The effect of birth weight on body composition: Evidence from a birth cohort and a Mendelian randomization study


Autoři: Junxi Liu aff001;  Shiu Lun Au Yeung aff001;  Baoting He aff001;  Man Ki Kwok aff001;  Gabriel Matthew Leung aff001;  C. Mary Schooling aff001
Působiště autorů: School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China aff001;  City University of New York Graduate School of Public Health and Health Policy, New York, New York, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222141

Souhrn

Background

Lower birth weight is associated with diabetes although the underlying mechanisms are unclear. Muscle mass could be a modifiable link and hence a target of intervention. We assessed the associations of birth weight with muscle and fat mass observationally in a population with little socio-economic patterning of birth weight and using Mendelian randomization (MR) for validation.

Methods

In the population-representative “Children of 1997” birth cohort (n = 8,327), we used multivariable linear regression to assess the adjusted associations of birth weight (kg) with muscle mass (kg) and body fat (%) at ~17.5 years. Genetically predicted birth weight (effect size) was applied to summary genetic associations with fat-free mass and fat mass (kg) from the UK Biobank (n = ~331,000) to obtain unconfounded estimates using inverse-variance weighting.

Results

Observationally, birth weight was positively associated with muscle mass (3.29 kg per kg birth weight, 95% confidence interval (CI) 2.83 to 3.75) and body fat (1.09% per kg birth weight, 95% CI 0.54 to 1.65). Stronger associations with muscle mass were observed in boys than in girls (p for interaction 0.004). Using MR, birth weight was positively associated with fat-free mass (0.77 kg per birth weight z-score, 95% CI 0.22 to 1.33) and fat mass (0.58, 95% CI 0.01 to 1.15). No difference by sex was evident.

Conclusion

Higher birth weight increasing muscle mass may be relevant to lower birth weight increasing the risk of diabetes and suggests post-natal muscle mass as a potential target of intervention.

Klíčová slova:

Biology and life sciences – Physiology – Physiological parameters – Birth weight – Biochemistry – Lipids – Fats – Computational biology – Genome-wide association studies – Genetics – Genomics – Genome analysis – Human genetics – Medicine and health sciences – Body weight – Endocrinology – Endocrine disorders – Metabolic disorders – Women's health – Maternal health – Birth – Obstetrics and gynecology – Research and analysis methods – Research design – Cohort studies – People and places – Population groupings – Age groups – Children – Families


Zdroje

1. Risnes KR, Vatten LJ, Baker JL, Jameson K, Sovio U, Kajantie E, et al. Birthweight and mortality in adulthood: a systematic review and meta-analysis. International journal of epidemiology. 2011;40(3):647–61. Epub 2011/02/18. doi: 10.1093/ije/dyq267 21324938.

2. Liu JX, Au Yeung SL, Kwok MK, Leung JYY, Lin SL, Hui LL, et al. Birth weight, gestational age and late adolescent liver function using twin status as instrumental variable in a Hong Kong Chinese birth cohort: "Children of 1997". Preventive medicine. 2018;111:190–7. Epub 2018/03/17. doi: 10.1016/j.ypmed.2018.03.006 29545162.

3. Wang T, Huang T, Li Y, Zheng Y, Manson JE, Hu FB, et al. Low birthweight and risk of type 2 diabetes: a Mendelian randomisation study. Diabetologia. 2016;59(9):1920–7. Epub 2016/06/24. doi: 10.1007/s00125-016-4019-z 27333884; PubMed Central PMCID: PMC4970938.

4. Zanetti D, Tikkanen E, Gustafsson S, Priest JR, Burgess S, Ingelsson E. Birthweight, Type 2 Diabetes Mellitus, and Cardiovascular Disease: Addressing the Barker Hypothesis With Mendelian Randomization. Circulation Genomic and precision medicine. 2018;11(6):e002054. Epub 2018/06/08. doi: 10.1161/CIRCGEN.117.002054 29875125.

5. Ahlgren M, Melbye M, Wohlfahrt J, Sørensen TIA. Growth Patterns and the Risk of Breast Cancer in Women. New England Journal of Medicine. 2004;351(16):1619–26. doi: 10.1056/NEJMoa040576 15483280.

