Journal of Obesity & Metabolic Syndrome

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J Obes Metab Syndr 2025; 34(1): 65-74

Published online January 30, 2025 https://doi.org/10.7570/jomes24018

Copyright © Korean Society for the Study of Obesity.

Separating the Effects of Early-Life and Adult Body Size on Chronic Kidney Disease Risk: A Mendelian Randomization Study

Xunliang Li1, Wenman Zhao1, Haifeng Pan2, Deguang Wang1,*

1Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei; 2Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China

Correspondence to:
Deguang Wang
https://orcid.org/0000-0003-4799-3241
Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei 230000, China
Tel: +86-13865808366
E-mail: wangdeguang@ahmu.edu.cn

Received: May 8, 2024; Reviewed : May 27, 2024; Accepted: September 19, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: Whether there is a causal relationship between childhood obesity and increased risk of chronic kidney disease (CKD) remains controversial. This study sought to explore how body size in childhood and adulthood independently affects CKD risk in later life using a Mendelian randomization (MR) approach.
Methods: Univariate and multivariate MR was used to estimate total and independent effects of body size exposures. Genetic associations with early-life and adult body size were obtained from a genome-wide association study of 453,169 participants in the U.K. Biobank, and genetic associations with CKD were obtained from the CKDGen and FinnGen consortia.
Results: A larger genetically predicted early-life body size was associated with an increased risk of CKD (odds ratio [OR], 1.27; 95% confidence interval [CI], 1.14 to 1.41; P=1.70E-05) and increased blood urea nitrogen (BUN) levels (β=0.010; 95% CI, 0.005 to 0.021; P=0.001). However, the association between the impact of early-life body size on CKD (OR, 1.12; 95% CI, 0.95 to 1.31; P=0.173) and BUN level (β=0.001; 95% CI, –0.010 to 0.012; P=0.853) did not remain statistically significant after adjustment for adult body size. Larger genetically predicted adult body size was associated with an increased risk of CKD (OR, 1.37; 95% CI, 1.21 to 1.54; P=4.60E-07), decreased estimated glomerular filtration rate (β=–0.011; 95% CI, –0.017 to –0.006; P=5.79E-05), and increased BUN level (β=0.010; 95% CI, 0.002 to 0.019; P=0.018).
Conclusion: Our research indicates that the significant correlation between early-life body size and CKD risk is likely due to maintaining a large body size into adulthood.

Keywords: Obesity, Chronic kidney disease, Epidemiology, Mendelian randomization, Risk factors

Obesity in children is a major public health problem worldwide, and its incidence has increased over the past three decades in virtually every region.1 Childhood obesity increases the risk of developing multiple comorbidities, including type 2 diabetes mellitus, hypertension, and dyslipidemia.2 Obesity has been linked to a greater risk of developing chronic kidney disease (CKD),3-5 and is an essential prognostic factor for poor kidney prognosis in patients with a single functioning kidney, immunoglobulin A nephropathy, and autosomal-dominant polycystic disease, apart from the distinct entity known as obesity-related glomerulopathy.6-8 Therefore, obesity is a major contributor to the development of CKD, which now affects 10% of the global population.9 Obesity in children has been linked to an increased chance of CKD in adults by two observational studies.10,11 Despite these relationships, however, it is difficult to draw causal conclusions about the effect of obesity in early life on the risk of CKD later in life because people who are obese in childhood are more likely to be obese in adulthood.12 Therefore, it is unclear whether the association between childhood obesity and CKD risk found in the past directly results from childhood obesity or is mediated by obesity later in life. In the latter instance, it would be possible to prevent the potential negative effects of childhood obesity by achieving and maintaining a healthy weight in adulthood.

Mendelian randomization (MR) is a method in epidemiology that uses genetic polymorphisms to improve the power of causal inference in establishing a relationship between an exposure and an outcome.13 Since genetic variants are randomly assorted at conception, they are unrelated to environmental and self-adopted factors that typically function as confounders, making MR less susceptible to confounding. Since fixed alleles are immune to the effects of disease development, this approach can also help reduce the likelihood of reverse causality.14 In addition, MR can estimate the separate effects of different exposures on disease outcomes in a multivariable model.15-17 Here, we present three hypotheses regarding the effect of early-life body size on CKD risk when adult body size is taken into account within a multivariate framework: (1) directly, without mediation by adult body size (Fig. 1A); (2) indirectly, via adult body size (Fig. 1B); and (3) both directly and indirectly (Fig. 1C). Several recent studies have used a similar approach to explore whether early-life body size increases the risk of many diseases in adulthood, such as inflammatory arthritis, type 2 diabetes mellitus, and fracture, and whether this effect is mediated by adult body size.18-20

In this study, we used two-sample MR to investigate the influence of early life and adult body size on CKD. Next, we used multivariate MR to examine whether early-life body size still influences CKD after adjusting for adult body size. This allowed us to determine whether the association between early-life body size and CKD risk is direct or mediated by adult body size.

