{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T03:46:13Z","timestamp":1792899973718,"version":"4.5.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>This study aimed to investigate potential causal relationships between circulating metabolites and breast cancer risk using Mendelian randomization (MR) analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>Summary-level genome-wide association study (GWAS) datasets for 249 circulating metabolites were obtained from the UK Biobank. GWAS datasets for estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancer were acquired from previous studies based on the Combined Oncoarray. Instrumental variables (IVs) were selected from single nucleotide polymorphisms (SNPs) associated with circulating metabolites, and MR analyses were conducted using the inverse-variance weighted (IVW) method as the primary analysis, with additional sensitivity analyses using other MR methods. Odds ratios (OR) and 95% confidence interval (CI) were used to estimate the association of circulating metabolites with breast cancer risk.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The IVW analysis revealed significant causal relationships between 79 circulating metabolites and ER + breast cancer risk, and 10 metabolites were significantly associated with ER-breast cancer risk. Notably, acetate (OR = 1.12, <jats:italic>P<\/jats:italic> = 0.03), HDL cholesterol (OR = 1.09, <jats:italic>P<\/jats:italic> &amp;lt; 0.001), ration of omega-6 fatty acids to total fatty acids ratio (OR = 1.09, <jats:italic>P<\/jats:italic> = 0.01), and phospholipids in large LDL (OR = 1.09, <jats:italic>P<\/jats:italic> &amp;lt; 0.001) were linked to an increased risk of ER + breast cancer, while linoleic acid (OR = 0.91, <jats:italic>P<\/jats:italic> &amp;lt; 0.001) monounsaturated fatty acids (OR = 0.91, <jats:italic>P<\/jats:italic> &amp;lt; 0.001), and total lipids in LDL (OR = 0.91, <jats:italic>P<\/jats:italic> &amp;lt; 0.001) were associated with a decreased risk. In ER-breast cancer, glycine, citrate, HDL cholesterol, cholesteryl esters in HDL, cholesterol to total lipids ratio in very large HDL, and cholesterol in large LDL were associated with an increased risk, while the free cholesterol to total lipids in very large HDL was linked to a decreased risk.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This MR approach underscores aberrant lipid metabolism as a key process in breast tumorigenesis, and may inform future prevention and treatment strategies. To further elucidate the underlying mechanisms and explore the potential clinical implications, additional research is warranted to validate the observed associations in this study.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fphar.2024.1442723","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T07:36:38Z","timestamp":1726040198000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Identification of circulating metabolites linked to the risk of breast cancer: a mendelian randomization study"],"prefix":"10.3389","volume":"15","author":[{"given":"Xiaosheng","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huai","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengjie","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honglin","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianqi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoliang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.tibs.2014.02.004","article-title":"Serine and glycine metabolism in cancer","volume":"39","author":"Amelio","year":"2014","journal-title":"Trends Biochem. 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