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Epidemiology and Population Health

Preschool age-specific obesity and later-life kidney health: a Mendelian randomization and colocalization study

Abstract

Objectives

While the association between obesity and kidney diseases has been found in previous studies, the relationship between preschool-age obesity and later-life kidney health remains unclear, posing challenges for effective interventions in this critical life period.

Methods

Utilizing the hitherto largest genome-wide association studies, we conducted two-sample mendelian randomization (MR) to estimate the association of preschool age-specific obesity on kidney health and diseases, including blood urea nitrogen (BUN), eGFRcrea, eGFRcys, chronic kidney disease (CKD), IgA nephropathy, and diabetic nephropathy. Then, we applied multivariable Mendelian randomization (MVMR) and stepwise MR to elucidate the role of adult obesity and 12 other potential factors in the pathway between preschool age-specific obesity and kidney health. Finally, we employed colocalization analysis to understand the mechanism of preschool age-specific obesity and kidney damage further by detecting shared causal variants.

Results

Our two-sample MR results indicated that preschool obesity could be associated with kidney health and disease. In addition, we observed a switch in the direction of associations between age-specific body mass index (BMI) and CKD, manifesting as negative associations before 3 years old and positive associations after 3 years old. Furthermore, MVMR and stepwise MR results suggested potential pathways linking preschool obesity to kidney health, involving factors such as adult BMI, circulating high-density lipoprotein cholesterol levels, and circulating C-reactive protein levels. Finally, we detected that preschool-age BMI and kidney function could share causal variants such as rs76111507, rs62107261, rs77165542 in the region of chromosome 2, and rs571312 in the region of chromosome 18.

Conclusion

Our study supports the association between preschool obesity and kidney health, emphasizing the role of adult BMI in this relationship. These findings underscore the importance of interventions starting in early childhood and continuing through adulthood to reduce the long-term risk of obesity-related kidney damage.

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Fig. 1: The design and methods used in this Mendelian randomization study.
Fig. 2: The IVW results of preschool age-specific obesity and kidney health and disease.
Fig. 3: The MVMR results of preschool age-specific obesity to eGFRcys.
Fig. 4: The putative pathway linking preschool obesity, adult obesity, correspondence candidate mediator, and kidney health in stepwise MR.
Fig. 5: The colocalization results of SNPs with PPH4 ≥ 0.75 and their sensitivity analysis results.

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Data availability

Summary statistics of birth weight and overall childhood BMI were downloaded from http://egg-consortium.org. Summary statistics of preschool age-specific BMI were downloaded from https://www.fhi.no/en/studies/moba/for-forskere-artikler/gwas-data-from-moba/. Summary statistics for CKDGen Consortium were obtained from https://ckdgen.imbi.uni-freiburg.de. The summary statistics of IgA nephropathy were obtained from the Scania Diabetes Registry (SDR), Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study, Steno Diabetes Centre, and Bergamo Nephrologic Diabetes Complications Trial (BENEDICT) A and B studies, which can be obtained from https://humandbs.biosciencedbc.jp/en/hum0197-v3-220. The summary statistics of diabetic nephropathy can be found on the website GWAS Catalog (http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST005001-GCST006000/GCST005880/).

References

  1. GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395:709–33.

    Article  Google Scholar 

  2. Chintam K, Chang AR. Strategies to treat obesity in patients with CKD. Am J Kidney Dis. 2021;77:427–39.

    Article  PubMed  Google Scholar 

  3. Bruci A, Tuccinardi D, Tozzi R, Balena A, Santucci S, Frontani R, et al. Very low-calorie ketogenic diet: a safe and effective tool for weight loss in patients with obesity and mild kidney failure. Nutrients 2020;12:333.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Al Salmi I, Hoy WE, Kondalsamy-Chennakes S, Wang Z, Healy H, Shaw JE. Birth weight and stages of CKD: a case-control study in an Australian population. Am J Kidney Dis. 2008;52:1070–78.

