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American Association for the Advancement of Science

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10.1126/sciadv.adq2452
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Psychological Science
Psychological Science
Evaluating the distinct effects of body mass index at childhood and adulthood on adult major psychiatric disorders
Childhood adiposity and psychiatric disorders
https://orcid.org/0000-0001-9976-7655
Xiao Pei Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Resources Software Supervision Validation Visualization Writing - original draft Writing - review & editing 1
Li Chi Conceptualization Investigation Resources Validation Visualization 2
https://orcid.org/0000-0003-0630-8447
Mi Jie Conceptualization Funding acquisition Investigation Project administration Supervision Validation Writing - review & editing 1 *
https://orcid.org/0000-0002-1601-1084
Wu Jinyi Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Resources Software Supervision Validation Visualization Writing - original draft Writing - review & editing 3 4 *
1 Center for Non-communicable Disease Management, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China.
2 Department of AIDS/STD Control and Prevention, Shijingshan District Center for Disease Control and Prevention, Beijing 100043, China.
3 Department of Public Health, Wuhan Fourth Hospital, Wuhan 430000, China.
4 School of Public Health, Fudan University, Shanghai 210000, China.
* Corresponding author. Email: wjypuai@outlook.com (J.W.); jiemi12@vip.sina.com (J.M.)
13 9 2024
13 9 2024
10 37 eadq245204 5 2024
06 8 2024
Copyright © 2024 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
2024
The Authors
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

Children with high body mass index (BMI) are at heightened risk of developing health issues in adulthood, yet the causality between childhood BMI and adult psychiatric disorders remains unclear. Using a life course Mendelian randomization (MR) framework, we investigated the causal effects of childhood and adulthood BMI on adult psychiatric disorders, including Alzheimer’s disease, anxiety, major depressive disorder, obsessive-compulsive disorder (OCD), and schizophrenia, using data from the Psychiatric Genomics Consortium and FinnGen study. Childhood BMI was significantly associated with an increased risk of schizophrenia, while adulthood BMI was associated with a decreased risk of OCD and schizophrenia. Multivariable MR analyses indicated a direct causal effect of childhood BMI on schizophrenia, independent of adulthood BMI and lifestyle factors. No evidence of causal associations was found between childhood BMI and other psychiatric outcomes. The sensitivity analyses yielded broadly consistent findings. These findings highlight the critical importance of early-life interventions to mitigate the long-term consequences of childhood adiposity.

The life course Mendelian randomization study revealed a causal effect of childhood BMI on the risk of adult schizophrenia.

http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 82204061 http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 82373589 BCH Young Investigator Program BCHYIP 3-1-014-01-33 Young Talent Development Program of Wuhan Fourth Hospital
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pmcINTRODUCTION

Childhood obesity is a critical public health issue that has been on the rise globally, triggering widespread concern due to its immediate and long-term health implications (1, 2). Body mass index (BMI) has been widely used to assess and classify obesity, and accumulating evidence suggests a link between BMI and psychiatric disorders, highlighting the complex interplay between obesity and mental health outcomes (3–6). However, the majority of previous studies examining this relationship have focused on adult populations, leaving a knowledge gap regarding the potential impact of early-life obesity on mental health outcomes (5, 7). Furthermore, while a limited number of studies have explored the relationship between childhood BMI and the risk of adult psychiatric disorders (8), these traditional epidemiological studies are prone to confounding bias and reverse causality (4–6). Consequently, the question remains as to whether interventions targeting childhood obesity can mitigate the risk of psychiatric disorders later in life.

Mendelian randomization (MR) offers a powerful approach for investigating causal relationships between exposures and outcomes in observational studies (9). By using genetic variants that are randomly allocated at conception, MR can mimic a randomized controlled trial setting, enhancing the robustness of causal inference and providing valuable evidence for informing public health policies and interventions (10). Recent advancements have introduced methods to ascertain whether multiple exposures affect an outcome through the same causal pathway or if they exert independent effects (11). One such advancement is multivariable MR (MVMR), which aids in determining whether early-life adiposity independently and enduringly influences disease risk or if its impact is solely mediated by later-life adiposity (12).

As depicted in Fig. 1, univariable MR (UVMR) can be used to estimate the total effect of childhood BMI on psychiatric disorders in later life without considering adulthood BMI (Fig. 1A). In contrast, MVMR enables the simultaneous estimation of two distinct effects of childhood BMI on psychiatric disorders in later life: the direct effect independent of adulthood BMI (Fig. 1B) and the indirect effect mediated through adulthood BMI (Fig. 1C). Despite recent MVMR studies investigating the causal associations between early-life BMI and several adulthood health outcomes (12–17), whether childhood BMI directly affects the risk of psychiatric disorders in later life remains largely unknown.

Fig. 1. Directed acyclic graphs used to illustrate the effects of childhood BMI on adult psychiatric disorders.

(A) The total effects of childhood BMI on adult psychiatric disorders estimated by UVMR. (B) The direct effect of childhood BMI on adult psychiatric disorders accounting for adulthood BMI estimated by multivariable MR. (C) The indirect effect of childhood BMI on adult psychiatric disorders accounting for adulthood BMI estimated by multivariable MR.

