Association of birth weight, childhood obesity, and age at menarche with ovarian function: an integrated Mendelian randomization study and bioinformatics analysis

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Methods We mainly uses the inverse variance weighted (IVW) method for analysis, conducting heterogeneity, horizontal pleiotropy and MR Steiger test to evaluate the robustness of the results and the strength of causal relationships. Finally, preliminary bioinformatics analysis was conducted to explore the underlying biological mechanisms. Results Causal associations were found between childhood obesity, age at menarche and ovarian function, with a positive association between childhood obesity and ovarian dysfunction and a negative association between age at menarche and ovarian function. A total of 10 hub genes were identified, which are interconnected in an interaction network and play a role in the synthesis and secretion of lipids and parathyroid hormone. Conclusions Our study genetically confirms the causal association between childhood obesity, age at menarche and ovarian function; childhood obesity increases the risk of primary ovarian failure, and the later the age at menarche, the lower the risk of ovarian failure, which may be related to alterations in metabolic pathways such as intracellular receptors-mediated alterations in lipids and hormones. Biological sciences/Computational biology and bioinformatics/Functional clustering Health sciences/Endocrinology/Endocrine system and metabolic diseases Health sciences/Diseases/Nutrition disorders/Obesity Biological sciences/Genetics/Medical genetics birth weight childhood obesity age at menarche ovarian function mendelian randomization integrated bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Ovarian dysfunction means that the number of eggs retained in a woman's ovaries fails to meet the threshold criteria, which leads to a decrease in female fertility [ 1 ] . Primary ovarian failure (POF) refers to ovarian failure that occurs before the age of 40 years and is characterised by amenorrhoea lasting 4 months or more, accompanied by a decrease in oestrogen and an increase in gonadotropin levels [ 2 ] . Ovarian reserve and ovarian aging have become a key area of research in current clinical work. There is a hypothesis suggesting that birth weight is influenced by genetic factors from both parents as well as the intrauterine environment [ 3 ] . Further research has indicated that girls born with small for gestational age (SGA) tend to have a reduced ovulation rate during pregnancy [ 4 ] , but limited sample sizes may introduce bias into these findings. Childhood obesity is a significant public health concern, with reported rates as high as 12.7% in the United States [ 5 ] . Previous clinical studies and meta-analyses have shown that adult obesity has a negative impact on ovarian function [ 6 ][ 7 ] . However, there has been limited research investigating whether childhood obesity similarly affects ovarian function. Age at menarche can serve as a reflection of a female's hormonal levels during puberty and is influenced by genetic, epigenetic, and environmental factors [ 8 ] . Some studies have shown no correlation between age at menarche and ovarian function [ 9 ] , but the number of studies is small and traditional observational studies may have methodological flaws and confounding bias. Since ovarian failure is still a problem of unknown etiology and difficult to treat in gynaecology, further studies are needed to clarify the correlation between the above three factors and ovarian function, with a view to early prevention and appropriate intervention. Mendelian randomization (MR) analysis, which use single nucleotide polymorphism (SNP) as an instrumental variable to infer causal associations between exposures and outcomes, can achieve a similar effect to that of random grouping without being interfered with by external environmental factors, and can compensate for the shortcomings of observational studies [ 10 ] . Therefore, in this study, we used MR integrated with bioinformatics to explore the causal associations between birth weight, childhood obesity, age at menarche and ovarian dysfunction and primary ovarian failure, and at the same time, to reveal the underlying biological mechanisms between the diseases. 2 Material and methods 2.1 Research design of MR After confirming that the Genome-wide Association Study (GWAS) pooled data for the exposure and outcome variables were independent of each other, this study investigated whether there was a causal effect between birth weight, childhood obesity, and age at menarche as the exposure variables, and ovarian dysfunction and primary ovarian failure as the outcome variables, respectively. The specific design is shown in Fig. 1 . MR analysis adheres to the following three key assumptions [ 11 ] : 1. Instrumental variables have a strong correlation with the exposure variables (birth weight, childhood obesity, age at menarche). 2. Instrumental variables are independent of both observed and unobserved confounding factors. 3. Instrumental variables solely influence the outcome (ovarian dysfunction, primary ovarian failure) through the exposure variables. 2.2 Data source Pooled GWAS data for birth weight (N = 143677), childhood obesity (N = 13848), and age at menarche (N = 182416) were obtained from the study by Horikoshi et al [ 12 ] , the Early Growth Genetics (EGG) consortium [ 13 ] , and the ReproGen consortium,, respectively [ 14 ] . Pooled GWAS data for ovarian dysfunction (N = 119170), and primary ovarian failure (N = 118482) were obtained from the Finngen database. GWAS pooled data for ovarian function-related indicators (FSH, LH, E 2 ) were obtained from the studies of Sun BB [ 15 ] , Ruth KS [ 16 ] et al. Pooled data for exposure and outcome were obtained from two different databases to prevent the possibility of duplicate data samples. The samples were all of European descent, as detailed in Supplement Excel 1. 2.3 Instrumental variable selection (1) Instrumental variables (IVs) correlation setting, in order to obtain strongly correlated exposure data, birth weight and age at menarche were screened with P < 5×10 − 8 [ 17 ] , however, most of the childhood obesity failed to find candidate SNPs under this genome-wide significance threshold, so this study set P < 1×10 − 6 to screen SNPs associated with childhood obesity; (2) Independence setting, PLINK aggregation method was used to calculate the linkage disequilibrium (LD) between SNPs, in which the linkage disequilibrium coefficient R 2 > 0.001 and SNPs with a physical distance of bases less than 10,000 kb were removed to ensure that the SNPs were independent of each other [ 18 ][ 19 ] ; (3) statistical strength setting, where the potency of instrumental variables was calculated by the statistic F, and weak instrumental variables with F < 10 were excluded [ 20 ] . Finally, SNPs with confounding factors or correlated with the results were excluded through the PhenoScanner database ( http://www.phenoscanner.medschl.cam.ac.uk/ ). 