
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12866-6
10.1016/j.heliyon.2024.e36835
e36835
Research Article
Mendelian randomization analysis separated the independent impact of childhood obesity and adult obesity on socioeconomic status, psychological status, and substance use
Cai Jiahao caijh6@mail2.sysu.edu.cn
ab⁎1
Zhao Lei c1
Li Nanfang d1
Xiao Zijin e
Huang Guiwu guiwu.huang@yale.edu
f⁎⁎
a School of Pediatrics, Guangzhou Medical University, China
b Department of Neurology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
c The Third Clinical Institute, Guangzhou Medical University, Guangzhou, China
d Graduate School of Human Science, Osaka University, Osaka, Japan
e Guangzhou Medical University, Guangzhou, China
f Department of Orthopaedics and Rehabilitation, Yale University School of Medicine, New Haven, CT, USA
⁎ Corresponding author. Department of Neurology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China. caijh6@mail2.sysu.edu.cn
⁎⁎ Corresponding author. guiwu.huang@yale.edu
1 Equal contribution.

23 8 2024
15 9 2024
23 8 2024
10 17 e3683525 12 2023
6 8 2024
22 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Background

Obesity is linked to a variety of psychosocial and behavioral outcomes but the causalities remain unclear yet. Determining the causalities and distinguishing between the separate effects of childhood and adult obesity is critical to develop more targeted strategies to prevent adverse outcomes.

Methods

With single nucleotide polymorphisms (SNPs) used as genetic variables, we employed univariable Mendelian randomization (UVMR) to explore the causalities between childhood and adult body mass index (BMI) and socioeconomic status, psychological status, and substance use. Genetic data for childhood and adult BMI came respectively from 47,541 children aged 10 years and 339,224 adult participants. The outcome data were obtained from corresponding consortia. The direct impact of childhood BMI and adult BMI was then examined using a multivariable MR (MVMR).

Results

UVMR found that higher childhood BMI was linked causally to lower household income (β = −0.06, 95 % CI = −0.08 ∼ −0.03, P = 4.86 × 10−5), decreased subjective well-being (β = −0.07, 95 % CI = −0.12 ∼ −0.03, P = 1.74 × 10−3), and an increased tendency of smoking regularly (OR = 1.12, 95 % CI = 1.04–1.20, P = 1.52 × 10−3). Similar results were observed in adult BMI. MVMR further revealed that after adjusting with adult BMI, childhood BMI remained an isolated impact on household income. The impacts of adult BMI on the outcomes were diminished when adjusting with childhood BMI.

Conclusion

The findings indicate the impacts of childhood obesity on subjective well-being and smoking initiation are a result of higher BMI sustaining into adulthood, whereas the effect on household income is attributed to a lasting impact of obesity in early life. The results would help facilitate more targeted strategies for obesity management to prevent adverse outcomes.

Keywords

Body mass index
Socioeconomic status
Psychological status
Substance use
Mendelian randomization
==== Body
pmc1 Introduction

1.1 Background: obesity and psychosocial outcomes & addictive behaviors

Obesity is a significant public health issue, given the unfavorable consequences it has brought to various health conditions, especially for non-communicable diseases (NCDs) like cancers and cardiovascular diseases [[1], [2], [3]]. As reported, obesity has contributed to the decrease of life expectancy by 5∼20 years due to long-term adverse health outcomes [4]. Over the past 40 years, the mean body mass index (BMI) and obesity prevalence have risen in most countries [5]. Specifically, the increased number of persons overweight from 1975 to 2016 was 213 million for children and 1.3 billion for adults across the globe, respectively [5]. Given the heavy burden of the global obesity pandemic on human society, reversing the increasing trends of BMI among both children and adults has been a priority for the World Health Organization (WHO), which was used as an index to measure obesity by the NCD study [6].

Epidemiological research has identified a disparity in the prevalence of low socioeconomic status, poor mental health, and addictive behaviors between the general population and those with adiposity [7]. However, the causality remains unclear. Individuals exposed to a lower socioeconomic status, suffering psychiatric disorders, or indulging in certain addictive behaviors might be more likely to develop obesity [8,9]. For instance, indigent material conditions might confine a person's acquisition of healthy food [10]. Previously, studies have reported that intake of unhealthy food, like fatty meats and sugar-sweetened beverages, was more frequent in the low-income population compared with the high-income population, which might partly explain why people in lower SES were vulnerable to developing adiposity [11,12]. There is also an alternative explanation in an opposite direction that obese persons are prone to developing adverse socio-psychosocial consequences or substance use. Segal et al. found childhood obesity significantly hindered education outcomes, with the effects predominantly observed in those with older ages (12+ years) [13]. Denoth et al. reported that smoking cigarettes, gambling, and worse school performance were more common in those with overweight [14]. Stress-pathways might partially explain the correlation between obesity and unfavorable health outcomes [15]. Indeed, discrimination towards persons with overweight is pervasive as they are usually perceived as lazy or lacking self-control in various social settings [10]. People who are obese and encounter prejudice or stereotypes of threat may experience high levels of stress, which might influence their decision-making, interpersonal behaviors, and cognitive function [16]. These negative stereotypes consequently contribute to developing “obesity stigma” that has been linked with lower socioeconomic status, psychological difficulties, and drug-seeking [17,18]. In this context, obesity might contribute to adverse psychosocial outcomes and addictive behaviors.