In the present study, we leveraged genetic instruments to genetically predict childhood and adulthood BMI as surrogate measures of obesity across different life stages, with the aim of providing a comprehensive estimation of the direct and indirect effects of childhood BMI on five major psychiatric disorders in later life, including Alzheimer’s disease, anxiety disorder, major depressive disorder, obsessive-compulsive disorder (OCD), and schizophrenia. Here, a two-stage life course MR analytic framework was conducted to achieve the objective. First, we applied UVMR to evaluate whether childhood BMI has a total effect on the risk of investigated outcomes. Subsequently, we used MVMR to discern whether childhood BMI has an independent effect on these outcomes when considering BMI in adulthood or whether the effect is mediated through BMI in later life.

RESULTS

Selection of instrumental variables

After the instrumental variables (IVs) selection process, a total of 16 and 446 single-nucleotide polymorphisms (SNPs) (table S1) were identified as genetic IVs and accounted for 2.3 and 4.9% of the variance in childhood and adulthood BMI, respectively. In all the UVMR analyses, the overall F-statistics for childhood and adulthood BMI exceeded 10, suggesting no evidence of weak instruments (table S2). However, the conditional F-statistics for childhood BMI decreased to below 10 when accounting for adulthood BMI, indicating potential weak instruments in MVMR. The smallest detectable odds ratios (ORs) for the associations between childhood BMI and the investigated outcomes ranged from 1.08 to 1.44 in the Psychiatric Genomics Consortium (PGC) dataset and from 1.08 to 1.41 in the FinnGen dataset, assuming 80% power to detect these outcomes (table S3).

Genetic correlation

We applied linkage disequilibrium score regression (LDSC) regression to summary statistics from the genome-wide association studies (GWASs) representing the investigated traits (table S4). The low LDSC intercepts for all traits, ranging from 1.00 to 1.13, indicate at most a small contribution of bias. The observed heritability (h2) of childhood BMI (h2, 0.206; SE, 0.018) was similar to that of adulthood BMI (h2, 0.212; SE, 0.007). Across the PGC psychiatric outcomes, the observed h2 varied, ranging from 0.014 (SE, 0.003) for Alzheimer’s disease to 0.355 (SE, 0.015) for schizophrenia. The heritability (h2) of psychiatric outcomes in the FinnGen dataset, with the exception of anxiety, which reached a maximum of 0.039, was consistently below 0.01 for all other traits. The genetic correlations (rg) between BMI at different stages and psychiatric outcomes from the PGC and FinnGen dataset were estimated using LDSC regression (Fig. 2 and tables S5 to S7). Notably, we found a negative association between adulthood BMI and both OCD and schizophrenia in both the PGC and FinnGen dataset, which remained significant after Bonferroni correction (P < 0.0024). However, no evidence of genetic correlation was detected between childhood BMI and psychiatric disorders. The rg for the same psychiatric outcome between the PGC and FinnGen dataset ranged from 0.500 (OCD) to 0.844 (schizophrenia) and remained statistically significant after Bonferroni correction except for OCD (table S7).

Fig. 2. Genetic correlations between the investigated traits.

Using LDSC, we calculated the genetic correlations between two exposures (childhood and adulthood BMI) and adult psychiatric disorders from the PGC (A) and FinnGen study (B), as well as the psychiatric outcomes between two consortia (C). A significance threshold of 0.05/n (where n is the number of comparisons) was used as the significance threshold after Bonferroni adjustment.

Total effects of childhood and adulthood BMI on psychiatric outcomes

Table 1 presents the results of the heterogeneity and pleiotropy tests using the MR-Egger method. Cochran’s Q statistics revealed a heterogeneity among genetic variants associated with BMI and all psychiatric outcomes except for anxiety disorder. The MR-Egger intercept test did not detect evidence of horizontal pleiotropy, except in the case of adulthood BMI concerning schizophrenia. The results of UVMR by random-effects inverse-variance weighted (IVW) method to examine the causal effects of childhood and adulthood BMI on psychiatric outcomes were presented in Fig. 3. The ORs derived from these analyses represent the change in odds associated with a 1-SD increase in childhood BMI and a 1 kg/m2 increase in adulthood BMI. The IVW method yielded strong evidence of a positive association between childhood BMI and the risk of schizophrenia [OR = 1.35, 95% confidence interval (CI) 1.05 to 1.75], whereas inverse associations were observed between adulthood BMI and the risk of schizophrenia (OR = 0.88, 95% CI 0.80 to 0.97) and OCD (OR = 0.67, 95% CI 0.53 to 0.85). These associations were directionally consistent in the replication analysis using the FinnGen dataset and robust in at least two sensitivity MR analyses (tables S8 and S9). Furthermore, we observed a positive association between adulthood BMI and the risk of major depressive disorder (OR = 1.13, 95% CI 1.05 to 1.21) in the PGC dataset, but this association was not statistically significant in the replication analysis using the FinnGen dataset. The scatterplots of the potential effects of SNPs on childhood and adulthood BMI versus significantly associated psychiatric outcomes, identified by the IVW method, were shown in figs. S1and S2, with the slope of each representing the evaluated effect size per method. Although horizontal pleiotropy was detected by the MR-Egger intercept test in the analysis of adulthood BMI versus schizophrenia, the pleiotropy-robust methods, including MR-Egger and MR-PRESSO (Pleiotropy RESidual Sum and Outlier), also supported the findings identified by the IVW method (tables S8 and S9). Figures S3 and S4 illustrate the results of leave-one-out analysis, which revealed that no single SNP was driving the whole effect in both the PGC and FinnGen datasets.

