Causal plasma metabolites for breast cancer risk: a two-sample Mendelian randomization study with colocalization evidence

Causal plasma metabolites for breast cancer risk: a two-sample Mendelian randomization study with colocalization evidence | 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 Research Article Causal plasma metabolites for breast cancer risk: a two-sample Mendelian randomization study with colocalization evidence Hanghang Chen, Yueyuan Xu, Zepeng Wang, Xufeng Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6417186/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background This study aimed to estimate causal effects of 1,400 human plasma metabolites on breast cancer (BC) risk using a two-sample Mendelian randomization (MR) framework. Methods We employed a two-sample Mendelian randomization framework to investigate causal associations between plasma metabolites and BC risk. We applied strict quality control with Bonferroni correction and conducted meta-analyses to verify result robustness. Colocalization analysis assessed shared genetic variants between causal metabolites and BC risk. Phenome-wide MR (PheWAS-MR) systematically evaluated metabolite associations across all FinnGen phenotypes. Results Five genetically determined plasma metabolites were identified as the potential causal biomarkers for BC risk, including 3,5-dichloro-2,6-dihydroxybenzoic acid (odds ratio [OR]: 0.90; 95% confidence interval [CI]: 0.87–0.94; p < 0.001), carnitine C14 (OR: 0.72; 95% CI: 0.64–0.83; p < 0.001) and epiandrosterone sulfate (OR: 1.04; 95% CI: 1.01–1.06; p < 0.001), Glyco-beta-muricholate (OR: 0.95; 95% CI: 0.93–0.97; p < 0.001), N4-acetylcytidine (OR: 0.93; 95% CI: 0.91–0.96; p < 0.001). Colocalization analysis indicates Glyco − beta − muricholate and Epiandrosterone sulfate were found strong colocalization evidence with BC risk (PPH4 = 1). Conclusions Genetically determined 3,5-dichloro-2,6-dihydroxybenzoic acid, carnitine C14, glyco-beta-muricholate, and N4-acetylcytidine were associated with reduced BC risk, while epiandrosterone sulfate correlated with increased risk. Glyco-beta-muricholate and epiandrosterone sulfate demonstrated colocalization with BC pathogenesis. Genome Wide Association Study (GWAS) Mendelian Randomization (MR) phenome-wide Mendelian randomization (PheWAS-MR) Colocalization Breast Cancer (BC) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Breast cancer (BC) has the highest cancer incidence in women worldwide 1 . In 2023, there were over 1,300,590 estimated new cases and over 43,700 estimated deaths worldwide 2 . Epidemiological investigations have identified risk factors for BC, including genetic factors (BRCA 1/2 genes), reproductive factors such as age at menarche and menopause, number of childbirths, period of breastfeeding or hormone replacement therapy, and modifiable risk factors such as alcohol consumption, smoking, physical inactivity, or obesity 3 . Previous epidemiological studies have investigated possible mediators for BC, but specific biomarkers still need further identification 4 ; the role of human plasma metabolites in BC development is unclear. Before embarking on expensive clinical trials, studying potential biomarkers associated with BC onset and progression is critical. The occurrence and development of BC undergoes a complex and multifaceted biochemical metabolic process 5 . The rapid advancement of metabolomics techniques has facilitated direct measurement of metabolite abundance 6 , enabling better identification of metabolites distinguishing BC patients from healthy controls 7 . Prior studies have highlighted significant differences in nucleotide and lipid metabolism between ER-positive and ER-negative BCs 8 . Additionally, β-carotene has demonstrated a capacity to reduce BC risk 9 . Examination of BC patients has yielded preliminary evidence implicating extensive dysregulation of oxidized phosphatidylcholines, sphingomyelins, and triacylglycerols in BC pathogenesis 10 . Furthermore, a study aimed at identifying novel BC biomarkers through Mendelian randomization revealed a potential inverse causal association between 1-oleoylglycerophosphocholine and BC, suggesting a potential clinical biomarker 11 . Nonetheless, large-scale metabolomics research linking BC to human plasma metabolites remains insufficient. Mendelian randomization (MR), an established epidemiological approach, enables causal inference between exposures and outcomes 12 , 13 . Previous MR studies have identified potential associations between plasma metabolites and BC. Comprehensive characterization of these metabolite-BC relationships remains crucial for elucidating disease mechanisms. We integrated large-scale metabQTLs, GWAS datasets, and MR analyses to systematically investigate causal effects of 1,400 genetically determined plasma metabolites on BC risk. Our multi-modal approach provides robust genetic evidence for metabolites' causal roles in BC pathogenesis, while demonstrating how molecular profiling combined with genetic associations advances etiological understanding. MATERIALS AND METHODS This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) guidelines 14 . Valid causal inference relies on three core assumptions: (1) genetic variants show strong associations with exposure; (2) genetic variants exhibit no associations with confounders; and (3) genetic variants influence outcomes exclusively through exposure-outcome pathways 15 . Data sources All exposure and outcome cohorts were restricted to individuals of European ancestry to both ensure access to large-scale metabQTL data and mitigate population stratification bias. Exposure and outcome characteristics are summarized in Table 1. MetabQTL data for 1,400 plasma metabolites were obtained from the Canadian Longitudinal Study on Aging (CLSA) 16 . Analyses utilized the CLSA Baseline Comprehensive Dataset version 4.0, with genome-wide genotyping performed on DNA samples from 8,192 randomly selected participants in the Comprehensive Cohort. To ensure analytical reproducibility, we analyzed three major GWAS summary datasets for outcome assessment: breast cancer (BC) risk data from the Breast Cancer Association Consortium (BCAC OncoArray, n = 138,508), BCAC iCOGS (n = 76,167) 17 and FinnGen Release 10 (R10, n = 201,713) 18 . Detailed data descriptions are provided in source publications. For phenome-wide Mendelian randomization (PheWAS-MR) analyses evaluating metabolite-disease associations, we included all FinnGen phenotype GWAS data (2,272 outcomes). Phenotypes with < 100 cases were excluded to maintain statistical robustness. Table 1 Detailed information on the exposures and outcomes. Exposure Outcome Sample size ncase ncontrol Consortium Ancestry Plasma metabolites / 8,192 / / CLSA European / BC risk 133,164 15,680 167,189 FinnGen European / BC risk 138,508 80,125 58,383 BCAC OncoArray European / BC risk 76,167 38,349 37,818 BCAC iCOGS European / BC survival 91,686 BCAC European Two sample MR analysis We employed “TwoSampleMR” R package 19 to conduct the two sample MR analysis. Inverse variance weighted (IVW) is the most potent and extensively utilized MR method 20 , and thus we selected IVW as our primary analysis method. Instrumental variables (IVs) were defined as SNPs that strongly correlated with exposure ( p < 5e-8). If there is just one IV available, then the Wald ratio method was the only choice 21 . First, we searched for the IVs for the plasma level of each metabolite. We next performed linkage disequilibrium (LD) clumping and identified independent genetic instrument variables using plink software (V1.9). LD was defined as R 2 < 0.1 within a clumping distance 100kb, and SNPs with LD were excluded 22,23 . F value of each instrumental variable was calculated using the formula: F = (\(\:\text{n}-2)\times\:{\text{R}}^{2}÷(1-{\text{R}}^{2})\) (R 2 : interpretability of instrumental variables, n: sample size). R 2 could be calculated using this formula: \(\:{R}^{2}={{\beta\:}}^{2}(1-\text{E}\text{A}\text{F})\times\:2\text{E}\text{A}\text{F}\) (EAF: effect elle frequency, β: effect size) 24 . Conventionally, an IV with F < 10 was defined as weak IV and was removed to avoid weak instrumental variable bias 21 . Secondly, we extracted the same SNPs from the outcome data. In cases where certain SNPs could not be identified in the outcome data, we opted not to seek proxies. Third, SNPs for an outcome and exposure were harmonized to be relative to the same allele. MR results could be obtained using “mr” function on the harmonized data. Odd ratio (OR) could be calculated using the formula: OR = exp(β). We used the “p.adjust” function to perform false discovery rate (FDR) correction to adjust the p values when conducting MR analysis between metabolites and BC risk. Sensitivity test We used Cochran’s Q statistics to test the heterogeneity 25 and the MR-Egger method to test the pleiotropy 26 of the harmonized data. A p value greater than 0.05 was considered indicative of the absence of heterogeneity or