1.2 Researching gaps of existing literature

So far, there are no randomized controlled trials (RCTs) to determine the causality between obesity and psychosocial outcomes and addictive behaviors, partially owing to the restriction of ethics and high costs. Prior observational studies are constrained by reverse causality and considerable undetectable confounders, potentially resulting in erroneous findings [19]. Besides, gaining excess weight in childhood is susceptible to maintaining obesity throughout the life course [20]. Due to the innate methodologic defects, observational studies are unable to distinguish the individual impact of childhood obesity from adult obesity. Therefore, the detrimental effect of childhood obesity after accounting for sustained overweight to adulthood is not yet established. Given that childhood obesity often ends up in obesity in adults, distinguishing their respective effects on health outcomes is of great importance for understanding disease heterogeneity and promoting appropriate strategies for disease prevention.

1.3 Objectives and rationales for this work

To this end, we employed genome-wide association study (GWAS) statistics and applied a two-sample Mendelian randomization (MR) framework in this work, aiming to elaborate two major issues: 1) whether socioeconomic status, neuropsychological status, and addictive behaviors are attributed to obesity, and 2) whether childhood or adult obesity exerts an independent effect on them. The MR technique is well developed and has been widely applied to infer causality in recent years. As the MR design utilizes genetic variants randomly allocated during conception prior to phenotype/disease formation, reverse causality and residual confounders commonly inevitably existing in observational studies are less likely in an MR work [21]. When RCTs are not available, MR is an alternative approach to help determine causality as most scientific issues are unable to be answered by RCTs. Thereby, we firstly assessed the impacts of childhood and adult BMI on socioeconomic status, neuropsychological status, and addictive behaviors using univariable MR (UVMR). We then evaluated the specific influence of childhood BMI taking adult BMI into account using multivariable MR (MVMR). As an extension of UVMR, MVMR consistently estimates the direct impacts two or more exposures, which has been successfully applied to distinguish the distinct impacts of child and adult body size on health outcomes [22]. Results in this work would 1) help determine whether adverse outcomes associated with childhood obesity could be alleviated or even reversed by early-life management of obesity and 2) whether adverse outcomes associated with adult obesity are attributed to the influence of overweight in early life, and would 3) have implications for clinicians and policymakers for developing more precise strategies for obesity management.

2 Methods

2.1 Study overview

To ascertain the causal impact of childhood and adult BMI on socioeconomic status, neuropsychological status, and substance use, we carried out a two-sample Mendelian randomization (MR) study. The cornerstones for a rational MR design include the relevance, exclusiveness, and independence assumptions, among which the latter two assumptions are collectively perceived as free from pleiotropic bias (Fig. 1A) [23]. All the analyses were carried out in the R program (4.0.2 version) with the “TwoSampleMR” packager (0.5.4 version) and the “MendelianRandomization” package (0.6.0 version).Fig. 1 Overview of the Mendelian randomization study.

Panel A presents the fundamental assumptions of the MR design. Panel B shows that average total household income before tax (ATHIBT), subjective well-being (SWB), and regular smoking (RS) are affected by both childhood and adult body mass index (BMI). Panel C shows that childhood BMI plays a long-term effect on ATHIBT, whereas SWA and RS are affected by life-long obesity.

Fig. 1

2.2 GWAS data for exposure-phenotypes

For childhood BMI, we retrieved genetic information from the Early Growth Genetics (EGG) consortium for the primary analysis. Briefly, Felix et al. conducted a GWAS on childhood BMI, which comprised 47,541 children aged two to 10 years (49.4 % females) [24]. All employed children were European, and syndromic cases of obesity were excluded. Precisely, the phenotype “childhood BMI” was measured as a continuous variable, which was adjusted with age and sex and was transformed into standard deviation (SD) scores.

For adult BMI, we leveraged summary-level statistics from the GIANT consortium. Considering that the outcome phenotypes employed in this study were primarily based on the United Kingdom Biobank (UKB), we leverage the GWAS conducted by Locke et al., which did not contain UKB participants. To mitigate population stratification, we also utilized GWAS data from people of European descent only [25]. Thereby, 339,224 participants were included in this MR analysis.