Ethics approval

Summary-level data used for analysis in this study were obtained from published studies and consortia. All original studies were approved by the appropriate ethics review boards, and participants provided informed consent. The contributing cohorts included the U.K. Biobank and FinnGen consortium. The U.K. Biobank has previously received ethical approval from the U.K. National Health Service’s National Research Ethics Service (11/NW/0382), while the coordinating ethics committee of the Hospital District of Helsinki and Uusimaa approved the FinnGen study protocol (HUS/990/2017). This research has been conducted using the U.K. Biobank Resource under Application Number 8614. In addition, this study did not use individual-level data and therefore did not require additional ethical approval.

Genetic instruments for early-life and adult body size

A genome-wide association study (GWAS) involving 453,169 participants of European descent included in the U.K. Biobank data identified genetic variants associated with early-life and adult body size.19 At the baseline, individuals were asked: “When you were 10 years old, compared to average, would you describe yourself as thinner, plumper, or about average?” This measurement is known as early-life body size. Participants’ body mass index (BMI) at baseline was used to determine adult body size, with a mean age of 57 years, and was divided into three groups to match the proportion of early-life body size for comparison purposes. Those without childhood or adult body mass data were left out of the analysis, and an rG=0.61 genetic correlation coefficient was found.19 Additionally, genetic instruments for these body size definitions have been independently tested in three cohorts, assuring that these genetic instruments can reliably distinguish between early-life and adult body size.19,21,22 Single-nucleotide polymorphisms (SNPs) associated with early-life and adult body size at the genome-wide significance level (P<5×10–8) were selected. The linkage disequilibrium of selected SNPs was estimated using the 1000 Genomes European reference panel, and SNPs in high linkage disequilibrium (R2<0.001) were excluded. As a result, a total of 295 SNPs and 557 SNPs were used as instrumental variables for early-life and adult body size, respectively (Supplementary Table 1).

CKD data sources

Genetic variants associated with CKD, estimated glomerular filtration rate (eGFR), and blood urea nitrogen (BUN) level were selected from a meta-analysis of Chronic Kidney Disease Genetics Consortium (CKDGen) and the Million Veteran Program (n=480,698 for CKD, including 41,395 CKD cases; n=567,460 for eGFR; and n=243,029 for BUN level).23 In this meta-analysis, eGFR was assessed using serum creatinine, and the presence of CKD was defined by eGFR <60 mL/min/1.73 m2. We also obtained CKD-related genetic variations from the FinnGen consortium (7,916 cases and 330,300 controls). We used summary information from the R8 version of the FinnGen consortium, which is a project that collects health and genetic data using the Finnish health registries. The 10th revision of the International Classification of Diseases (ICD-10) was used to diagnose instances of CKD in the FinnGen consortium. In addition, we obtained genetic variants associated with eGFR and BUN level from a meta-analysis of CKDGen and the U.K. Biobank (n=1,004,040 for eGFR and n=679,531 for BUN level).24 Table 1 summarizes the detailed information for each GWAS. All the studies included in this research received approval from the corresponding institutional review boards and ethical committees, and all participants provided informed consent through signed consent forms.

Statistical analysis

The random-effects multiplicative inverse variance weighting (IVW) method was used as the primary MR method for analyzing the association between genetically predicted early-life and adult body size and CKD, respectively. The fixed-effects meta-analysis method combined MR estimates for each outcome from multiple studies. We conducted multivariate IVW MR analyses, mutually adjusted for early-life and adult body size, to evaluate the direct effects of early-life and adult body size on CKD risk. Similarly, we combined the MR estimates for each outcome from different sources using the fixed-effects meta-analysis method.