2.4 Mendelian randomization analysis The inverse variance weighted (IVW) method [ 21 ] was used as the main analytical method to determine causality, and the most accurate results were obtained from IVW in the absence of heterogeneity and horizontal pleiotropy. In addition, we applied several complementary methods, including MR-Egger regression, weighted median (WME), and weighted mode (WM) based on plurality, to estimate causality under different conditions. Heterogeneity was assessed using Cochran's Q test, and if heterogeneity existed, potential outliers were eliminated and re-analysed using the MR pleiotropy residual sum and outlier (MR-PRESSO) [ 22 ] . The MR-Egger intercept was used to detect horizontal pleiotropy, and the absence of horizontal pleiotropy (P > 0.05) indicates that the results of the MR analysis are reliable [ 23 ] . To ensure the reliability of the results and to exclude specific SNPs that may influence the causal effect, MR Steiger test was used to further assess the directionality of the causal relationship, and "leave-one-out" (LOO) analysis was performed for sensitivity analyses to assess whether the results were robust [ 24 ] . 2.5 Pilot bioinformatics analysis Open Targets Genetics ( https://genetics.opentargets.org/ ) integrates a large amount of functional genomics data and quantitative trait loci to generate an overall variant-to-gene (V2 G) score that can be used to select the best candidate genes. Genes with the highest overall V2 G scores were selected and subsequently subjected to protein-interaction network construction as well as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. 3 Results 3.1 Causal effects of birth weight, childhood obesity and age at menarche on ovarian dysfunction After the selection and coordination of IVs, 42, 7, and 54 SNPs associated with birth weight, childhood obesity, and age at menarche were obtained, respectively, and the detailed information of each SNP is shown in Supplementary Excel 2.The results of the IVW analyses were used as the main reference indexes because of the absence of heterogeneity and horizontal pleiotropy. As shown in Fig. 2 , childhood obesity increased the risk of ovarian dysfunction (OR = 1.378, 95% CI: 1.113 ~ 1.705, P = 0.003), and the later the age of menarche, the lower the risk of ovarian dysfunction (OR = 0.639, 95% CI: 0.468 ~ 0.871, P = 0.005). Cochran's Q test showed that the respective SNPs were free of heterogeneity,and the test for multivariate validity showed that the P-values of the MR Egger test were all greater than 0.05, suggesting that there was no horizontal multivariate validity, and that the causal relationships were all in the expected direction. The LOO analysis showed robust results, as shown in Supplementary Excel 3 and Supplementary Fig. 1. 3.2 Causal effects of birth weight, childhood obesity and age at menarche on primary ovarian failure After the selection and coordination of IVs, 48, 7, and 60 SNPs associated with birth weight, childhood obesity, and age at menarche were obtained, respectively, and the detailed information of each SNP is shown in Supplementary Excel 4. The results of the IVW analyses were used as the main reference indexes because of the absence of heterogeneity and horizontal pleiotropy. As shown in Fig. 3 , the later the age at menarche, the lower the risk of primary ovarian failure (OR = 0.510, 95% CI: 0.283–0.918, P = 0.025), and there were no causal associations between birth weight (P = 0.092), childhood obesity (P = 0.704), and primary ovarian failure. Cochran's Q test showed that there was no heterogeneity in any of SNPs.The test for pleiotropy showed that the p-values of MR Egger's test were all greater than 0.05, suggesting that there was no horizontal pleiotropy and that the causal relationships were all in the expected direction.LOO analyses showed that the results were robust, as shown in Supplementary Excel 5 and Supplementary Fig. 2. We further examined whether childhood obesity, age at menarche, and ovarian function-related indexes were causally associated with each other. The results showed that childhood obesity, age at menarche were not causally associated with FSH, LH and E 2 , while birth weight showed a positive correlation with E 2 levels (OR = 1.013, 95% CI: 1.005–1.022, P = 0.003) (Supplementary Excel 6, Supplementary Figs. 3 and 4). 3.3 Genetic architecture mediating causal effects We mapped the mutation loci for the childhood obesity-ovarian dysfunction and age at menarche-primary ovarian failure relationships to the gene database, respectively. After removing duplicates, we ended up matching 60 independent SNPs for age at menarche-primary ovarian failure to 59 independent genes with the highest V2 G scores because of the relatively small number of genes at the childhood obesity-ovarian dysfunction locus (Supplementary Excel 7). Protein-interaction network analysis screened 18 interacting genes (Fig. 4 A). Subsequently, the top ten hub genes with the highest extent were identified, which were RHOA, ARNTL, NOB1, RXRG, ERCC4, PHF21A, SEC16B, SAR1B, ANXA2, and DST. KEGG analysis showed that hub genes were mainly enriched in lipids, synthetic secretion of parathyroid hormone, and cGMP-PKG signalling pathway. GO analysis showed that hub genes were mainly enriched in hormones, intracellular receptor-mediated signalling pathways, regulation of cellular senescence, vesicles, nuclear receptors and transcription factor activity (Fig. 4 B). 