2.3 GWAS data for outcome-phenotypes

In this study, phenotypes grouped into socioeconomic status included educational attainment (EA) and average total household income before tax. Years of schooling were measured for EA in Lee et al.’s GWAS (mean = 16.8 years, standard deviation = 4.2), involving 766,345 European participants with the 23andMe cohort excluded due to data privacy and protection [26]. The genetic information for the average total household income before tax came from the IEU consortium (https://gwas.mrcieu.ac.uk/). It was categorically ordered based on a 5-point scale (1 being less than £18,000, 2 being £18,000-£29,999, 3 being £30,000-£51,999, 4 being £52,000-£100,000 and 5 being greater than £100,000).

Four phenotypes were classified into neuropsychological status, including depressive symptoms, neuroticism, subjective well-being, and anxiety & stress-related disorders (ASRD). Among them, the GWAS statistics for depressive symptoms, neuroticism, and subjective well-being came from the research from Okbay and colleagues [27]. The genetic information for ASRD came from the research from Sandra et al. [28], which could be obtained from the iPSYCH consortium.

Depressive symptoms were majorly determined by two questions focusing on the frequency of feeling down, depressed or hopeless and the frequency of feeling little interest or pleasure in doing things over the past two weeks. There were five answers for chosen: “Not at all”, “Several days”, “More than half the days”, “Nearly every day” and “Do not know/Prefer not to answer”. Neuroticism was predominantly measured by scoring on the Eysenck Personality Inventory Neuroticism scale, which include 12 items. Subjective well-being was determined by two domains, including positive affect and life satisfaction. Positive affect was measured by two questions, including “During the past week, I was happy?” and “How would you rate your emotional wellbeing at present?”. Life satisfaction referred to a long-term assessment of an individual's life and was gauged by the question “How satisfied are you with your life as a whole?”. As for ASRD, individuals were identified by a psychiatrist in accordance with ICD-10 (F40.0-F41.9; F43.0-F43.9).

For addictive behaviors, we enrolled three phenotypes, including regular smoking, alcohol intake frequency, and cannabis use disorder (CUD). The GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN) consortium provided genetic information for regular smoking [29]. The IEU consortium provided effect estimates for alcohol intake frequency. For CUD, full GWAS summary data was downloaded from the PGC consortium [30]. Table 1 shows specific information for all the data sources.Table 1 Sources for the GWAS data used for Mendelian randomization analysis.

Table 1Phenotypes	PMID	Consortium	Ancestry	Sample size	Websites	
Obesity-related phenotypes	
Childhood BMI	33045005	EGG	European	47,541	http://egg-consortium.org/	
Adult BMI	25673413	GIANT	European	339,224	https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium	
Socioeconomic status	
Educational attainment	30038396	SSGAC	European	766,345	https://www.thessgac.org/	
Average total household income before tax	NA	UKB	European	397,751	https://gwas.mrcieu.ac.uk/	
Neuropsychological status	
Depressive symptoms	27089181	SSGAC	European	161,460	https://www.thessgac.org/	
Neuroticism	27089181	SSGAC	European	170,911	https://www.thessgac.org/	
Subjective well-being	27089181	SSGAC	European	298,420	https://www.thessgac.org/	
anxiety & stress-related disorders	31116379	iPSYCH	European	12,665 cases, 19,225 controls	https://ipsych.dk/en/about-ipsych	
Addictive behaviors	
Regular smoking	30643251	GSCAN	European	607,291	https://gwas.mrcieu.ac.uk/	
Alcohol intake frequency	NA	UKB	European	462,346	https://gwas.mrcieu.ac.uk/	
Cannabis use disorder	33096046	PGC	European	20,916 cases, 363,116 control	https://pgc.unc.edu/	
BMI, body mass index; EGG, early growth genetics; GIANT, The Genetic Investigation of Anthropometric Traits; SSGAC, Social Science Genetic Association Consortium; UKB, United kingdom Biobank; iPSYCH, Integrative Psychiatric Research; GSCAN, GWAS & Sequencing Consortium of Alcohol and Nicotine use; PGC, Psychiatric Genomics Consortium; NA, not applicable.

2.4 Genetic variant selection

We conducted rigorous SNPs filtration to obtain eligible SNPs to proxy childhood BMI and adult BMI. First, to obtain instrumental variables (IVs) tightly correlated with the exposures, we screened SNPs from the full GWAS set at P < 5 × 10−8, a threshold of genome-wide significance. Second, we pruned the SNPs in the 10,000 kb window using the most stringent parameters with linkage disequilibrium (LD) r2 < 0.001. Third, in the outcome full GWAS datasets, we retrieved and extracted the exposure-SNPs. To mitigate violation of MR assumptions, the SNPs were excluded if correlated with the outcome phenotypes at genome-wide significance. Fourth, we aligned the alleles of exposure and outcome SNPs and the SNPs with alleles being palindromic or incompatible were discarded. Fifth, we calculated F-statistics for the SNPs with the formula described elsewhere [31]. SNPs with insufficient strength (F < 10) were discarded to prevent bias from weak IVs. MR- Pleiotropy Residual Sum and Outlier (MR-PRESSO) was carried out ahead of MR estimation to identify and rule out outliers with potential pleiotropy finally [32]. The included SNPs for MR analysis were shown in Table S1∼S18.

