Circulating Metabolome and White Matter Hyperintensities in Women and Men

Eeva Sliz, Jean Shin, NeuroCHARGE Working Group, Shahzad Ahmad, Dylan M Williams, Stefan Frenzel, Friederike Gauß, Sarah E Harris, Ann-Kristin Henning, Maria Valdes Hernandez, Yi-Han Hu, Beatriz Jiménez, Muralidharan Sargurupremraj, Carole Sudre, Ruiqi Wang, Katharina Wittfeld, Qiong Yang, Joanna M Wardlaw, Henry Völzke, Meike W VernooijJonathan M Schott, Marcus Richards, Petroula Proitsi, Matthias Nauck, Matthew R Lewis, Lenore Launer, Norbert Hosten, Hans J Grabe, Mohsen Ghanbari, Ian J Deary, Simon R Cox, Nishi Chaturvedi, Josephine Barnes, Jerome I Rotter, Stephanie Debette, M Arfan Ikram, Myriam Fornage, Tomas Paus, Sudha Seshadri, Zdenka Pausova*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)


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)-adjusted P values (PFDR)<0.05 were considered significant.

RESULTS: In the meta-analysis of results from the basic models, we identified 30 metabolomic measures associated with WMH (PFDR<0.05), 7 of which remained significant in the fully adjusted models. The most significant association was with higher level of hydroxyphenylpyruvate in men (PFDR.full.adj=1.40×10-7) and in both the pooled sample (PFDR.full.adj=1.66×10-4) and statin-untreated (PFDR.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 (PFDR=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.

Original languageEnglish
Pages (from-to)1040-1052
Number of pages13
Issue number14
Publication statusPublished - 5 Apr 2022

Bibliographical note

Sources of Funding
Supported in part by the National Center for Advancing Translational Sciences, Clinical and Translational Sciences Institute (grant UL1TR001881), and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (grant DK063491) to the Southern California Diabetes Endocrinology Research Center. Dr Schott is supported by the University College London/University College London Hospitals National Institute for Health Research Biomedical Research Center. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam; Netherlands Organization for Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Ministry of Education, Culture and Science; the Ministry for Health, Welfare and Sports; the European Commission (DG XII); and the Municipality of Rotterdam. Metabolomics measurements of the Nightingale platform were funded by Biobanking and Biomolecular Resources Research Infrastructure (grant 184.021.007). The Saguenay Youth Study was funded by the Canadian Institutes of Health Research, Heart and Stroke Foundation of Canada, Canadian Foundation for Innovation, and National Institutes for Health (R01AG056726). Insight 46 is principally funded by grants from Alzheimer’s Research UK, the Medical Research Council Dementias Platform UK, the Wolfson Foundation, and the British Heart Foundation. The National Survey of Health and Development is funded by the Medical Research Council. The Lothian Birth Cohort 1936 is supported by Age UK (the Disconnected Mind project, which supports Dr Harris), the Medical Research Council (grants G0701120, G1001245, MR/M013111/1, and MR/R024065/1, which support Dr Cox), and the University of Edinburgh. Magnetic resonance imaging was supported by the Medical Research Council (grants G0701120, G1001245, MR/M013111/1, and MR/R024065/1). Metabolomics were supported by the Dementia Platform UK, the Medical Research Council (grant MR/L023784/2), and the Medical Research Council and National Institute for Health Research (grant MC_PC_12025). Infrastructure support was provided by the National Institute for Health Research Imperial Biomedical Research Center.


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