Table 1. Heterogeneity and pleiotropy test using MR-Egger methods.

Data source	Outcome	Exposure	MR-Egger	
Cochran’s Q statistic	df	P value	Intercept	Standard error	P value	
The PGC	Alzheimer’s disease	Childhood BMI	26.358	14	2.33 × 10−02	0.00367	0.017132	8.33 × 10−01	
Anxiety disorder	Childhood BMI	14.474	13	3.41 × 10−01	−0.03920	0.039588	3.40 × 10−01	
Major depressive disorder	Childhood BMI	40.566	14	2.08 × 10−04	−0.01434	0.019074	4.65 × 10−01	
OCD	Childhood BMI	28.008	14	1.42 × 10−02	0.05384	0.045718	2.59 × 10−01	
Schizophrenia	Childhood BMI	37.424	14	3.55 × 10−04	−0.02553	0.018722	1.96 × 10−01	
Alzheimer’s disease	Adulthood BMI	605.507	441	2.99 × 10−07	0.00203	0.001609	2.07 × 10−01	
Anxiety disorder	Adulthood BMI	477.434	442	1.18 × 10−01	−0.00195	0.003993	6.26 × 10−01	
Major depressive disorder	Adulthood BMI	723.178	444	1.08 × 10−15	−0.00091	0.001574	5.64 × 10−01	
OCD	Adulthood BMI	691.892	442	2.59 × 10−13	0.00656	0.004478	1.44 × 10−01	
Schizophrenia	Adulthood BMI	1424.172	435	2.03 × 10−105	−0.00779	0.002177	3.82 × 10−04	
The FinnGen	Alzheimer’s disease	Childhood BMI	26.358	14	2.33 × 10−02	0.00367	0.017132	8.33 × 10−01	
Anxiety disorder	Childhood BMI	14.474	13	3.41 × 10−01	−0.03920	0.039588	3.40 × 10−01	
Major depressive disorder	Childhood BMI	40.566	14	2.08 × 10−04	−0.01434	0.019074	4.65 × 10−01	
OCD	Childhood BMI	28.008	14	1.42 × 10−02	0.05384	0.045718	2.59 × 10−01	
Schizophrenia	Childhood BMI	37.424	14	3.55 × 10−04	−0.02553	0.018722	1.96 × 10−01	
Alzheimer’s disease	Adulthood BMI	605.507	441	2.99 × 10−07	0.00203	0.001609	2.07 × 10−01	
Anxiety disorder	Adulthood BMI	477.434	442	1.18 × 10−01	−0.00195	0.003993	6.26 × 10−01	
Major depressive disorder	adulthood BMI	723.178	444	1.08 × 10−15	−0.00091	0.001574	5.64 × 10−01	
OCD	Adulthood BMI	691.892	442	2.59 × 10−13	0.00656	0.004478	1.44 × 10−01	
Schizophrenia	Adulthood BMI	1424.172	435	2.03 × 10−105	−0.00779	0.002177	3.82 × 10−04	

Fig. 3. UVMR estimates for the total causal effects of childhood and adulthood BMI on adult psychiatric disorders by using the IVW method.

*P < 0.05. SNP, single nucleotide polymorphism.

Table 2 presents the robust analysis results after removing the outliers identified by the RadialMR package. The radial IVW method still demonstrated a significant and consistent causal association, wherein adulthood BMI was linked to a decreased risk of OCD and schizophrenia, while childhood BMI was associated with an increased risk of schizophrenia. Accordingly, figs. S5 and S6 visualize Radial plots of both the IVW and MR-Egger analyses, including a Radial curve to highlight the ratio estimate for each genetic variant, as well as the overall estimate. To assess bias due to sample overlap, the results of robust analysis derived from the MRlap package were presented in Table 3. After correcting for sample overlap, the reverse association between adulthood BMI and the risk of schizophrenia remained significant (PGC OR = 0.93, P = 5.42 × 10−03; FinnGen OR = 0.96, P = 4.03 × 10−05), but the effect size decreased compared to that before correction (P for pre-post correction difference < 0.05). The findings from MRlap analysis consistently supported a causal link between childhood BMI and an increased risk of schizophrenia (PGC OR = 1.08, P = 4.98 × 10−02; FinnGen OR = 1.05, P = 4.34 × 10−02), which was in line with the main results (P for pre-post correction difference > 0.05). However, the association between adulthood BMI and the risk of OCD in the PGC dataset became nonsignificant (P = 6.75 × 10−01) after correcting for sample overlap.

Table 2. Robust analysis of the concordant causality with outlier-filtering approaches.

IVW, inverse-variance weighting.