6. Zhou CK, Sutcliffe S, Welsh J, Mackinnon K, Kuh D, Hardy R, et al. Is birthweight associated with total and aggressive/lethal prostate cancer risks? A systematic review and meta-analysis. British journal of cancer. 2016;114(7):839–48. Epub 2016/03/02. doi: 10.1038/bjc.2016.38 26930450; PubMed Central PMCID: PMC4955914.

7. Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Statistics in medicine. 2008;27(8):1133–63. doi: 10.1002/sim.3034 17886233.

8. Mathews F, Yudkin P, Neil A. Influence of maternal nutrition on outcome of pregnancy: prospective cohort study. BMJ (Clinical research ed). 1999;319(7206):339–43. Epub 1999/08/06. doi: 10.1136/bmj.319.7206.339 10435950; PubMed Central PMCID: PMC28185.

9. Fisher D, Baird J, Payne L, Lucas P, Kleijnen J, Roberts H, et al. Are infant size and growth related to burden of disease in adulthood? A systematic review of literature. International journal of epidemiology. 2006;35(5):1196–210. Epub 2006/07/18. doi: 10.1093/ije/dyl130 16845132.

10. Chomtho S, Wells JC, Williams JE, Lucas A, Fewtrell MS. Associations between birth weight and later body composition: evidence from the 4-component model. The American journal of clinical nutrition. 2008;88(4):1040–8. Epub 2008/10/10. doi: 10.1093/ajcn/88.4.1040 18842792.

11. Yliharsila H, Kajantie E, Osmond C, Forsen T, Barker DJ, Eriksson JG. Birth size, adult body composition and muscle strength in later life. International journal of obesity (2005). 2007;31(9):1392–9. Epub 2007/03/16. doi: 10.1038/sj.ijo.0803612 17356523.

12. Strasser B, Siebert U, Schobersberger W. Resistance training in the treatment of the metabolic syndrome: a systematic review and meta-analysis of the effect of resistance training on metabolic clustering in patients with abnormal glucose metabolism. Sports medicine (Auckland, NZ). 2010;40(5):397–415. Epub 2010/05/04. doi: 10.2165/11531380-000000000-00000 20433212.

13. Schooling CM, Jiang C, Zhang W, Lam TH, Cheng KK, Leung GM. Adolescent build and diabetes: the Guangzhou Biobank Cohort Study. Annals of epidemiology. 2011;21(1):61–6. Epub 2010/12/07. doi: 10.1016/j.annepidem.2010.08.010 21130371.

14. Hou WW, Tse MA, Lam TH, Leung GM, Schooling CM. Adolescent testosterone, muscle mass and glucose metabolism: evidence from the 'Children of 1997' birth cohort in Hong Kong. Diabetic medicine: a journal of the British Diabetic Association. 2015;32(4):505–12. Epub 2014/10/14. doi: 10.1111/dme.12602 25307068.

15. Madden D. The relationship between low birth weight and socioeconomic status in Ireland. Journal of biosocial science. 2014;46(2):248–65. Epub 2013/05/02. doi: 10.1017/S0021932013000187 23631865.

16. Gigante DP, Horta BL, Matijasevich A, Mola CL, Barros AJ, Santos IS, et al. Gestational age and newborn size according to parental social mobility: an intergenerational cohort study. Journal of epidemiology and community health. 2015;69(10):944–9. Epub 2015/06/26. doi: 10.1136/jech-2014-205377 26109560; PubMed Central PMCID: PMC4602273.

17. Leung JY, Leung GM, Schooling CM. Socioeconomic disparities in preterm birth and birth weight in a non-Western developed setting: evidence from Hong Kong's 'Children of 1997' birth cohort. Journal of epidemiology and community health. 2016;70(11):1074–81. Epub 2016/05/12. doi: 10.1136/jech-2015-206668 27165846.

18. Schooling CM, Yau C, Cowling BJ, Lam TH, Leung GM. Socio-economic disparities of childhood Body Mass Index in a newly developed population: evidence from Hong Kong's 'Children of 1997' birth cohort. Archives of disease in childhood. 2010;95(6):437–43. Epub 2010/04/27. doi: 10.1136/adc.2009.168542 20418337.

