Plasma metabolites may inhibit childhood obesity by regulating ferroptosis through SMPD1 and SIRT3

Plasma metabolites may inhibit childhood obesity by regulating ferroptosis through SMPD1 and SIRT3 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Plasma metabolites may inhibit childhood obesity by regulating ferroptosis through SMPD1 and SIRT3 Ji-Gan Wang, Hui-Hong Dou, Yan Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6338689/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Nov, 2025 Read the published version in International Journal of Obesity → Version 1 posted 9 You are reading this latest preprint version Abstract Objective To investigate the causal relationship between plasma metabolites and ferroptosis-related genes in childhood obesity and to explore the potential mediating role of ferroptosis-related genes in the association between plasma metabolites and childhood obesity risk. Methods A bidirectional two-step Mendelian randomization (MR) approach was applied, leveraging publicly available genome-wide association study (GWAS) datasets to analyze the causal relationship among 1,400 plasma metabolites, 159 ferroptosis-related genes, and childhood obesity. In the first step, protein quantitative trait loci (pQTL) data corresponding to ferroptosis-related genes were identified as mediators to evaluate the causal effects of plasma metabolites and ferroptosis-related genes on childhood obesity. In the second step, MR analysis was conducted on ferroptosis-related genes and plasma metabolites identified in the first step to confirm their causal association. The inverse-variance weighted (IVW) method was primarily used for meta-analysis, while MR-PRESSO was employed to detect pleiotropy and outliers. Results Four ferroptosis-related genes (SMPD1 and SIRT3 suppressing obesity, GSTZ1 and ADAMTS13 promoting obesity) and nine plasma metabolites were found to be significantly associated with childhood obesity (six negatively correlated and three positively correlated). Further mediation analysis indicated that the ferroptosis mechanism regulated by SMPD1 and SIRT3 partially mediated the association between specific plasma metabolites and childhood obesity, with the highest mediation proportion reaching 9.62%. Sensitivity analysis confirmed the robustness of the results (no heterogeneity or horizontal pleiotropy), and reverse Mendelian randomization ruled out causal interference. Conclusion This study is the first to reveal, through Mendelian randomization analysis, the potential mediating role of ferroptosis-related genes in the association between plasma metabolites and childhood obesity. It suggests that the ferroptosis mechanism may influence childhood obesity risk by regulating specific metabolites. These findings contribute to understanding the role of ferroptosis in the pathological mechanisms of childhood obesity and provide novel molecular targets and intervention strategies for obesity prevention and treatment in children. Biological sciences/Immunology/Immunological disorders Health sciences/Diseases/Nutrition disorders Childhood obesity Plasma metabolites Ferroptosis MR GWAS Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Childhood obesity has become a major global public health challenge. Its occurrence not only affects children's growth and development but also significantly increases the risk of metabolic diseases in adulthood, such as type 2 diabetes, cardiovascular diseases, and non-alcoholic fatty liver disease (NAFLD)[ 1 , 2 ]. The development of obesity is influenced by genetic, environmental, and metabolic factors, among which metabolic disorders play a crucial role in the pathological mechanisms of childhood obesity[ 3 ]. In recent years, ferroptosis, a unique mode of regulated cell death, has garnered extensive attention in the progression of various diseases [ 4 ]. However, whether ferroptosis regulates childhood obesity risk by influencing plasma metabolites remains unclear due to a lack of systematic causal evidence. Ferroptosis is an iron-dependent form of regulated cell death characterized by lipid peroxidation of the cell membrane, with its core mechanisms involving iron metabolism, lipid metabolism, and the glutathione antioxidant system[ 5 , 6 ]. Previous studies have suggested that ferroptosis may affect lipid metabolism, thereby linking it to obesity and related metabolic disorders[ 7 ]. Meanwhile, plasma metabolites play a key role in regulating energy homeostasis, inflammation, and insulin resistance[8 ].Therefore, ferroptosis-related genes may influence the occurrence and progression of childhood obesity by modulating plasma metabolites. However, most existing studies are observational in nature, making it difficult to rule out confounding factors and reverse causality, leaving the true causal relationship between ferroptosis, plasma metabolites, and childhood obesity unclear. Mendelian randomization (MR) is a causal inference method based on genetic variation, which uses genetic variants as instrumental variables to assess the causal effect of an exposure (such as plasma metabolites or ferroptosis-related genes) on an outcome (childhood obesity)[ 9 ]. Compared to traditional observational studies, MR analysis can mitigate the influence of confounding factors and reduce bias from reverse causality[ 10 ]. In this study, we employed a bidirectional two-step two-sample Mendelian randomization approach based on publicly available genome-wide association study (GWAS) datasets to explore the causal relationship between ferroptosis-related genes, plasma metabolites, and childhood obesity risk. Additionally, we investigated whether ferroptosis acts as a mediator between plasma metabolites and childhood obesity. The findings of this study will contribute to a deeper understanding of the role of ferroptosis in the pathogenesis of obesity and provide new scientific evidence for the prevention and treatment of childhood obesity. Materials and Methods 1.1 Study Design This study employed a bidirectional two-step two-sample Mendelian randomization (MR) approach using publicly available datasets to investigate the potential mediating role of ferroptosis-related genes in the relationship between 1,400 plasma metabolites and childhood obesity risk. In the first step, protein quantitative trait loci (pQTL) data corresponding to ferroptosis-related genes were identified as mediators. A two-sample Mendelian randomization analysis was then conducted separately to assess the causal effects of plasma metabolites and ferroptosis-related genes on childhood obesity, yielding beta_all and beta1 , respectively.In the second step, ferroptosis-related genes and plasma metabolites that showed significant associations in the first step were further analyzed through Mendelian randomization to determine the causal effect of plasma metabolites on ferroptosis-related genes, yielding beta2 . A mediation analysis can only be performed if a causal relationship exists in the triangular structure, meaning that the exposure (plasma metabolites) must have a causal effect on the outcome (childhood obesity), the mediator (ferroptosis-related genes) must have a causal effect on the outcome, and the exposure must have a causal effect on the mediator. The mediation effect was calculated as beta1 × beta2 , and the mediation proportion was determined as (beta1 × beta2) / beta_all ( see Fig. 1 ). 1.2 Data Sources 1.2.1 Plasma Metabolites Data on 1,400 plasma metabolites were obtained from a publicly available genome-wide association study (GWAS) dataset with accession numbers GCST90199621-90201020[ 11 ]. The study population consisted entirely of individuals of European ancestry. 1.2.2 Childhood Obesity Childhood obesity data were sourced from a publicly available GWAS dataset with accession number ieu-a-1096, which included 5,530 cases and 8,318 controls, all of European ancestry[ 12 ]. 