2.5 Univariable Mendelian randomization (UVMR) analysis

For the primary analysis for causal inference, the random-effect inverse weighted variance (IVW) method was undertaken. As the most powerful method, IVW was typically used as the predominant approach in an MR study [33]. We used Benjamini-Hochberg corrected P values to limit the false discovery rate (FDR) to determine the significant IVW estimates (PFDR < 0.05) [34]. Besides, an online tool was applied to compute the power of the estimates of the IVW method (https://sb452.shinyapps.io/power/). A statistical power over 80 % means the MR estimate is sufficient in statistical power. For comparison, the causal effects were estimated using two alternative MR models: the MR-Egger regression and the weighted median (WM) method. The WM method supposes that at least half of instruments are valid, with the statistical power slightly less than IVW [35]. It served as a crucial complement of the IVW method as it typically provided more conservative estimates. MR-Egger regression supposes that SNPs are all invalid and has the least power among the three MR models [36]. As the method with the least statistical power, consistent estimate direction of MR-Egger with other MR models typically enhanced the dependability of the MR estimates.

Complementary sensitivity analyses based on distinct statistical assumptions were undertaken to verify the causal inference's robustness. The Cochran Q test of the IVW model was conducted to detect heterogeneity. Specifically, the IVW-derived Q should follow a χ2 distribution with Qdf (degrees of freedom) equal to the number of SNPs minus one. An IVW-derived Q-value with P < 0.05 indicated excessive heterogeneity, suggesting violation of the MR assumptions [37]. To alleviate the influence of heterogeneity, this work employed the random-effect IVW model as mentioned above. To ascertain the presence of horizontal pleiotropy, we utilized the intercept term of the MR-Egger regression [38]. Specifically, in the MR-Egger model, the intercept is not constrained to zero, and an intercept with P < 0.05 indicated potential pleiotropy existing in our study, suggesting the observed MR estimates might be biased and the causal inference should not be established. We also conducted Rucker's Q′ test to calculate Q−Q′. It suggests that the IVW model was more appropriate for causal inference than the MR-Egger regression model with the negligible difference of Q−Q′ [39] otherwise, the IVW model might be biased by invalid instruments (P < 0.05).

2.6 Multivariable Mendelian randomization (MRMR) analysis

Given that UVMR only determined the total impacts of BMI on the outcome phenotypes, MVMR was then employed to distinguish the influence of childhood and adult BMI. MVMR is the extension of UVMR, which is widely applied to assess the independent impact of the exposure after adjusting with covariates. Compared with UVMR that yields the total effect, this method could alleviate the impact of confounding to a greater extent.

Specifically, the outcome phenotypes affected by both childhood and adult BMI in UVMR analysis were further included for MVMR analysis. Similarly, three MR methods were used, including MV-IVW, MV-WM, and MV-Egger regression. Heterogeneity was evaluated with the Cochran Q test. In case when heterogeneity was detected, the MV-WM was used for causal inference, which has been described elsewhere. Similarly, to exam pleiotropy, the intercept term for MV-Egger regression was evaluated.

3 Results

3.1 UVMR results

UVMR detected strong evidence of the impacts of childhood BMI on several outcome phenotypes (Fig. 1B; Table 2). Specifically, genetically predicted higher childhood BMI was linked to lower average total household income before tax (β = −0.06, 95 % CI = −0.08 ∼ −0.03, P = 4.86 × 10−5) and subjective well-being (β = −0.07, 95 % CI = −0.12 ∼ −0.03, P = 1.74 × 10−3), but was correlated with an incremental tendency for regular smoking (β = 0.11, 95 % CI = 0.04–0.18, P = 1.52 × 10−3) and increased alcohol intake frequency (β = 0.15, 95 % CI = 0.10–0.21, P = 1.73 × 10−7). MR estimates across different models showed concordant estimations (Table 3). Additionally, there was scant evidence of the relationships of childhood BMI with EA, depressive symptoms, neuroticism, ASRD, or CUD (Table 2).Table 2 The Mendelian randomization estimates derived from the inverse variance weighted (IVW) method.