    Article  CAS  PubMed  Google Scholar 

  5. Esmeijer K, de Vries AP, Mook-Kanamori DO, de Fijter JW, Rosendaal FR, Rabelink TJ, et al. Low birth weight and kidney function in middle-aged men and women: the Netherlands epidemiology of obesity study. Am J Kidney Dis. 2019;74:751–60.

    Article  CAS  PubMed  Google Scholar 

  6. Eriksson JG, Salonen MK, Kajantie E, Osmond C. Prenatal growth and CKD in older adults: longitudinal findings from the Helsinki Birth Cohort Study, 1924–1944. Am J Kidney Dis. 2018;71:20–26.

    Article  PubMed  Google Scholar 

  7. van Dam MJCM, Pottel H, Vreugdenhil ACE. Relation between obesity-related comorbidities and kidney function estimation in children. Pediatr Nephrol. 2023;38:1867–76.

    Article  PubMed  Google Scholar 

  8. Liu C, Tian J, Jose MD, Dwyer T, Venn AJ. BMI trajectories from childhood to midlife are associated with subclinical kidney damage in midlife. Obes (Silver Spring). 2021;29:1058–66.

    Article  Google Scholar 

  9. Yan Y, Zheng W, Ma Q, Chu C, Hu J, Wang K, et al. Child-to-adult body mass index trajectories and the risk of subclinical renal damage in middle age. Int J Obes (Lond). 2021;45:1095–04.

    Article  PubMed  Google Scholar 

  10. Aarestrup J, Blond K, Vistisen D, Jørgensen ME, Frimodt-Møller M, Jensen BW, et al. Childhood body mass index trajectories and associations with adult-onset chronic kidney disease in Denmark: a population-based cohort study. PLoS Med. 2022;19:e1004098.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Silverwood RJ, Pierce M, Hardy R, Thomas C, Ferro C, Savage C, et al. Early-life overweight trajectory and CKD in the 1946 British birth cohort study. Am J Kidney Dis. 2013;62:276–84.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Simmonds M, Burch J, Llewellyn A, Griffiths C, Yang H, Owen C, et al. The use of measures of obesity in childhood for predicting obesity and the development of obesity-related diseases in adulthood: a systematic review and meta-analysis. Health Technol Assess. 2015;19:1–336.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wahab A, Dey AK, Bandyopadhyay D, Katikineni V, Chopra R, Vedantam KS, et al. Obesity, systemic hypertension, and pulmonary hypertension: a tale of three diseases. Curr Probl Cardiol. 2021;46:100599.

    Article  PubMed  Google Scholar 

  14. Nguyen NT, Nguyen XM, Lane J, Wang P. Relationship between obesity and diabetes in a US adult population: findings from the National Health and Nutrition Examination Survey, 1999-2006. Obes Surg. 2011;21:351–5.

    Article  PubMed  Google Scholar 

  15. Prospective Studies Collaboration, Whitlock G, Lewington S, Sherliker P, Clarke R, Emberson J, et al. Body-mass index and cause-specific mortality in 900,000 adults: collaborative analyses of 57 prospective studies. Lancet. 2009;373:1083–96.

    Article  PubMed Central  Google Scholar 

  16. Zhu J, Zhang Y, Wu Y, Xiang Y, Tong X, Yu Y, et al. Obesity and dyslipidemia in chinese adults: a cross-sectional study in Shanghai, China. Nutrients 2022;14:2321.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Burgess S, Daniel RM, Butterworth AS, Thompson SG. EPIC-InterAct Consortium. Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. Int J Epidemiol. 2015;44:484–95.

    Article  PubMed  Google Scholar 

  19. Zuber V, Grinberg NF, Gill D, Manipur I, Slob EAW, Patel A, et al. Combining evidence from Mendelian randomization and colocalization: review and comparison of approaches. Am J Hum Genet. 2022;109:767–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. Nat Genet. 2019;51:804–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Helgeland Ø, Vaudel M, Sole-Navais P, Flatley C, Juodakis J, Bacelis J, et al. Characterization of the genetic architecture of infant and early childhood body mass index. Nat Metab. 2022;4:344–58.