1.2.3 pQTL (Plasma Protein Quantitative Trait Loci) Data pQTL data were derived from a GWAS conducted on 35,559 Icelandic individuals, which measured plasma protein levels using 4,907 oligonucleotide aptamers[ 13 ]. The dataset is available at deCODE Genetics. 1.2.4 Ferroptosis-Related Genes Ferroptosis-related genes were retrieved from FerrDb ( http://www.zhounan.org/ferrdb/current/ ), the world’s first database dedicated to ferroptosis regulators and ferroptosis-disease associations. 1.3 Selection of Single Nucleotide Polymorphisms (SNPs) Selection of instrumental variables for PQTL and plasma metabolites: Set P < 5×10^-8, linkage disequilibrium parameters (r² = 0.1, kb = 10000). For palindromic SNPs, the forward strand alleles were determined using allele frequency information. The F-statistics for the selected instrumental variables (IVs) reached a threshold of > 10, ensuring that the causal estimations had no weak instrument bias. 1.4 Statistical Analysis This study utilized the TwoSampleMR package in R software (version 4.3.3) to conduct bidirectional two-sample Mendelian randomization (MR) analysis, aiming to clarify the causal relationships among the study variables. The primary analytical method was the inverse variance weighted (IVW) meta-analysis, which effectively integrates causal effect estimates from multiple genetic instrumental variables (single nucleotide polymorphisms, SNPs) to obtain robust and efficient overall effect estimates[ 14 ].To ensure the robustness and reliability of the MR analysis, various approaches were employed for effect assessment and sensitivity analysis. The MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) method was used to detect and correct pleiotropy by identifying and removing outlier SNPs with abnormal effects, enhancing the validity of causal inference. Cochran’s Q test was applied to examine the heterogeneity in effect estimates among SNPs, where a P-value < 0.05 indicated heterogeneity among the genetic instrumental variables, necessitating careful interpretation of causal estimates. The MR-Egger intercept test was used to evaluate whether horizontal pleiotropy was present; a significant deviation of the MR-Egger intercept from zero ( P < 0.05 ) suggested the existence of pleiotropy that might compromise the reliability of the analysis. Additionally, a leave-one-out sensitivity analysis was performed by sequentially excluding individual SNPs to assess their influence on the overall causal effect estimate, ensuring the stability of the results[ 15 ].Furthermore, a mediation analysis was conducted within the MR framework to clarify the mediating role of specific variables in the causal pathway between exposure and outcome variables. The IVW method was used to estimate the effect of each causal pathway, and by comparing the significance and effect sizes of direct and indirect effects, the underlying mediation mechanism was explored. All statistical tests were two-sided, with the significance level set at α = 0.05 . Results 2.1 Causal Association Between Ferroptosis and Childhood Obesity After removing linkage disequilibrium (LD), 4,377 pQTLs remained. From the FerrDb database, a total of 483 ferroptosis-related genes with clearly defined roles in either promoting or inhibiting ferroptosis were identified. By intersecting these 483 ferroptosis-related genes with the pQTL dataset, a total of 159 ferroptosis-related genes corresponding to pQTLs were obtained (Fig. 2). These 159 ferroptosis-related genes were treated as the exposures, and childhood obesity was set as the outcome for bidirectional Mendelian randomization analysis. The results identified four ferroptosis-related genes that exhibited a causal relationship with childhood obesity. Among them, SMPD1, GSTZ1, and SIRT3 were found to suppress childhood obesity, while ADAMTS13 was found to promote childhood obesity (OR = 1.288, 95% CI = 1.084–1.529, P = 0.004) (Fig. 3A). 2.2 Plasma Metabolites and Childhood Obesity Our analysis identified a total of nine plasma metabolites that exhibited a causal relationship with childhood obesity. Among them, 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4), 1-(1-enyl-palmitoyl)-2-oleoyl-GPC (P-16:0/18:1), 1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6), Hexadecenedioate (C16:1-DC), 1-stearoyl-2-arachidonoyl-GPC (18:0/20:4), and N-acetylputrescine to (N(1) + N(8))-acetylspermidine ratio were negatively associated with childhood obesity, with the strongest correlation observed for 1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6) (OR = 0.655, 95% CI = 0.470–0.913, P = 0.012). Conversely, 1-oleoyl-2-linoleoyl-GPE (18:1/18:2), Butyrylcarnitine (C4), and Ethylmalonate were identified as risk factors for childhood obesity, with 1-oleoyl-2-linoleoyl-GPE (18:1/18:2) exhibiting the strongest positive correlation (OR = 1.167, 95% CI = 1.034–1.318, P = 0.012) (Fig. 3B). 2.3 Plasma Metabolites and Ferroptosis-Related Genes Based on the previous results, four ferroptosis-related genes were identified as potential mediators. A Mendelian randomization analysis was then conducted to examine the effect of these nine plasma metabolites on the ferroptosis-related genes, with plasma metabolites set as the exposure and ferroptosis-related genes as the outcome. A total of 15 significant pairings were identified, with specific results presented in Fig. 4. 2.4 Mediation Analysis A total of five mediation relationships were identified. SMPD1-regulated ferroptosis mediated the effect of 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4), 1-(1-enyl-palmitoyl)-2-oleoyl-GPC (P-16:0/18:1), and 1-stearoyl-2-arachidonoyl-GPC (18:0/20:4) in inhibiting childhood obesity, with mediation proportions of 8.24%, 9.62%, and 8.05%, respectively. Similarly, SIRT3-regulated ferroptosis mediated the effect of 1-stearoyl-2-arachidonoyl-GPC (18:0/20:4) and 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4) in inhibiting childhood obesity, with mediation proportions of 7.23% and 7.72%, respectively ( Table ). 2.5 Sensitivity and Heterogeneity Analysis A sensitivity analysis was performed on the relationships among plasma metabolites, ferroptosis-related genes, and childhood obesity. The MR-Egger regression confirmed the absence of horizontal pleiotropy, while Cochran’s Q test using the IVW method indicated no heterogeneity among SNPs ( Supplementary Table ). 2.6 Reverse Mendelian Randomization Analysis To verify whether there was a reverse causal relationship between plasma metabolites, ferroptosis-related genes, and childhood obesity, a reverse Mendelian randomization (MR) analysis was conducted. The selected SNPs did not show any significant effect of childhood obesity on the identified plasma metabolites ( Supplementary Fig. 1 ) or ferroptosis-related genes ( Supplementary Fig. 2 ), confirming the absence of reverse causality. Discussion This study utilized a Mendelian randomization (MR) approach to explore the potential mediating role of ferroptosis-related genes in the relationship between plasma metabolites and childhood obesity risk. The results indicated that certain ferroptosis-related genes, such as SMPD1, GSTZ1, and SIRT3, may suppress childhood obesity by regulating the ferroptosis process, while ADAMTS13 may promote childhood obesity. Additionally, nine plasma metabolites were found to have a causal relationship with childhood obesity, with six metabolites negatively associated and three metabolites identified as obesity risk factors. Further mediation analysis revealed that ferroptosis may serve as a key regulatory factor, indirectly influencing childhood obesity by modulating plasma metabolites. This finding not only strengthens the link between ferroptosis and metabolic