Evidence map›Paper›PMID 40355763›Full record

ArticleDiscover oncology2025

Causal links between plasma lipidome and ovarian cancer risk: evidence from Mendelian randomization.

Huke Dong, Chen Zhang, Hua Wang, Ying Dai

Abstract read
In one paragraph

Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Huke Dong *Department of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Chen Zhang *Lu'an Hospital of Traditional Chinese Medicine, Lu'an, China.
Hua WangDepartment of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China. wanghua@ahmu.edu.cn.
Ying DaiDepartment of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China. dabora20051@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

S. Plasma lipids in circulation are integral to the physiopathological processes of the ovary and may impact the development of various ovarian conditions, including ovarian cancer (OC). This study utilized a two-sample Mendelian randomization method to examine the causal link between changes in 179 plasma lipid groups and ovarian cancer (OC) to gain deeper insights into this association. We used the inverse variance weighted (IVW) method as the main tool for analysis. We utilized statistical data from plasma lipidomics involving 7,174 Finnish individuals and OC data from the FinnGen consortium, including 2,339 European OC patients and 222,078 European healthy controls. Our analysis revealed that elevated levels of four plasma lipids-Phosphatidylcholine (14:0_16:0, O-18:2_18:2, 16:0_20:4)-are linked to an increased risk of OC, while Sphingomyelin (d34:2) seems to act as a protective factor(all P < 0.05). We also conducted tests for heterogeneity and pleiotropy in the MR results. Additionally, reverse MR analysis indicated that OC does not affect plasma levels of these lipids. To determine whether the observed significant plasma lipids influence OC through common risk factors, we selected BMI as a confounder for multivariable Mendelian randomization (MVMR) analysis. The results showed that Sphingomyelin (d34:2) levels remained significantly associated with OC even after including BMI as an exposure factor. Furthermore, we investigated whether these four lipids mediated the effect of BMI on OC but found no evidence supporting their mediating role. In summary, our findings confirm a causal link between certain plasma lipid species and OC, providing fresh perspectives for risk evaluation and potential therapeutic strategies.

Indexed as

179 plasma lipidomeGWASMendelian randomizationOvarian cancer

Identifiers

PMID40355763
PMCPMC12069180

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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.