Data source	Disease	Exposure	Method	OR (95% CI)	P value	
The PGC	Alzheimer’s disease	Childhood BMI	Radial IVW	1.03 (0.92–1.14)	6.36 × 10−01	
Radial MR-Egger	0.64 (0.39–1.03)	8.88 × 10−02	
Adulthood BMI	Radial IVW	1.01 (0.94–1.10)	7.30 × 10−01	
Radial MR-Egger	0.85 (0.69–1.05)	1.28 × 10−01	
Anxiety disorder	Childhood BMI	Radial IVW	1.27 (0.99–1.62)	8.00 × 10−02	
Radial MR-Egger	0.78 (0.27–2.26)	6.61 × 10−01	
Adulthood BMI	Radial IVW	1.01 (0.84–1.22)	9.25 × 10−01	
Radial MR-Egger	1.24 (0.75–2.05)	4.03 × 10−01	
Major depressive disorder	Childhood BMI	Radial IVW	1.12 (0.99–1.26)	6.49 × 10−02	
Radial MR-Egger	1.25 (0.68–2.30)	4.92 × 10−01	
Adulthood BMI	Radial IVW	1.13 (1.05–1.22)*	1.82 × 10−03	
Radial MR-Egger	1.14 (0.93–1.40)	1.98 × 10−01	
OCD	Childhood BMI	Radial IVW	0.78 (0.55–1.11)	1.90 × 10−01	
Radial MR-Egger	1.01 (0.18–5.57)	9.92 × 10−01	
Adulthood BMI	Radial IVW	0.66 (0.51–0.86)*	2.06 × 10−03	
Radial MR-Egger	0.81 (0.42–1.56)	5.30 × 10−01	
Schizophrenia	Childhood BMI	Radial IVW	1.37 (1.04–1.79)*	3.81 × 10−02	
Radial MR-Egger	0.25 (0.01–23.5)	5.57 × 10−01	
Adulthood BMI	Radial IVW	0.87 (0.77–0.98)*	2.18 × 10−02	
Radial MR-Egger	1.39 (1.05–1.84)*	2.24 × 10−03	
The FinnGen	Alzheimer’s disease	Childhood BMI	Radial IVW	0.99 (0.85–1.15)	8.98 × 10−01	
Radial MR-Egger	1.02 (0.58–1.81)	9.49 × 10−01	
Adulthood BMI	Radial IVW	1.00 (0.90–1.12)	9.31 × 10−01	
Radial MR-Egger	1.03 (0.77–1.37)	8.35 × 10−01	
Anxiety disorder	Childhood BMI	Radial IVW	1.01 (0.91–1.13)	8.39 × 10−01	
Radial MR-Egger	1.00 (0.63–1.61)	9.87 × 10−01	
Adulthood BMI	Radial IVW	1.01 (0.94–1.08)	8.37 × 10−01	
Radial MR-Egger	1.04 (0.87–1.26)	6.57 × 10−01	
Major depressive disorder	Childhood BMI	Radial IVW	0.92 (0.67–1.27)	6.31 × 10−01	
Radial MR-Egger	1.37 (0.43–4.35)	6.00 × 10−01	
Adulthood BMI	Radial IVW	0.98 (0.78–1.24)	8.95 × 10−01	
Radial MR-Egger	0.94 (0.51–1.74)	8.55 × 10−01	
OCD	Childhood BMI	Radial IVW	0.83 (0.60–1.15)	2.85 × 10−01	
Radial MR-Egger	0.63 (0.17–2.31)	4.96 × 10−01	
Adulthood BMI	Radial IVW	0.70 (0.57–0.87)*	1.22 × 10−03	
Radial MR-Egger	0.59 (0.34–1.03)	6.19 × 10−02	
Schizophrenia	Childhood BMI	Radial IVW	1.40 (1.13–1.73)*	7.86 × 10−03	
Radial MR-Egger	1.39 (0.60–3.21)	4.55 × 10−01	
Adulthood BMI	Radial IVW	0.73 (0.61–0.89)*	1.34 × 10−03	
Radial MR-Egger	0.93 (0.56–1.56)	7.85 × 10−01	
*P < 0.05.

Table 3. Robust analysis of the concordant causality with sample overlap correction approach.

Data source	Disease	Exposure	MRlap corrected OR (95% CI)	MRlap corrected P value	P for difference	
The PGC	Alzheimer’s disease	Childhood BMI	1.00 (0.97–1.04)	8.87 × 10−01	8.88 × 10−01	
Adulthood BMI	1.00 (0.98–1.02)	8.36 × 10−01	6.62 × 10−01	
Anxiety disorder	Childhood BMI	1.10 (0.96–1.27)	1.71 × 10−01	1.18 × 10−01	
Adulthood BMI	0.99 (0.91–1.07)	7.75 × 10−01	1.30 × 10−01	
Major depressive disorder	Childhood BMI	1.07 (0.99–1.16)	1.06 × 10−01	1.14 × 10−01	
Adulthood BMI	1.05 (1.02–1.09)*	1.94 × 10−03	1.08 × 10−01	
OCD	Childhood BMI	1.07 (0.84–1.35)	5.91 × 10−01	5.52 × 10−01	
Adulthood BMI	1.02 (0.92–1.14)	6.57 × 10−01	3.55 × 10−01	
Schizophrenia	Childhood BMI	1.08 (1.00–1.17)*	4.98 × 10−02	9.18 × 10−02	
Adulthood BMI	0.93 (0.88–0.98)*	5.42 × 10−03	4.85 × 10−02	
The FinnGen	Alzheimer’s disease	Childhood BMI	1.00 (0.97–1.03)	8.97 × 10−01	8.24 × 10−01	
Adulthood BMI	0.99 (0.98–1.01)	4.63 × 10−01	4.97 × 10−01	
Anxiety disorder	Childhood BMI	1.00 (0.95–1.05)	9.77 × 10−01	9.10 × 10−01	
Adulthood BMI	1.01 (0.98–1.03)	5.44 × 10−01	6.23 × 10−01	
Major depressive disorder	Childhood BMI	0.99 (0.95–1.03)	6.55 × 10−01	6.18 × 10−01	
Adulthood BMI	1.00 (0.98–1.02)	9.47 × 10−01	4.91 × 10−01	
OCD	Childhood BMI	0.97 (0.94–1.01)	1.40 × 10−01	1.67 × 10−01	
Adulthood BMI	0.97 (0.95–0.98)*	3.26 × 10−05	2.94 × 10−03	
Schizophrenia	Childhood BMI	1.05 (1.01–1.08)*	4.34 × 10−02	8.89 × 10−01	
Adulthood BMI	0.96 (0.95– 0.98)*	4.03 × 10−05	6.85 × 10−03	
*P < 0.05.