disorders but also provides new insights into the pathophysiological mechanisms of childhood obesity. In recent years, ferroptosis, as a unique mode of cell death, has been increasingly recognized as being closely associated with various metabolic diseases. Ferroptosis primarily depends on iron ion accumulation and lipid peroxidation, leading to cell membrane damage and ultimately triggering cell death[ 16 ]. Previous studies have demonstrated that ferroptosis plays a significant role in energy metabolism regulation, insulin sensitivity, and inflammatory responses, suggesting that it may be a key factor influencing the development of obesity[ 17 , 18 ]. Adipose tissue serves as the body's primary energy storage site, and its metabolic homeostasis is regulated by oxidative stress, lipid metabolism, and inflammatory responses[ 19 ]. The occurrence of ferroptosis may impair adipocyte function, leading to disrupted lipid breakdown and exacerbated chronic inflammation, both of which are crucial mechanisms in obesity pathogenesis[ 20 , 21 ]. Our findings suggest that regulating key ferroptosis-related genes, such as SMPD1 and SIRT3, could play a beneficial role in the prevention and intervention of childhood obesity. SIRT3 (Sirtuin 3) is a NAD⁺-dependent deacetylase predominantly localized in the mitochondria, where it is involved in regulating energy metabolism, oxidative stress, and apoptosis[ 22 , 23 ]. Studies have shown that SIRT3 is modulated under metabolic stress conditions (e.g., high-fat diet), and its inhibition is linked to the accelerated progression of metabolic syndrome. In mouse models fed a high-fat diet, SIRT3 downregulation leads to excessive mitochondrial protein acetylation, ultimately disrupting metabolic homeostasis[ 24 ]. Plasma metabolites, as key indicators of the body's metabolic state, play a crucial role in the development and progression of obesity. This study found that certain phosphatidylcholine metabolites (e.g., 1-linoleoyl-2-arachidonoyl-GPC) may have protective effects, while some short-chain fatty acid metabolites (e.g., butyrylcarnitine) may promote childhood obesity. These metabolites are primarily involved in lipid metabolism, redox balance, and inflammation regulation and may influence obesity risk by affecting energy expenditure, fat storage, and insulin sensitivity[ 25 ]. The SMPD1 gene encodes acid sphingomyelinase (ASMase), which plays a critical role in sphingolipid metabolism by hydrolyzing sphingomyelin (SM) into ceramide and phosphocholine. Ceramide is a key product of SMPD1-mediated sphingolipid metabolism[26]. The generation of ceramide can rapidly alter the physical properties of the cell membrane, forming ceramide-enriched membrane domains that influence protein aggregation and signal transduction. Additionally, ceramide can be further metabolized into other bioactive lipids, such as sphingosine-1-phosphate (S1P), which regulates cell proliferation, apoptosis, and inflammation[ 27 ]. These findings reveal the complex interactions between ferroptosis-related genes and plasma metabolites, highlighting the bridge role of metabolites in the relationship between ferroptosis and obesity. Future research could further explore the specific biological functions of these metabolites and their mechanistic roles in obesity development. lthough this study is based on large-scale genomic data and employs the Mendelian randomization (MR) method to reduce the impact of confounding factors, certain limitations remain. First, the analysis included only a limited set of plasma metabolites, and it did not cover all potential metabolic factors influencing childhood obesity. Future research should integrate larger-scale metabolomics datasets to obtain a more comprehensive understanding. Second, while Mendelian randomization provides robust causal inferences, this study does not directly reveal the specific biological mechanisms of ferroptosis in childhood obesity. Therefore, further cellular and animal model experiments are needed to validate the functional roles of these genes and metabolites. Ferroptosis-related genes have complex functions, and their roles may vary across different tissues and cell types. This study only investigated the relationship between ferroptosis-related genes and childhood obesity at the overall level, necessitating further research into their tissue- and cell-specific mechanisms. In conclusion, this study suggests that ferroptosis-related genes may play a key mediating role in the relationship between plasma metabolites and childhood obesity, shedding light on the potential mechanisms of ferroptosis in childhood obesity. These findings provide a new perspective on the molecular mechanisms underlying childhood obesity and may serve as a scientific foundation for future precision prevention and intervention strategies. Future research should further explore the specific role of ferroptosis in childhood obesity, combining experimental studies to validate the functions of these genes and metabolites in obesity, ultimately contributing to more precise targets for early diagnosis and intervention. Abbreviations MR ： Mendelian randomization GWAS ： Genome-wide association studies SNP ： single nucleotide polymorphism LPS ： lipopolysaccharides IVW ： Inverse Variance Weighted Declarations Data availability statement The datasets analyzed during the current study are available in the Finngen database (https://www.finngen.fi/en) and IEU OpenGWAS (https://gwas.mrcieu.ac.uk/). Ethics Statement In this MR study, we used publicly available aggregate data; therefore, no separate ethical approval is required. Funding Support This study was funded by the Guangxi Science and Technology Program Project (Guike AD22035121). Conflict of interest The authors declare no conflicts of interest Acknowledgement We would like to thank all the authors who contributed to the drafting of the manuscript Author Contributions Ji-Gan Wang designed the study and interpreted the results. Ji-Gan Wang and Xiu-Hua Pan was responsible for the conceptualization, methodology, data analysis, and manuscript writing. Xiu-Hua Pan and Yan Li participated in supervising the study, project management, and funding acquisition, and reviewed and edited the manuscript. References Kelly AS, Armstrong SC, Michalsky MP, Fox CK. Obesity in Adolescents: A Review. JAMA. 2024;332:738-48. Myette RL, Flynn JT. The ongoing impact of obesity on childhood hypertension. Pediatr Nephrol. 2024;39:2337-46. Yuan C, Dong Y, Chen H, Ma L, Jia L, Luo J, et al. Determinants of childhood obesity in China. Lancet Public Health. 2024;9:e1105-14. Dixon SJ, Olzmann JA. The cell biology of ferroptosis. Nat Rev Mol Cell Biol. 2024;25:424-42. 