pleiotropy. Furthermore, the MR Steiger directionality test was employed to exclude potential reverse causalities 27 . Meta analysis We intersected all significant metabolites from three outcomes to ensure the repeatability of analysis results. Furthermore, to ensure the robustness of the results, we performed meta analysis for all associations using the “meta” R package (V6.2-1). Cochran’s Q test and Higgins’s I 2 test were used to test the heterogeneity among studies 28 . If p 50%, heterogeneity was considered to exist among studies and the random effects model was used, otherwise, fixed effects model was selected as the main meta method 29 . Colocalization analysis To verify that causal metabolites and BC share causal variants in designated gene regions, we used coloc R package 30 for genetic colocalization analysis. We defined the 1Mb region upstream and downstream of the lead SNP for each protein as the validation region. If the posterior probability 4 (PH4) of shared causal variation is ≥ 0.8 31 , the two traits were considered to have strong colocalization support. MR analysis of metabolites and BC subtypes Based on the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), BC can be defined as five molecular subtypes: (1) luminal A-like, (2) luminal B/HER2-negative-like, (3) luminal B-like, (4) HER2-enriched-like and (5) triple-negative or basal-like. We also used the same MR and sensitivity analysis methods to explore the causal associations between causal metabolites and specific BC subtypes. PheWAS-MR analysis To figure out the potential functions and side effects, we conducted PheWAS-MR between the significant plasma metabolites associated with BC risk and all health outcomes in FinnGen. The calculation parameters were selected as above-mentioned. Statistical methods MR analysis were conducted using IVW methods if there is more than one shared SNP in the exposure and outcome data. The Wald ratio method was used when there was only one shared SNP. An F-value greater than 10 was considered a prerequisite to mitigate the risk of weak instrumental variable bias. FDR and Bonferroni corrections were used to adjust the p values and adjusted p < 0.05 was considered indicative of causal associations between the two traits. In the meta analysis, Cochran’s Q test and Higgins’s I 2 test were used to test heterogeneity between studies. RESULTS The overall study design is illustrated in Fig. 1 . We employed a two-sample MR framework to integrate causal associations between exposures (1,400 human plasma metabolites) and the outcomes (BC risk). Subsequently, meta-analysis was conducted to assess the robustness and reliability of all identified associations. The associations between metabolites and BC risk Through a two sample MR analysis for causal assessment between each plasma metabolite and the outcome, the significant associations and the sensitivity analysis results between human plasma metabolites and BC (FinnGen) were displayed in Table S1 . Results with p > 0.05 in the sensitivity analysis was defined as lacking heterogeneity or pleiotropy. 81 plasma metabolites were found to pass the sensitivity test and were causally associated with BC risk after FDR correction (FinnGen). The effect size and FDR-corrected p value of all significant metabolites associated BC risk (FinnGen) were displayed in Fig. 2 A. The significant associations and the sensitivity analysis results between plasma metabolites and BC (BCAC OncoArray) were displayed in Table S2. 86 plasma metabolites passed the sensitivity test and causally associated with BC after FDR correction (BCAC OncoArray). The effect size and FDR-corrected p values of all significant metabolites associated BC risk (BCAC OncoArray) were displayed in Fig. 2 B. The significant associations and the sensitivity analysis results between plasma metabolites and BC (BCAC iCOGS) were displayed in Table S3. 82 plasma metabolites passed the sensitivity test and were causally associated with BC risk after FDR correction (BCAC iCOGS). The effect size and FDR-corrected p values of all significant metabolites associated BC risk (BCAC iCOGS) were displayed in Fig. 2 C. To obtain more reliable results, the significantly positive results of the above three analyses are intersected, which are shown in the Venn Diagram in Fig. 2 D. Five plasma metabolites are all significantly associated with BC risk from FinnGen, BCAC (OncoArray) and BCAC (iCOGS), including 3,5 − dichloro − 2,6 − dihydroxybenzoic acid, Carnitine C14, Epiandrosterone sulfate, Glyco − beta − muricholate, and N4 − acetylcytidine. Significant results were displayed in Table 2 . The proxy SNPs for all metabolites were displayed in Table S4. The effect size of MR results between these five metabolites and three BC outcomes were showed in figure S1 A. Figure S1 B shows the OR and 95% CI of MR results between five FDR-corrected significant plasma metabolites and three BC outcomes. Table 2 Significant Mendelian randomization (MR) results between plasma metabolites and breast cancer risk. Exposure Outcome OR lci95 uci95 pval FDR 3,5-dichloro-2,6-dihydroxybenzoic acid FinnGen 0.897 0.832 0.968 0.005 0.046 BCAC_OncoArray 0.902 0.853 0.955 0.0004 0.006 BCAC_iCOGS 0.912 0.856 0.973 0.005 0.047 Carnitine C14 FinnGen 0.644 0.569 0.730 5.13E-12 6.26E-10 BCAC_OncoArray 0.811 0.746 0.881 7.85E-07 4.05E-05 BCAC_iCOGS 0.714 0.639 0.798 2.87E-09 5.50E-07 Epiandrosterone sulfate FinnGen 1.056 1.040 1.072 2.86E-12 5.57E-10 BCAC_OncoArray 1.022 1.008 1.036 0.002234 0.023197 BCAC_iCOGS 1.028 1.009 1.047 0.003190 0.035567 Glyco-beta-muricholate FinnGen 0.929 0.907 0.951 1.40E-09 8.51E-08 BCAC_OncoArray 0.967 0.947 0.987 0.002 0.02 BCAC_iCOGS 0.956 0.929 0.983 0.002 0.023 N4-acetylcytidine FinnGen 0.923 0.880 0.968 0.001 0.015 BCAC_OncoArray 0.947 0.915 0.980 0.002 0.022 BCAC_iCOGS 0.914 0.869 0.962 0.0005 0.01 OR: odds ratio. lci95: lower 95% confidence interval. uci95: upper 95% confidence interval. FDR: false discovery rate. Meta analysis validated the robustness of the MR results All meta results of the two MR results between five significant plasma metabolites and two BC outcomes were displayed in Fig. 3 . The heterogeneity was considered to exist among studies between carnitine C14, Epiandrosterone sulfate, Glyco-beta-muricholate and BC risk, and random effects model was used. 3,5-dichloro-2,6-dihydroxybenzoic acid is associated with reduced BC risk (Fig. 3 A, odds ratio [OR]: 0.90; 95% confidence interval [CI]: 0.87–0.94; p < 0.001). Carnitine C14 is associated with reduced BC risk (Fig. 3 B, OR: 0.72; 95% CI: 0.64–0.83; p < 0.001). Epiandrosterone sulfate is associated with elevated BC risk (Fig. 3 C, OR: 1.04; 95% CI: 1.01–1.06; p < 0.001). Glyco-beta-muricholate is associated with reduced BC risk (Fig. 3 D, OR: 0.95; 95% CI: 0.93–0.97; p < 0.001). N4-acetylcytidine is associated with reduced BC risk (Fig. 3 E, OR: 0.93; 95% CI: 0.91–0.96; p < 0.001). Colocalization analysis indicate shared variants between metabolites and BC risk Colocalization analysis can verify whether there are shared variants between traits in the specified gene region. PPH4 > 0.8 means strong colocalization evidence. Among the 5 metabolites that have causal associations with BC risk, Glyco − beta − muricholate and Epiandrosterone sulfate were found strong colocalization evidence with BC risk (PPH4 = 1, Fig. 4 ). The associations between five metabolites and BC subtypes Potential causal associations were identified between four of the five metabolites and four BC subtypes (Fig. 5 , Table 3 ). 3,5-dichloro-2,6-dihydroxybenzoic acid is negatively associated with Luminal_B_HER2Neg and TNBC risk. Epiandrosterone sulfate could potentially elevate the Luminal subtype risk. Glyco-beta-muricholate could also elevate the Luminal subtype risk except Luminal_B. N4-acetylcytidine is only associated with reduced Luminal_A risk. Table 3 Significant MR results between plasma metabolites and breast cancer subtype risk. Exposure Outcome OR lci95 uci95 pval 3,5-dichloro-2,6-dihydroxybenzoic acid Luminal_B_HER2Neg TNBC 0.822 0.751 0.900 2.27E-05 3,5-dichloro-2,6-dihydroxybenzoic acid 0.816 0.711 0.936 0.004 Epiandrosterone sulfate Luminal_A 1.040 1.024 1.056 3.03E-07 Epiandrosterone sulfate Luminal_B 1.051 1.018 1.085 0.002 Epiandrosterone sulfate Luminal_B_HER2Neg 1.065 1.036 1.095 8.21E-06 Glyco-beta-muricholate Luminal_A 0.945 0.925 0.965 2.20E-07 Glyco-beta-muricholate Luminal_B_HER2Neg 0.937 0.893 0.983 0.008 N4-acetylcytidine Luminal_A 0.952 0.918 0.987 0.007 OR: odds ratio. lci95: lower 95% confidence interval. uci95: upper 95% confidence interval. The associations between five metabolites and all health outcomes To figure out the other potential functions and side effects of five metabolites, the PheWAS-MR was conducted between five metabolites and all health outcomes in FinnGen. 