Evidence map›Paper›PMID 40934270›Full record

ArticlePloS one2025

Genetic association of lipids characteristics and lipid lowering drug target genes with sepsis.

Yu Wang, Haiyue Zhang, Yuanyuan Zhan, Zhuoran Li, Sujing Li, Yingchao Zhang, Shubin Guo

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Article in PloS one, 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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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yu WangEmergency Medicine Clinical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, P.R. China.
Haiyue ZhangThrombosis Research Center, Beijing Jishuitan Hospital, Capital Medical University, Xicheng District, Beijing, China.
Yuanyuan ZhanDepartment of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, P.R. China.
Zhuoran LiEmergency Medicine Clinical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, P.R. China.
Sujing LiDepartment of dermatology, Zhengzhou People's Hospital, Zhengzhou, China.
Yingchao ZhangDepartment of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, P.R. China.
Shubin GuoEmergency Medicine Clinical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, P.R. China.ORCID https://orcid.org/0000-0003-2121-9223

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSepsis is a severe systemic infection that can result in organ dysfunction and mortality. Dyslipidemia emerges as a key player in the intricate web of sepsis pathogenesis. Yet, the causal relationship between blood lipid profiles and sepsis risk remains uncertain. This study aims to investigate the association between genetically predicted lipid traits, drug targets, and sepsis.

methodsThe UK Biobank's Genome-wide association studies (GWAS) produced data on lipid and apolipoprotein characteristics. Four independent GWAS datasets were used to generate the sepsis statistics. The study utilized the two-sample Mendelian randomization (MR) approach, which incorporates multivariable (MVMR) models, to assess the correlations between sepsis risk and lipid-related parameters. To gain further insight, expression quantitative trait loci (eQTL) data were used to investigate the significant drug targets for lipid-lowering.

resultsIncreasing ApoA-1 levels was associated with a diminished risk of sepsis (under 75) (OR 0.927, 95% CI 0.861-0.999; p = 0.047). This inverse correlation persevered even after performing multivariable MR. Elevated levels of HDL-C were associated with a decreased risk of sepsis (under 75) (OR 0.897, 95% CI 0.838-0.960; P = 0.002) and incidence of sepsis (OR 0.883, 95% CI 0.820-0.951; P = 0.001), which was consistent across sensitivity analyses. Furthermore, a decrease in total cholesterol exhibited a causal effect on sepsis in multivariable MR (OR 0.779, 95% CI 0.642-0.944; P = 0.01). The genetic variants related to lowering LDL-C, located near the HMGCR and LDLR genes, were predicted to elevate the risk of sepsis. Moreover, genetic mimicry near the ANGPTL3 and LPL gene suggested that reducing the activity of ANGPTL3 and LPL (mimicking antisense anti-ANGPTL3 and LPL agents) was forecasted to decrease sepsis risk.

conclusionGenetically inferred elevated ApoA-1, total cholesterol, and HDL-C manifest a protective effect against sepsis. Within the 9 lipid-lowering drug targets investigated ANGPTL3 and LPL exhibit potential as candidate drug targets for sepsis.

Indexed as

LipidsSepsisApolipoprotein A-ICholesterol, HDLFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansHypolipidemic AgentsMaleMendelian Randomization AnalysisMiddle AgedPolymorphism, Single NucleotideQuantitative Trait LociAPOA1 protein, humanApolipoprotein A-ICholesterol, HDLHypolipidemic AgentsLipids

Identifiers

PMID40934270
PMCPMC12425328

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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.