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Table Table : Mediation analysis results of ferroptosis in the pathway between plasma metabolites and childhood obesity Exposure Ferroptosis Outcome beta_all Mediating effect Direct effect Intermediate ratio 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) SMPD1 Childhood obesity -0.145 -0.012 -0.133 8.24% 1-(1-enyl-palmitoyl)-2-oleoyl-gpc (p-16:0/18:1) SMPD1 Childhood obesity -0.392 -0.038 -0.354 9.62% 1-stearoyl-2-arachidonoyl-gpc (18:0/20:4) SMPD1 Childhood obesity -0.105 -0.008 -0.097 8.05% 1-stearoyl-2-arachidonoyl-gpc (18:0/20:4) SIRT3 Childhood obesity -0.105 -0.008 -0.098 7.23% 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) SIRT3 Childhood obesity -0.145 -0.011 -0.134 7.72% Additional Declarations There is NO conflict of interest to disclose Supplementary Files Supplementarytable.doc Supplementary Table: Pleiotropy and heterogeneity analysis of nine plasma metabolites and four ferroptosis-related genes SupplementaryFigure1.tif Supplementary Figure 1: Reverse Mendelian randomization analysis results of plasma metabolites and childhood obesity SupplementaryFigure2.tif Supplementary Figure 2: Reverse Mendelian randomization analysis results of ferroptosis and childhood obesity Cite Share Download PDF Status: Published Journal Publication published 17 Nov, 2025 Read the published version in International Journal of Obesity → Version 1 posted Editorial decision: revise 17 Jul, 2025 Review # 2 received at journal 04 Jul, 2025 Review # 1 received at journal 04 Jul, 2025 Reviewer # 2 agreed at journal 22 Jun, 2025 Reviewer # 1 agreed at journal 13 Jun, 2025 Reviewers invited by journal 01 Apr, 2025 Submission checks completed at journal 31 Mar, 2025 First submitted to journal 30 Mar, 2025 Editor assigned by journal 30 Mar, 2025 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-6338689","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":437067893,"identity":"3511240c-a17b-4ace-add0-0c1e2d719648","order_by":0,"name":"Ji-Gan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYLACCQMgwd7Y+PADaVp4DjcbS5BoVXqbAA8xCg2Onz38wqKgVt5c8mEbgwSDnZxuAyEtZ/LSLCQMjhvunJ3Y9qCAIdnY7AABLWYHcswMJAyOMW64ndhuIMFwIHEbQS3n34C12G+4ebBNgocoLTdyjB9IGNQkbrjBSKQW+xtvzICBfCB5w5lEYCAbEOEXyf4c488Sf+psNxw//vDhhwo7OYJagIBNWoLhMJRtQFg5CDB//MBQR5zSUTAKRsEoGJkAAEOrRkMhX5RaAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-7775-3308","institution":"[email protected]","correspondingAuthor":true,"prefix":"","firstName":"Ji-Gan","middleName":"","lastName":"Wang","suffix":""},{"id":437067894,"identity":"e42ddcca-b1ff-4b43-b66b-ffed18eb2db9","order_by":1,"name":"Hui-Hong Dou","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hui-Hong","middleName":"","lastName":"Dou","suffix":""},{"id":437067895,"identity":"aa5fd3e3-3c10-4bb8-9707-57697e109962","order_by":2,"name":"Yan Li","email":"","orcid":"","institution":"Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-03-30 13:35:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6338689/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6338689/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41366-025-01951-x","type":"published","date":"2025-11-17T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81700095,"identity":"e2bbc4d6-09a2-44a4-b852-8014155c0c1b","added_by":"auto","created_at":"2025-04-30 13:04:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":838725,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design of ferroptosis-mediated relationship between plasma metabolites and childhood obesity\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6338689/v1/2a577f63c5e9128e9bb7a021.png"},{"id":81699868,"identity":"87f3c099-73e7-4a0d-b857-453148bad895","added_by":"auto","created_at":"2025-04-30 13:03:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":278979,"visible":true,"origin":"","legend":"\u003cp\u003eIntersection and matching results of ferroptosis-related genes and pQTLs\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6338689/v1/d1562e95d3681631064f2c30.png"},{"id":81700066,"identity":"98ffe1a1-47b4-4eb0-aaf2-f8371e65d777","added_by":"auto","created_at":"2025-04-30 13:04:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":480942,"visible":true,"origin":"","legend":"\u003cp\u003ea. Causal relationship between ferroptosis and childhood obesity\u003cbr\u003e\n\u003cstrong\u003eB\u003c/strong\u003e: Causal relationship between plasma metabolites and childhood obesity\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6338689/v1/bc9cf205f9079c984052bba7.png"},{"id":81700100,"identity":"0718043c-ee62-4a54-a5d2-a2abe084dcde","added_by":"auto","created_at":"2025-04-30 13:04:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":793326,"visible":true,"origin":"","legend":"\u003cp\u003eCausal relationship between plasma metabolites and ferroptosis\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6338689/v1/eb49a6fdfff7735b92c7da42.png"},{"id":96154842,"identity":"74dbe0b5-a471-4622-947f-fd158cef2255","added_by":"auto","created_at":"2025-11-18 08:15:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2773803,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6338689/v1/a7500d02-1cbb-4807-8174-192c450d0a8d.pdf"},{"id":81699738,"identity":"bb3ae779-cd31-43b3-8659-18a5b496fa33","added_by":"auto","created_at":"2025-04-30 13:03:54","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":60416,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table\u003c/strong\u003e: Pleiotropy and heterogeneity analysis of nine plasma metabolites and four ferroptosis-related genes\u003c/p\u003e","description":"","filename":"Supplementarytable.doc","url":"https://assets-eu.researchsquare.com/files/rs-6338689/v1/edd6f993bd9ea26717b77bc3.doc"},{"id":81699761,"identity":"4764983b-3b3c-455f-bfda-edbc9c9ee441","added_by":"auto","created_at":"2025-04-30 13:03:55","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2362692,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e: Reverse Mendelian randomization analysis results of plasma metabolites and childhood obesity\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6338689/v1/79d08e34f3bdb19d825458a4.tif"},{"id":81700127,"identity":"9500ec94-69c2-4c7c-bd86-b3126a59c63b","added_by":"auto","created_at":"2025-04-30 13:04:30","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1181442,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e: Reverse Mendelian randomization analysis results of ferroptosis and childhood obesity\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-6338689/v1/fd913fd721863ccfb509e58d.tif"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Plasma metabolites may inhibit childhood obesity by regulating ferroptosis through SMPD1 and SIRT3","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChildhood obesity has become a major global public health challenge. Its occurrence not only affects children's growth and development but also significantly increases the risk of metabolic diseases in adulthood, such as type 2 diabetes, cardiovascular diseases, and non-alcoholic fatty liver disease (NAFLD)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The development of obesity is influenced by genetic, environmental, and metabolic factors, among which metabolic disorders play a crucial role in the pathological mechanisms of childhood obesity[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In recent years, ferroptosis, a unique mode of regulated cell death, has garnered extensive attention in the progression of various diseases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, whether ferroptosis regulates childhood obesity risk by influencing plasma metabolites remains unclear due to a lack of systematic causal evidence.\u003c/p\u003e \u003cp\u003eFerroptosis is an iron-dependent form of regulated cell death characterized by lipid peroxidation of the cell membrane, with its core mechanisms involving iron metabolism, lipid metabolism, and the glutathione antioxidant system[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Previous studies have suggested that ferroptosis may affect lipid metabolism, thereby linking it to obesity and related metabolic disorders[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Meanwhile, plasma metabolites play a key role in regulating energy homeostasis, inflammation, and insulin resistance[8 ].Therefore, ferroptosis-related genes may influence the occurrence and progression of childhood obesity by modulating plasma metabolites. However, most existing studies are observational in nature, making it difficult to rule out confounding factors and reverse causality, leaving the true causal relationship between ferroptosis, plasma metabolites, and childhood obesity unclear.