19. VanderWeele TJ. Explanation in causal inference: methods for mediation: Oxford University Press; 2015.

20. Warrington NM, Beaumont RN, Horikoshi M, Day FR, Helgeland Ø, Laurin C, et al. Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. Nature genetics. 2019;51(5):804–14. doi: 10.1038/s41588-019-0403-1 31043758

21. Howrigan D. DETAILS AND CONSIDERATIONS OF THE UK BIOBANK GWAS: THE NEALE LAB; September 20, 2017. Available from: http://www.nealelab.is/blog/2017/9/11/details-and-considerations-of-the-uk-biobank-gwas.

22. Schooling CM, Hui LL, Ho LM, Lam TH, Leung GM. Cohort profile: 'children of 1997': a Hong Kong Chinese birth cohort. Int J Epidemiol. 2012;41(3):611–20. doi: 10.1093/ije/dyq243 21224275.

23. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife. 2018;7:e34408. doi: 10.7554/eLife.34408 29846171

24. Cohen J. Statistical power analysis for the behavioral sciences: Academic Press; 1977.

25. Seaman SR, White IR. Review of inverse probability weighting for dealing with missing data. Statistical methods in medical research. 2013;22(3):278–95. Epub 2011/01/12. doi: 10.1177/0962280210395740 21220355.

26. Seaman SR, White IR, Copas AJ, Li L. Combining Multiple Imputation and Inverse-Probability Weighting. Biometrics. 2012;68(1):129–37. doi: 10.1111/j.1541-0420.2011.01666.x PMC3412287. 22050039

27. Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two‐sample Mendelian randomization. Genetic Epidemiology. 2016;40(7):597–608. doi: 10.1002/gepi.21998 PMC5082560. 27625185

28. Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan NA, Thompson JR. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. International journal of epidemiology. 2016;45(6):1961–74. Epub 2016/09/13. doi: 10.1093/ije/dyw220 27616674; PubMed Central PMCID: PMC5446088.

29. Burgess S, Bowden J., Fall T., Ingelsson E., Thompson S. G. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology (Cambridge, Mass). 2016.

30. Altman DG, Bland JM. Interaction revisited: the difference between two estimates. BMJ (Clinical research ed). 2003;326(7382):219. Epub 2003/01/25. doi: 10.1136/bmj.326.7382.219 12543843; PubMed Central PMCID: PMC1125071.

31. Freeman G, Cowling BJ, Schooling CM. Power and sample size calculations for Mendelian randomization studies using one genetic instrument. Int J Epidemiol. 2013;42(4):1157–63. Epub 2013/08/13. doi: 10.1093/ije/dyt110 23934314.

32. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304–14. Epub 2016/04/12. doi: 10.1002/gepi.21965 27061298; PubMed Central PMCID: PMC4849733.

33. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International journal of epidemiology. 2015;44(2):512–25. Epub 2015/06/08. doi: 10.1093/ije/dyv080 26050253; PubMed Central PMCID: PMC4469799.

34. Verbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature genetics. 2018;50(5):693–8. doi: 10.1038/s41588-018-0099-7 29686387

35. Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. International journal of epidemiology. 2017;46(6):1734–9. Epub 2017/04/12. doi: 10.1093/ije/dyx034 28398548; PubMed Central PMCID: PMC5510723.

36. Dodds R, Denison HJ, Ntani G, Cooper R, Cooper C, Sayer AA, et al. Birth weight and muscle strength: A systematic review and meta-analysis. The journal of nutrition, health & aging. 2012;16(7):609–15. doi: 10.1007/s12603-012-0053-9 22836701

37. Simpson J, Smith AD, Fraser A, Sattar N, Lindsay RS, Ring SM, et al. Programming of Adiposity in Childhood and Adolescence: Associations With Birth Weight and Cord Blood Adipokines. The Journal of clinical endocrinology and metabolism. 2017;102(2):499–506. Epub 2016/11/15. doi: 10.1210/jc.2016-2342 27841944; PubMed Central PMCID: PMC5413167.