4 Discussion In this study, we applied MR and bioinformatics method to investigate the causal relationship and biological mechanisms between birth weight, childhood obesity and age at menarche and ovarian function. The results showed that childhood obesity increased the risk of ovarian dysfunction in females (OR = 1.378, 95% CI: 1.113–1.705, P = 0.003), and the later the age at menarche, the lower the risk of ovarian dysfunction (OR = 0.639, 95% CI: 0.468–0.871, P = 0.005) and primary ovarian failure (OR = 0.510, 95% CI: 0.283–0.918, P = 0.025). This may be related to lipid and parathyroid hormone synthesis and secretion processes, and there was no causal relationship between birth weight and ovarian dysfunction and primary ovarian failure. Due to the original ovarian follicle pool being established within the uterus, it may be influenced by parental characteristics and the intrauterine environment [ 25 ] . Additionally, a study in 2007 [ 26 ] suggested that newborns with relatively lower birth weights have smaller ovarian volumes, raising the hypothesis that smaller ovarian volume and ovarian dysfunction in females may originate during fetal development. However, a study in 2011 found no significant differences in follicular-phase LH, FSH, AMH levels, or the response to endogenous GnRH between small for gestational age and appropriate for gestational age females [ 27 ] . This suggests that there may be no correlation between female ovarian function and birth weight. The contrasting results between these two studies could be attributed to the challenges of conducting extensive clinical follow-ups, resulting in limited sample sizes and potential biases influenced by various confounding factors. Our study found that birth weight, although not causally associated with ovarian function, resulted in elevated E 2 levels. This is consistent with the findings of a previous study [ 28 ] that neonates with relatively low birth weight have more pronounced activation of the hypothalamic-pituitary-gonadal axis and relatively higher E 2 levels than term infants, but genetically this does not have an impact on ovarian function. A study conducted in 2014, which included 103 participants, found a higher prevalence of ovarian dysfunction among females who had insufficient weight during childhood [ 29 ] . Recent research has consistently indicated that childhood obesity leads to an earlier onset of puberty in females, increases the risk of developing polycystic ovary syndrome in adulthood, and affects fertility, among other factors [ 30 , 31 ] . However, studies exploring the relationship between childhood obesity and adult ovarian function have been lacking. Inconsistent with previous studies, our study found that women who were obese in childhood had a higher risk of ovarian dysfunction, which may be related to the coordinated action of various hormones such as leptin, insulin, and epinephrine [ 32 ] . Leptin receptors are present in the hypothalamus and anterior pituitary gland in gonadotrophoblasts, ovarian follicular cells, and mesenchymal stromal cells, which accelerate the pulsation of gonadotropin-releasing hormone (GnRH) that leading to increased gonadotropin secretion and gonadal maturation, and in obese individuals, inhibitory effects on the gonads due to the presence of leptin resistance [ 33 ] . For example, rs9941349 is located in the intron of the FTO gene, and it has been found that FTO abnormalities are associated with oocyte maturation disorders and premature ovarian failure, but the exact mechanism still needs to be elucidated in subsequent studies [ 34 ] . The earliest study in 2012 [ 35 ] found that women with earlier menarche had higher AMH levels at a younger age, higher numbers of follicles in the ovaries, and better ovarian reserve function, but failed to conduct a study of longer duration. On the contrary, a 2013 study by Andrea Weghofer et al [ 36 ] showed that the earlier the age at menarche, the greater the risk of ovarian hypoplasia, which may be related to the size of the follicular pool and/or the rate of follicular recruitment. 2018 and 2021 studies [ 37 , 38 ] , however, found no correlation between the age at menarche and women's ovarian function. Considering that in addition to genetics, menarche is also influenced by socioeconomic and environmental factors, such as race, BMI, geography, nutritional habits, and exercise have been shown to affect age at menarche, there has been controversy as to whether age at menarche is correlated or causally related to ovarian function in females, and it is difficult for observational studies to come up with a definitive answer. Our study confirmed genetically that the later the age at menarche, the lower the risk of ovarian failure. Parathyroid hormone responsive-B1 (PTHB1) gene expression responds to parathyroid hormone, and BBS is a genetically heterogeneous autosomal recessive disorder [ 39 ] , whose main symptoms are obesity [ 40 ] and ovarian hypoplasia [ 41 ] showed that PTHB 1 is expressed early in human adipogenesis and has a high correlation with POF, so it is hypothesised that PTHB 1 variants increase the risk of developing POF. Unlike previous observational studies that were susceptible to confounding factors and the disadvantage that RCT experiments are difficult to carry out in large numbers in the clinic, MR studies minimise the inherent bias due to confounding factors or reverse causality, while combining the functional enrichment and protein-protein interaction network approaches to provide new insights into the specific mechanisms underlying the relationship between childhood obesity, age at menarche and ovarian function. insights. However, there are several limitations of our study. Firstly, the MR analysis could only use the available genetic data and could not exclude other non-genetic factors, such as lifestyle, which may influence the development of the disease; secondly, the data obtained did not have more detailed cohort data, such as age and sex, and could not be further analysed in subgroups; and lastly, the data used in this study was ethnically homogeneous, and it is important to be cautious when extending the results to other populations with different lifestyles and cultural traditions. Finally, the data used in the study were ethnically homogeneous and caution should be exercised when generalising the results to other populations with different lifestyles and cultural traditions. 