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a causal inference method based on genetic variation, which uses genetic variants as instrumental variables to assess the causal effect of an exposure (such as plasma metabolites or ferroptosis-related genes) on an outcome (childhood obesity)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Compared to traditional observational studies, MR analysis can mitigate the influence of confounding factors and reduce bias from reverse causality[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we employed a bidirectional two-step two-sample Mendelian randomization approach based on publicly available genome-wide association study (GWAS) datasets to explore the causal relationship between ferroptosis-related genes, plasma metabolites, and childhood obesity risk. Additionally, we investigated whether ferroptosis acts as a mediator between plasma metabolites and childhood obesity. The findings of this study will contribute to a deeper understanding of the role of ferroptosis in the pathogenesis of obesity and provide new scientific evidence for the prevention and treatment of childhood obesity.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Study Design\u003c/h2\u003e \u003cp\u003eThis study employed a bidirectional two-step two-sample Mendelian randomization (MR) approach using publicly available datasets to investigate the potential mediating role of ferroptosis-related genes in the relationship between 1,400 plasma metabolites and childhood obesity risk.\u003c/p\u003e \u003cp\u003eIn the first step, protein quantitative trait loci (pQTL) data corresponding to ferroptosis-related genes were identified as mediators. A two-sample Mendelian randomization analysis was then conducted separately to assess the causal effects of plasma metabolites and ferroptosis-related genes on childhood obesity, yielding \u003cb\u003ebeta_all\u003c/b\u003e and \u003cb\u003ebeta1\u003c/b\u003e, respectively.In the second step, ferroptosis-related genes and plasma metabolites that showed significant associations in the first step were further analyzed through Mendelian randomization to determine the causal effect of plasma metabolites on ferroptosis-related genes, yielding \u003cb\u003ebeta2\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eA mediation analysis can only be performed if a causal relationship exists in the triangular structure, meaning that the exposure (plasma metabolites) must have a causal effect on the outcome (childhood obesity), the mediator (ferroptosis-related genes) must have a causal effect on the outcome, and the exposure must have a causal effect on the mediator. The mediation effect was calculated as \u003cb\u003ebeta1 × beta2\u003c/b\u003e, and the mediation proportion was determined as \u003cb\u003e(beta1 × beta2) / beta_all\u003c/b\u003e (\u003cb\u003esee\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.2 Data Sources\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.2.1 Plasma Metabolites\u003c/h2\u003e \u003cp\u003eData on 1,400 plasma metabolites were obtained from a publicly available genome-wide association study (GWAS) dataset with accession numbers GCST90199621-90201020[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The study population consisted entirely of individuals of European ancestry.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.2.2 Childhood Obesity\u003c/h3\u003e\n\u003cp\u003eChildhood obesity data were sourced from a publicly available GWAS dataset with accession number ieu-a-1096, which included 5,530 cases and 8,318 controls, all of European ancestry[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003e1.2.3 pQTL (Plasma Protein Quantitative Trait Loci) Data\u003c/h3\u003e\n\u003cp\u003epQTL data were derived from a GWAS conducted on 35,559 Icelandic individuals, which measured plasma protein levels using 4,907 oligonucleotide aptamers[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The dataset is available at deCODE Genetics.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1.2.4 Ferroptosis-Related Genes\u003c/h2\u003e \u003cp\u003eFerroptosis-related genes were retrieved from \u003cb\u003eFerrDb\u003c/b\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.zhounan.org/ferrdb/current/\u003c/span\u003e\u003cspan address=\"http://www.zhounan.org/ferrdb/current/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the world’s first database dedicated to ferroptosis regulators and ferroptosis-disease associations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.3 Selection of Single Nucleotide Polymorphisms (SNPs)\u003c/h3\u003e\n\u003cp\u003eSelection of instrumental variables for PQTL and plasma metabolites: Set P \u0026lt; 5×10^-8, linkage disequilibrium parameters (r² = 0.1, kb = 10000). For palindromic SNPs, the forward strand alleles were determined using allele frequency information. The F-statistics for the selected instrumental variables (IVs) reached a threshold of \u0026gt; 10, ensuring that the causal estimations had no weak instrument bias.\u003c/p\u003e\n\u003ch3\u003e1.4 Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eThis study utilized the TwoSampleMR package in R software (version 4.3.3) to conduct bidirectional two-sample Mendelian randomization (MR) analysis, aiming to clarify the causal relationships among the study variables. The primary analytical method was the inverse variance weighted (IVW) meta-analysis, which effectively integrates causal effect estimates from multiple genetic instrumental variables (single nucleotide polymorphisms, SNPs) to obtain robust and efficient overall effect estimates[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].To ensure the robustness and reliability of the MR analysis, various approaches were employed for effect assessment and sensitivity analysis. The MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) method was used to detect and correct pleiotropy by identifying and removing outlier SNPs with abnormal effects, enhancing the validity of causal inference. Cochran’s Q test was applied to examine the heterogeneity in effect estimates among SNPs, where a \u003cb\u003eP-value \u0026lt; 0.05\u003c/b\u003e indicated heterogeneity among the genetic instrumental variables, necessitating careful interpretation of causal estimates. The MR-Egger intercept test was used to evaluate whether horizontal pleiotropy was present; a significant deviation of the MR-Egger intercept from zero (\u003cb\u003eP \u0026lt; 0.05\u003c/b\u003e) suggested the existence of pleiotropy that might compromise the reliability of the analysis. Additionally, a leave-one-out sensitivity analysis was performed by sequentially excluding individual SNPs to assess their influence on the overall causal effect estimate, ensuring the stability of the results[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].Furthermore, a mediation analysis was conducted within the MR framework to clarify the mediating role of specific variables in the causal pathway between exposure and outcome variables. The IVW method was used to estimate the effect of each causal pathway, and by comparing the significance and effect sizes of direct and indirect effects, the underlying mediation mechanism was explored. All statistical tests were two-sided, with the significance level set at \u003cb\u003eα = 0.05\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003ch2\u003e2.1 Causal Association Between Ferroptosis and Childhood Obesity\u003c/h2\u003e\u003cp\u003eAfter removing linkage disequilibrium (LD), 4,377 pQTLs remained. From the FerrDb database, a total of 483 ferroptosis-related genes with clearly defined roles in either promoting or inhibiting ferroptosis were identified. By intersecting these 483 ferroptosis-related genes with the pQTL dataset, a total of 159 ferroptosis-related genes corresponding to pQTLs were obtained (Fig.