2099 phenotypes with more than 100 cases in FinnGen were collected as outcomes. Through strict Bonferroni correction and sensitivity analysis, 3,5-dichloro-2,6-dihydroxybenzoic acid is significantly associated with 6 phenotypes; carnitine C14 is significantly associated with 96 phenotypes; epiandrosterone sulfate is significantly associated with 78 phenotypes; Glyco-beta-muricholate is significantly associated with 45 phenotypes; N4-acetylcytidine is significantly associated with one phenotype. The associations were shown in Fig. 6 and Table S5. DISCUSSION This integrative metabolomics-genomics MR study advances biomarker discovery for BC by identifying novel causal associations. Genetic influences on mutations related to BC susceptibility have been increasingly reported in experimental and observational research 32 . Through unbiased two-sample MR analysis of 1,400 plasma metabolites, we identified five causal biomarkers for BC risk: 3,5-dichloro-2,6-dihydroxybenzoic acid, carnitine C14, glyco-beta-muricholate, N4-acetylcytidine, and epiandrosterone sulfate. Four plasma metabolites showed inverse associations with BC risk, with carnitine C14 demonstrating the strongest effect. This long-chain acylcarnitine facilitates mitochondrial fatty acid transport 33 , supports ketogenesis, and regulates energy metabolism 34 . While acylcarnitine dysregulation has disease implications 35 , carnitine C14 remains underexplored in oncology compared to other acylcarnitines. Existing evidence links carnitine C14 to reduced prostate cancer progression 36 and its utility in esophageal squamous cell carcinoma profiling 33 . Notably, carnitine accumulation via CPT2 downregulation promotes hepatocarcinogenesis through STAT3 activation 37 . Our study reveals carnitine C14's protective association against BC. However, PheWAS-MR analyses indicate positive associations with respiratory/circulatory disorders including asthma, COPD, and atrial fibrillation. These dual effects necessitate comprehensive reevaluation of carnitine C14's therapeutic potential and systemic impacts. Epiandrosterone sulfate, the most abundant steroid hormone in adult women, exhibits age-dependent concentration declines 38 . Prior studies have linked elevated levels to increased BC risk in women 39 , 40 , aligning with our findings. Despite extensive research on its BC associations, its utility as a plasma biomarker for risk prediction remains unexplored 41 , 42 . Mechanistically, epiandrosterone sulfate may influence BC pathogenesis via androgen receptor interactions beyond its role as an androgen precursor. PheWAS-MR analysis revealed associations with acne conglobata and rhinophyma, consistent with established biological pathways 43 . Notably, these analyses support its safety profile as a potential BC therapeutic target. This study features several strengths. First, we employed the most comprehensive metabQTL dataset as exposure alongside three BC risk outcome datasets to enhance reproducibility. Second, rigorous quality controls were implemented, including stringent instrumental variable selection, LD removal in MR analyses, and p-value corrections. Third, meta-analyses were conducted to aggregate and validate findings, improving precision in estimating genetic susceptibility effects. Finally, PheWAS-MR analyses systematically evaluated metabolites' functional associations and safety profiles. This study has several limitations. First, our analysis was restricted to genetic and statistical evidence; functional validation through in vitro/in vivo experiments remains necessary to confirm biological mechanisms. Second, the absence of individual-level data precluded subgroup analyses by potential effect modifiers like age, smoking status, or hormonal profiles. Third, while focusing on European-ancestry populations minimized confounding through large-scale cohorts, generalization to other ethnic groups requires further investigation. Our study demonstrates how integrating metabQTL and GWAS data elucidates metabolic mechanisms in BC. We identified causal metabolites influencing BC risk, advancing both mechanistic understanding and potential biomarker/therapeutic target discovery. Future investigations should elucidate biological pathways underlying these metabolite-BC associations. CONCLUSION The present systematic MR analysis revealed that genetically determined 3,5-dichloro-2,6-dihydroxybenzoic acid, carnitine C14, Glyco-beta-muricholate and N4-acetylcytidine are associated with reduced BC risk and epiandrosterone sulfate is associated with elevated BC risk. Glyco − beta − muricholate and Epiandrosterone sulfate were revealed strong colocalization evidence with BC risk. Epiandrosterone sulfate might be promising drug target for BC. Abbreviations BC: Breast cancer BCAC: Breast Cancer Association Consortium β: effect size CLSA: the Canadian Longitudinal Study on Aging CI: confidence interval EAF: effect elle frequency GWAS: Genome wide association study IV: Instrumental variable IVW: Inverse variance weighted LD: linkage disequilibrium MR: Mendelian randomization OR: Odds ratio QTL: Quantitative trait loci SNP: Single nucleotide polymorphism STROBE-MR: the Reporting of Observational Studies in Epidemiology using Mendelian Randomization Declarations Ethics Statement This study used publicly available GWAS summary statistics data. No individual-level data were accessed or processed and no personal identifiers or private health information were involved in this research, and thus no ethical approval was required. Consent for publication This study has been approved by all authors for publication. Competing interests The authors declare that they have no competing interests. Clinical trial number Not applicable. Funding Special Program for Scientific Research of Chinese Medicine from Henan Province, China (2022ZY1048); Natural Science Foundation of Henan Province of China (232300421183). Doctoral Research Foundation of the First Affiliated Hospital of Henan University of Chinese Medicine (Grant No. 2024BSJJ044). Authors' contributions Hanghang Chen: Conceptualization, Methodology, Formal analysis, Software, Visualization, Funding acquisition. Yueyuan Xu: Validation, Data curation, Writing original draft, Formal analysis, Resources. Zepeng Wang: Validation, Data curation, Resources. Xufeng Cheng: Conceptualization, Methodology, Writing-review & Editing, Funding acquisition. Acknowledgments We want to acknowledge the participants and investigators of the CLSA, FinnGen Biobank, and BCAC studies. The breast cancer genome-wide association analyses for BCAC and CIMBA were supported by Cancer Research UK (PPRPGM-Nov20\100002, C1287/A10118, C1287/A16563, C1287/A10710, C12292/A20861, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565) and the Gray Foundation, The National Institutes of Health (CA128978, X01HG007492- the DRIVE consortium), the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSRSIIRI-701 grant, the Quebec Breast Cancer Foundation, the American Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), the American Union's Horizon 2020 Research and Innovation Programme (634935 and 633784), the Post-Cancer GWAS initiative (U19 CA148537, CA148065 and CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer (CRN-87521), the Komen Foundation for the Cure, the Breast Cancer Research Foundation and the Ovarian Cancer Research Fund. All studies and funders are listed in Zhang H et al (Nat Genet, 2020). BCAC is funded by Cancer Research UK [C1287/A16563], the European Union's Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST respectively), and by the European Community´s Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). The EU Horizon 2020 Research and Innovation Programme funding source had no role in study design, data collection, data analysis, data interpretation or writing of the report. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer Research UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSRSIIRI-701 grant, and the Quebec Breast Cancer Foundation. Funding for the iCOGS infrastructure came from: the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, and Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The DRIVE Consortium was funded by U19 CA148065. Availability of data and material The GWAS summary statistics data is available at https://finngen.gitbook.io/documentation/data-download and http://bcac.ccge.medschl.cam.ac.uk/bcacdata/, respectively. 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Pre-diagnostic metabolite concentrations and prostate cancer risk in 1077 cases and 1077 matched controls in the European Prospective Investigation into Cancer and Nutrition. BMC medicine 15 , 122, doi:10.1186/s12916-017-0885-6 (2017). Fujiwara, N. et al. CPT2 downregulation adapts HCC to lipid-rich environment and promotes carcinogenesis via acylcarnitine accumulation in obesity. Gut 67 , 1493-1504, doi:10.1136/gutjnl-2017-315193 (2018). Hána, V. et al. Novel GC-MS/MS Technique Reveals a Complex Steroid Fingerprint of Subclinical Hypercortisolism in Adrenal Incidentalomas. The Journal of clinical endocrinology and metabolism 104 , 3545-3556, doi:10.1210/jc.2018-01926 (2019). El Kihel, L. Oxidative metabolism of dehydroepiandrosterone (DHEA) and biologically active oxygenated metabolites of DHEA and epiandrosterone (EpiA)--recent reports. Steroids 77 , 10-26, doi:10.1016/j.steroids.2011.09.008 (2012). Chatterton, R. T. Functions of dehydroepiandrosterone in relation to breast cancer. Steroids 179 , 108970, doi:https://doi.org/10.1016/j.steroids.2022.108970 (2022). Gabrielson, M. et al. Inclusion of Endogenous Plasma Dehydroepiandrosterone Sulfate and Mammographic Density in Risk Prediction Models for Breast Cancer. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 29 , 574-581, doi:10.1158/1055-9965.Epi-19-1120 (2020). Livshits, G. et al. An omics investigation into chronic widespread musculoskeletal pain reveals epiandrosterone sulfate as a potential biomarker. Pain 156 , 1845-1851, doi:10.1097/j.pain.0000000000000200 (2015). Nag, A. et al. Genome-wide scan identifies novel genetic loci regulating salivary metabolite levels. Human molecular genetics 29 , 864-875, doi:10.1093/hmg/ddz308 (2020). Supplementary Figure S1 The Supplementary Figure S1 file is not available with this version. Additional Declarations No competing interests reported. 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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-6417186","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450528573,"identity":"ab9fb63d-da37-4e6f-a43c-973a79def697","order_by":0,"name":"Hanghang Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYBACAyBmBjEY2xsbH8OFeYjRwtxzuNkYSEsQr4V9hnubNFFazNnPHn5dUHPHrncGY1t1QY1dHf+0A4wP3rYxyJvj0GLZk5dmPePYs+SZsxvbbs84liwhcTuB2XBuG4PhzgYcDjuQY2bMw3Y42XDOwbbbPGwHJAykE9ikedsYEgwO4NBy/g1Qy7/DyfY3EtuKef6BtbD/xqvlRo7xY962w3aMMxLbmHnbILYw49fyxoyZt+9wAmPPwWZp3r5kyRm3E5sl55yTMNyA02E5xp95vh22Z2xvfwhk2PHzz04++OFNmY08LluAgA0UFYkNCAFGEFsCp3ogYP4AJOzxqRgFo2AUjIIRDgCXRV4ReuGvCgAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Henan University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Hanghang","middleName":"","lastName":"Chen","suffix":""},{"id":450528574,"identity":"e2c93e3e-b122-42b2-b667-296b0d1314ba","order_by":1,"name":"Yueyuan Xu","email":"","orcid":"","institution":"The First Affiliated Hospital of Henan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yueyuan","middleName":"","lastName":"Xu","suffix":""},{"id":450528576,"identity":"8767e11b-6150-42c7-b1b2-d5213c98142e","order_by":2,"name":"Zepeng Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Henan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zepeng","middleName":"","lastName":"Wang","suffix":""},{"id":450528579,"identity":"2dbff10a-c7c2-4f31-9c7b-6b8029f00ca2","order_by":3,"name":"Xufeng Cheng","email":"","orcid":"","institution":"The First Affiliated Hospital of Henan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xufeng","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2025-04-10 06:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6417186/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6417186/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82324425,"identity":"e7f04827-b455-4f73-8f14-1f72f83a386c","added_by":"auto","created_at":"2025-05-09 06:01:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":366124,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design overview. Exposure 1,400 plasma metabolite metabQTLs from the CLSA. Outcome BC GWAS summary statistics from FinnGen R10 and BCAC cohorts. Analytical framework: two-sample MR with meta-analysis for robustness. Abbreviations: CLSA (Canadian Longitudinal Study on Aging); MR (Mendelian randomization).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6417186/v1/9075cef7ca2931bed7fbdc17.png"},{"id":82324426,"identity":"99ea70e9-90a7-43a9-925a-ff80704df687","added_by":"auto","created_at":"2025-05-09 06:01:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":430973,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plots of metabolite-BC associations across cohorts. \u003cstrong\u003eA\u003c/strong\u003e) CLSA metabolites vs. FinnGen BC; \u003cstrong\u003eB\u003c/strong\u003e) CLSA metabolites vs. BCAC OncoArray BC; \u003cstrong\u003eC\u003c/strong\u003e) CLSA metabolites vs. BCAC iCOGS BC; \u003cstrong\u003eD\u003c/strong\u003e) Intersection analysis identifying five significant metabolites. Axes: −log10(FDR-corrected p-value) vs. MR effect size (β).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6417186/v1/dbfcbaac2860bcf4ce632711.png"},{"id":82324430,"identity":"b6bce03c-b755-4e24-99cb-bbb8e1cc3f70","added_by":"auto","created_at":"2025-05-09 06:01:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":547732,"visible":true,"origin":"","legend":"\u003cp\u003eMeta-analysis of five metabolites' causal effects on BC risk. Forest plots showing: \u003cstrong\u003eA\u003c/strong\u003e) 3,5-dichloro-2,6-dihydroxybenzoic acid; \u003cstrong\u003eB\u003c/strong\u003e) Carnitine C14; \u003cstrong\u003eC\u003c/strong\u003e) Epiandrosterone sulfate; \u003cstrong\u003eD\u003c/strong\u003e) Glyco-beta-muricholate; \u003cstrong\u003eE\u003c/strong\u003e) N4-acetylcytidine. Heterogeneity assessed via Cochran’s Q and Higgins’ I² statistics (random/fixed effects models applied accordingly).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6417186/v1/a4c9ad2bdf088179a31900c9.png"},{"id":82326136,"identity":"92026708-b267-49fc-bdca-9dc7e67efac1","added_by":"auto","created_at":"2025-05-09 06:17:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":148093,"visible":true,"origin":"","legend":"\u003cp\u003eColocalization evidence for metabolite-BC risk loci. Glyco-beta-muricholate and Epiandrosterone sulfate show strong shared genetic architecture with BC (PP.H4 \u0026gt; 0.8).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6417186/v1/91075897b9d21b5da341ac91.png"},{"id":82326134,"identity":"31fca5e2-9371-4099-a8fe-536d30cd9a81","added_by":"auto","created_at":"2025-05-09 06:17:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":178905,"visible":true,"origin":"","legend":"\u003cp\u003eSubtype-specific associations between metabolites and BC. Key findings: 3,5-dichloro-2,6-dihydroxybenzoic acid inversely associates with Luminal_B_HER2Neg/TNBC; Epiandrosterone sulfate and Glyco-beta-muricholate positively associate with Luminal subtypes; N4-acetylcytidine reduces Luminal_A risk.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6417186/v1/6b5dd11ef21fb1ad32bb2eee.png"},{"id":82324433,"identity":"1f875f44-d694-469f-b217-718cac1b5266","added_by":"auto","created_at":"2025-05-09 06:01:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1220222,"visible":true,"origin":"","legend":"\u003cp\u003ePheWAS-MR associations between metabolites and FinnGen health outcomes. Circular heatmap displays effect directions (positive/negative associations) across 2,272 phenotypes.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6417186/v1/a6e0173d6e212d915b5fa598.png"},{"id":82327227,"identity":"b0d6f715-496e-43bc-a31e-3f58f0e3cdab","added_by":"auto","created_at":"2025-05-09 06:33:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3180530,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6417186/v1/a9c20c6d-b030-4e9a-a63b-5149de85cca9.pdf"},{"id":82326898,"identity":"4e26f35c-7390-4015-8be7-7acaab52d5f9","added_by":"auto","created_at":"2025-05-09 06:25:21","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1996588,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaltables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6417186/v1/4f57c38534c012be11fb8cb6.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal plasma metabolites for breast cancer risk: a two-sample Mendelian randomization study with colocalization evidence","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBreast cancer (BC) has the highest cancer incidence in women worldwide \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In 2023, there were over 1,300,590 estimated new cases and over 43,700 estimated deaths worldwide \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Epidemiological investigations have identified risk factors for BC, including genetic factors (BRCA 1/2 genes), reproductive factors such as age at menarche and menopause, number of childbirths, period of breastfeeding or hormone replacement therapy, and modifiable risk factors such as alcohol consumption, smoking, physical inactivity, or obesity \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Previous epidemiological studies have investigated possible mediators for BC, but specific biomarkers still need further identification \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e; the role of human plasma metabolites in BC development is unclear. Before embarking on expensive clinical trials, studying potential biomarkers associated with BC onset and progression is critical.