5 Conclusions In conclusion, our study finds that childhood obesity increases the risk of ovarian dysfunction, and the later the age at menarche, the lower the risk of ovarian failure, which may be related to intracellular receptor-mediated alterations in metabolic pathways such as lipids and parathyroid hormone. This finding reinforces the positive association between age at menarche and ovarian function, and women with early age at menarche need to be alerted to the risk of ovarian failure, as well as the importance of maintaining a healthy BMI from childhood to protect ovarian function. However, larger experimental studies are needed to elucidate the complex epigenetic interactions between childhood obesity, age at menarche, and ovarian function, as ovarian hypoplasia remains a disease of unknown etiology. Abbreviations MR = mendelian randomization, POF = Primary ovarian failure, SNP = single nucleotide polymorphism, IVs = instrumental variables, GWAS = genome-wide association study, LD = linkage disequilibrium, IVW = inverse variance weighted, WME = weighted median, WM = weighted mode, LOO = leave-one-out Declarations Acknowledgments This work benefited from the publicly available statistics of GWAS. We thank the contributors to the original GWAS database. Author Contributions CX. D. and PF. L. provided the overall design of the study. CX. D. wrote the body of the manuscript. JX. L. and X.Y. conducted the main analyses of this study and summarized and organized the results into tables. All authors reviewed the the manuscript. Funding This work was supported by the National Natural Science Foundation of China (No.82104917), the Natural Science Foundation of Shandong Province (No.ZR2021MH079). Data Availability All data generated or analysed during this study are included in this published article and its supplementary information files. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no conflict of interest. References Al Rashid K, Taylor A, Lumsden MA, Goulding N, Lawlor DA, Nelson SM. Association of the functional ovarian reserve with serum metabolomic profiling by nuclear magnetic resonance spectroscopy: a cross-sectional study of ~ 400 women. BMC Med. 2020;18(1):247. doi: 10.1186/s12916-020-01700-z . De Vos M, Devroey P, Fauser BC. Primary ovarian insufficiency. Lancet. 2010;376(9744):911–21. doi: 10.1016/S0140-6736(10)60355-8 . Lima MLS, Romão GS, Bettiol H, Barbieri MA, Ferriani RA, Navarro PA. 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The cardinal manifestations of Bardet-Biedl syndrome, a form of Laurence-Moon-Biedl syndrome. N Engl J Med. 1989;321(15):1002–9. doi: 10.1056/NEJM198910123211503 . Schachat AP, Maumenee IH. Bardet-Biedl syndrome and related disorders. Arch Ophthalmol. 1982;100(2):285–8. doi: 10.1001/archopht.1982.01030030287011 . Forti E, Aksanov O, Birk RZ. Temporal expression pattern of Bardet-Biedl syndrome genes in adipogenesis. Int J Biochem Cell Biol. 2007;39(5):1055–62. doi: 10.1016/j.biocel.2007.02.014 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryExcel.xlsx SupplementaryFigures.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4119845","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":286959763,"identity":"c8b21cb8-9f7a-4e00-acf0-7a2be1ef1a50","order_by":0,"name":"Chunxiao Dang","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chunxiao","middleName":"","lastName":"Dang","suffix":""},{"id":286959764,"identity":"6271a94c-3964-45fd-8c7f-d5127aaa8416","order_by":1,"name":"Pengfei Liu","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Liu","suffix":""},{"id":286959765,"identity":"22b23841-3c0d-4007-86cd-e2855c9d3dc0","order_by":2,"name":"Jinxing Liu","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinxing","middleName":"","lastName":"Liu","suffix":""},{"id":286959766,"identity":"17f5bf8f-7a3d-4c25-8c9d-d5827227d168","order_by":3,"name":"Xiao Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACAwbGB0DSRs7+eOMDkAAPH2EtzAYMDAVpxgxnDhuAtbARp+XD4cSGG8lgLQwEtZizNzN+YDA4nNg48zHjY94cOxk2BuaHj27g0WLZc5hZgsEg3bhZOpnZcOa2ZKDD2IyNc/A57Eb+AaAWa9k26fxjEh+3MQO18LBJ49Vy/zHzDwYDZsYeycNsEonb6onQcoOZDWiLs+IMCSDj47bDRGg5k8xmkWCQZmzAA/bLcR42ZkJ+OX6Y+caHPzZyBuyHgSG2rdqen7354WN8WsAgAYXHTEj5KBgFo2AUjAKCAADzi0EB18i49gAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-03-18 04:29:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4119845/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4119845/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54068999,"identity":"955a6dd2-537e-4633-a3e5-6a4b9897229f","added_by":"auto","created_at":"2024-04-04 06:47:23","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39171,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of instrumental variable screening for MR method analysis. SNPs: single nucleotide polymorphisms\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4119845/v1/27d7513683c208f5567043f2.jpeg"},{"id":54069448,"identity":"f083f864-1897-441f-85bb-9b030c6376be","added_by":"auto","created_at":"2024-04-04 06:55:23","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101575,"visible":true,"origin":"","legend":"\u003cp\u003eForrest plot for causal associations of birth weighe, childhood obesity and age at menarche with ovarian dysfunction risk based on four MR methods. Abbreviations: CI = confidence interval;\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4119845/v1/e1e3e67f1117905d2177d665.jpeg"},{"id":54069004,"identity":"aa4fa942-9670-4985-8201-d1be3a55e6fb","added_by":"auto","created_at":"2024-04-04 06:47:23","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44068,"visible":true,"origin":"","legend":"\u003cp\u003eForrest plot for causal associations of birth weighe, childhood obesity and age at menarche with primary ovarian failure risk based on four MR methods. Abbreviations: CI = confidence interval;\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4119845/v1/4388821b522fe2699e66448e.jpeg"},{"id":54069005,"identity":"d7dffd61-8737-4460-8a43-367cf549ae4d","added_by":"auto","created_at":"2024-04-04 06:47:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":425412,"visible":true,"origin":"","legend":"\u003cp\u003eThe biological functions and Interactions of mediator genes. (A) Interaction network of target genes was constructed using the STRING database. (B) The top 3 GO and KEGG enriched pathways\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4119845/v1/22c93e84828608f1b9a889fe.png"},{"id":54733561,"identity":"96da83e4-cef5-4a16-ae3d-a4ddae072597","added_by":"auto","created_at":"2024-04-16 02:13:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":585489,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4119845/v1/2d905531-bf53-4fdd-a48e-5012ad912777.pdf"},{"id":54069001,"identity":"01ccf203-53e7-4442-8723-56ac95726ed4","added_by":"auto","created_at":"2024-04-04 06:47:23","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40083,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryExcel.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4119845/v1/6753c246e43b8e6983792d7c.xlsx"},{"id":54069002,"identity":"cd7a6bb2-6b8c-46e7-b7bb-3309f554e829","added_by":"auto","created_at":"2024-04-04 06:47:23","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1121627,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4119845/v1/3f3f29767de68f7940472bc3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of birth weight, childhood obesity, and age at menarche with ovarian function: an integrated Mendelian randomization study and bioinformatics analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eOvarian dysfunction means that the number of eggs retained in a woman's ovaries fails to meet the threshold criteria, which leads to a decrease in female fertility \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Primary ovarian failure (POF) refers to ovarian failure that occurs before the age of 40 years and is characterised by amenorrhoea lasting 4 months or more, accompanied by a decrease in oestrogen and an increase in gonadotropin levels \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Ovarian reserve and ovarian aging have become a key area of research in current clinical work.\u003c/p\u003e \u003cp\u003eThere is a hypothesis suggesting that birth weight is influenced by genetic factors from both parents as well as the intrauterine environment \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Further research has indicated that girls born with small for gestational age (SGA) tend to have a reduced ovulation rate during pregnancy \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, but limited sample sizes may introduce bias into these findings. Childhood obesity is a significant public health concern, with reported rates as high as 12.7% in the United States \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Previous clinical studies and meta-analyses have shown that adult obesity has a negative impact on ovarian function \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. However, there has been limited research investigating whether childhood obesity similarly affects ovarian function. Age at menarche can serve as a reflection of a female's hormonal levels during puberty and is influenced by genetic, epigenetic, and environmental factors \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Some studies have shown no correlation between age at menarche and ovarian function \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, but the number of studies is small and traditional observational studies may have methodological flaws and confounding bias. Since ovarian failure is still a problem of unknown etiology and difficult to treat in gynaecology, further studies are needed to clarify the correlation between the above three factors and ovarian function, with a view to early prevention and appropriate intervention.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) analysis, which use single nucleotide polymorphism (SNP) as an instrumental variable to infer causal associations between exposures and outcomes, can achieve a similar effect to that of random grouping without being interfered with by external environmental factors, and can compensate for the shortcomings of observational studies \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Therefore, in this study, we used MR integrated with bioinformatics to explore the causal associations between birth weight, childhood obesity, age at menarche and ovarian dysfunction and primary ovarian failure, and at the same time, to reveal the underlying biological mechanisms between the diseases.\u003c/p\u003e"},{"header":"2 Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research design of MR\u003c/h2\u003e \u003cp\u003eAfter confirming that the Genome-wide Association Study (GWAS) pooled data for the exposure and outcome variables were independent of each other, this study investigated whether there was a causal effect between birth weight, childhood obesity, and age at menarche as the exposure variables, and ovarian dysfunction and primary ovarian failure as the outcome variables, respectively. The specific design is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. MR analysis adheres to the following three key assumptions \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e: 1. Instrumental variables have a strong correlation with the exposure variables (birth weight, childhood obesity, age at menarche). 2. Instrumental variables are independent of both observed and unobserved confounding factors. 3. Instrumental variables solely influence the outcome (ovarian dysfunction, primary ovarian failure) through the exposure variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data source\u003c/h2\u003e \u003cp\u003ePooled GWAS data for birth weight (N\u0026thinsp;=\u0026thinsp;143677), childhood obesity (N\u0026thinsp;=\u0026thinsp;13848), and age at menarche (N\u0026thinsp;=\u0026thinsp;182416) were obtained from the study by Horikoshi et al \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, the Early Growth Genetics (EGG) consortium \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, and the ReproGen consortium,, respectively \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Pooled GWAS data for ovarian dysfunction (N\u0026thinsp;=\u0026thinsp;119170), and primary ovarian failure (N\u0026thinsp;=\u0026thinsp;118482) were obtained from the Finngen database. GWAS pooled data for ovarian function-related indicators (FSH, LH, E\u003csub\u003e2\u003c/sub\u003e) were obtained from the studies of Sun BB \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, Ruth KS \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e et al. Pooled data for exposure and outcome were obtained from two different databases to prevent the possibility of duplicate data samples. The samples were all of European descent, as detailed in Supplement Excel 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Instrumental variable selection\u003c/h2\u003e \u003cp\u003e(1) Instrumental variables (IVs) correlation setting, in order to obtain strongly correlated exposure data, birth weight and age at menarche were screened with P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, however, most of the childhood obesity failed to find candidate SNPs under this genome-wide significance threshold, so this study set P\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e to screen SNPs associated with childhood obesity; (2) Independence setting, PLINK aggregation method was used to calculate the linkage disequilibrium (LD) between SNPs, in which the linkage disequilibrium coefficient R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.001 and SNPs with a physical distance of bases less than 10,000 kb were removed to ensure that the SNPs were independent of each other \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e][\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e; (3) statistical strength setting, where the potency of instrumental variables was calculated by the statistic F, and weak instrumental variables with F\u0026thinsp;\u0026lt;\u0026thinsp;10 were excluded \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Finally, SNPs with confounding factors or correlated with the results were excluded through the PhenoScanner database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.cam.