\u0026nbsp;2). These 159 ferroptosis-related genes were treated as the exposures, and childhood obesity was set as the outcome for bidirectional Mendelian randomization analysis. The results identified four ferroptosis-related genes that exhibited a causal relationship with childhood obesity. Among them, SMPD1, GSTZ1, and SIRT3 were found to suppress childhood obesity, while ADAMTS13 was found to promote childhood obesity (OR = 1.288, 95% CI = 1.084–1.529, P = 0.004) (Fig.\u0026nbsp;3A).\u003c/p\u003e\u003ch2\u003e2.2 Plasma Metabolites and Childhood Obesity\u003c/h2\u003e\u003cp\u003eOur analysis identified a total of nine plasma metabolites that exhibited a causal relationship with childhood obesity. Among them, 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4), 1-(1-enyl-palmitoyl)-2-oleoyl-GPC (P-16:0/18:1), 1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6), Hexadecenedioate (C16:1-DC), 1-stearoyl-2-arachidonoyl-GPC (18:0/20:4), and N-acetylputrescine to (N(1) + N(8))-acetylspermidine ratio were negatively associated with childhood obesity, with the strongest correlation observed for 1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6) (OR = 0.655, 95% CI = 0.470–0.913, P = 0.012). Conversely, 1-oleoyl-2-linoleoyl-GPE (18:1/18:2), Butyrylcarnitine (C4), and Ethylmalonate were identified as risk factors for childhood obesity, with 1-oleoyl-2-linoleoyl-GPE (18:1/18:2) exhibiting the strongest positive correlation (OR = 1.167, 95% CI = 1.034–1.318, P = 0.012) (Fig.\u0026nbsp;3B).\u003c/p\u003e\u003ch2\u003e2.3 Plasma Metabolites and Ferroptosis-Related Genes\u003c/h2\u003e\u003cp\u003eBased on the previous results, four ferroptosis-related genes were identified as potential mediators. A Mendelian randomization analysis was then conducted to examine the effect of these nine plasma metabolites on the ferroptosis-related genes, with plasma metabolites set as the exposure and ferroptosis-related genes as the outcome. A total of 15 significant pairings were identified, with specific results presented in \u003cb\u003eFig.\u0026nbsp;4.\u003c/b\u003e\u003c/p\u003e\u003ch2\u003e2.4 Mediation Analysis\u003c/h2\u003e\u003cp\u003eA total of five mediation relationships were identified. SMPD1-regulated ferroptosis mediated the effect of 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4), 1-(1-enyl-palmitoyl)-2-oleoyl-GPC (P-16:0/18:1), and 1-stearoyl-2-arachidonoyl-GPC (18:0/20:4) in inhibiting childhood obesity, with mediation proportions of 8.24%, 9.62%, and 8.05%, respectively. Similarly, SIRT3-regulated ferroptosis mediated the effect of 1-stearoyl-2-arachidonoyl-GPC (18:0/20:4) and 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4) in inhibiting childhood obesity, with mediation proportions of 7.23% and 7.72%, respectively (\u003cb\u003eTable\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e2.5 Sensitivity and Heterogeneity Analysis\u003c/p\u003e\u003cp\u003eA sensitivity analysis was performed on the relationships among plasma metabolites, ferroptosis-related genes, and childhood obesity. The MR-Egger regression confirmed the absence of horizontal pleiotropy, while Cochran’s Q test using the IVW method indicated no heterogeneity among SNPs (\u003cb\u003eSupplementary Table\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e2.6 Reverse Mendelian Randomization Analysis\u003c/p\u003e\u003cp\u003eTo verify whether there was a reverse causal relationship between plasma metabolites, ferroptosis-related genes, and childhood obesity, a reverse Mendelian randomization (MR) analysis was conducted. The selected SNPs did not show any significant effect of childhood obesity on the identified plasma metabolites (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e) or ferroptosis-related genes (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e), confirming the absence of reverse causality.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study utilized a Mendelian randomization (MR) approach to explore the potential mediating role of ferroptosis-related genes in the relationship between plasma metabolites and childhood obesity risk. The results indicated that certain ferroptosis-related genes, such as SMPD1, GSTZ1, and SIRT3, may suppress childhood obesity by regulating the ferroptosis process, while ADAMTS13 may promote childhood obesity. Additionally, nine plasma metabolites were found to have a causal relationship with childhood obesity, with six metabolites negatively associated and three metabolites identified as obesity risk factors. Further mediation analysis revealed that ferroptosis may serve as a key regulatory factor, indirectly influencing childhood obesity by modulating plasma metabolites. This finding not only strengthens the link between ferroptosis and metabolic disorders but also provides new insights into the pathophysiological mechanisms of childhood obesity.\u003c/p\u003e \u003cp\u003eIn recent years, ferroptosis, as a unique mode of cell death, has been increasingly recognized as being closely associated with various metabolic diseases. Ferroptosis primarily depends on iron ion accumulation and lipid peroxidation, leading to cell membrane damage and ultimately triggering cell death[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Previous studies have demonstrated that ferroptosis plays a significant role in energy metabolism regulation, insulin sensitivity, and inflammatory responses, suggesting that it may be a key factor influencing the development of obesity[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdipose tissue serves as the body's primary energy storage site, and its metabolic homeostasis is regulated by oxidative stress, lipid metabolism, and inflammatory responses[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The occurrence of ferroptosis may impair adipocyte function, leading to disrupted lipid breakdown and exacerbated chronic inflammation, both of which are crucial mechanisms in obesity pathogenesis[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our findings suggest that regulating key ferroptosis-related genes, such as SMPD1 and SIRT3, could play a beneficial role in the prevention and intervention of childhood obesity.\u003c/p\u003e \u003cp\u003eSIRT3 (Sirtuin 3) is a NAD⁺-dependent deacetylase predominantly localized in the mitochondria, where it is involved in regulating energy metabolism, oxidative stress, and apoptosis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Studies have shown that SIRT3 is modulated under metabolic stress conditions (e.g., high-fat diet), and its inhibition is linked to the accelerated progression of metabolic syndrome. In mouse models fed a high-fat diet, SIRT3 downregulation leads to excessive mitochondrial protein acetylation, ultimately disrupting metabolic homeostasis[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePlasma metabolites, as key indicators of the body's metabolic state, play a crucial role in the development and progression of obesity. This study found that certain phosphatidylcholine metabolites (e.g., 1-linoleoyl-2-arachidonoyl-GPC) may have protective effects, while some short-chain fatty acid metabolites (e.g., butyrylcarnitine) may promote childhood obesity. These metabolites are primarily involved in lipid metabolism, redox balance, and inflammation regulation and may influence obesity risk by affecting energy expenditure, fat storage, and insulin sensitivity[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The SMPD1 gene encodes acid sphingomyelinase (ASMase), which plays a critical role in sphingolipid metabolism by hydrolyzing sphingomyelin (SM) into ceramide and phosphocholine. Ceramide is a key product of SMPD1-mediated sphingolipid metabolism[26]. The generation of ceramide can rapidly alter the physical properties of the cell membrane, forming ceramide-enriched membrane domains that influence protein aggregation and signal transduction. Additionally, ceramide can be further metabolized into other bioactive lipids, such as sphingosine-1-phosphate (S1P), which regulates cell proliferation, apoptosis, and inflammation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These findings reveal the complex interactions between ferroptosis-related genes and plasma metabolites, highlighting the bridge role of metabolites in the relationship between ferroptosis and obesity. Future research could further explore the specific biological functions of these metabolites and their mechanistic roles in obesity development.