\u003c/p\u003e \u003cp\u003eThe occurrence and development of BC undergoes a complex and multifaceted biochemical metabolic process \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The rapid advancement of metabolomics techniques has facilitated direct measurement of metabolite abundance \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, enabling better identification of metabolites distinguishing BC patients from healthy controls \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Prior studies have highlighted significant differences in nucleotide and lipid metabolism between ER-positive and ER-negative BCs \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Additionally, β-carotene has demonstrated a capacity to reduce BC risk \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Examination of BC patients has yielded preliminary evidence implicating extensive dysregulation of oxidized phosphatidylcholines, sphingomyelins, and triacylglycerols in BC pathogenesis \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Furthermore, a study aimed at identifying novel BC biomarkers through Mendelian randomization revealed a potential inverse causal association between 1-oleoylglycerophosphocholine and BC, suggesting a potential clinical biomarker \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Nonetheless, large-scale metabolomics research linking BC to human plasma metabolites remains insufficient.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR), an established epidemiological approach, enables causal inference between exposures and outcomes \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Previous MR studies have identified potential associations between plasma metabolites and BC. Comprehensive characterization of these metabolite-BC relationships remains crucial for elucidating disease mechanisms.\u003c/p\u003e \u003cp\u003eWe integrated large-scale metabQTLs, GWAS datasets, and MR analyses to systematically investigate causal effects of 1,400 genetically determined plasma metabolites on BC risk. Our multi-modal approach provides robust genetic evidence for metabolites' causal roles in BC pathogenesis, while demonstrating how molecular profiling combined with genetic associations advances etiological understanding.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eThis study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) guidelines \u003csup\u003e14\u003c/sup\u003e. Valid causal inference relies on three core assumptions: (1) genetic variants show strong associations with exposure; (2) genetic variants exhibit no associations with confounders; and (3) genetic variants influence outcomes exclusively through exposure-outcome pathways \u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eData sources\u003c/h2\u003e\n \u003cp\u003eAll exposure and outcome cohorts were restricted to individuals of European ancestry to both ensure access to large-scale metabQTL data and mitigate population stratification bias. Exposure and outcome characteristics are summarized in Table 1. MetabQTL data for 1,400 plasma metabolites were obtained from the Canadian Longitudinal Study on Aging (CLSA) \u003csup\u003e16\u003c/sup\u003e. Analyses utilized the CLSA Baseline Comprehensive Dataset version 4.0, with genome-wide genotyping performed on DNA samples from 8,192 randomly selected participants in the Comprehensive Cohort.\u003c/p\u003e\n \u003cdiv\u003eTo ensure analytical reproducibility, we analyzed three major GWAS summary datasets for outcome assessment: breast cancer (BC) risk data from the Breast Cancer Association Consortium (BCAC OncoArray, n = 138,508), BCAC iCOGS (n = 76,167) \u003csup\u003e17\u003c/sup\u003e and FinnGen Release 10 (R10, n = 201,713) \u003csup\u003e18\u003c/sup\u003e. Detailed data descriptions are provided in source publications.\u003c/div\u003e\n \u003cp\u003eFor phenome-wide Mendelian randomization (PheWAS-MR) analyses evaluating metabolite-disease associations, we included all FinnGen phenotype GWAS data (2,272 outcomes). Phenotypes with \u0026lt; 100 cases were excluded to maintain statistical robustness.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDetailed information on the exposures and outcomes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003encase\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003encontrol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConsortium\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAncestry\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasma metabolites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCLSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBC risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133,164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167,189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinnGen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBC risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138,508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80,125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58,383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCAC OncoArray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBC risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76,167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38,349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37,818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCAC iCOGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBC survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91,686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eTwo sample MR analysis\u003c/h3\u003e\n\u003cp\u003eWe employed “TwoSampleMR” R package \u003csup\u003e19\u003c/sup\u003e to conduct the two sample MR analysis. Inverse variance weighted (IVW) is the most potent and extensively utilized MR method \u003csup\u003e20\u003c/sup\u003e, and thus we selected IVW as our primary analysis method. Instrumental variables (IVs) were defined as SNPs that strongly correlated with exposure (\u003cem\u003ep\u003c/em\u003e \u0026lt; 5e-8). If there is just one IV available, then the Wald ratio method was the only choice \u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFirst, we searched for the IVs for the plasma level of each metabolite. We next performed linkage disequilibrium (LD) clumping and identified independent genetic instrument variables using plink software (V1.9). LD was defined as R\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.1 within a clumping distance 100kb, and SNPs with LD were excluded \u003csup\u003e22,23\u003c/sup\u003e. F value of each instrumental variable was calculated using the formula: F = (\\(\\:\\text{n}-2)\\times\\:{\\text{R}}^{2}÷(1-{\\text{R}}^{2})\\) (R\u003csup\u003e2\u003c/sup\u003e: interpretability of instrumental variables, n: sample size). R\u003csup\u003e2\u003c/sup\u003e could be calculated using this formula: \\(\\:{R}^{2}={{\\beta\\:}}^{2}(1-\\text{E}\\text{A}\\text{F})\\times\\:2\\text{E}\\text{A}\\text{F}\\) (EAF: effect elle frequency, β: effect size) \u003csup\u003e24\u003c/sup\u003e. Conventionally, an IV with F \u0026lt; 10 was defined as weak IV and was removed to avoid weak instrumental variable bias \u003csup\u003e21\u003c/sup\u003e. Secondly, we extracted the same SNPs from the outcome data. In cases where certain SNPs could not be identified in the outcome data, we opted not to seek proxies. Third, SNPs for an outcome and exposure were harmonized to be relative to the same allele. MR results could be obtained using “mr” function on the harmonized data. Odd ratio (OR) could be calculated using the formula: OR = exp(β). We used the “p.adjust” function to perform false discovery rate (FDR) correction to adjust the \u003cem\u003ep\u003c/em\u003e values when conducting MR analysis between metabolites and BC risk.\u003c/p\u003e\n\u003ch3\u003eSensitivity test\u003c/h3\u003e\n\u003cp\u003eWe used Cochran’s Q statistics to test the heterogeneity \u003csup\u003e25\u003c/sup\u003e and the MR-Egger method to test the pleiotropy \u003csup\u003e26\u003c/sup\u003e of the harmonized data. A \u003cem\u003ep\u003c/em\u003e value greater than 0.05 was considered indicative of the absence of heterogeneity or pleiotropy. Furthermore, the MR Steiger directionality test was employed to exclude potential reverse causalities \u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eMeta analysis\u003c/h3\u003e\n\u003cp\u003eWe intersected all significant metabolites from three outcomes to ensure the repeatability of analysis results. Furthermore, to ensure the robustness of the results, we performed meta analysis for all associations using the “meta” R package (V6.2-1). Cochran’s Q test and Higgins’s I\u003csup\u003e2\u003c/sup\u003e test were used to test the heterogeneity among studies \u003csup\u003e28\u003c/sup\u003e. If \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 or I\u003csup\u003e2\u003c/sup\u003e \u0026gt; 50%, heterogeneity was considered to exist among studies and the random effects model was used, otherwise, fixed effects model was selected as the main meta method \u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eColocalization analysis\u003c/h3\u003e\n\u003cp\u003eTo verify that causal metabolites and BC share causal variants in designated gene regions, we used coloc R package \u003csup\u003e30\u003c/sup\u003e for genetic colocalization analysis. We defined the 1Mb region upstream and downstream of the lead SNP for each protein as the validation region. If the posterior probability 4 (PH4) of shared causal variation is ≥ 0.8 \u003csup\u003e31\u003c/sup\u003e, the two traits were considered to have strong colocalization support.