Table 2Outcomes	Childhood BMI	Adult BMI	
nSNP	Effect (95 % CI)	P	PFDR	Power	nSNP	Effect (95 % CI)	P	PFDR	Power	
Educational attainmenta	12	−0.01 (−0.03, 0.01)	0.38	0.49	18.2 %	65	−0.09 (−0.12, −0.06)	2.83×10−9	2.55 × 10−8	100 %	
Average total household income before taxa	13	−0.06 (−0.08, −0.03)	4.86×10−5	1.75 × 10−4	99.7 %	73	−0.09 (−0.12, −0.05)	1.21×10−6	5.45 × 10−6	100 %	
Depressive symptomsa	12	0.04 (0.0003, 0.07)	0.05	0.82	52.6 %	78	0.05 (0.01, 0.09)	0.01	0.02	69.4 %	
Neuroticisma	12	0.01 (−0.03, 0.05)	0.63	0.67	7.5 %	76	−0.03 (−0.08, 0.02)	0.20	0.30	32.5 %	
Subjective well-beinga	12	−0.07 (−0.12, −0.03)	1.74×10−3	4.47 × 10−3	99.8 %	75	−0.05 (−0.08, −0.01)	7.31×10−3	0.01	90.5 %	
anxiety & stress-related disorders	14	1.06 (0.89, 1.25)	0.53	0.63	9.8 %	74	1.06 (0.87, 1.30)	0.56	0.63	9.1 %	
Regular smokinga	10	0.11 (0.04, 0.18)	1.52×10−3	4.47 × 10−3	100 %	68	0.10 (0.04, 0.15)	2.13×10−3	4.79 × 10−3	100 %	
Alcohol intake frequencya	11	0.15 (0.10, 0.21)	1.73×10−7	1.04 × 10−6	100 %	69	0.25 (0.21, 0.30)	5.94×10−26	1.07 × 10−24	100 %	
Cannabis use disorder	15	1.01 (0.83, 1.24)	0.90	0.90	3.8 %	72	1.10 (0.91, 1.33)	0.33	0.46	37.5 %	
a The causal estimates were reported as raw beta since the outcome phenotypes were measured as continuous variables.

Table 3 Sensitivity analysis of the Mendelian randomization estimates.

Table 3Exposure	Outcomes	Weighted median	MR-Egger	Heterogeneity	Pleiotropy	Rucker Q′	
Effect (95 % CI)	P	Effect (95 % CI)	P	Cochran Q	P	Intercept	P	Q – Q′	P	
Childhood BMI	Educational attainmenta	−0.01 (−0.03, 0.02)	0.66	−0.02 (−0.10, 0.07)	0.68	15.75	0.15	0.0006	0.84	0.06	0.80	
Average total household income before taxa	−0.06 (−0.08, −0.03)	3.96 × 10−4	−0.01 (−0.11, 0.10)	0.90	9.33	0.67	−0.003	0.36	0.91	0.34	
Depressive symptomsa	0.04 (0.99, 1.09)	0.10	0.08 (−0.08, 0.25)	0.36	11.41	0.41	−0.003	0.59	0.35	0.55	
Neuroticisma	0.01 (−0.04, 0.06)	0.65	0.06 (−0.14, 0.25)	0.58	17.87	0.08	0.006	0.65	0.39	0.53	
Subjective well-beinga	−0.06 (−0.11, −0.01)	1.81 × 10−2	−0.09 (−0.31, 0.12)	0.42	24.93	0.01	0.001	0.87	0.07	0.79	
anxiety & stress-related disorders	1.02 (0.82, 1.27)	0.83	1.24 (0.61, 2.50)	0.56	11.11	0.60	−0.01	0.65	0.22	0.64	
Smoking initiationa	0.14 (0.07, 0.21)	1.22 × 10−4	0.17 (−0.09, 0.44)	0.24	21.41	0.01	−0.004	0.65	0.58	0.45	
Alcohol intake frequencya	0.13 (0.08, 0.18)	1.60 × 10−6	−0.06 (−0.24, 0.12)	0.54	29.12	0.001	0.01	0.04	11.17	8.32 × 10−4	
Cannabis use disorder	1.08 (0.87, 1.35)	0.49	1.17 (0.52, 2.62)	0.71	21.76	0.08	−0.01	0.73	0.21	0.64	
Adult BMI	Educational attainmenta	−0.08 (−0.11, −0.05)	3.07 × 10−6	−0.05 (−0.14, 0.04)	0.24	158.12	6.27 × 10−10	0.001	0.40	1.79	0.18	
Average total household income before taxa	−0.06 (−0.11, −0.02)	3.07 × 10−3	−0.04 (−0.13, 0.04)	0.32	139.67	3.09 × 10−6	0.001	0.26	2.50	0.11	
Depressive symptomsa	0.08 (0.03, 0.13)	1.81 × 10−3	0.07 (−0.02, 0.16)	0.15	121.03	0.001	0.001	0.66	0.31	0.58	
Neuroticisma	0.00 (−0.05, 0.05)	1.00	0.03 (−0.09, 0.15)	0.66	178.93	1.78 × 10−10	−0.001	0.30	2.56	0.11	
Subjective well-beinga	−0.05 (−0.10, −0.01)	0.03	−0.10 (−0.19, −0.02)	0.02	111.35	0.003	0.002	0.16	2.94	0.09	
anxiety & stress-related disorders	0.88 (0.67, 1.14)	0.33	0.68 (0.43, 1.09)	0.12	109.62	0.003	0.01	0.04	6.27	0.01	
Smoking initiationa	0.09 (0.01, 0.15)	0.02	0.05 (−0.09, 0.19)	0.53	188.62	1.78 × 10−13	0.001	0.49	1.37	0.24	
Alcohol intake frequencya	0.25 (0.19, 0.31)	3.46 × 10−16	0.28 (0.17, 0.39)	4.90 × 10−6	172.32	5.15 × 10−11	−0.001	0.57	0.84	0.36	
Cannabis use disorder	1.21 (0.93, 1.57)	0.15	1.07 (0.68, 1.68)	0.77	107.20	0.004	0.001	0.90	0.02	0.88	
a The causal estimates were reported as raw beta since the outcome phenotypes were measured as continuous variables.