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.phenoscanner.medschl.cam.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Mendelian randomization analysis\u003c/h2\u003e \u003cp\u003eThe inverse variance weighted (IVW) method \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e was used as the main analytical method to determine causality, and the most accurate results were obtained from IVW in the absence of heterogeneity and horizontal pleiotropy. In addition, we applied several complementary methods, including MR-Egger regression, weighted median (WME), and weighted mode (WM) based on plurality, to estimate causality under different conditions. Heterogeneity was assessed using Cochran's Q test, and if heterogeneity existed, potential outliers were eliminated and re-analysed using the MR pleiotropy residual sum and outlier (MR-PRESSO) \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The MR-Egger intercept was used to detect horizontal pleiotropy, and the absence of horizontal pleiotropy (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) indicates that the results of the MR analysis are reliable \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. To ensure the reliability of the results and to exclude specific SNPs that may influence the causal effect, MR Steiger test was used to further assess the directionality of the causal relationship, and \"leave-one-out\" (LOO) analysis was performed for sensitivity analyses to assess whether the results were robust \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Pilot bioinformatics analysis\u003c/h2\u003e \u003cp\u003eOpen Targets Genetics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genetics.opentargets.org/\u003c/span\u003e\u003cspan address=\"https://genetics.opentargets.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) integrates a large amount of functional genomics data and quantitative trait loci to generate an overall variant-to-gene (V2 G) score that can be used to select the best candidate genes. Genes with the highest overall V2 G scores were selected and subsequently subjected to protein-interaction network construction as well as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Causal effects of birth weight, childhood obesity and age at menarche on ovarian dysfunction\u003c/h2\u003e \u003cp\u003eAfter the selection and coordination of IVs, 42, 7, and 54 SNPs associated with birth weight, childhood obesity, and age at menarche were obtained, respectively, and the detailed information of each SNP is shown in Supplementary Excel 2.The results of the IVW analyses were used as the main reference indexes because of the absence of heterogeneity and horizontal pleiotropy. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, childhood obesity increased the risk of ovarian dysfunction (OR\u0026thinsp;=\u0026thinsp;1.378, 95% CI: 1.113\u0026thinsp;~\u0026thinsp;1.705, P\u0026thinsp;=\u0026thinsp;0.003), and the later the age of menarche, the lower the risk of ovarian dysfunction (OR\u0026thinsp;=\u0026thinsp;0.639, 95% CI: 0.468\u0026thinsp;~\u0026thinsp;0.871, P\u0026thinsp;=\u0026thinsp;0.005). Cochran's Q test showed that the respective SNPs were free of heterogeneity,and the test for multivariate validity showed that the P-values of the MR Egger test were all greater than 0.05, suggesting that there was no horizontal multivariate validity, and that the causal relationships were all in the expected direction. The LOO analysis showed robust results, as shown in Supplementary Excel 3 and Supplementary Fig.\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Causal effects of birth weight, childhood obesity and age at menarche on primary ovarian failure\u003c/h2\u003e \u003cp\u003eAfter the selection and coordination of IVs, 48, 7, and 60 SNPs associated with birth weight, childhood obesity, and age at menarche were obtained, respectively, and the detailed information of each SNP is shown in Supplementary Excel 4. The results of the IVW analyses were used as the main reference indexes because of the absence of heterogeneity and horizontal pleiotropy. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the later the age at menarche, the lower the risk of primary ovarian failure (OR\u0026thinsp;=\u0026thinsp;0.510, 95% CI: 0.283\u0026ndash;0.918, P\u0026thinsp;=\u0026thinsp;0.025), and there were no causal associations between birth weight (P\u0026thinsp;=\u0026thinsp;0.092), childhood obesity (P\u0026thinsp;=\u0026thinsp;0.704), and primary ovarian failure. Cochran's Q test showed that there was no heterogeneity in any of SNPs.The test for pleiotropy showed that the p-values of MR Egger's test were all greater than 0.05, suggesting that there was no horizontal pleiotropy and that the causal relationships were all in the expected direction.LOO analyses showed that the results were robust, as shown in Supplementary Excel 5 and Supplementary Fig.\u0026nbsp;2.\u003c/p\u003e \u003cp\u003eWe further examined whether childhood obesity, age at menarche, and ovarian function-related indexes were causally associated with each other. The results showed that childhood obesity, age at menarche were not causally associated with FSH, LH and E\u003csub\u003e2\u003c/sub\u003e, while birth weight showed a positive correlation with E\u003csub\u003e2\u003c/sub\u003e levels (OR\u0026thinsp;=\u0026thinsp;1.013, 95% CI: 1.005\u0026ndash;1.022, P\u0026thinsp;=\u0026thinsp;0.003) (Supplementary Excel 6, Supplementary Figs.\u0026nbsp;3 and 4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Genetic architecture mediating causal effects\u003c/h2\u003e \u003cp\u003eWe mapped the mutation loci for the childhood obesity-ovarian dysfunction and age at menarche-primary ovarian failure relationships to the gene database, respectively. After removing duplicates, we ended up matching 60 independent SNPs for age at menarche-primary ovarian failure to 59 independent genes with the highest V2 G scores because of the relatively small number of genes at the childhood obesity-ovarian dysfunction locus (Supplementary Excel 7).