\u003c/p\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eMR analysis of metabolites and BC subtypes\u003c/h2\u003e\n \u003cp\u003eBased on the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), BC can be defined as five molecular subtypes: (1) luminal A-like, (2) luminal B/HER2-negative-like, (3) luminal B-like, (4) HER2-enriched-like and (5) triple-negative or basal-like. We also used the same MR and sensitivity analysis methods to explore the causal associations between causal metabolites and specific BC subtypes.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePheWAS-MR analysis\u003c/h3\u003e\n\u003cp\u003eTo figure out the potential functions and side effects, we conducted PheWAS-MR between the significant plasma metabolites associated with BC risk and all health outcomes in FinnGen. The calculation parameters were selected as above-mentioned.\u003c/p\u003e\n\u003ch3\u003eStatistical methods\u003c/h3\u003e\n\u003cp\u003eMR analysis were conducted using IVW methods if there is more than one shared SNP in the exposure and outcome data. The Wald ratio method was used when there was only one shared SNP. An F-value greater than 10 was considered a prerequisite to mitigate the risk of weak instrumental variable bias. FDR and Bonferroni corrections were used to adjust the \u003cem\u003ep\u003c/em\u003e values and adjusted \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 was considered indicative of causal associations between the two traits. In the meta analysis, Cochran’s Q test and Higgins’s I\u003csup\u003e2\u003c/sup\u003e test were used to test heterogeneity between studies.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe overall study design is illustrated in Fig. \u003cspan\u003e1\u003c/span\u003e. We employed a two-sample MR framework to integrate causal associations between exposures (1,400 human plasma metabolites) and the outcomes (BC risk). Subsequently, meta-analysis was conducted to assess the robustness and reliability of all identified associations.\u003c/p\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eThe associations between metabolites and BC risk\u003c/h2\u003e\n \u003cp\u003eThrough a two sample MR analysis for causal assessment between each plasma metabolite and the outcome, the significant associations and the sensitivity analysis results between human plasma metabolites and BC (FinnGen) were displayed in Table \u003cspan\u003eS1\u003c/span\u003e. Results with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 in the sensitivity analysis was defined as lacking heterogeneity or pleiotropy. 81 plasma metabolites were found to pass the sensitivity test and were causally associated with BC risk after FDR correction (FinnGen). The effect size and FDR-corrected \u003cem\u003ep\u003c/em\u003e value of all significant metabolites associated BC risk (FinnGen) were displayed in Fig. \u003cspan\u003e2\u003c/span\u003eA.\u003c/p\u003e\n \u003cp\u003eThe significant associations and the sensitivity analysis results between plasma metabolites and BC (BCAC OncoArray) were displayed in Table S2. 86 plasma metabolites passed the sensitivity test and causally associated with BC after FDR correction (BCAC OncoArray). The effect size and FDR-corrected \u003cem\u003ep\u003c/em\u003e values of all significant metabolites associated BC risk (BCAC OncoArray) were displayed in Fig. \u003cspan\u003e2\u003c/span\u003eB.\u003c/p\u003e\n \u003cp\u003eThe significant associations and the sensitivity analysis results between plasma metabolites and BC (BCAC iCOGS) were displayed in Table S3. 82 plasma metabolites passed the sensitivity test and were causally associated with BC risk after FDR correction (BCAC iCOGS). The effect size and FDR-corrected \u003cem\u003ep\u003c/em\u003e values of all significant metabolites associated BC risk (BCAC iCOGS) were displayed in Fig. \u003cspan\u003e2\u003c/span\u003eC. To obtain more reliable results, the significantly positive results of the above three analyses are intersected, which are shown in the Venn Diagram in Fig. \u003cspan\u003e2\u003c/span\u003eD.\u003c/p\u003e\n \u003cp\u003eFive plasma metabolites are all significantly associated with BC risk from FinnGen, BCAC (OncoArray) and BCAC (iCOGS), including 3,5\u0026thinsp;\u0026minus;\u0026thinsp;dichloro\u0026thinsp;\u0026minus;\u0026thinsp;2,6\u0026thinsp;\u0026minus;\u0026thinsp;dihydroxybenzoic acid, Carnitine C14, Epiandrosterone sulfate, Glyco\u0026thinsp;\u0026minus;\u0026thinsp;beta\u0026thinsp;\u0026minus;\u0026thinsp;muricholate, and N4\u0026thinsp;\u0026minus;\u0026thinsp;acetylcytidine. Significant results were displayed in Table \u003cspan\u003e2\u003c/span\u003e. The proxy SNPs for all metabolites were displayed in Table S4. The effect size of MR results between these five metabolites and three BC outcomes were showed in figure \u003cspan\u003eS1\u003c/span\u003eA. Figure \u003cspan\u003eS1\u003c/span\u003eB shows the OR and 95% CI of MR results between five FDR-corrected significant plasma metabolites and three BC outcomes.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSignificant Mendelian randomization (MR) results between plasma metabolites and breast cancer risk.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elci95\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003euci95\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epval\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e3,5-dichloro-2,6-dihydroxybenzoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinnGen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCAC_OncoArray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCAC_iCOGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCarnitine C14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinnGen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.13E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e6.26E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCAC_OncoArray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.85E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e4.05E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCAC_iCOGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.87E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e5.50E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eEpiandrosterone sulfate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinnGen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e1.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e5.57E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCAC_OncoArray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e1.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e0.023197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCAC_iCOGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e1.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e0.035567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGlyco-beta-muricholate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinnGen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e8.51E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCAC_OncoArray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCAC_iCOGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eN4-acetylcytidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinnGen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCAC_OncoArray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCAC_iCOGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.1709%;\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.9683%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eOR: odds ratio. lci95: lower 95% confidence interval. uci95: upper 95% confidence interval. FDR: false discovery rate.