    Article  CAS  PubMed  Google Scholar 

  22. Vogelezang S, Bradfield JP, Ahluwalia TS, Curtin JA, Lakka TA, Grarup N, et al. Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits. PLoS Genet. 2020;16:e1008718.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wuttke M, Li Y, Li M, Sieber KB, Feitosa MF, Gorski M, et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet. 2019;51:957–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Stanzick KJ, Li Y, Schlosser P, Gorski M, Wuttke M, Thomas LF, et al. Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals. Nat Commun. 2021;12:4350.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Sakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53:1415–24.

    Article  CAS  PubMed  Google Scholar 

  26. van Zuydam NR, Ahlqvist E, Sandholm N, Deshmukh H, Rayner NW, Abdalla M, et al. A genome-wide association study of diabetic kidney disease in subjects with Type 2 diabetes. Diabetes 2018;67:1414–27.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Correia-Costa L, Azevedo A, Caldas Afonso A. Childhood obesity and impact on the kidney. Nephron 2019;143:8–11.

    Article  PubMed  Google Scholar 

  28. Lengton R, Dekker FW, van Rossum EFC, de Fijter JW, Rosendaal FR, van Dijk KW, et al. Hypertension and diabetes, but not leptin and adiponectin, mediate the relationship between body fat and chronic kidney disease. Endocrine. 2024;85:1141–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Fritz J, Brozek W, Concin H, Nagel G, Kerschbaum J, Lhotta K, et al. The association of excess body weight with risk of ESKD is mediated through insulin resistance, hypertension, and hyperuricemia. J Am Soc Nephrol. 2022;33:1377–89.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Emanuelsson F, Wade K, Varbo A, Tybjaerg-hansen A, Smith GD, Nordestgaard BG, et al. Cardiometabolic risk factors as causal mediators of the relationship between high body mass index and chronic kidney disease: a two-step mendelian randomization study and mediation analyses. Circulation 2021;144:A10469.

    Article  Google Scholar 

  31. Lee S, Kang S, Joo YS, Lee C, Nam KH, Yun HR, et al. Smoking, smoking cessation, and progression of chronic kidney disease: results from KNOW-CKD study. Nicotine Tob Res. 2021;23:92–98.

    Article  PubMed  Google Scholar 

  32. Burgess S, Thompson SG, CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40:755–64.

    Article  PubMed  Google Scholar 

  33. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65.

    Article  PubMed  PubMed Central  Google Scholar 

  34. 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;40:304–14.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants. Epidemiology 2017;28:30–42.

    Article  PubMed  Google Scholar 

  38. Sanderson E, Spiller W, Bowden J. Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization. Stat Med. 2021;40:5434–52.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Sucharda P. Kourení a obezita. Smoking and obesity. Vnitr Lek. 2010;56:1053–7.

    PubMed  Google Scholar 

  40. Traversy G, Chaput JP. Alcohol consumption and obesity: an update. Curr Obes Rep. 2015;4:122–30.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10:e1004383.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Kia DA, Zhang D, Guelfi S, Manzoni C, Hubbard L, Reynolds RH, et al. Identification of candidate Parkinson disease genes by integrating genome-wide association study, expression, and epigenetic data sets. JAMA Neurol. 2021;78:464–72.

    Article  PubMed  Google Scholar 

  43. Lin S, Zhang H, Qi M, Cooper DN, Yang Y, Yang Y, et al. Inferring the genetic relationship between brain imaging-derived phenotypes and risk of complex diseases by Mendelian randomization and genome-wide colocalization. Neuroimage 2023;279:120325.

    Article  CAS  PubMed  Google Scholar 

  44. Nehus E. Obesity and chronic kidney disease. Curr Opin Pediatr. 2018;30:241–6.

    Article  PubMed  Google Scholar 

  45. Verrotti A, Penta L, Zenzeri L, Agostinelli S, De Feo P. Childhood obesity: prevention and strategies of intervention. A systematic review of school-based interventions in primary schools. J Endocrinol Invest. 2014;37:1155–64.