38. Patro B, Liber A, Zalewski B, Poston L, Szajewska H, Koletzko B. Maternal and paternal body mass index and offspring obesity: a systematic review. Annals of nutrition & metabolism. 2013;63(1–2):32–41. Epub 2013/07/28. doi: 10.1159/000350313 23887153.

39. Tyrrell J, Richmond RC, Palmer TM, et al. Genetic evidence for causal relationships between maternal obesity-related traits and birth weight. Jama. 2016;315(11):1129–40. doi: 10.1001/jama.2016.1975 26978208

40. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ (Clinical research ed). 2018;362:k601. doi: 10.1136/bmj.k601 30002074

41. Evans DM, Moen G-H, Hwang L-D, Lawlor DA, Warrington NM. Elucidating the role of maternal environmental exposures on offspring health and disease using two-sample Mendelian randomization. International journal of epidemiology. 2019;48(3):861–75. doi: 10.1093/ije/dyz019 30815700

42. Lawlor D, Richmond R, Warrington N, McMahon G, Smith G, Bowden J, et al. Using Mendelian randomization to determine causal effects of maternal pregnancy (intrauterine) exposures on offspring outcomes: Sources of bias and methods for assessing them [version 1; peer review: 4 approved]. Wellcome Open Research. 2017;2(11). doi: 10.12688/wellcomeopenres.10567.1 28405635

43. Nelson VR, Nadeau JH. Transgenerational genetic effects. Epigenomics. 2010;2(6):797–806. doi: 10.2217/epi.10.57 22122083.

44. Du M, Yan X, Tong JF, Zhao J, Zhu MJ. Maternal obesity, inflammation, and fetal skeletal muscle development. Biology of reproduction. 2010;82(1):4–12. Epub 2009/06/12. doi: 10.1095/biolreprod.109.077099 19516021; PubMed Central PMCID: PMC2802110.

45. Chiavaroli V, Derraik JG, Hofman PL, Cutfield WS. Born Large for Gestational Age: Bigger Is Not Always Better. The Journal of pediatrics. 2016;170:307–11. Epub 2015/12/29. doi: 10.1016/j.jpeds.2015.11.043 26707580.

46. Spalding KL, Arner E, Westermark PO, Bernard S, Buchholz BA, Bergmann O, et al. Dynamics of fat cell turnover in humans. Nature. 2008;453(7196):783–7. Epub 2008/05/06. doi: 10.1038/nature06902 18454136.

47. Yeung CHC, Au Yeung SL, Fong SSM, Schooling CM. Lean mass, grip strength and risk of type 2 diabetes: a bi-directional Mendelian randomisation study. Diabetologia. 2019. Epub 2019/02/25. doi: 10.1007/s00125-019-4826-0 30798333.

48. Pan WH, Flegal KM, Chang HY, Yeh WT, Yeh CJ, Lee WC. Body mass index and obesity-related metabolic disorders in Taiwanese and US whites and blacks: implications for definitions of overweight and obesity for Asians. The American journal of clinical nutrition. 2004;79(1):31–9. Epub 2003/12/20. doi: 10.1093/ajcn/79.1.31 14684394.

49. Spanakis EK, Golden SH. Race/Ethnic Difference in Diabetes and Diabetic Complications. Current diabetes reports. 2013;13(6):10.1007/s11892-013-0421-9. doi: 10.1007/s11892-013-0421-9 PMC3830901. 24037313

50. Madan A, Holland S, Humbert JE, Benitz WE. Racial Differences in Birth Weight of Term Infants in a Northern California Population. Journal Of Perinatology. 2002;22:230. doi: 10.1038/sj.jp.7210703 11948387

51. Silva AM, Shen W, Heo M, Gallagher D, Wang Z, Sardinha LB, et al. Ethnicity-Related Skeletal Muscle Differences Across the Lifespan. American journal of human biology: the official journal of the Human Biology Council. 2010;22(1):76–82. doi: 10.1002/ajhb.20956 PMC2795070. 19533617

52. Lear SA, Kohli S, Bondy GP, Tchernof A, Sniderman AD. Ethnic Variation in Fat and Lean Body Mass and the Association with Insulin Resistance. The Journal of Clinical Endocrinology & Metabolism. 2009;94(12):4696–702. doi: 10.1210/jc.2009-1030 19820012


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