Evidence map›Paper›PMID 41680800›Full record

ArticleLipids in health and disease2026

Plasma lipidomic profiling reveals the potential protective role of lipids in cerebral small vessel disease.

Chao Wu, Li-Yan Gao, Wei Sun, Ben-Ke Zhao, Ya-Hui Ma, Hai-Qing Song, Alzheimer’s Disease Neuroimaging Initiative

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Article in Lipids in health and disease, 2026. 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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5 · Who and what money

Authors and funding

7 authors.

Chao WuDepartment of Neurology, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China.
Li-Yan GaoDepartment of Neurology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, China.
Wei SunDepartment of Neurology, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China.
Ben-Ke ZhaoDepartment of Neurology, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China.
Ya-Hui MaDepartment of Neurology, The Affiliated Hospital of Qingdao University, No.16 Jiangsu Road, Qingdao, 266003, China. yahuiweixiao@163.com.
Hai-Qing SongDepartment of Neurology, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China. songhq@xwhosp.org.
Alzheimer’s Disease Neuroimaging Initiative

Funding

National Natural Science Foundation of China 82401662Taishan Scholar Project of Shandong Province tsqn202312391
6 · The paper itself

Abstract

backgroundLimited understanding of the pathophysiology of cerebral small vessel disease (CSVD) has hampered the development of effective treatments. Lipidomics offers a promising approach for identifying molecular signatures, clarifying underlying pathogenic mechanisms, and predicting disease severity and progression.

methodsA total of 1161 participants with lipidomic data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were included and matched to each neuroimaging marker of CSVD separately, including cerebral microbleeds (CMBs, n = 578), white matter hyperintensities (WMHs, n = 650), lacunes (n = 1125), and CSVD burden (n = 546). Three complementary classification strategies (class-based grouping, model-based clustering, and individual lipid species) were employed to investigate lipid signatures across various CSVD markers. A multimodel regression framework followed by a series of sensitivity analyses was used to further identify lipid species showing robust associations with each CSVD marker.

resultsA total of 46 lipid classes, 25 lipid clusters, and 749 lipid species were quantified. Individuals with CSVD presented broadly reduced plasma lipid levels, particularly those of glycerophospholipids and glycerolipids. A multimodel regression framework initially screened 32 lipid species associated with the presence or progression of CSVD. Subsequent sensitivity analyses narrowed these to 13 robust species, including phosphatidylcholine, triacylglycerol, phosphatidylethanolamine, alkenyl-phosphatidylethanolamine, and sphingomyelin. Among these, elevated levels of PC(36:4)[+ OH] and TG(56:6)[NL-20:4] were associated with lower odds of CMBs and reduced WMH volumes, respectively, whereas higher levels of PC(15:0_20:3) and SM(d16:1/19:0) were linked to lower odds of lacunes, with all associations consistently observed across baseline, year 1, and year 2.

conclusionThis study revealed dysregulated lipid metabolism across distinct magnetic resonance imaging phenotypes of CSVD and revealed multiple lipid species that are consistently associated with the presence and progression of these phenotypes, underscoring the potential of lipidomics for the earlier identification and prevention of CSVD and informing future diagnostic development and mechanistic studies.

Indexed as

Alzheimer DiseaseCerebral Small Vessel DiseasesLipidomicsLipidsAgedAged, 80 and overBiomarkersFemaleGlycerophospholipidsHumansMagnetic Resonance ImagingMaleWhite MatterBiomarkersGlycerophospholipidsLipidsCerebral Small Vessel DiseaseGlycerolipidsGlycerophospholipidsInflammationLipidomics

Identifiers

PMID41680800
PMCPMC12997876

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