IVW also detected that genetic liability to higher adult BMI was causally linked to fewer years of schooling (β = −0.09, 95 % CI = −0.12 ∼ −0.06, P = 2.83 × 10−9), average total household income before tax (β = −0.09, 95 % CI = −0.12 ∼ −0.05, P = 1.21 × 10−6), and subjective well-being (β = −0.05, 95 % CI = −0.08 ∼ −0.01, P = 7.31 × 10−3), but was associated with increased frequency of regular smoking (β = 0.10, 95 % CI = 0.04–0.15, P = 2.13 × 10−3) and alcohol intake (β = 0.25, 95 % CI = 0.21–0.30, P = 5.94 × 10−26) (Fig. 1B; Table 2).

In sensitivity analysis, Heterogeneity was detected in certain MR results. According to the Egger intercept, no horizontal pleiotropy was observed. Using Rucker's Q test, we noticed that the detected influence of childhood BMI on alcohol intake frequency derived from IVW might be invalid (Table 3).

3.2 MVMR results

Based on the results from UVMR and sensitivity analyses, we determined a set of outcomes causally affected by both childhood and adult BMI, including average total household income before tax, subjective well-being, and regular smoking (Fig. 1B). Therefore, MVMR was further conducted to distinguish the independent impact. As considerable heterogeneity was detected in MVMR, the WM method was recommended for causal inference. The intercepts of Egger regression suggested that there was no pleiotropic bias.

Upon adjusting with adult BMI, the WM method indicated that genetically predicted childhood obesity directly affected average total household income before tax (β = −0.057, 95 % CI = −0.105 ∼ −0.008, P = 0.02). For subjective well-being and smoking initiation, the effect of childhood BMI was significantly attenuated after adjustment of adult BMI (Fig. 1C; Table 4).Table 4 Results of multivariable Mendelian randomization.

Table 4Outcomes	Methods	Childhood BMI	Adult BMI	Heterogeneity P	Pleiotropy	
Effect	95 % CI	P	Effect	95 % CI	P	intercept	P	
ATHIBTa	MV-IVW	−0.042	(-0.087, 0.003)	0.07	−0.044	(-0.100, 0.130)	0.13	<0.001	−0.001	0.237	
MV-median	−0.057	(-0.105, −0.008)	0.02	−0.015	(-0.081, 0.052)	0.67	
MV-Egger	−0.031	(-0.079, 0.018)	0.21	−0.034	(-0.093, 0.025)	0.26	
SWBa	MV-IVW	−0.018	(-0.068, 0.031)	0.46	−0.045	(-0.107, 0.017)	0.16	<0.001	0.001	0.182	
MV-median	−0.015	(-0.072, 0.043)	0.62	−0.042	(-0.117, 0.032)	0.27	
MV-Egger	−0.032	(-0.084, 0.021)	0.24	−0.059	(-0.123, 0.006)	0.08	
RS	MV-IVW	1.030	(0.950, 1.120)	0.46	1.070	(0.970, 1.190)	0.15	<0.001	0.001	0.513	
MV-median	1.020	(0.920, 1.130)	0.74	1.070	(0.970, 1.190)	0.18	
MV-Egger	1.020	(0.930, 1.110)	0.68	1.060	(0.960, 1.180)	0.25	
ATHIBT, average total household income before tax; SWB, subjective well-being; RS, regular smoking; BMI, body mass index; CI, confidence interval.

a The causal estimates were reported as raw beta since the outcome phenotypes were measured as continuous variables.

No evidence was observed for the isolated impact of adult BMI on average total household income before tax, subjective well-being, or smoking initiation (Table 4).

