Evidence map›Paper›PMID 41428133›Full record

ArticleDiscover oncology2025

Deciphering the causal effects of plasma metabolomics and lipids on breast cancer risk: a Mendelian randomization analysis.

Lingyao Zhou, Yangwen Du, Kumar Ganesan, Sitong Xian, Caiyue Lin, Hongbing Liu, Jianhua Chen, Chen Lin, Jianping Chen

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Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Lingyao Zhou *Liuzhou Traditional Chinese Medical Hospital, Liuzhou, 545001, People's Republic of China.
Yangwen Du *Liuzhou Traditional Chinese Medical Hospital, Liuzhou, 545001, People's Republic of China.
Kumar GanesanSchool of Chinese Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Sitong XianSchool of Chinese Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Caiyue LinSchool of Chinese Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Hongbing LiuSchool of Chinese Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Jianhua ChenThe Affiliated Hospital of Chengdu University of Traditional Medicine, Chengdu, China.
Chen LinGuangxi University of Chinese Medicine, Nanning, 530001, People's Republic of China. doctorlc@qq.com.
Jianping ChenSchool of Chinese Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China. abchen@hku.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer, a complex disease influenced by metabolic and lipid profiles, requires a deeper understanding of causal relationships between dietary preferences, plasma metabolites, and cancer risk. This study employed Mendelian randomization (MR) to explore causal links between dietary preferences, plasma metabolites, and ER-negative/ER-positive breast cancer risk, alongside underlying mechanisms. The genome-wide association study (GWAS) data regarding dietary preferences, plasma metabolites, and plasma lipids were obtained from the GWAS database, with sample sizes of 161,625, 8,299, and 7,174 respectively. Meanwhile, the GWAS data related to breast cancer and its subtypes (ER + breast cancer and ER- breast cancer) were sourced from the IEU Open GWAS project, with sample sizes of 228,951, 175,475, and 127,442 respectively. Reverse MR and mediation analyses were conducted to assess bidirectional relationships and mediating roles of plasma biomarkers. The analysis revealed significant causal effects of dietary preferences and plasma metabolites on breast cancer risk. Mediation analysis identified key pathways: curry preference (β = -0.015, 95% CI [-0.029, -0.001], P = 0.034) reduced breast cancer risk through increased phosphatidylcholine levels, while tomato preference (β = 0.021, 95% CI [0.002, 0.041], P = 0.031) increased risk via the serine to α-tocopherol ratio. Additionally, bitter beer preference (β = -0.012, 95% CI [-0.024, -0.000], P = 0.038) was inversely associated with ER-negative breast cancer risk, mediated by specific metabolites, whereas sweetened tea preference (β = 0.015, 95% CI [0.030, 0.059], P = 0.026) positively correlated with ER-negative risk through distinct plasma biomarkers. These findings underscore the importance of dietary factors and plasma metabolites in modulating breast cancer risk across subtypes. The study highlights potential mediation mechanisms, offering insights into the complex interplay between diet, metabolism, and cancer. These results advance our understanding of breast cancer etiology and may inform personalized prevention and treatment strategies, emphasizing the role of targeted dietary interventions and biomarker-driven approaches.

Indexed as

Breast cancer riskCausal linksLipidsMendelian randomizationPlasma metabolomics

Identifiers

PMID41428133
PMCPMC13221510

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.