
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39252271
MD-D-24-07960
00058
10.1097/MD.0000000000039548
3
4300
Research Article
Observational Study
Causal link between childhood obesity and adult osteoporosis: An investigation through Mendelian randomization
https://orcid.org/0009-0002-1290-6152
Ying Dawen MM a*
Ying Minzheng BD minzhongying@126.com
b
a Department of Orthopaedics, Yueyang Hospital Affiliated of Hunan Normal University, Yueyang, China
b Health Management Center, Yueyang Hospital Affiliated of Hunan Normal University, Yueyang, China.
* Correspondence: Dawen Ying, Department of Orthopaedics, Yueyang Hospital Affiliated of Hunan Normal University, Yueyang 414000, China (e-mail: yingdawen@foxmail.com).
06 9 2024
06 9 2024
103 36 e3954812 7 2024
11 8 2024
13 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

The intricate link between childhood obesity and adult osteoporosis has been a subject of numerous clinical inquiries, yet the genetic underpinnings of this association remain enigmatic. Our research aims to unravel the association between adult osteoporosis and childhood obesity using genome-wide association study data for Mendelian randomization (MR) analysis. Utilizing a pool of single-nucleotide polymorphism data associated with childhood obesity obtained from a previous genome-wide association study report involving a study population of 13,848 people in Europe, alongside data of adult osteoporosis sourced from Neale Lab (5266 cases and 331,893 controls). Various methods for MR were used in our research, including weighted mode, simple mode, weighted median, MR-Egger, and the inverse-variance weighted (IVW). We also used Cochran Q test of IVW to assess for heterogeneity, MR-Egger intercept and MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) analysis for pleiotropy, and leave-one-out analysis for the result stability. The instrumental variables associated with 11 single-nucleotide polymorphisms were selected. MR analyses unveiled a noteworthy link between genetically forecasted childhood obesity and the onset of adult osteoporosis based on the odds ratio, 95% confidence interval, and P-value from the results of IVW, MR-Egger, weighted median: simple mode, and weighted mode analyses. No significant heterogeneity was found by the assessment using MR-Egger and IVW. Similarly, there was no indication of pleiotropy based on the MR-PRESSO and MR-Egger analyses. Leave-one-out analysis confirmed the stability of the results. Our research suggests that childhood obesity, as predicted by genetic factors, may pose a significant risk for the development of osteoporosis in adulthood.

childhood obesity
genetic variants
Mendelian randomization
osteoporosis
single-nucleotide polymorphisms
OPEN-ACCESSTRUE
==== Body
pmc1. Introduction

As economic globalization accelerates and living standards continue to improve, the incidence of childhood obesity is still steadily increasing. Since 1980, the global rate of childhood obesity has more than doubled,[1] presenting a growing public health concern. Extensive research has highlighted the adverse effects of obesity on children’s organ systems, which include heightened risk of respiratory disorders, cardiovascular diseases, and type 2 diabetes in later life.[2]

Osteoporosis, a chronic metabolic bone disease that causes decreased bone strength, microarchitectural deterioration of bone tissue, and diminished bone mass, leads to greater susceptibility to fractures.[3] The interplay between obesity and osteoporosis is intricate. On the one hand, increased body weight stimulates bone turnover and fosters the accumulation of bone mineral content.[4] On the other hand, excessive fat accumulation induces metabolic irregularities, alters hormone levels, and triggers inflammatory processes, potentially disrupting bone metabolism and causing bone loss.[5,6] Although much attention has been directed towards the prevention of childhood obesity and adult osteoporosis, the mechanisms underlying their relationship remain relatively unexplored. Emerging evidence suggests that childhood obesity could exert adverse effects on adult bone health, potentially predisposing individuals to osteoporosis due to impaired bone development.[7]

Previous investigations into the link between childhood obesity and osteoporosis have predominantly relied upon observational study designs, which have thus been constrained by potential inherent limitations such as confounding variables related to lifestyle and health conditions. Randomized controlled trials, despite being regarded as the gold standard for establishing causality, often demand substantial time and resources, rendering them impractical for numerous research scenarios. Mendelian randomization (MR) offers a methodological alternative for elucidating causal associations. By utilizing genetic variants as instrumental variables (IVs), MR allows for the assessment of causal effects between outcomes and exposure factors, free from confounding biases.[8]

Given the random distribution of genetic variants, which remains consistent across diseases, MR transcends the limitations typically encountered in observational studies. Thus, in our investigation, we employed a two-sample MR approach to unravel the potential causal relationship between childhood obesity and the risk of adult osteoporosis. MR effortlessly circumvents the typical limitations of the consistent random distribution of genetic variants across diseases that are encountered within observational studies.