4 Discussion

4.1 Main findings

In this MR research, we explored the causal impacts of childhood and adult BMI on socioeconomic status, neuropsychological status, and addictive behaviors. While the relationships between adult BMI and health outcomes were well-documented, the role of childhood BMI was limited. Univariable analyses demonstrated that average total household income before tax, subjective well-being, and regular smoking were affected by both childhood and adult BMI. Multivariable analyses indicated little evidence of a direct impact of childhood BMI or adult BMI on subjective well-being or regular smoking, suggesting that the decreased subjective well-being and increased tendency to regular smoking were attributed to life-long obesity. Besides, the MVMR identified a direct effect of childhood BMI but not adult BMI on average total household income before tax. This suggested that the estimated impact of between adult BMI on income was facilitated by childhood BMI and that childhood BMI exerted a persistent effect on average total household income before tax.

4.2 Contribution of life-long obesity

Existing evidence of the relationships between obesity and health-related consequences is broad. Especially in the adult population, the contribution of obesity to developing adverse health outcomes is well-documented. However, the pathways from obesity to health outcomes remain further explored for several reasons. First, conventional observational studies were subjective to methodologic defects and hence were vulnerable to residual confounding and reverse causality. Second, compared with adult obesity, childhood obesity caught much less attention in terms of its contribution to disease development. Third, few studies distinguished the separated influence of childhood obesity and adult obesity. As estimated, childhood obesity is growing at almost twice the rate of adults [40]. It has also been reported that childhood obesity often persisted in adulthood [41], and traditional observational studies often failed to disentangle the isolated impact of childhood and adult BMI. Whether health outcomes were attributed to solely childhood obesity or adult obesity, or life-long obesity, is not yet determined.

Though obesity has been an established risk factor for various health outcomes, elucidating the exact effect of time-dependent obesity maintenance is crucial for facilitating more specific strategies of disease prevention. Rogers et al. found that people becoming obese at a later age had a lower risk of physical difficulties compared with those becoming obese from an early age, suggesting that physical difficulties were more likely attributed to the accumulated adverse effects of persisting obesity from earlier ages [42]. In the study of Power et al., adult BMI remained a significant direct effect on cardiovascular events after accounting for childhood BMI, indicating that the later onset of obesity still exerted a considerable influence on cardiovascular diseases [43]. On the other hand, the effect of childhood BMI vanished when accounting for adult BMI, implying that childhood obesity did not maintain a persistent effect on the risk of cardiovascular diseases [43]. Similar conclusions were yielded in other health outcomes, like major depressive disorder and asthma [44,45]. In the present study, UVMR show strong evidence of the associations between obesity and decreased subjective well-being and increased propensity to smoke regularly. All the three MR models yielded similar results. Specifically, though the WM method assumes less than half of IVs are invalid and the Egger regression assumes none of the IVs are eligible, they still obtain consistent and comparable estimates, suggesting the causal inference was robust. Though heterogeneity was detected, application of the random-effect IVW model could still balance and mitigate overall heterogeneity and obtain a more conservative estimate. Egger intercept revealed no evidence of pleiotropic bias, and the Rucker framework further indicated the IVW model was less likely to be biased. MVMR further showed no direct effect of BMI was observed in children or adults. This suggests that decreased subjective well-being or increased propensity to smoke regularly may be due to the cumulative and persistent effects of lifelong obesity. From the perspective of formulating public health strategies, these finding highlighted that the detrimental effect of childhood obesity could be reversed by curbing overweight persisting into adulthood, and the exact timing node should be determined in future investigations.

4.3 Contribution of childhood obesity

The current study reported a direct hazardous impact of childhood BMI but not adult BMI on total household income before tax, indicating a far-reaching influence of adiposity from childhood on future income independent of adult obesity. The long-term impact of childhood obesity has been identified in other health-related outcomes. For example, O'Nunain et al. detected a long-term impact of childhood obesity on heart structure upon accounting for adult obesity [46]. Whereas in our study, we detected a direct influence of childhood adiposity on later household income. This is the initial MR study that establishes the direct impact of childhood adiposity on economic conditions, to our best acknowledgement. As mentioned above, the previous study design failed to disentangle the isolated impact of childhood and adult adiposity owing to methodologic defects, and thus the exact impact of distinct obesity trajectories across the life course could not be explored. Combined with previous findings, we believe that obesity management should be initiated from the childhood period to mitigate the risk for some unfavorable outcomes in later life, as childhood obesity did initiate a long-term impact independent of adult obesity on certain adverse outcomes, like worse economic conditions discovered in our study.

4.4 Possible explanations for the adverse impact of obesity

Generally, we presented evidence that obesity could causally lead to unfavorable consequences, like smoking initiation, poor subjective well-being, and lower household income. In terms of the underlying mechanisms, “stigma” might be a plausible explanation for the hazardous influence of obesity on socioeconomic and psychological, and behavioral characteristics [47].