Direct effects of childhood and adulthood BMI on psychiatric disorders

In the MVMR analyses of model 1, where both childhood and adulthood BMI were simultaneously included in the models (Fig. 4A), we found that genetically determined adulthood BMI remained significantly associated with a decreased risk of OCD (PGC: multivariable IVW OR = 0.59, P = 1.42 × 10−03; FinnGen: multivariable IVW OR = 0.69, P = 7.36 × 10−03) and schizophrenia (PGC: multivariable IVW OR = 0.84, P = 1.36 × 10−02; FinnGen: multivariable IVW OR = 0.67, P = 1.76 × 10−03) in both the PGC and FinnGen datasets, consistently across all three multivariable methods. Besides, after adjusting for adulthood BMI, genetically determined childhood BMI remained significantly associated with an increased risk of schizophrenia, with an OR of 1.09 (P = 3.72 × 10−02) in the PGC dataset and 1.18 (P = 4.52 × 10−02) in the FinnGen dataset, according to the multivariable IVW method. Notwithstanding the conditional F-statistics of childhood BMI versus schizophrenia being below 10, the results obtained from the multivariable MR-Egger method (OR = 1.17, P = 4.13 × 10−02) remained broadly consistent with the main findings in the PGC dataset. Figure 4B presents the results of MVMR analyses using model 2, which included model 1 and was further adjusted for sleep duration, physical activity, alcoholic drinking, smoking, household income, and educational attainment. After adjusting for lifestyle factors, all three multivariable methods still supported the causal relationships between adulthood BMI and OCD, as well as schizophrenia, in both the PGC and FinnGen datasets. For childhood, the PGC dataset consistently showed an association with the increased risk of schizophrenia across all multivariable methods, whereas the FinnGen dataset only replicated this finding with the multivariable lasso method.

Fig. 4. Multivariable MR estimates for the direct causal effects of childhood and adulthood BMI on adult psychiatric disorder.

Model 1 (A) included both the childhood and adulthood BMI. Model 2 (B) extended model 1 by further adjusting for sleep duration, physical activity, alcoholic drinking, smoking, household income, and educational attainment. *P < 0.05.

DISCUSSION

In this study, we investigated the independent causal effects of childhood and adulthood BMI on the risk of Alzheimer’s disease, anxiety disorder, major depressive disorder, OCD, and schizophrenia in adulthood using a life course MR approach. Our UVMR results revealed a significant causal association between genetically predicted childhood BMI and the subsequent risk of schizophrenia in adulthood, as well as a link between adulthood BMI and the decreased risk of OCD and schizophrenia. Notably, our MVMR analyses revealed a robust direct causal effect of childhood BMI on the risk of schizophrenia in adulthood, independent of adulthood BMI, after adjusting for potential lifestyle confounding factors. This suggests that the influence of childhood BMI on schizophrenia is not solely mediated by its impact on adulthood BMI; rather, childhood BMI has a distinct and independent effect on schizophrenia in adulthood. Besides, the MVMR results were in agreement with the UVMR findings, indicating that adulthood BMI was causally associated with a decreased risk of OCD and schizophrenia.

Our findings align with previous research highlighting the association between BMI and mental health outcomes (7, 18–21). However, most previous studies have focused on adult populations, neglecting the potential impact of early-life body weight on psychiatric disorders in life. Our study addresses this gap by specifically examining the role of childhood BMI in psychiatric outcomes, contributing to a more comprehensive understanding of the long-term consequences of obesity on health. A cohort study by Gallagher et al. (8) examined the relationships between BMI trajectories from childhood to mid-adulthood and anxiety and depression outcomes in middle age. The study revealed that individuals with a persistently high BMI from childhood to mid-adulthood, as well as those with an average childhood BMI that increased consistently throughout adulthood, had a greater risk of depression in middle age. However, the study design may have led to residual confounding and failed to fully disentangle the distinct effects of childhood BMI independent of adulthood BMI. In contrast, a series of MR analyses revealed that adulthood BMI, rather than childhood BMI, was causally associated with an increased risk of major depressive disorder in the PGC dataset, although this association was not replicated in the FinnGen dataset. Similarly, Yan et al. (13) conducted a two-sample MR analysis to explore the causal effect of childhood BMI on depression, followed by an MVMR analysis to investigate the potential role of adulthood BMI in mediating this effect. Consistent with our findings, their MVMR results revealed that the impact of childhood BMI on major depressive disorder risk was primarily mediated by adulthood BMI. However, our study took a substantial step forward by extending the outcome phenomenon to Alzheimer’s disease, anxiety, OCD, and schizophrenia while also adjusting for lifestyle confounding factors. Our study may contribute to the field by suggesting a potential link between childhood BMI and the risk of schizophrenia onset in adulthood. This finding could have important implications for early intervention and prevention strategies. Previous genome-wide cross-trait analyses have elucidated a shared genetic basis architecture between schizophrenia and BMI (3, 22, 23), thereby providing strong supportive evidence for the causal association observed in our study.