Therefore, in our study, we opted to use a two-sample MR strategy to uncover the potential causal association between childhood obesity and susceptibility to adult osteoporosis.

2. Materials and methods

Our study hinged upon a MR design, utilizing publicly accessible datasets sourced from genome-wide association studies (GWAS) to scrutinize the interplay between risk factors and diseases. In this framework, genetic variation acts as an IVs, facilitating causal inference. Mendelian-based randomization methodologies address unmeasured confounding variables, thus enhancing the strength and reliability of causal conclusions. The study design relied upon 3 pivotal assumptions: firstly, that genetic variation is strongly associated with the exposure being investigated; secondly, that genetic variation remains independent of other potential confounders; and thirdly, that genetic variation exclusively influences the outcome through the examined exposure. We obtained findings from institutional review board approved studies using web-accessible data, thus negating the requirement for additional ethics clearance. Employing a two-sample MR method,[9] we explored the causal connection between childhood obesity and the likelihood of adult osteoporosis (see Fig. 1).

Figure 1. MR overview and assumptions. Three key assumptions related to the validity of MR. Assumption 1: IVs related to SNPs must be reliably essential to the exposure under investigation. Assumption 2: There is no any link between potential confounding variables and the selected IVs. Assumption 3: SNPs utilized as IVs should only impact the risk of the disease via the exposure being studied, without affecting any alternative pathways. IVs = instrumental variables; MR = Mendelian randomization; SNPs = single-nucleotide polymorphisms.

2.1. Data source

Our investigation relied on data sourced from 2 esteemed databases: the Neale Lab and the IEU OpenGWAS database (accessible at gwas.mrcieu.ac.uk). Within the second database, the childhood obesity dataset, provided by the early growth genetics Consortium under ID ieu-a-1096, includes European children aged 2 to 18 years. The research involved 5530 children classified with obesity (body mass index [BMI] ≥ 95th percentile) and 8318 control children with a BMI < 50th percentile, encompassing both genders. This comprehensive study analyzed 2442,739 single-nucleotide polymorphisms (SNPs).[10]

Additionally, osteoporosis data were obtained from the Neale Lab under ID Ukb-b-87 within the UK Biobank, featuring a diverse European cohort of 337,159 individuals aged 40 to 69 years, encompassing both sexes. The dataset relies on self-reported physician diagnoses and includes 5266 cases of osteoporosis and 331,893 unaffected controls. Genetic information for 10,894,596 SNPs was analyzed.[11]

To pinpoint those IVs strongly linked to adult osteoporosis, we applied stringent (P < 5 × 10−8) and relaxed (P < 5 × 10−6) thresholds, as per previous research, when examining the influence of childhood obesity on adult osteoporosis.[12,13] We guaranteed the choice of autonomous instruments by specifically choosing SNPs that were not correlated (r2 < 0.001) and were positioned a minimum of 10,000 kb apart, thereby minimizing the likelihood of linkage disequilibrium between them.

These chosen SNPs underwent cross-referencing with the outcome GWAS dataset and further refinement via the task of “harmonise_data” to align outcome datasets and exposure. Moreover, potential confounding factors were identified and mitigated through inquiries on the PhenoScanner website.

Evaluation of the IV strength was carried out utilizing the F-statistic, with a retention threshold set at 10.[14] This threshold was crucial for ensuring the robustness of the correlation between exposure and IVs, thereby minimizing instrumental bias in the MR analysis. Detailed information regarding the ultimately identified IVs is presented in Table 1.

Table 1 The detailed information of finalized SNPs in exposure and outcomes.