\u003c/p\u003e \u003cp\u003elthough this study is based on large-scale genomic data and employs the Mendelian randomization (MR) method to reduce the impact of confounding factors, certain limitations remain. First, the analysis included only a limited set of plasma metabolites, and it did not cover all potential metabolic factors influencing childhood obesity. Future research should integrate larger-scale metabolomics datasets to obtain a more comprehensive understanding. Second, while Mendelian randomization provides robust causal inferences, this study does not directly reveal the specific biological mechanisms of ferroptosis in childhood obesity. Therefore, further cellular and animal model experiments are needed to validate the functional roles of these genes and metabolites. Ferroptosis-related genes have complex functions, and their roles may vary across different tissues and cell types. This study only investigated the relationship between ferroptosis-related genes and childhood obesity at the overall level, necessitating further research into their tissue- and cell-specific mechanisms.\u003c/p\u003e \u003cp\u003eIn conclusion, this study suggests that ferroptosis-related genes may play a key mediating role in the relationship between plasma metabolites and childhood obesity, shedding light on the potential mechanisms of ferroptosis in childhood obesity. These findings provide a new perspective on the molecular mechanisms underlying childhood obesity and may serve as a scientific foundation for future precision prevention and intervention strategies. Future research should further explore the specific role of ferroptosis in childhood obesity, combining experimental studies to validate the functions of these genes and metabolites in obesity, ultimately contributing to more precise targets for early diagnosis and intervention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eMR\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eMendelian randomization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eGenome-wide association studies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003esingle nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLPS\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003elipopolysaccharides\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIVW\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eInverse Variance Weighted\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Data availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the Finngen database (https://www.finngen.fi/en) and IEU OpenGWAS (https://gwas.mrcieu.ac.uk/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this MR study, we used publicly available aggregate data; therefore, no separate ethical approval is required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Guangxi Science and Technology Program Project (Guike AD22035121).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWe would like to thank all the authors who contributed to the drafting of the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJi-Gan Wang designed the study and interpreted the results. Ji-Gan Wang and Xiu-Hua Pan was responsible for the conceptualization, methodology, data analysis, and manuscript writing. Xiu-Hua Pan and Yan Li participated in supervising the study, project management, and funding acquisition, and reviewed and edited the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKelly AS, Armstrong SC, Michalsky MP, Fox CK. Obesity in Adolescents: A Review. JAMA. 2024;332:738-48.\u003c/li\u003e\n\u003cli\u003eMyette RL, Flynn JT. The ongoing impact of obesity on childhood hypertension. Pediatr Nephrol. 2024;39:2337-46.\u003c/li\u003e\n\u003cli\u003eYuan C, Dong Y, Chen H, Ma L, Jia L, Luo J, et al. Determinants of childhood obesity in China. Lancet Public Health. 2024;9:e1105-14.\u003c/li\u003e\n\u003cli\u003eDixon SJ, Olzmann JA. The cell biology of ferroptosis. Nat Rev Mol Cell Biol. 2024;25:424-42.\u003c/li\u003e\n\u003cli\u003eDai E, Chen X, Linkermann A, Jiang X, Kang R, Kagan VE, et al. A guideline on the molecular ecosystem regulating ferroptosis. Nat Cell Biol. 2024;26:1447-57.\u003c/li\u003e\n\u003cli\u003eLi S, Zhang G, Hu J, Tian Y, Fu X. Ferroptosis at the nexus of metabolism and metabolic diseases. Theranostics. 2024;14:5826-52.\u003c/li\u003e\n\u003cli\u003eMa W, Jia L, Xiong Q, Du H. Iron Overload Protects from Obesity by Ferroptosis. Foods. 2021;10.\u003c/li\u003e\n\u003cli\u003eGijbels A, Jardon KM, Trouwborst I, Manusama KC, Goossens GH, Blaak EE, et al. Fasting and postprandial plasma metabolite responses to a 12-wk dietary intervention in tissue-specific insulin resistance: a secondary analysis of the PERSonalized glucose Optimization through Nutritional intervention (PERSON) randomized trial. Am J Clin Nutr. 2024;120:347-59.\u003c/li\u003e\n\u003cli\u003eBurgess S, Thompson SG. Use of allele scores as instrumental variables for Mendelian randomization. Int J Epidemiol. 2013;42:1134-44.\u003c/li\u003e\n\u003cli\u003eSekula P, Del GMF, Pattaro C, Kottgen A. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol. 2016;27:3253-65.\u003c/li\u003e\n\u003cli\u003eChen Y, Lu T, Pettersson-Kymmer U, Stewart ID, Butler-Laporte G, Nakanishi T, et al. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat Genet. 2023;55:44-53.\u003c/li\u003e\n\u003cli\u003eBradfield JP, Taal HR, Timpson NJ, Scherag A, Lecoeur C, Warrington NM, et al. A genome-wide association meta-analysis identifies new childhood obesity loci. Nat Genet. 2012;44:526-31.\u003c/li\u003e\n\u003cli\u003eFerkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53:1712-21.\u003c/li\u003e\n\u003cli\u003eBowden J, Holmes MV. Meta-analysis and Mendelian randomization: A review. Res Synth Methods. 2019;10:486-96.\u003c/li\u003e\n\u003cli\u003eBurgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res. 2017;26:2333-55.\u003c/li\u003e\n\u003cli\u003eJiang X, Stockwell BR, Conrad M. Ferroptosis: mechanisms, biology and role in disease. Nat Rev Mol Cell Biol. 2021;22:266-82.\u003c/li\u003e\n\u003cli\u003eSun Y, Chen P, Zhai B, Zhang M, Xiang Y, Fang J, et al. The emerging role of ferroptosis in inflammation. Biomed Pharmacother. 2020;127:110108.\u003c/li\u003e\n\u003cli\u003eChen X, Kang R, Kroemer G, Tang D. Ferroptosis in infection, inflammation, and immunity. J Exp Med. 2021;218.\u003c/li\u003e\n\u003cli\u003eCho CH, Patel S, Rajbhandari P. Adipose tissue lipid metabolism: lipolysis. Curr Opin Genet Dev. 2023;83:102114.\u003c/li\u003e\n\u003cli\u003ePope LE, Dixon SJ. Regulation of ferroptosis by lipid metabolism. Trends Cell Biol. 2023;33:1077-87.\u003c/li\u003e\n\u003cli\u003eLin Z, Liu J, Kang R, Yang M, Tang D. Lipid Metabolism in Ferroptosis. Adv Biol (Weinh). 2021;5:e2100396.\u003c/li\u003e\n\u003cli\u003eZhou L, Pinho R, Gu Y, Radak Z. The Role of SIRT3 in Exercise and Aging. Cells. 2022;11.\u003c/li\u003e\n\u003cli\u003eDiao Z, Ji Q, Wu Z, Zhang W, Cai Y, Wang Z, et al. SIRT3 consolidates heterochromatin and counteracts senescence. Nucleic Acids Res. 2021;49:4203-19.\u003c/li\u003e\n\u003cli\u003eGreen MF, Hirschey MD. SIRT3 weighs heavily in the metabolic balance: a new role for SIRT3 in metabolic syndrome. J Gerontol A Biol Sci Med Sci. 2013;68:105-7.