Although the exact mechanism underlying the relationship between childhood BMI and schizophrenia remains unclear, several plausible pathways have been suggested to explain why excess BMI in childhood may increase vulnerability to schizophrenia. One potential mechanism is that childhood obesity may lead to alterations in brain structure and function during a critical period of neurodevelopment, which in turn contribute to the development of psychiatric disorders (24). In addition, alterations in the gut microbiota composition and function, which are often associated with obesity, may also play a role in the pathogenesis of schizophrenia (25). Furthermore, the hypothalamic-pituitary-adrenal axis, which is critical for regulating the stress response, may be dysregulated in individuals with a high BMI, leading to an increased risk of schizophrenia (26). Besides, emerging evidence suggests that psychosocial factors related to childhood obesity (27), such as social stigma, bullying, and interpersonal stressors, may contribute to the development of maladaptive coping strategies and psychological vulnerabilities, thereby potentially increasing susceptibility to schizophrenia (28).

On the other hand, we observed an inverse relationship between adulthood BMI and schizophrenia, which is in line with the findings of two prospective cohort studies conducted in Sweden and Denmark (29, 30), as well as a MR study by Chen et al. (19). These findings support the classical “somatotype” theory, suggesting that obesity in schizophrenia may be a consequence of factors such as antipsychotics, lifestyle changes, and unhealthy habits (23). Our study also revealed that adulthood BMI was associated with a decreased risk of OCD, which is consistent with previous studies (19, 31). The biological mechanisms underpinning the associations between adulthood BMI and lower risk of schizophrenia and OCD are not clear, but clues may be gained from previous studies. Gonçalves et al. (32) proposed that hormonal changes linked to higher BMI, such as increased estrogen levels, may play a role in reducing the risk of schizophrenia. In addition, adipose tissue produces various neurotrophic factors, such as leptin and adiponectin, that may have neuroprotective effects (33, 34).

The strengths of our study lie in the rigorous application of MR methods, the use of large-scale genetic data, the replication in the FinnGen dataset, and the comprehensive assessment of both direct and indirect effects of childhood BMI on psychiatric outcomes. Besides, we used MRlap method accounting for MR biases such as sample overlap and Winner’s curse. Although this study benefits from the robustness of MR analysis in addressing confounding and reverse causality, several limitations should be acknowledged. The analysis was limited to individuals of European descent, which may affect the generalizability of the findings to other populations. In addition, the genetic instruments used for MR analysis may not fully capture the complexity of BMI and its effects on major psychiatric disorders. Last, in the present study, sex differences and nonlinearity in the causal pattern between childhood BMI and the risk of schizophrenia could not be evaluated due to limited available data.

In conclusion, our study provides compelling evidence for a direct and enduring effect of childhood BMI on the risk of schizophrenia later in life, independent of adulthood BMI and lifestyle factors. These findings underscore the critical importance of early-life interventions to mitigate the long-term mental health consequences of childhood obesity. Overall, our study provides robust evidence for policymakers and health care professionals to develop targeted interventions aimed at reducing childhood obesity and mitigating its long-term consequences for mental health. The findings emphasize the need for preventive strategies starting in childhood to promote better mental health outcomes in adulthood.

MATERIALS AND METHODS

Study design and ethics

As illustrated in Fig. 5, in the discovery stage, we performed a two-sample MR analysis followed by sensitivity analyses to examine the causal associations between BMI at two life stages—childhood (between 2 and 10 years of age) and adulthood (above 18 years of age)—and psychiatric outcomes in adulthood using summary data from the PGC. Then, we replicated the MR analysis in the same outcomes of the FinnGen study, with the aim of validating the identified associations in the discovery stage. Ethical approval and participant consent were previously obtained from the original GWAS, as detailed in table S10, which also provides web links for data access. Therefore, separate ethical approval for this MR analysis of publicly available summary-level GWAS data was not necessary. This study adhered to the reporting guidelines outlined in the Strengthening the Reporting of Observational Studies in Epidemiology using MR (table S11) (35, 36).

Fig. 5. Overview of the life course MR study design.

MAF, minor allele frequency; LD, linkage disequilibrium. This MR study comprised two analysis phases. In phase 1, we assessed the total effects of childhood and adulthood BMI on psychiatric outcomes separately by applying UVMR. In phase 2, we further performed multivariable MR to investigate whether childhood BMI independently affects the outcomes, separate from the influence of adulthood BMI. All the analyses were conducted in both the PGC (discovery) and FinnGen datasets (replication).

Data source

Exposures

We extracted summary-level GWAS data on childhood BMI from the Early Growth Genetics Consortium, which was based on a two-stage GWAS meta-analysis involving up to 61,111 children of European ancestry aged between 2 and 10 years (37). The sex- and age-adjusted SD scores for BMI were created using the same software and external reference across all studies (LMS growth, http://healthforallchildren.co.uk). A total of 47 independent SNPs were identified using fixed-effects inverse-variance weighted meta-analysis in the discovery stage, which included data from 26 studies (ndiscovery = 39,620). These SNPs were subsequently analyzed in 15 replication cohorts (nreplication = 21,491), and the results from both stages were combined. Summary-level statistics for adulthood BMI were extracted from a fixed-effect IVW meta-analysis of data from up to 681,275 European ancestry individuals, including 456,426 UK Biobank participants (aged 37 to 73 years) and publicly available adult BMI data from the Genetic Investigation of Anthropometric Traits (GIANT) consortium (38).