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eMeta analysis validated the robustness of the MR results\u003c/h2\u003e\n \u003cp\u003eAll meta results of the two MR results between five significant plasma metabolites and two BC outcomes were displayed in Fig. \u003cspan\u003e3\u003c/span\u003e. The heterogeneity was considered to exist among studies between carnitine C14, Epiandrosterone sulfate, Glyco-beta-muricholate and BC risk, and random effects model was used. 3,5-dichloro-2,6-dihydroxybenzoic acid is associated with reduced BC risk (Fig. \u003cspan\u003e3\u003c/span\u003eA, odds ratio [OR]: 0.90; 95% confidence interval [CI]: 0.87\u0026ndash;0.94; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Carnitine C14 is associated with reduced BC risk (Fig. \u003cspan\u003e3\u003c/span\u003eB, OR: 0.72; 95% CI: 0.64\u0026ndash;0.83; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Epiandrosterone sulfate is associated with elevated BC risk (Fig. \u003cspan\u003e3\u003c/span\u003eC, OR: 1.04; 95% CI: 1.01\u0026ndash;1.06; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Glyco-beta-muricholate is associated with reduced BC risk (Fig. \u003cspan\u003e3\u003c/span\u003eD, OR: 0.95; 95% CI: 0.93\u0026ndash;0.97; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). N4-acetylcytidine is associated with reduced BC risk (Fig. \u003cspan\u003e3\u003c/span\u003eE, OR: 0.93; 95% CI: 0.91\u0026ndash;0.96; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eColocalization analysis indicate shared variants between metabolites and BC risk\u003c/h2\u003e\n \u003cp\u003eColocalization analysis can verify whether there are shared variants between traits in the specified gene region. PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.8 means strong colocalization evidence. Among the 5 metabolites that have causal associations with BC risk, Glyco\u0026thinsp;\u0026minus;\u0026thinsp;beta\u0026thinsp;\u0026minus;\u0026thinsp;muricholate and Epiandrosterone sulfate were found strong colocalization evidence with BC risk (PPH4\u0026thinsp;=\u0026thinsp;1, Fig. \u003cspan\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eThe associations between five metabolites and BC subtypes\u003c/h2\u003e\n \u003cp\u003ePotential causal associations were identified between four of the five metabolites and four BC subtypes (Fig. \u003cspan\u003e5\u003c/span\u003e, Table \u003cspan\u003e3\u003c/span\u003e). 3,5-dichloro-2,6-dihydroxybenzoic acid is negatively associated with Luminal_B_HER2Neg and TNBC risk. Epiandrosterone sulfate could potentially elevate the Luminal subtype risk. Glyco-beta-muricholate could also elevate the Luminal subtype risk except Luminal_B. N4-acetylcytidine is only associated with reduced Luminal_A risk.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSignificant MR results between plasma metabolites and breast cancer subtype risk.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elci95\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003euci95\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epval\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,5-dichloro-2,6-dihydroxybenzoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLuminal_B_HER2Neg\u003c/p\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.27E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,5-dichloro-2,6-dihydroxybenzoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEpiandrosterone sulfate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal_A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.03E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEpiandrosterone sulfate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal_B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEpiandrosterone sulfate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal_B_HER2Neg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.21E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlyco-beta-muricholate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal_A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.20E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlyco-beta-muricholate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal_B_HER2Neg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN4-acetylcytidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal_A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eOR: odds ratio. lci95: lower 95% confidence interval. uci95: upper 95% confidence interval.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eThe associations between five metabolites and all health outcomes\u003c/h2\u003e\n \u003cp\u003eTo figure out the other potential functions and side effects of five metabolites, the PheWAS-MR was conducted between five metabolites and all health outcomes in FinnGen. 2099 phenotypes with more than 100 cases in FinnGen were collected as outcomes.\u003c/p\u003e\n \u003cp\u003eThrough strict Bonferroni correction and sensitivity analysis, 3,5-dichloro-2,6-dihydroxybenzoic acid is significantly associated with 6 phenotypes; carnitine C14 is significantly associated with 96 phenotypes; epiandrosterone sulfate is significantly associated with 78 phenotypes; Glyco-beta-muricholate is significantly associated with 45 phenotypes; N4-acetylcytidine is significantly associated with one phenotype. The associations were shown in Fig. \u003cspan\u003e6\u003c/span\u003e and Table S5.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis integrative metabolomics-genomics MR study advances biomarker discovery for BC by identifying novel causal associations. Genetic influences on mutations related to BC susceptibility have been increasingly reported in experimental and observational research \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Through unbiased two-sample MR analysis of 1,400 plasma metabolites, we identified five causal biomarkers for BC risk: 3,5-dichloro-2,6-dihydroxybenzoic acid, carnitine C14, glyco-beta-muricholate, N4-acetylcytidine, and epiandrosterone sulfate.\u003c/p\u003e \u003cp\u003eFour plasma metabolites showed inverse associations with BC risk, with carnitine C14 demonstrating the strongest effect. This long-chain acylcarnitine facilitates mitochondrial fatty acid transport \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, supports ketogenesis, and regulates energy metabolism \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. While acylcarnitine dysregulation has disease implications \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, carnitine C14 remains underexplored in oncology compared to other acylcarnitines. Existing evidence links carnitine C14 to reduced prostate cancer progression \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and its utility in esophageal squamous cell carcinoma profiling \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Notably, carnitine accumulation via CPT2 downregulation promotes hepatocarcinogenesis through STAT3 activation \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Our study reveals carnitine C14's protective association against BC. However, PheWAS-MR analyses indicate positive associations with respiratory/circulatory disorders including asthma, COPD, and atrial fibrillation. These dual effects necessitate comprehensive reevaluation of carnitine C14's therapeutic potential and systemic impacts.\u003c/p\u003e \u003cp\u003eEpiandrosterone sulfate, the most abundant steroid hormone in adult women, exhibits age-dependent concentration declines \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Prior studies have linked elevated levels to increased BC risk in women \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, aligning with our findings. Despite extensive research on its BC associations, its utility as a plasma biomarker for risk prediction remains unexplored \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Mechanistically, epiandrosterone sulfate may influence BC pathogenesis via androgen receptor interactions beyond its role as an androgen precursor. PheWAS-MR analysis revealed associations with acne conglobata and rhinophyma, consistent with established biological pathways \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Notably, these analyses support its safety profile as a potential BC therapeutic target.\u003c/p\u003e \u003cp\u003eThis study features several strengths. First, we employed the most comprehensive metabQTL dataset as exposure alongside three BC risk outcome datasets to enhance reproducibility. Second, rigorous quality controls were implemented, including stringent instrumental variable selection, LD removal in MR analyses, and p-value corrections. Third, meta-analyses were conducted to aggregate and validate findings, improving precision in estimating genetic susceptibility effects. Finally, PheWAS-MR analyses systematically evaluated metabolites' functional associations and safety profiles.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, our analysis was restricted to genetic and statistical evidence; functional validation through in vitro/in vivo experiments remains necessary to confirm biological mechanisms. Second, the absence of individual-level data precluded subgroup analyses by potential effect modifiers like age, smoking status, or hormonal profiles. Third, while focusing on European-ancestry populations minimized confounding through large-scale cohorts, generalization to other ethnic groups requires further investigation.\u003c/p\u003e \u003cp\u003eOur study demonstrates how integrating metabQTL and GWAS data elucidates metabolic mechanisms in BC. We identified causal metabolites influencing BC risk, advancing both mechanistic understanding and potential biomarker/therapeutic target discovery. Future investigations should elucidate biological pathways underlying these metabolite-BC associations.