			Exposure (childhood obesity)	Outcome (adult osteoporosis)		
SNP	EA	OA	Beta	SE	P value	Beta	SE	P value	F	
rs13130484	T	C	0.143	0.027	1.30E‐0.7	4.38E‐04	3.04E‐04	.150	27.8	
rs17697518	T	C	0.186	0.039	1.85E‐0.6	2.00E‐04	4.51E‐04	.656	22.7	
rs4833407	A	C	0.123	0.027	3.88E‐06	‐1.86E‐04	3.06E‐04	.543	21.4	
rs4854344	T	G	0.245	0.035	3.22E‐12	9.37E‐04	3.98E‐04	.018	48.5	
rs4864201	C	T	‐0.136	0.028	1.41E‐06	8.44E‐05	3.15E‐04	.789	23.3	
rs571312	A	C	0.199	0.031	1.25E‐10	3.11E-04	3.54E-04	.380	41.3	
rs6752378	A	C	0.170	0.026	1.05E‐10	‐1.59E‐04	3.01E‐047	.596	41.9	
rs7138803	A	G	0.167	0.027	6.50E‐10	9.92E‐04	3.12E‐04	.001	38.1	
rs9299	T	C	0.134	0.028	1.91E‐06	7.02E‐04	3.16E‐04	.026	22.7	
rs9568856	A	G	0.191	0.039	1.36E‐06	6.32E‐04	4.50E‐04	.160	23.4	
rs9941349	T	C	0.198	0.027	1.16E‐13	7.97E‐04	3.05E‐04	.009	54.9	
EA = effect allele, OA = other allele, SE = standard errors.

2.2. Statistical analysis

In our statistical examination, we immersed ourselves in unraveling the intricate interplay between the susceptibility to adult osteoporosis and childhood obesity through a two-sample MR framework. At the heart of our analysis lay the inverse variance weighted (IVW) method, which is esteemed for its proficiency in unveiling causal relationships. Its core assumption relies on the lack of horizontal pleiotropy or the validity of all SNP contribution effects,[15] making IVW indispensable for elucidating causal pathways in our inquiry.[16]

To bolster our conclusions, we augmented IVW with a weighted mode, simple mode, weighted median, and MR-Egger techniques. While MR-Egger offers valuable insights, its focus on effect size and direction over statistical significance necessitates cautious interpretation due to its diminished statistical power.[17]

To ensure the robustness and credibility of our findings, we conducted an exhaustive range of sensitivity analyses in our research. These analyses encompassed evaluations for heterogeneity, pleiotropy, and the leave-one-out (LOO) method.

We scrutinized heterogeneity utilizing both the MR-Egger and IVW methods, which were included in the Cochran Q statistic as a quantifiable measure.[18] We utilized random-effects IVW to effectively address heterogeneity, thus ensuring the integrity of our results.

To assess horizontal pleiotropy, we primarily relied upon MR-PRESSO global analysis and the MR-Egger intercept test. The second method facilitated the estimation of potential horizontal pleiotropy by computing the intercept derived from linear regression analysis,[19] thus gaining valuable understanding of the potential existence of any confounding factors. We also used the Globle test of MR-PRESSO analysis for evaluating the overall pleiotropy of the study and identifying potential outlier SNPs that may exhibit horizontal pleiotropy.[20] In our study, we established a significance threshold (P < .05) to detect potential instances of heterogeneity or horizontal pleiotropy. This threshold guided our result interpretation, aiding in the identification of any factors that might introduce bias into our analysis.

Additionally, we utilized the LOO method as a method for evaluating sensitivity to assess the resilience of our IVW estimates. This approach involved systematically excluding each SNP from the MR analysis and recalculating the IVW estimate accordingly. By observing whether the exclusion of any particular SNP significantly altered the combined IVW estimate, we could evaluate the impact of individual SNPs on our overall conclusions.[21] Consistency in IVW estimates following the exclusion of each SNP suggested robustness, indicating that our findings were not disproportionately influenced by any single SNP.

Furthermore, R software (version 4.1.0) was used for all data analyses in this study.

3. Results

3.1. Impact of childhood obesity on adult osteoporosis

To broaden our scope in the MR analysis, a relaxed threshold was established (P < 5 × 10−6), by which more SNPs associated with childhood obesity can be captured. Following the exclusion of specific SNPs for various reasons, such as being palindromic or confounding, we refined our selection to 11 SNPs. Importantly, each of these SNPs demonstrated robust instrumental strength, with an F statistic value between 21 and 48 (all surpassing the threshold of 10), thus reinforcing the credibility of our instruments.

Utilizing 5 distinct MR techniques, including the weighted median, simple mode, weighted mode, MR-Egger, and IVW, we extensively explored the causal relationship between childhood obesity and susceptibility to adult osteoporosis (see Fig. 2 for reference). The IVW analysis unveiled a positive correlation between adult osteoporosis and childhood obesity (odds ratio [OR] = 1.002, 95% CI 1.001–1.003, P = 6.531E-04). Consistent results surfaced from the weighted mode (OR = 1.003, 95% CI 1.001–1.006, P = .035) and the weighted median (OR = 1.003, 95% CI 1.001–1.004, P = 2.67E‐04) methods. Although the simple mode approach did not demonstrate a significant association statistically, it neared significance (OR = 1.003, 95% CI 1.001–1.006; P = .056). However, the MR-Egger analysis failed to establish a significant causal relationship (OR = 1.006, 95% CI 0.999–1.013; P = .12) (see Fig. 3). Figure 4 provides comprehensive insight into the causal effects of individual genetic variants on adult osteoporosis.