\u003c/p\u003e \u003cp\u003eProtein-interaction network analysis screened 18 interacting genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Subsequently, the top ten hub genes with the highest extent were identified, which were RHOA, ARNTL, NOB1, RXRG, ERCC4, PHF21A, SEC16B, SAR1B, ANXA2, and DST. KEGG analysis showed that hub genes were mainly enriched in lipids, synthetic secretion of parathyroid hormone, and cGMP-PKG signalling pathway. GO analysis showed that hub genes were mainly enriched in hormones, intracellular receptor-mediated signalling pathways, regulation of cellular senescence, vesicles, nuclear receptors and transcription factor activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this study, we applied MR and bioinformatics method to investigate the causal relationship and biological mechanisms between birth weight, childhood obesity and age at menarche and ovarian function. The results showed that childhood obesity increased the risk of ovarian dysfunction in females (OR\u0026thinsp;=\u0026thinsp;1.378, 95% CI: 1.113\u0026ndash;1.705, P\u0026thinsp;=\u0026thinsp;0.003), and the later the age at menarche, the lower the risk of ovarian dysfunction (OR\u0026thinsp;=\u0026thinsp;0.639, 95% CI: 0.468\u0026ndash;0.871, P\u0026thinsp;=\u0026thinsp;0.005) and primary ovarian failure (OR\u0026thinsp;=\u0026thinsp;0.510, 95% CI: 0.283\u0026ndash;0.918, P\u0026thinsp;=\u0026thinsp;0.025). This may be related to lipid and parathyroid hormone synthesis and secretion processes, and there was no causal relationship between birth weight and ovarian dysfunction and primary ovarian failure.\u003c/p\u003e \u003cp\u003eDue to the original ovarian follicle pool being established within the uterus, it may be influenced by parental characteristics and the intrauterine environment \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Additionally, a study in 2007 \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e suggested that newborns with relatively lower birth weights have smaller ovarian volumes, raising the hypothesis that smaller ovarian volume and ovarian dysfunction in females may originate during fetal development. However, a study in 2011 found no significant differences in follicular-phase LH, FSH, AMH levels, or the response to endogenous GnRH between small for gestational age and appropriate for gestational age females \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. This suggests that there may be no correlation between female ovarian function and birth weight. The contrasting results between these two studies could be attributed to the challenges of conducting extensive clinical follow-ups, resulting in limited sample sizes and potential biases influenced by various confounding factors. Our study found that birth weight, although not causally associated with ovarian function, resulted in elevated E\u003csub\u003e2\u003c/sub\u003e levels. This is consistent with the findings of a previous study \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e that neonates with relatively low birth weight have more pronounced activation of the hypothalamic-pituitary-gonadal axis and relatively higher E\u003csub\u003e2\u003c/sub\u003e levels than term infants, but genetically this does not have an impact on ovarian function.\u003c/p\u003e \u003cp\u003eA study conducted in 2014, which included 103 participants, found a higher prevalence of ovarian dysfunction among females who had insufficient weight during childhood \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Recent research has consistently indicated that childhood obesity leads to an earlier onset of puberty in females, increases the risk of developing polycystic ovary syndrome in adulthood, and affects fertility, among other factors \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. However, studies exploring the relationship between childhood obesity and adult ovarian function have been lacking. Inconsistent with previous studies, our study found that women who were obese in childhood had a higher risk of ovarian dysfunction, which may be related to the coordinated action of various hormones such as leptin, insulin, and epinephrine \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Leptin receptors are present in the hypothalamus and anterior pituitary gland in gonadotrophoblasts, ovarian follicular cells, and mesenchymal stromal cells, which accelerate the pulsation of gonadotropin-releasing hormone (GnRH) that leading to increased gonadotropin secretion and gonadal maturation, and in obese individuals, inhibitory effects on the gonads due to the presence of leptin resistance \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. For example, rs9941349 is located in the intron of the FTO gene, and it has been found that FTO abnormalities are associated with oocyte maturation disorders and premature ovarian failure, but the exact mechanism still needs to be elucidated in subsequent studies \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe earliest study in 2012 \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e found that women with earlier menarche had higher AMH levels at a younger age, higher numbers of follicles in the ovaries, and better ovarian reserve function, but failed to conduct a study of longer duration. On the contrary, a 2013 study by Andrea Weghofer et al \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e showed that the earlier the age at menarche, the greater the risk of ovarian hypoplasia, which may be related to the size of the follicular pool and/or the rate of follicular recruitment. 2018 and 2021 studies \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e, however, found no correlation between the age at menarche and women's ovarian function. Considering that in addition to genetics, menarche is also influenced by socioeconomic and environmental factors, such as race, BMI, geography, nutritional habits, and exercise have been shown to affect age at menarche, there has been controversy as to whether age at menarche is correlated or causally related to ovarian function in females, and it is difficult for observational studies to come up with a definitive answer. Our study confirmed genetically that the later the age at menarche, the lower the risk of ovarian failure. Parathyroid hormone responsive-B1 (PTHB1) gene expression responds to parathyroid hormone, and BBS is a genetically heterogeneous autosomal recessive disorder \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, whose main symptoms are obesity \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e and ovarian hypoplasia \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e showed that PTHB 1 is expressed early in human adipogenesis and has a high correlation with POF, so it is hypothesised that PTHB 1 variants increase the risk of developing POF.