Outcomes

Summary-level statistics of the largest GWASs published to date for Alzheimer’s disease (n = 39,918 cases, 318,222 controls) (39), anxiety disorder (n = 7016 cases, 14,745 controls) (40), major depressive disorder (n = 41,636 cases, 101,010 controls) (41), OCD (n = 2688 cases, 7037 controls) (42), and schizophrenia (n = 52,017 cases, 75,889 controls) (43) in adults were obtained from the PGC. To minimize potential population stratification bias, the study exclusively included European individuals. In addition, we excluded UK Biobank participants and removed 23andMe samples from the PGC outcomes to minimize potential overlap between exposure and outcome data. To test the robustness of MR inference in the PGC outcomes, we extracted another set of summary-level statistics on these five psychiatric outcomes from the FinnGen consortium study (R10 release) (44), with sample sizes detailed in table S10. In the FinnGen dataset, the diagnoses of outcomes were based on International Classification of Diseases (ICD)–9 or ICD-10 criteria. The median age of the FinnGen sample was approximately 41.5 years, with females accounting for approximately 55.9% of the sample. The genetic associations were adjusted for sex, age, genetic principal components, and genotyping batch effects.

Covariates

In the MVMR analysis, we further adjusted for several potential lifestyle confounding factors. As detailed in table S10, the GWAS summary data on these lifestyle traits can be accessed through the Integrative Epidemiology Unit (IEU) open GWAS project (https://ebi.ac.uk/gwas/): sleep duration (GWAS ID: ukb-b-4424), moderate to vigorous physical activity levels (GWAS ID: ebi-a-GCST006097), alcoholic drinks per week (GWAS ID: ieu-b-73), current tobacco smoking (GWAS ID: ukb-b-223), average total household income before tax (GWAS ID: ukb-b-7408), and educational attainment (GWAS ID: ebi-a-GCST90029013). These datasets comprised up to 460,099, 377,234, 335,394, 462,434, 397,751, and 461,457 individuals of European ancestry, respectively.

Genetic IVs

A series of quality control procedures were used to identify eligible instrumental SNPs. Initially, SNPs associated with childhood and adulthood BMI with genome-wide significance (P < 5 × 10−8 and minor allele frequency > 0.01) were extracted. Subsequently, a clumping process (r2 < 0.001, window size = 10,000 kb) was carried out using European samples from the 1000 Genomes Project to assess linkage disequilibrium (LD) between SNPs. Then, the above selected SNPs were extracted from the psychiatric disorder GWAS summary. If a requested SNP was absent in the outcome GWAS, then a proxy SNP in LD with the requested SNP identified using 1000 Genomes Project European sample data was sought instead. Last, ambiguous SNPs with discordant alleles and palindromic SNPs with ambiguous strands were either corrected or excluded during the harmonization process. We calculated the proportion of phenotypic variation explained by IVs (R2) and F-statistics to assess the tool strength of the IVs. Subsequently, we applied Steiger filtering to mitigate reverse causality, removing variants with stronger associations with the outcome than the exposure (45). Conventionally, an F-statistic greater than 10 is regarded as a strong genetic instrument for explaining phenotypic variations, representing a 10% relative bias toward the null in the two-sample MR setting. In MVMR analysis, a conditional F-statistic, which has the same distribution as the univariate F-statistic, was calculated to quantify the instrument strength.

Statistical analysis

Genetic correlation analysis

We performed LDSC analysis to assess the genetic correlation between the investigated traits using GWAS summary-level data (46). To ensure data quality, we specifically included SNPs that met the following criteria: (i) were involved in the 1000 Genomes Project and HapMap Projects, (ii) had a minor allele frequency of at least 1%, and (iii) had an imputation quality of ≥0.9. To address the issue of multiple hypothesis testing, we applied a Bonferroni-adjusted significance threshold, calculated as 0.05 divided by the number of comparisons, in the LDSC analyses.

UVMR and MVMR

MR analysis necessitates fulfilling three core assumptions: (i) The genetic instruments are strongly associated with the exposure; (ii) the use of genetic instruments does not correlate with any confounders; and (iii) the genetic instruments influence the outcome solely through exposure. We initially applied UVMR to estimate the total effects of childhood and adulthood on each psychiatric outcome. Given the likely strong correlation between genetic determinants of BMI during childhood and adulthood, the effect of childhood BMI on outcomes might be partly or entirely mediated by adulthood BMI. Considering the temporal sequence, childhood BMI can affect adulthood BMI but not vice versa. Consequently, we incorporated both childhood and adulthood BMI into the MVMR model (model 1) to examine the independent effects of BMI at different life stages on each psychiatric outcome. Subsequently, we extended the MVMR model by further adjusting for lifestyle factors (model 2) to control for potential confounding effects. This analytical strategy enabled us to concurrently assess the independent effects of childhood and adulthood BMI on psychiatric outcomes while controlling for potential confounding factors. As the IVW method offers a precise estimation of risk under the assumption that all IVs are valid, we used the random-effects IVW method as the primary analysis in UVMR and the MV-IVW method as the primary analysis in MVMR.