Sensitivity analyses

We calculated the F-statistic for each exposure in univariate MR and the conditional F-statistic in multivariate MR to assess the instrument strength; ultimately, an F-statistic >10 suggested a sufficiently powerful instrument.25,26 To examine the stability of univariate IVW estimates against horizontal pleiotropy, we used MR-Egger, weighted median, and MR pleiotropy residual sum and outlier (MR-PRESSO) tests. By performing the embedded intercept test, MR-Egger regression can identify horizontal pleiotropy and provide estimates after adjusting for pleiotropic effects. Assuming that >50% of the weight comes from true SNPs, the weighted median technique can produce valid MR estimates. Separately, the MR-PRESSO approach may identify and fix potential outliers, and the MR-PRESSO global test can assess horizontal pleiotropy due to heterogeneity among estimations of SNPs. In the multivariate setting, we applied the MR-Egger method for this objective. To further investigate SNP heterogeneity in each MR relationship, we used the Cochran Q test. Bonferroni correction was applied to account for multiple testing of the associations between two traits (i.e., early-life and adult body size) and three kidney outcomes (i.e., CKD, eGFR, and BUN level). A two-sided P-value of <0.008 [0.05/(2×3)] was considered statistically significant. Due to the conservatism of Bonferroni correction, estimates with P=0.05–0.008 were considered to suggest an association. All analyses were two-sided and performed using the R packages TwoSampleMR, multivariable MR (MVMR), and MR-PRESSO in R version 4.2.2 (R Foundation for Statistical Computing).

Total effects of early-life and adult body size on CKD

Univariable analyses indicated evidence that larger genetically predicted early-life body size is associated with an increased risk of CKD (odds ratio [OR], 1.27; 95% confidence interval [CI], 1.14 to 1.41; P=1.70E-05) (Fig. 2). No statistical significance was found between early-life body size and eGFR (β=0.002; 95% CI, −0.003 to 0.006; P=0.471) (Fig. 3). Meanwhile, larger genetically predicted early-life body size was associated with higher BUN levels (β=0.013; 95% CI, 0.005 to 0.021; P=0.001) (Fig. 4).

Separately, we determined that larger genetically predicted adult body size increased the risk of CKD (OR, 1.38; 95% CI, 1.26 to 1.51; P=1.09E-12) (Fig. 2) and was associated with lower eGFRs (β=−0.007; 95% CI, −0.010 to −0.003; P=0.001) and higher BUN levels (β=0.015; 95% CI, 0.009 to 0.021; P=2.34E-06) (Figs. 3 and 4). These associations were consistent across the two data sources.

Direct effects of early-life and adult body size on CKD

Genetically predicted early-life body size was not found to affect CKD risk after adult body size was accounted for in the multivariable MR model (OR, 1.12; 95% CI, 0.95 to 1.31; P=0.173) (Fig. 2). Similarly, in multivariable models for eGFR and BUN level, there was little evidence of a direct effect of genetically predicted early-life body size (β=0.0002; 95% CI, −0.007 to 0.007; P=0.957 and β=0.001; 95% CI, −0.010 to 0.012; P=0.853, respectively) (Figs. 3 and 4).

The effect of higher adult body size on an increased risk of CKD remained robust when adjusting for early-life body size in the multivariable MR model (OR, 1.37; 95% CI, 1.21 to 1.54; P=4.60E-07) (Fig. 2). In multivariable models for eGFR, greater adult body size remained associated with a lower eGFR (β=−0.011; 95% CI, −0.017 to −0.006; P=5.79E-05) (Fig. 3). However, after adjusting for early-life body size, the association between higher adult body size and higher BUN level changed to a suggestive correlation (β=0.010; 95% CI, 0.002 to 0.019; P=0.018) (Fig. 4).

Sensitivity analyses

The F-statistics for each SNP under the univariable were all >10, suggesting that all SNPs had sufficient validity (Supplementary Table 1). In multivariable MR, the F-statistics for early-life and adult body size similarly exceeded 10, indicating little evidence of weak instrumental bias (Supplementary Table 2). Genetically predicted early-life body size and adult body size results on CKD risk were consistent in the direction of sensitivity analyses in both univariate and multivariate analyses (Supplementary Tables 3 and 4). Most analyses showed evidence of heterogeneity, as denoted by the Q-statistics (Supplementary Tables 4 and 5). No horizontal pleiotropy was observed in the MR-Egger intercept analysis (Supplementary Tables 4 and 5). Although MR-PRESSO detected outliers in the univariate analysis, the associations persisted and remained significant after removing these outlying SNPs (Supplementary Table 5).