\u003c/li\u003e\n\u003cli\u003eHuang Y, Sulek K, Stinson SE, Holm LA, Kim M, Trost K, et al. Lipid profiling identifies modifiable signatures of cardiometabolic risk in children and adolescents with obesity. Nat Med. 2025;31:294-305.\u003c/li\u003e\n\u003cli\u003eUeda S, Manabe Y, Kubo N, Morino N, Yuasa H, Shiotsu M, et al. Early secretory pathway-resident Zn transporter proteins contribute to cellular sphingolipid metabolism through activation of sphingomyelin phosphodiesterase 1. Am J Physiol Cell Physiol. 2022;322:C948-59.\u003c/li\u003e\n\u003cli\u003eGorelik A, Illes K, Heinz LX, Superti-Furga G, Nagar B. Crystal structure of mammalian acid sphingomyelinase. Nat Commun. 2016;7:12196.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table ","content":"\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e: Mediation analysis results of ferroptosis in the pathway between plasma metabolites and childhood obesity\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"874\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eFerroptosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003ebeta_all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eMediating effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eIntermediate ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003e1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eSMPD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eChildhood obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.145\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.012\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.133\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e8.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003e1-(1-enyl-palmitoyl)-2-oleoyl-gpc (p-16:0/18:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eSMPD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eChildhood obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.392\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.038\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.354\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e9.62%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003e1-stearoyl-2-arachidonoyl-gpc (18:0/20:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eSMPD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eChildhood obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.105\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.097\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e8.05%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003e1-stearoyl-2-arachidonoyl-gpc (18:0/20:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eSIRT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eChildhood obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.105\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.098\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7.23%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003e1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eSIRT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eChildhood obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.145\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.134\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Childhood obesity, Plasma metabolites, Ferroptosis, MR, GWAS","lastPublishedDoi":"10.21203/rs.3.rs-6338689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6338689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eObjective\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo investigate the causal relationship between plasma metabolites and ferroptosis-related genes in childhood obesity and to explore the potential mediating role of ferroptosis-related genes in the association between plasma metabolites and childhood obesity risk.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA bidirectional two-step Mendelian randomization (MR) approach was applied, leveraging publicly available genome-wide association study (GWAS) datasets to analyze the causal relationship among 1,400 plasma metabolites, 159 ferroptosis-related genes, and childhood obesity. In the first step, protein quantitative trait loci (pQTL) data corresponding to ferroptosis-related genes were identified as mediators to evaluate the causal effects of plasma metabolites and ferroptosis-related genes on childhood obesity. In the second step, MR analysis was conducted on ferroptosis-related genes and plasma metabolites identified in the first step to confirm their causal association. The inverse-variance weighted (IVW) method was primarily used for meta-analysis, while MR-PRESSO was employed to detect pleiotropy and outliers.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFour ferroptosis-related genes (SMPD1 and SIRT3 suppressing obesity, GSTZ1 and ADAMTS13 promoting obesity) and nine plasma metabolites were found to be significantly associated with childhood obesity (six negatively correlated and three positively correlated). Further mediation analysis indicated that the ferroptosis mechanism regulated by SMPD1 and SIRT3 partially mediated the association between specific plasma metabolites and childhood obesity, with the highest mediation proportion reaching 9.62%. Sensitivity analysis confirmed the robustness of the results (no heterogeneity or horizontal pleiotropy), and reverse Mendelian randomization ruled out causal interference.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study is the first to reveal, through Mendelian randomization analysis, the potential mediating role of ferroptosis-related genes in the association between plasma metabolites and childhood obesity. It suggests that the ferroptosis mechanism may influence childhood obesity risk by regulating specific metabolites. These findings contribute to understanding the role of ferroptosis in the pathological mechanisms of childhood obesity and provide novel molecular targets and intervention strategies for obesity prevention and treatment in children.\u003c/p\u003e","manuscriptTitle":"Plasma metabolites may inhibit childhood obesity by regulating ferroptosis through SMPD1 and SIRT3","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 12:20:43","doi":"10.21203/rs.3.rs-6338689/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-07-17T14:11:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-07-04T21:19:47+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-07-04T06:23:20+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-06-22T22:17:18+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-06-13T18:22:35+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-04-01T20:33:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-31T11:34:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Obesity","date":"2025-03-30T13:34:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-30T13:34:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ff353943-88f1-44a5-adfc-97e9161e0404","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46540526,"name":"Biological sciences/Immunology/Immunological disorders"},{"id":46540527,"name":"Health sciences/Diseases/Nutrition disorders"}],"tags":[],"updatedAt":"2025-11-18T08:15:05+00:00","versionOfRecord":{"articleIdentity":"rs-6338689","link":"https://doi.org/10.1038/s41366-025-01951-x","journal":{"identity":"international-journal-of-obesity","isVorOnly":false,"title":"International Journal of Obesity"},"publishedOn":"2025-11-17 05:00:00","publishedOnDateReadable":"November 17th, 2025"},"versionCreatedAt":"2025-04-30 12:20:43","video":"","vorDoi":"10.1038/s41366-025-01951-x","vorDoiUrl":"https://doi.org/10.1038/s41366-025-01951-x","workflowStages":[]},"version":"v1","identity":"rs-6338689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6338689","identity":"rs-6338689","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}