Circulating Metabolome and White Matter Hyperintensities in Women and Men
Author(s) -
Eeva Sliz,
Jean Shin,
Shahzad Ahmad,
Dylan M. Williams,
Stefan Frenzel,
Friederike Gauß,
Sarah E. Harris,
AnnKristin Henning,
María Valdés Hernández,
YiHan Hu,
Beatriz Jiménez,
Muralidharan Sargurupremraj,
Carole H. Sudre,
Ruiqi Wang,
Katharina Wittfeld,
Qiong Yang,
Joanna M. Wardlaw,
Henry Völzke,
Meike W. Vernooij,
Jonathan M. Schott,
Marcus Richards,
Petroula Proitsi,
Matthias Nauck,
Matthew R. Lewis,
Lenore J. Launer,
Norbert Hosten,
Hans J. Grabe,
Mohsen Ghanbari,
Ian J. Deary,
Simon R. Cox,
Nish Chaturvedi,
Josephine Barnes,
Jerome I. Rotter,
Stéphanie Debette,
M. Arfan Ikram,
Myriam Fornage,
Tomáš Paus,
Sudha Seshadri,
Zdenka Pausová
Publication year - 2022
Publication title -
circulation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.795
H-Index - 607
eISSN - 1524-4539
pISSN - 0009-7322
DOI - 10.1161/circulationaha.121.056892
Subject(s) - medicine , hyperintensity , metabolome , linear regression , population , false discovery rate , cardiology , magnetic resonance imaging , statistics , metabolite , radiology , biochemistry , chemistry , mathematics , environmental health , gene
Background: White matter hyperintensities (WMH), identified on T2-weighted magnetic resonance images of the human brain as areas of enhanced brightness, are a major risk factor of stroke, dementia, and death. There are no large-scale studies testing associations between WMH and circulating metabolites. Methods: We studied up to 9290 individuals (50.7% female, average age 61 years) from 15 populations of 8 community-based cohorts. WMH volume was quantified from T2-weighted or fluid-attenuated inversion recovery images or as hypointensities on T1-weighted images. Circulating metabolomic measures were assessed with mass spectrometry and nuclear magnetic resonance spectroscopy. Associations between WMH and metabolomic measures were tested by fitting linear regression models in the pooled sample and in sex-stratified and statin treatment–stratified subsamples. Our basic models were adjusted for age, sex, age×sex, and technical covariates, and our fully adjusted models were also adjusted for statin treatment, hypertension, type 2 diabetes, smoking, body mass index, and estimated glomerular filtration rate. Population-specific results were meta-analyzed using the fixed-effect inverse variance–weighted method. Associations with false discovery rate (FDR)–adjustedP values (P FDR )<0.05 were considered significant.Results: In the meta-analysis of results from the basic models, we identified 30 metabolomic measures associated with WMH (P FDR <0.05), 7 of which remained significant in the fully adjusted models. The most significant association was with higher level of hydroxyphenylpyruvate in men (P FDR.full.adj =1.40×10−7 ) and in both the pooled sample (P FDR.full.adj =1.66×10− 4 ) and statin-untreated (P FDR.full.adj =1.65×10− 6 ) subsample. In men, hydroxyphenylpyruvate explained 3% to 14% of variance in WMH. In men and the pooled sample, WMH were also associated with lower levels of lysophosphatidylcholines and hydroxysphingomyelins and a larger diameter of low-density lipoprotein particles, likely arising from higher triglyceride to total lipids and lower cholesteryl ester to total lipids ratios within these particles. In women, the only significant association was with higher level of glucuronate (P FDR =0.047).Conclusions: Circulating metabolomic measures, including multiple lipid measures (eg, lysophosphatidylcholines, hydroxysphingomyelins, low-density lipoprotein size and composition) and nonlipid metabolites (eg, hydroxyphenylpyruvate, glucuronate), associate with WMH in a general population of middle-aged and older adults. Some metabolomic measures show marked sex specificities and explain a sizable proportion of WMH variance.
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