Figure 2. The causal effect of childhood obesity on the risk of adult osteoporosis. A scatter plot visually represents the causal impact of childhood obesity on the risk of adult osteoporosis across various individuals or populations, depicting causal estimates derived from the 5 MR methods mentioned in the Results section. Each method’s causal estimate is represented by a line fitted through these data points, indicating the strength and direction of the causal effect inferred by the respective MR method. MR = Mendelian randomization.

Figure 3. Odds ratios with corresponding 95% CI of the effect estimates associated with childhood obesity and adult osteoporosis. In this figure, the odds ratios indicate the likelihood of developing osteoporosis in adulthood among individuals with childhood obesity compared to those without. The 95% CI furnish a spectrum wherein the genuine effect estimate is anticipated to be situated. This graphical representation allows for a visual assessment of the strength and precision of the interplay between childhood obesity and adult osteoporosis, helping the interpretation of the impact of obesity during early life stages on bone health in later years. CI = confidence interval.

Figure 4. The effect of SNPs on the risk of adult osteoporosis. A forest plot visually represents the causal effect estimates for each SNP and their respective confidence intervals, where a horizontal line represents one type of SNP, with a marker indicating the point estimate, positioned along the line to signify the magnitude of the effect. SNPs to the right of the null line suggest a potential causal effect, indicating an elevated risk of adult osteoporosis, while those to the left imply a negative effect. The width of the confidence interval reflects the precision of the estimate, with narrower intervals indicating greater precision. Researchers can utilize the plot to identify SNPs with significant causal effects and assess their consistency across different genetic variants, facilitating the interpretation of genetic impacts on the susceptibility to osteoporosis and guiding further investigation. SNPs = single-nucleotide polymorphisms.

Our sensitivity analyses have provided additional insights into the reliability and resilience of our conclusions. Notably, both the IVW (Q = 13.652, P = .133) and MR-Egger (Q = 15.308, P = .1212) methods revealed no evidence of heterogeneity, indicating coherence across different analytical approaches. Additionally, the MR-Egger intercept test for evaluating horizontal pleiotropy did not show significant results statistically (P = .323), suggesting the absence of horizontal pleiotropy. Similarly, the MR-PRESSO analysis detected no outliers, and the global test did not identify significant pleiotropy (P = .167). Moreover, the MR LOO sensitivity analysis further validated the stability of our results. The analysis revealed that removing any individual SNP could not substantially impact the causal effect, as depicted in Figure 5. This underscores the robustness of our results against potential influences from individual SNPs.

Figure 5. MR leave-one-out sensitivity analysis for childhood obesity on adult osteoporosis. This analysis assesses whether the comprehensive effect of the remaining SNPs is consistent with the main effect after removing 1 SNP at a time. This method can be used to determine the robustness of the results.

4. Discussion

Our findings based on the analysis using the two-sample MR approach shed light on the causal association between childhood obesity and the susceptibility to adult osteoporosis. Our research showed a remarkable elevated risk of adult osteoporosis associated with childhood obesity, highlighting the potential long-term consequences of early-life adiposity on bone health. Although the ORs observed are relatively modest, they hold statistical significance, underscoring the clinical and public health significance of our findings at both individual and population scales. Given the worldwide increase in childhood obesity, even a slight elevation in risk can aggregate across extensive populations, potentially amplifying the public health impact of osteoporosis in adulthood.

Importantly, our sensitivity analyses further strengthened the robustness of these findings, thereby affirming the reliability of our conclusions. These results carry significant implications for public health interventions, emphasizing the critical role of weight management interventions in childhood. By addressing obesity in early life, we could potentially mitigate the risk of developing osteoporosis later in adulthood. Moreover, our study provides genetic evidence that supports understanding of the underlying pathogenesis of adult osteoporosis, thereby advancing our understanding of the intricate interplay between genetic factors and environmental influences on bone health.

