| Title: | Examining the link between 179 lipid species and 7 diseases using genetic predictors |
| Journal: | EBioMedicine |
| Published: | 28 Mar 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40157129/ |
| DOI: | https://doi.org/10.1016/j.ebiom.2025.105671 |
Publication 15824
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Abstract
BACKGROUND: Genome-wide association studies of lipid species have identified several loci shared with various diseases, however, the relationship between lipid species and disease risk remains poorly understood. Here we investigated whether the plasma levels of lipid species are causally linked to disease risk.</p>
METHODS: We built genetic predictors of 179 lipid species, measured in 7174 Finnish individuals, by utilising either 11 high-impact genomic loci or genome-wide polygenic scores (PGS). We assessed the impact of the lipid species on seven diseases by performing disease association across FinnGen (n = 500,348), UK Biobank (n = 420,531), and Generation Scotland (n = 20,032). We performed univariable Mendelian randomisation (MR) and multivariable MR (MVMR) analyses to examine whether lipid species impact disease risk independently of standard lipids.</p>
FINDINGS: PGS explained >4% of the variance for 34 lipid species but variants outside the high-impact loci had only a marginal contribution. Variants within the high-impact loci showed association with all seven diseases. MVMR supported a causal role of ApoB in ischaemic heart disease after accounting for lipid species. Phosphatidylethanolamine-increasing LIPC variants seemed to lower age-related macular degeneration risk independently of HDL-cholesterol. MVMR suggested a protective effect of four lipid species containing arachidonic acid on cholelithiasis risk independently of Total Cholesterol.</p>
INTERPRETATION: Our study demonstrates how genetic predictors of lipid species can be utilised to gain insights into disease risk. We report potential links between lipid species and age-related macular degeneration and cholelithiasis risk, which can be explored for their utility in disease risk prediction and therapy.</p>
FUNDING: The funders had no role in the study design, data analyses, interpretation, or writing of this article.</p>
13 Authors
- Linda Ottensmann
- Rubina Tabassum
- Sanni E. Ruotsalainen
- Mathias J. Gerl
- Christian Klose
- Daniel L. McCartney
- Elisabeth Widén
- FinnGen
- Kai Simons
- Samuli Ripatti
- Veronique Vitart
- Caroline Hayward
- Matti Pirinen
1 Application
| Application ID | Title |
|---|---|
| 22627 | Fine-mapping genotype-phenotype associations |
Enabling scientific discoveries that improve human health