    Article  CAS  PubMed  Google Scholar 

  46. Richter LM, Daelmans B, Lombardi J, Heymann J, Boo FL, Behrman JR, et al. Investing in the foundation of sustainable development: pathways to scale up for early childhood development. Lancet 2017;389:103–18.

    Article  PubMed  Google Scholar 

  47. Mahajan A, Taliun D, Thurner M, Robertson NR, Torres JM, Rayner NW, et al. Fine-mapping Type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet. 2018;50:1505–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Sohn JW, Harris LE, Berglund ED, Liu T, Vong L, Lowell BB, et al. Melanocortin 4 receptors reciprocally regulate sympathetic and parasympathetic preganglionic neurons. Cell 2013;152:612–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Waterfield S, Richardson TG, Davey Smith G, O’Keeffe LM, Bell JA. Life course effects of genetic susceptibility to higher body size on body fat and lean mass: prospective cohort study. Int J Epidemiol. 2023;52:1377–87.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Hukku A, Pividori M, Luca F, Pique-Regi R, Im HK, Wen X. Probabilistic colocalization of genetic variants from complex and molecular traits: promise and limitations. Am J Hum Genet. 2021;108:25–35.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We express our gratitude to the patients and investigators who have contributed to the EGG Consortium, the CKDGen Consortium, MoBa Cohort, the Scania Diabetes Registry (SDR), Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study, Steno Diabetes Centre, and Bergamo Nephrologic Diabetes Complications Trial (BENEDICT) A and B studies.

Funding

This study was supported by the National Natural Science Foundation of China (U20A20411, Zhenmi Liu), the Fundamental Research Funds for the Central Universities (2022SCU12021, Chenghan Xiao), and the Natural Science Foundation of Sichuan Province (2023NSFSC0652, Ling Zhang). The funders played no role in the design of the study, collection and analysis of data, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

Authors

Contributions

Xin Jin: conceptualization, formal analysis, visualization, writing—original draft, writing—review and editing. Yujue Wang, Sixuan Zeng, Jiarui Cai, Kerui Wang, Xinxi Li, Yu Tong, Xiaoli Luo, Menghan Yang, and Weidong Zhang: writing—review and editing. Qiaoyue Ge and Lu Zhang verified data. Chuan Yu: conceptualization, writing—review and editing. Ling Zhang: funding acquisition, writing—review and editing. Chenghan Xiao and Zhenmi Liu: conceptualization, funding acquisition, writing—review and editing. All authors read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Chuan Yu, Chenghan Xiao or Zhenmi Liu.

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The authors declare no competing interests.

Ethics approval and consent to participate

The study adheres to STROBE-MR Guidelines and each included GWAS has institutional review board approval and participant consent. The UK Biobank has ethics approval from the North West Multi-Centre Research Ethics Committee. Age-specific BMI GWAS received ethical approval from the Avon Longitudinal Study of Parents and Children (ALSPAC) Ethics and Law Committee and Local Research Ethics Committees, with consent under the Human Tissue Act (2004). MoBa’s study protocol was approved by the Norwegian Institute of Public Health’s administrative board, with data collection authorized by the Norwegian Data Protection Agency and The Regional Committee for Medical Research Ethics (no. 2012/67). The Amsterdam Born Children and their Development- Genetic Enrichment (ABCD) study protocol received approval from the Dutch Central Committee on Research Involving Human Subjects, the medical ethics review committees of the hospitals involved, and the Amsterdam Municipality’s Registration Committee.

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Jin, X., Wang, Y., Zeng, S. et al. Preschool age-specific obesity and later-life kidney health: a Mendelian randomization and colocalization study. Int J Obes 49, 649–657 (2025). https://doi.org/10.1038/s41366-024-01686-1

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