 

  21. Alexander TA, Machiela MJ. LDpop: an interactive online tool to calculate and visualize geographic LD patterns. BMC Bioinform. 2020;21:14.

    Article  CAS  Google Scholar 

  22. Burgess S, Thompson SG. Use of allele scores as instrumental variables for Mendelian randomization. Int J Epidemiol. 2013;42:1134–44.

    Article  Google Scholar 

  23. Stock JH, Yogo M. Testing for Weak Instruments in Linear IV Regression. In Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg. Cambridge University Press. 2005. p. 80–108.

  24. Kanter R, Caballero B. Global gender disparities in obesity: a review. Adv Nutr. 2012;3:491–8.

    Article  Google Scholar 

  25. Janssen HC, Emmelot-Vonk MH, Verhaar HJ, van der Schouw YT. Determinants of vitamin D status in healthy men and women aged 40-80 years. Maturitas. 2013;74:79–83.

    Article  CAS  Google Scholar 

  26. Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism. 2019;92:6–10.

    Article  CAS  Google Scholar 

  27. Guessous I, McClellan W, Kleinbaum D, Vaccarino V, Zoller O, Theler JM, et al. Comparisons of serum vitamin D levels, status, and determinants in populations with and without chronic kidney disease not requiring renal dialysis: a 24-hour urine collection population-based study. J Ren Nutr. 2014;24:303–12.

    Article  CAS  Google Scholar 

  28. Davies NM, Hill WD, Anderson EL, Sanderson E, Deary IJ, Davey Smith G. Multivariable two-sample Mendelian randomization estimates of the effects of intelligence and education on health. Elife. 2019;8:e43990.

    Article  Google Scholar 

  29. Liu X, Baylin A, Levy PD. Vitamin D deficiency and insufficiency among US adults: prevalence, predictors and clinical implications. Br J Nutr. 2018;119:928–36.

    Article  CAS  Google Scholar 

  30. Asvold BO, Bjorngaard JH, Carslake D, Gabrielsen ME, Skorpen F, Smith GD, et al. Causal associations of tobacco smoking with cardiovascular risk factors: a Mendelian randomization analysis of the HUNT Study in Norway. Int J Epidemiol. 2014;43:1458–70.

    Article  Google Scholar 

  31. Mousavi SE, Amini H, Heydarpour P, Amini Chermahini F, Godderis L. Air pollution, environmental chemicals, and smoking may trigger vitamin D deficiency: Evidence and potential mechanisms. Environ Int. 2019;122:67–90.

    Article  CAS  Google Scholar 

  32. Lawlor DA, Nordestgaard BG, Benn M, Zuccolo L, Tybjaerg-Hansen A, Davey Smith G. Exploring causal associations between alcohol and coronary heart disease risk factors: findings from a Mendelian randomization study in the Copenhagen General Population Study. Eur Heart J. 2013;34:2519–28.

    Article  CAS  Google Scholar 

  33. Touvier M, Deschasaux M, Montourcy M, Sutton A, Charnaux N, Kesse-Guyot E, et al. Determinants of vitamin D status in Caucasian adults: influence of sun exposure, dietary intake, sociodemographic, lifestyle, anthropometric, and genetic factors. J Investig Dermatol. 2015;135:378–88.

    Article  CAS  Google Scholar 

  34. Doherty A, Smith-Byrne K, Ferreira T, Holmes MV, Holmes C, Pulit SL, et al. GWAS identifies 14 loci for device-measured physical activity and sleep duration. Nat Commun. 2018;9:5257.

    Article  Google Scholar 

  35. Hibler EA, Sardo Molmenti CL, Dai Q, Kohler LN, Warren Anderson S, Jurutka PW, et al. Physical activity, sedentary behavior, and vitamin D metabolites. Bone. 2016;83:248–55.

    Article  CAS  Google Scholar 

  36. Teumer A. Common Methods for Performing Mendelian Randomization. Front Cardiovasc Med. 2018;5:51.

    Article  Google Scholar 

  37. Wu D-M. Alternative Tests of Independence between Stochastic Regressors and Disturbances: Finite Sample Results. Econometrica. 1974;42:529–46.

    Article  Google Scholar 

  38. Holick MF, Binkley NC, Bischoff-Ferrari HA, Gordon CM, Hanley DA, Heaney RP, et al. Evaluation, treatment, and prevention of vitamin D deficiency: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. 2011;96:1911–30.

    Article  CAS  Google Scholar 

  39. Saneei P, Salehi-Abargouei A, Esmaillzadeh A. Serum 25-hydroxy vitamin D levels in relation to body mass index: a systematic review and meta-analysis. Obes Rev. 2013;14:393–404.