This study determined whether or not early-life body size increases the risk of CKD in adulthood and whether or not this putative causative effect happens independently or via the same causation pathway as body size in adulthood, respectively. Genetically predicted early-life body size was associated with a higher risk of CKD and an increased BUN level in a univariate MR analysis. However, the direct effect estimates for early-life body size were considerably attenuated and fully compatible with, and close to, the null compared to the estimates of the total effects, suggesting that the influences of early-life body size on these outcomes are mediated by body size in later life when analyzed in a multivariable framework. These results suggest that the elevated risk of CKD seen in observational studies of childhood obesity is probably attributable to those who maintain their big body size into adulthood. This indicates a potential window of opportunity to reduce the risk that being overweight as a child poses for developing CKD later in life.

Few observational studies have examined the association between childhood obesity and the development of CKD in adulthood. A study of 5,362 singleton children born in England, Scotland, and Wales showed that being overweight throughout early life or becoming overweight from puberty to age 20 years was associated with CKD in later life.11 These associations disappeared after multivariate adjustment for adult BMI. Vivante et al.27 studied a large Israeli cohort, linking data from military conscription exams at age 17 years to the Israeli end stage renal disease (ESRD) registry; ultimately, they found a strong association between overweight and obesity at age 17 years and the risk of treated ESRD in adulthood. Another study by a Swedish research team employed a matched case-control design to investigate the incidence of ESRD up to the age of 40 years with consideration of risk factors measured in adolescence.28 These authors found that elevated BMI at conscription was associated with an increased ESRD risk, suggesting a dose-effect relationship. However, both the Vivante et al.27 and Swedish research team studies lacked data on information on important potential confounders, including adult body size.

These observational studies indicate a potential link between early-life adiposity and increased CKD risk. However, it is important to consider the limitations inherent in observational research. Confounding factors, such as socioeconomic status, lifestyle behaviors, and genetic predisposition, may contribute to the observed associations. Furthermore, reverse causation is a potential concern, as individuals with underlying kidney dysfunction may experience changes in body size due to the disease itself, leading to a spurious association. To overcome these limitations and provide stronger evidence about causality, our study used MR methods. By leveraging genetic variants as instrumental variables, we minimized the impact of confounding factors and reduced the likelihood of reverse causation. This approach strengthens the validity of our findings and allows for more robust causal inferences.

Our univariable MR analysis demonstrated a significant association between genetically predicted early-life body size and both an increased risk of CKD and increased BUN levels. However, it is important to note that univariable analyses do not account for potential confounding factors or mediating pathways. To further elucidate the independent effects of early-life adiposity on CKD risk, we conducted a multivariable MR analysis that included early-life and adult body sizes as exposures. Interestingly, the direct-effect estimates for early-life body size on CKD risk were considerably attenuated when adjusted for adult body size. Notably, these attenuated estimates were close to the null, indicating that the direct causal effect of early-life adiposity on CKD risk is minimal when considering body size in later life. These results suggest that the observed associations between early-life obesity and an increased risk of CKD are largely mediated by body size in later life. In other words, individuals with a large body size in childhood that persists into adulthood are more likely to experience the detrimental impact of early-life adiposity on CKD risk. This finding highlights the importance of considering the long-term effects of adiposity and the cumulative impact of body size over the life course.

Previous research has shown that childhood body size plays a critical role in determining adult body size. Longitudinal studies have demonstrated tracking of body size from childhood to adulthood, indicating that individuals who are larger during childhood are more likely to maintain a larger body size into adulthood.29 Additionally, twin and family studies have provided evidence for substantial genetic contributions to both pediatric and adult body size, suggesting that genetic factors influencing body size in childhood also exert long-term effects on adult body size.30 Moreover, environmental factors during critical periods of growth and development, such as the prenatal and early childhood stages, have been shown to influence adult body size.31 These findings underscore the importance of early-life factors in shaping adult body size and highlight the potential implications for chronic disease risk later in life.

The mediating effect of body size in later life implies that interventions targeting the prevention or reduction of obesity in childhood may have the potential to mitigate the increased risk of CKD. By intervening early and promoting healthy body size throughout the life course, it may be possible to reduce the burden of CKD and its associated complications. It is important to acknowledge that our multivariate MR analysis provides evidence of mediation but cannot establish the specific mechanisms through which body size mediates the relationship between early-life adiposity and CKD risk. Further research is needed to explore the underlying biological pathways involved and identify potential intervention targets.