MR sensitivity analysis

The robustness of our findings against weak violations of MR assumptions was assessed through a series of MR sensitivity analyses. We calculated Cochran’s Q statistic to assess SNP heterogeneity for exposure and evaluated horizontal pleiotropy using the MR-Egger intercept. A proximity of the intercept to zero indicates a lower likelihood of horizontal pleiotropy. The power calculations in MR analysis were performed using the web tool mRnd (https://github.com/kn3in/mRnd). To validate the robustness of the IVW results based on various assumptions in UNMR analysis, we conducted several traditional MR analysis methods, including weighted median, weighted mode, MR-Egger, robust adjusted profile score, and MR-PRESSO methods. The weighted median method was used because it provides reliable causal estimates even when up to 50% of the IVs violate MR assumptions due to horizontal pleiotropy. Similarly, the weighted mode method can provide unbiased estimates when the largest cluster of SNPs contributing to the analysis is valid. To address potential issues of directional pleiotropy, we incorporated the MR-Egger method to provide bias-corrected causal estimates even in situations where all IVs may be affected by pleiotropy. We used the MR-PRESSO method to assess directional pleiotropic bias by identifying and evaluating outlying SNPs potentially influenced by horizontal pleiotropy. We also carried out the leave-one-out analysis to assess whether the overall effect was driven by a single SNP. In addition, we used the Radial MR package (47) to identify and remove outliers, followed by reapplying the IVW and MR-Egger method. To address the issue of sample overlap, in addition to excluding the UK Biobank (UKB) and 23andMe population from the outcome sample, we used MRlap package (https://github.com/n-mounier/MRlap) (48) to adjust for the sample overlap effect and obtain corrected estimates. MRlap is a relatively new method that addresses several biases in the MR analyses, including weak instrument bias and winner’s curse. By accounting for sample overlap, MRlap corrects for these biases and provides an analytical derivation of the expected value of the standard IVW causal effect estimate, assuming a spike-and-slab genomic architecture for the exposure.

In the multivariable setting, we used the multivariable MR-Egger method, an extension of the MR-Egger approach, to account for both measured and unmeasured pleiotropy. We also performed a multivariable MR-Lasso method (49), which applies lasso-type penalization to the direct effects of genetic variants on the outcome, to identify valid instruments and estimate causal effects. This method extends the multivariable IVW model by incorporating intercept terms for each genetic variant, representing associations between variants and the outcome that bypass the risk factors. The lasso penalty shrinks the intercept terms corresponding to invalid instruments to zero, and the method involves two steps: identifying valid instruments and estimating causal effects using standard multivariable IVW.

All analyses in this study were performed using R software (version 4.2.3) and the following packages: TwoSampleMR (version 0.5.6), MVMR (version 0.3.0), MRPRESSO (version 1.0.0), RadialMR (version 1.1.0), ldscr (version 0.1.0), and MRlap (version 0.0.3.0). Statistical significance was defined as a P value < 0.05. Causal inference based on the IVW estimates was only established if these estimates exhibited consistent directionality and statistical significance in at least one sensitivity analysis, and no evidence of pleiotropy was found (P for Egger intercept, >0.05).

Acknowledgments

We are grateful to the Early Growth Genetics Consortium for releasing the childhood BMI GWAS summary statistics, the GIANT consortium for releasing the adulthood BMI GWAS summary statistics, and the PGC and the FinnGen study for releasing the psychiatric disorder GWAS summary statistics.

Funding: This study was supported by the National Natural Science Foundation of China (82204061 to P.X. and 82373589 to J.M.), the BCH Young Investigator Program (BCHYIP 3-1-014-01-33 to P.X.), and Young Talent Development Program of Wuhan Fourth Hospital (no award/grant number to J.W.).

Author contributions: Conceptualization and investigation: P.X., C.L., J.M., and J.W.; writing—original draft and supervision: P.X. and J.W.; writing—review and editing: P.X., J.M., and J.W.; formal analysis: P.X. and J.W.; visualization: P.X., C.L., and J.W.

Competing interests: The authors declare that they have no competing interests.

Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. The GWAS summary statistics for childhood BMI are available on the Early Growth Genetics Consortium website (http://egg-consortium.org/). The GWAS summary statistics for adulthood BMI are available on the GIANT consortium website (https://pgc.unc.edu/for-researchers/download-results/). The GWAS data for psychiatric disorders can be obtained from the PGC website (https://ebi.ac.uk/gwas/studies/GCST90014290) and FinnGen (https://finngen.fi/fi) resources. The GWAS data for sleep duration (GWAS ID ukb-b-4424), physical activity (GWAS ID ebi-a-GCST006097), alcoholic drinking (GWAS ID ieu-b-73), current tobacco smoking (GWAS ID ukb-b-223), average total household income before tax (GWAS ID ukb-b-7408), and educational attainment (GWAS ID ebi-a-GCST90029013) can be obtained at https://gwas.mrcieu.ac.uk/. The data and analytical code can be provided by J.W. at Wuhan Fourth Hospital pending scientific review and a completed material transfer agreement. Requests for the data should be submitted to J.W. at wjypuai@outlook.com.

Supplementary Materials

This PDF file includes:

Figs. S1 to S6

Tables S1 to S11
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