    Article  CAS  Google Scholar 

  40. Dorjgochoo T, Shi J, Gao YT, Long J, Delahanty R, Xiang YB, et al. Genetic variants in vitamin D metabolism-related genes and body mass index: analysis of genome-wide scan data of approximately 7000 Chinese women. Int J Obes. 2012;36:1252–5.

    Article  CAS  Google Scholar 

  41. Meng X, Li X, Timofeeva MN, He Y, Spiliopoulou A, Wei WQ, et al. Phenome-wide Mendelian-randomization study of genetically determined vitamin D on multiple health outcomes using the UK Biobank study. Int J Epidemiol. 2019;48:1425–34.

    Article  Google Scholar 

  42. Himbert C, Ose J, Delphan M, Ulrich CM. A systematic review of the interrelation between diet- and surgery-induced weight loss and vitamin D status. Nutr Res. 2017;38:13–26.

    Article  CAS  Google Scholar 

  43. Mallard SR, Howe AS, Houghton LA. Vitamin D status and weight loss: a systematic review and meta-analysis of randomized and nonrandomized controlled weight-loss trials. Am J Clin Nutr. 2016;104:1151–9.

    Article  CAS  Google Scholar 

  44. Vanlint S. Vitamin D and Obesity. Nutrients. 2013;5:949–56.

    Article  CAS  Google Scholar 

  45. Drincic AT, Armas LAG, Van Diest EE, Heaney RP. Volumetric Dilution, Rather Than Sequestration Best Explains the Low Vitamin D Status of Obesity. Obesity. 2012;20:1444–8.

    Article  CAS  Google Scholar 

  46. Earthman CP, Beckman LM, Masodkar K, Sibley SD. The link between obesity and low circulating 25-hydroxyvitamin D concentrations: considerations and implications. Int J Obes. 2012;36:387–96.

    Article  CAS  Google Scholar 

  47. Ference BA, Julius S, Mahajan N, Levy PD, Williams KA, Flack JM. Clinical Effect of Naturally Random Allocation to Lower Systolic Blood Pressure Beginning Before the Development of Hypertension. Hypertension. 2014;63:1182–8.

    Article  CAS  Google Scholar 

  48. Palmer TM, Lawlor DA, Harbord RM, Sheehan NA, Tobias JH, Timpson NJ, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res. 2012;21:223–42.

    Article  Google Scholar 

  49. Ebrahim S, Smith GD. Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology? Hum Genet. 2008;123:15–33.

    Article  Google Scholar 

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Acknowledgements

The Guangzhou Biobank Cohort Study investigators include: Guangzhou Twelfth People’s Hospital: WSZ, M Cao, T Zhu, B Liu, CQJ (Co-PI); The University of Hong Kong: CM Schooling, SM McGhee, GM Leung, R Fielding, THL (Co-PI); The University of Birmingham: P Adab, GN Thomas, KKC (Co-PI).

Funding

This work was supported by the Natural Science Foundation of China (No. 81941019), the National Key R&D Program of China (2017YFC0907100), Natural Science Foundation of Guangdong (2018A030313140), the Guangzhou Science and Technology Bureau (201704030132), the Major Infectious Disease Prevention and Control of the National Science and Technique Major Project (2018ZX10715004) and the University of Birmingham, UK.

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Authors and Affiliations

  1. School of Public Health, Sun Yat-sen University, Guangzhou, China

    Ying Yue Huang & Lin Xu

  2. Guangzhou Twelfth People’s Hospital, Guangzhou, China

    Wei Sen Zhang, Chao Qiang Jiang, Feng Zhu, Ya Li Jin & Tai Hing Lam

  3. Institute of Applied Health Research, University of Birmingham, Birmingham, UK

    Kar Keung Cheng

  4. School of Public Health, the University of Hong Kong, Hong Kong, China

    Tai Hing Lam & Lin Xu

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Contributions

YYH, LX, THL, WSZ, FZ, YLJ, CQJ, and KKC have substantial contributions to conception and design, acquisition of funding, data and interpretation of data; YYH, LX, CQJ and THL analyzed the data, FZ and YLJ verified the data, YYH, LX, CQJ, WSZ and KKC drafted the article, LX, THL and KKC revised it critically for important intellectual content. All authors read and approved the final paper.

Corresponding authors

Correspondence to Tai Hing Lam or Lin Xu.

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The authors declare no competing interests.

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The Guangzhou Medical Ethics Committee of the Chinese Medical Association approved the study, including the use of genetic data.

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Huang, Y.Y., Zhang, W.S., Jiang, C.Q. et al. Mendelian randomization on the association of obesity with vitamin D: Guangzhou Biobank Cohort Study. Eur J Clin Nutr 77, 195–201 (2023). https://doi.org/10.1038/s41430-022-01234-y

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