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe present systematic MR analysis revealed that genetically determined 3,5-dichloro-2,6-dihydroxybenzoic acid, carnitine C14, Glyco-beta-muricholate and N4-acetylcytidine are associated with reduced BC risk and epiandrosterone sulfate is associated with elevated BC risk. Glyco\u0026thinsp;\u0026minus;\u0026thinsp;beta\u0026thinsp;\u0026minus;\u0026thinsp;muricholate and Epiandrosterone sulfate were revealed strong colocalization evidence with BC risk. Epiandrosterone sulfate might be promising drug target for BC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBC: Breast cancer\u003c/p\u003e\n\u003cp\u003eBCAC: Breast Cancer Association Consortium\u003c/p\u003e\n\u003cp\u003e\u0026beta;: effect size\u003c/p\u003e\n\u003cp\u003eCLSA: the Canadian Longitudinal Study on Aging\u003c/p\u003e\n\u003cp\u003eCI: confidence interval\u003c/p\u003e\n\u003cp\u003eEAF: effect elle frequency\u003c/p\u003e\n\u003cp\u003eGWAS: Genome wide association study\u003c/p\u003e\n\u003cp\u003eIV: Instrumental variable\u003c/p\u003e\n\u003cp\u003eIVW: Inverse variance weighted\u003c/p\u003e\n\u003cp\u003eLD: linkage disequilibrium\u003c/p\u003e\n\u003cp\u003eMR: Mendelian randomization\u003c/p\u003e\n\u003cp\u003eOR: Odds ratio\u003c/p\u003e\n\u003cp\u003eQTL: Quantitative trait loci\u003c/p\u003e\n\u003cp\u003eSNP: Single nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003eSTROBE-MR:\u0026nbsp;the Reporting of Observational Studies in Epidemiology using\u003c/p\u003e\n\u003cp\u003eMendelian Randomization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used publicly available GWAS summary statistics data. No individual-level data were accessed or processed and no personal identifiers or private health information were involved in this research, and thus no ethical approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been approved by all authors for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecial\u0026ensp;Program\u0026ensp;for Scientific\u0026ensp;Research of Chinese Medicine from Henan Province, China (2022ZY1048); Natural Science Foundation of Henan Province of China (232300421183). Doctoral Research Foundation of the First Affiliated Hospital of Henan University of Chinese Medicine (Grant No. 2024BSJJ044).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHanghang Chen: Conceptualization, Methodology, Formal analysis, Software, Visualization, Funding acquisition. Yueyuan Xu: Validation, Data curation, Writing original draft, Formal analysis, Resources. Zepeng Wang: Validation, Data curation, Resources. Xufeng Cheng: Conceptualization, Methodology, Writing-review \u0026amp; Editing, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to acknowledge the participants and investigators of the CLSA, FinnGen Biobank, and BCAC studies. The breast cancer genome-wide association analyses for BCAC and CIMBA were supported by Cancer Research UK (PPRPGM-Nov20\\100002, C1287/A10118, C1287/A16563, C1287/A10710, C12292/A20861, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565) and the Gray Foundation, The National Institutes of Health (CA128978, X01HG007492- the DRIVE consortium), the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and the Minist\u0026egrave;re de l\u0026rsquo;\u0026Eacute;conomie, Science et Innovation du Qu\u0026eacute;bec through Genome Qu\u0026eacute;bec and the PSRSIIRI-701 grant, the Quebec Breast Cancer Foundation, the American Community\u0026apos;s Seventh Framework Programme under grant agreement n\u0026deg; 223175 (HEALTH-F2-2009-223175) (COGS), the American Union\u0026apos;s Horizon 2020 Research and Innovation Programme (634935 and 633784), the Post-Cancer GWAS initiative (U19 CA148537, CA148065 and CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer (CRN-87521), the Komen Foundation for the Cure, the Breast Cancer Research Foundation and the Ovarian Cancer Research Fund. All studies and funders are listed in Zhang H et al (Nat Genet, 2020).\u003c/p\u003e\n\u003cp\u003eBCAC is funded by Cancer Research UK [C1287/A16563], the European Union\u0026apos;s Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST respectively), and by the European Community\u0026acute;s Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). The EU Horizon 2020 Research and Innovation Programme funding source had no role in study design, data collection, data analysis, data interpretation or writing of the report. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer Research UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Minist\u0026egrave;re de l\u0026rsquo;\u0026Eacute;conomie, Science et Innovation du Qu\u0026eacute;bec through Genome Qu\u0026eacute;bec and the PSRSIIRI-701 grant, and the Quebec Breast Cancer Foundation. Funding for the iCOGS infrastructure came from: the European Community\u0026apos;s Seventh Framework Programme under grant agreement n\u0026deg; 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, and Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The DRIVE Consortium was funded by U19 CA148065.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GWAS summary statistics data is available at https://finngen.gitbook.io/documentation/data-download and http://bcac.ccge.medschl.cam.ac.uk/bcacdata/, respectively. The metabQTL data of CLSA data is available at https://www.clsa-elcv.ca. LD reference data for the European super population can be downloaded directly through this link (http://fileserve.mrcieu.ac.uk/ld/1kg.v3.tgz).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung, H.\u003cem\u003e et al.\u003c/em\u003e Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA: a cancer journal for clinicians\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 209-249, doi:10.3322/caac.21660 (2021).\u003c/li\u003e\n\u003cli\u003eSiegel, R. 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We applied strict quality control with Bonferroni correction and conducted meta-analyses to verify result robustness. Colocalization analysis assessed shared genetic variants between causal metabolites and BC risk. Phenome-wide MR (PheWAS-MR) systematically evaluated metabolite associations across all FinnGen phenotypes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFive genetically determined plasma metabolites were identified as the potential causal biomarkers for BC risk, including 3,5-dichloro-2,6-dihydroxybenzoic acid (odds ratio [OR]: 0.90; 95% confidence interval [CI]: 0.87\u0026ndash;0.94; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), carnitine C14 (OR: 0.72; 95% CI: 0.64\u0026ndash;0.83; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and epiandrosterone sulfate (OR: 1.04; 95% CI: 1.01\u0026ndash;1.06; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Glyco-beta-muricholate (OR: 0.95; 95% CI: 0.93\u0026ndash;0.97; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), N4-acetylcytidine (OR: 0.93; 95% CI: 0.91\u0026ndash;0.96; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Colocalization analysis indicates Glyco\u0026thinsp;\u0026minus;\u0026thinsp;beta\u0026thinsp;\u0026minus;\u0026thinsp;muricholate and Epiandrosterone sulfate were found strong colocalization evidence with BC risk (PPH4\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eGenetically determined 3,5-dichloro-2,6-dihydroxybenzoic acid, carnitine C14, glyco-beta-muricholate, and N4-acetylcytidine were associated with reduced BC risk, while epiandrosterone sulfate correlated with increased risk. Glyco-beta-muricholate and epiandrosterone sulfate demonstrated colocalization with BC pathogenesis.\u003c/p\u003e","manuscriptTitle":"Causal plasma metabolites for breast cancer risk: a two-sample Mendelian randomization study with colocalization evidence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 06:01:16","doi":"10.21203/rs.3.rs-6417186/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-06T06:01:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T02:05:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191993437002424597961277963451136161192","date":"2025-07-03T13:29:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-25T09:49:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29444538635176522552084546758732932301","date":"2025-05-24T02:28:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152281270911763117575943538288947988688","date":"2025-05-23T17:56:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T10:03:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164118990825308045815418713834877311775","date":"2025-05-12T11:06:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-01T06:39:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-28T03:01:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-28T02:58:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-04-10T06:41:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae6ee750-42a0-44f6-a54e-8cee1c7468cf","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T13:09:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 06:01:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6417186","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6417186","identity":"rs-6417186","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}