Obesity stigma is widespread. Discrimination towards obese people might in part be ascribed to the prevailing opinion that obesity is a personal choice that one could voluntarily choose to eat less and exercise more to reverse obesity [47]. Therefore, lack of self-discipline, weak will, gluttony, and laziness, are common labels tightly stuck to people with overweight. These negative impressions typically motivated considerable harm to individuals. Previously, studies have reported that weight stigma has contributed significantly to worse social outcomes, poor mental health, and increased risk of substance use [48,49]. Notably, our study separated the effect of childhood and adult obesity using an MVMR design and noticed a heterogeneity for distinct health outcomes regarding the susceptibility to be affected by obesity, for which further mechanisms should be investigated in future studies.

4.5 Strengths and limitations

In this investigation, the application of MVMR is the major strength. MVMR is the extension of the conventional MR, which has been widely leveraged to detect the direct effect of exposures to the outcomes and access more targeted strategies for disease prevention. Using MVMR, we disentangled childhood obesity from adult obesity concerning its impact on health outcomes. The findings would help clinicians understand distinct effects of obesity in different period and make personal obesity management for patients. The findings also shed light on public health strategies formulating, which might imply policymakers to formulate more targeted policies in obesity management based on the distinct effect of obesity in various life courses. We believe this is critical as we did find the detrimental effect of obesity on certain adverse social outcomes that would largely increase the health and economic burden of the society and these might be reversed by management of obesity through more targeted strategies. Thereby, this work might also have broad implication on improving socioeconomic burden, mental health, and adverse social behaviors, through intervention of obesity. Second, by integrating results from three different MR models and an array of statistical methods for sensitivity analysis, including heterogeneity detection based on Cochran's Q test, horizontal pleiotropy detection based on the Egger intercept test, and the Rucker framework to exam whether the IVW was biased, we could infer more reliable causalities. Meanwhile, this work was based on high-quality and large-scale GWAS data sources, including consortium-based GWAS like EGG, GIANT, and so on, which might enhance the reliability of the MR results. Third, we limited the participants to European descent, and this would largely mitigate population stratification and improve the validity and reliability of the causal inference.

Several limitations should be noticed. First, it should be noted that time heterogeneity commonly exists concerning the impacts of disease risk factors. Likewise, the susceptibility to the effects of obesity could be distinct in different periods of one's lifespan. Though our study separated the effect of childhood and adult obesity, future studies should focus on identifying the exact time points when the detrimental effects of obesity become immutable. Second, self-report bias might exist in some phenotypes, like subjective well-being, which might result in biasing the causal inference. Application of GWAS phenotypes determined by biological markers would largely mitigate this kind of bias when they are publicly available in future. Third, though we delved to the independent impact of childhood and adult adiposity on psychosocial consequences and addictive behaviors in this work, the latent mechanisms remain further investigating though it is out of the scope of this work. Further work should focus on the bio-functional pathways underlying these associations. Meanwhile, though the MR is powerful in causality inference and complementary sensitivity analyses were applied in this work, residual confounding could not be fully ruled out. Interaction among the outcomes likes smoking, drinking, and depression, might also increase the risk of overestimating about the effect of obesity. Fourth, some results in this work showed insufficient statistical power (<80 %), which might be partially explained by a relatively small effect size or sample size. Future studies using GWAS with larger sample sizes should be performed to validate our result.

5 Conclusions

In summary, genetically predicted obesity was causally associated with certain adverse social outcomes. Some of them might be influence by the long-term effect of childhood obesity, whereas others were facilitated by the accumulated effects of life-long obesity. This distinguished susceptibility towards obesity across various social outcomes would imply researchers to develop more targeted prevention strategies by obesity management.

Data and code availability

All the data generated in this was is presented in the main text and supplementary materials. The GWAS data leveraged in this research is publicly available, with the website where the data deposited shown in Table 1. The codes used in this work are presented in the Supplementary material.

Ethics approval

Only publicly available GWAS data was employed in the present Mendelian randomization study, and the ethics approval has been obtained in the original GWAS research.

Funding

This work was supported by Research Foundation of Guangzhou Women and Children's Medical Center for Clinical Doctor (NO. 2021BS012 ) and Guangzhou Science and Technology Program (NO. 2023A04J1895 )

CRediT authorship contribution statement

Jiahao Cai: Writing – original draft, Supervision, Project administration, Methodology, Conceptualization. Lei Zhao: Visualization, Software, Methodology, Data curation, Conceptualization. Nanfang Li: Visualization, Formal analysis, Data curation. Zijin Xiao: Validation. Guiwu Huang: Supervision, Methodology, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Multimedia component 2

Multimedia component 2

Acknowledgement

We appreciate all the researchers for making GWAS summary-level statistics publicly available.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36835.
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