\u003c/p\u003e \u003cp\u003eUnlike previous observational studies that were susceptible to confounding factors and the disadvantage that RCT experiments are difficult to carry out in large numbers in the clinic, MR studies minimise the inherent bias due to confounding factors or reverse causality, while combining the functional enrichment and protein-protein interaction network approaches to provide new insights into the specific mechanisms underlying the relationship between childhood obesity, age at menarche and ovarian function. insights. However, there are several limitations of our study. Firstly, the MR analysis could only use the available genetic data and could not exclude other non-genetic factors, such as lifestyle, which may influence the development of the disease; secondly, the data obtained did not have more detailed cohort data, such as age and sex, and could not be further analysed in subgroups; and lastly, the data used in this study was ethnically homogeneous, and it is important to be cautious when extending the results to other populations with different lifestyles and cultural traditions. Finally, the data used in the study were ethnically homogeneous and caution should be exercised when generalising the results to other populations with different lifestyles and cultural traditions.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn conclusion, our study finds that childhood obesity increases the risk of ovarian dysfunction, and the later the age at menarche, the lower the risk of ovarian failure, which may be related to intracellular receptor-mediated alterations in metabolic pathways such as lipids and parathyroid hormone. This finding reinforces the positive association between age at menarche and ovarian function, and women with early age at menarche need to be alerted to the risk of ovarian failure, as well as the importance of maintaining a healthy BMI from childhood to protect ovarian function. However, larger experimental studies are needed to elucidate the complex epigenetic interactions between childhood obesity, age at menarche, and ovarian function, as ovarian hypoplasia remains a disease of unknown etiology.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMR = mendelian randomization, POF = Primary ovarian failure, SNP = single nucleotide polymorphism, IVs = instrumental variables, GWAS = genome-wide association study, LD = linkage disequilibrium, IVW = inverse variance weighted, WME = weighted median, WM = weighted mode, LOO = leave-one-out\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e This work benefited from the publicly available statistics of GWAS. We thank the contributors to the original GWAS database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e CX. D. and PF. L. provided the overall design of the study. CX. D. wrote the body of the manuscript. JX. L. and X.Y. conducted the main analyses of this study and summarized and organized the results into tables. All authors reviewed the the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was supported by the National Natural Science Foundation of China (No.82104917), the Natural Science Foundation of Shandong Province (No.ZR2021MH079).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e All data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl Rashid K, Taylor A, Lumsden MA, Goulding N, Lawlor DA, Nelson SM. 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Int J Biochem Cell Biol. 2007;39(5):1055\u0026ndash;62. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.biocel.2007.02.014\u003c/span\u003e\u003cspan address=\"10.1016/j.biocel.2007.02.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"birth weight, childhood obesity, age at menarche, ovarian function, mendelian randomization, integrated bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-4119845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4119845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective \u003c/strong\u003eObservational studies have shown that birth weight, childhood obesity and age at menarche are associated with ovarian function, but there is still some controversy.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eWe mainly uses the inverse variance weighted (IVW) method for analysis, conducting heterogeneity, horizontal pleiotropy and MR Steiger test to evaluate the robustness of the results and the strength of causal relationships. Finally, preliminary bioinformatics analysis was conducted to explore the underlying biological mechanisms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Causal associations were found between childhood obesity, age at menarche and ovarian function, with a positive association between childhood obesity and ovarian dysfunction and a negative association between age at menarche and ovarian function. A total of 10 hub genes were identified, which are interconnected in an interaction network and play a role in the synthesis and secretion of lipids and parathyroid hormone.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eOur study genetically confirms the causal association between childhood obesity, age at menarche and ovarian function; childhood obesity increases the risk of primary ovarian failure, and the later the age at menarche, the lower the risk of ovarian failure, which may be related to alterations in metabolic pathways such as intracellular receptors-mediated alterations in lipids and hormones.\u003c/p\u003e","manuscriptTitle":"Association of birth weight, childhood obesity, and age at menarche with ovarian function: an integrated Mendelian randomization study and bioinformatics analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-04 06:47:18","doi":"10.21203/rs.3.rs-4119845/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"770d0787-2911-4ef5-bdac-c7bbea4378f4","owner":[],"postedDate":"April 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30204775,"name":"Biological sciences/Computational biology and bioinformatics/Functional clustering"},{"id":30204776,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases"},{"id":30204777,"name":"Health sciences/Diseases/Nutrition disorders/Obesity"},{"id":30204778,"name":"Biological sciences/Genetics/Medical genetics"}],"tags":[],"updatedAt":"2024-04-16T02:05:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-04 06:47:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4119845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4119845","identity":"rs-4119845","version":["v1"]},"buildId":"ApUGefWb6u5IBVtyqm6d5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}