The mechanisms underlying the observed mediation by later-life body size remain to be fully elucidated. The persistence of large body size may contribute to metabolic dysregulation, including insulin resistance, dyslipidemia, and hypertension—known risk factors for CKD.28 Additionally, chronic inflammation and oxidative stress, which are often associated with adiposity, may further amplify the renal damage and progression of CKD.32

This study has several strengths. First, the MR study design reduces confounding and reverse causality bias. Second, only European populations are included in our summary-level data, so the population structure bias is unlikely to influence our results. Finally, the meta-analysis pooled the estimated relationships from several data sources, ensuring sufficient statistical power and the reliability of the results.

This study also has several limitations. First, we cannot rule out the possibility of recall bias in the measures because the body size phenotype in early childhood was based on self-reported questionnaire data rather than actual measurements. However, the SNPs for assessing early-life body size have been satisfactorily verified in three separate investigations using direct assessments of childhood BMI.19,21,22 Second, we may have been influenced by the potential for bias due to the high degree of participant overlap between the GWASs used in the two-sample MR.33 A recent simulation study has shown that two-sample MR methods can be securely employed for one-sample MR performed inside large cohorts, as in this work; therefore, any bias is expected to be low.33,34 Unfortunately, this does not apply to the multivariable MR method. Nonetheless, this possible bias was probably insignificant, given that our multivariable MR analysis values are similar across two independent datasets. Third, this study used data from Wuttke et al.23 and Stanzick et al.24 for meta-analysis. However, the same CKDGen data were included in both studies, which could lead to problems with data overlap that may impact the results. Fourth, the two GWAS datasets used in this study differed in their CKD diagnostic criteria; specifically, the study by Wuttke et al.23 relied on eGFR measurements to define CKD, while the FinnGen consortium relied on the ICD-10 diagnostic criteria for CKD. This discrepancy in diagnostic criteria could introduce heterogeneity in the CKD classification across datasets and potentially impact the comparability and interpretation of results. Future studies may benefit from harmonizing diagnostic criteria for CKD to ensure consistency and accuracy in the assessment of genetic associations. Finally, our study only included Europeans; thus, it is unclear if the discovered correlations hold true for people of other ethnicities.

In conclusion, this MR study suggests that the association between early-life body size and CKD may be because people who were overweight or obese in childhood remain that way later in life. Future research should further explore the role of childhood obesity in the development of CKD and identify effective interventions to mitigate this risk.

The authors express their gratitude to the researchers and individuals who participated in the U.K. Biobank, CKDGen, and FinnGen consortium for their valuable contributions to this study. This work was supported by grants from the Natural Science Foundation of Anhui Province (2008085MH244) and the Anhui Medical University 2021 Clinical and Pre-disciplinary Co-Construction (2021lcxk032). No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Study concept and design: XL and DW; acquisition of data: XL; analysis and interpretation of data: XL, WZ, and HP; drafting of the manuscript: XL; critical revision of the manuscript: HP and DW; statistical analysis: XL; obtained funding: HP and DW; administrative, technical, or material support: DW; and study supervision: DW.

Fig. 1. Directed acyclic graphs summarizing analyses of early-life body size and chronic kidney disease risk in adulthood. (A) Early-life body size has a direct effect on chronic kidney disease risk independent of adult body size, (B) early-life body size has an indirect effect on chronic kidney disease risk through body size in adulthood, and (C) early-life body size exerts both direct and indirect effects on chronic kidney disease risk in adulthood.
Fig. 2. Forest plot illustrating the direct and indirect effects of genetically predicted early-life and adult body size on the risk of chronic kidney disease. CI, confidence interval; MR, Mendelian randomization.
Fig. 3. Forest plot illustrating the direct and indirect effects of genetically predicted early-life and adult body size on the risk of estimated glomerular filtration rate increase. CI, confidence interval; MR, Mendelian randomization.
Fig. 4. Forest plot illustrating the direct and indirect effects of genetically predicted early-life and adult body size on the risk of blood urea nitrogen level increase. CI, confidence interval; MR, Mendelian randomization.

Genome-wide association study outcome information and data sources

Phenotype (unit of measure) Total no. or case no./Total no. Data source
CKD 41,395/439,303 Wuttke et al. (2019)23
CKD 7,916/330,300 FinnGen
eGFR (mL/min/1.73 m2) 567,460 Wuttke et al. (2019)23
eGFR (mL/min/1.73 m2) 1,004,040 Stanzick et al. (2021)24
BUN (mg/dL) 243,029 Wuttke et al. (2019)23
BUN (mg/dL) 679,531 Stanzick et al. (2021)24

CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; BUN, blood urea nitrogen.

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