A wealth of research has underscored that childhood obesity not only impacts children’s immediate health but also signals a heightened risk of developing diseases in adulthood. Childhood obesity has been found to be linked to various adult diseases, including hypertension, hyperlipidemia, type 2 diabetes, and asthma.[2] A study conducted in Britain found a link between childhood obesity and elevated risk of dysglycemia and stroke in adulthood.[13] Similarly, another investigation highlighted the strong interplay between childhood obesity and heightened risk of colorectal cancer, kidney cancer, endometrial cancer, and esophageal cancer in adults.[22]

Numerous investigations have delved into the nexus between obesity and osteoporosis, yet a dearth of population-centric evidence addresses the repercussion of childhood obesity on adult osteoporosis. While antecedent studies imply that obesity augments mechanical loading and may confer a protective shield for bones,[23] it is noteworthy that bone density undergoes augmentation only within a delimited spectrum of BMI. Conversely, when BMI expands beyond a certain threshold, bone density experiences a decline.[24]

The formative years encompassing preschool and adolescence wield considerable influence over peak adult bone mass, laying down the bedrock for future bone health.[25] Alas, contemporary trends indicate a shift towards sedentary lifestyles among children, marked by diminished outdoor activities and an upsurge in screen time pursuits. The rising prevalence of childhood obesity dovetails with this sedentarism, precipitating a reduction in dynamic load on bones, thereby predisposing those children to fragility in adulthood.[26] Observational studies have underscored the correlation between childhood obesity and compromised bone density, particularly among girls[27] and those with abdominal adiposity,[28] portending a substantial impact on osteoporosis development in later life.

Our study constitutes a pioneering endeavor in exhaustively probing the causal effect of childhood obesity on adult osteoporosis. Plausible mechanisms underpinning this relationship encompass hormonal disruptions induced by childhood obesity,[29] differentiation of mesenchymal stem cells into fat cells,[7] and the release of inflammatory factors from adipose tissue within bone marrow.[30] Furthermore, diminished physical activity, inherent to childhood obesity, poses a formidable impediment to optimal bone formation.[31]

Our study boasts several notable strengths. The application of MR enables us to delve into the causal link between exposure and outcome using genetic data, thereby circumventing issues such as reverse causation and confounding factors. Moreover, this approach circumvents the formidable costs and constraints associated with randomized controlled trials, including small sample sizes and ethical considerations. Additionally, the utilization of a vast sample size and closely correlated SNPs enhanced our ability to discern causal effects with heightened precision.

Nonetheless, our study is not devoid of limitations. The scarcity of suitable SNPs compelled us to relax our significance criteria, potentially introducing IV bias. Moreover, the absence of detailed subgroup data hindered our exploration of the gender- or age-specific nuances in the causal relationship between obesity and osteoporosis. Furthermore, our reliance on GWAS data exclusively of European populations necessitates caution in generalizing our findings to other demographic groups, therefore warranting further investigation.

5. Conclusion

In summary, our genetics-focused inquiry, leveraging publicly available databases and extensive GWAS data, with analysis using a two-sample MR method, underscores the genetic underpinnings of the interplay between enhanced susceptibility to osteoporosis and childhood obesity. Our findings underscore the urgency of early interventions targeting childhood obesity to mitigate the risk of adult osteoporosis. Future research endeavors should prioritize elucidating the intricate biological mechanisms that mediate this association.

Acknowledgments

We thank Medjaden Inc. for scientific editing of this manuscript.

Author contributions

Conceptualization: Dawen Ying, Minzheng Ying.

Data curation: Dawen Ying.

Formal analysis: Dawen Ying.

Funding acquisition: Minzheng Ying.

Investigation: Dawen Ying.

Methodology: Dawen Ying.

Project administration: Dawen Ying.

Resources: Dawen Ying.

Software: Dawen Ying.

Supervision: Minzheng Ying.

Validation: Minzheng Ying.

Visualization: Dawen Ying.

Writing – original draft: Dawen Ying.

Writing – review & editing: Dawen Ying, Minzheng Ying.

Abbreviations:

BMI body mass index

GWAS genome-wide association study

IVW inverse-variance weighted

LOO leave-one-out

MR Mendelian randomization

ORs odds ratios

SNP single-nucleotide polymorphism.

The participants provided their written informed consent to participate in this study.

Ethical approval was not required for the studies involving humans because the human studies did not require additional ethical approval, the publicly available GWAS summary datasets were collected in studies that had already received the necessary ethical approval. The studies were conducted in accordance with the local legislation and institutional requirements.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

How to cite this article: Ying D, Ying M. Causal link between childhood obesity and adult osteoporosis: An investigation through Mendelian randomization. Medicine 2024;103:36(e39548).
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