Circulating Metabolome and White Matter Hyperintensities in Women and Men, 2022, Sliz et al.

Circulating Metabolome and White Matter Hyperintensities in Women and Men
Eeva Sliz; Jean Shin; 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. Vernooij; Jonathan 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; for the NeuroCHARGE Working Group

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.

What Is New?

• This is the first large-scale study to identify circulating metabolomic measures associated with white matter hyperintensities (WMH) volume, which is the most common brain imaging marker of small vessel disease and a major risk factor for incident stroke, dementia, and all-cause mortality.

• The metabolomic profile of WMH volume appears largely sex specific.

• The most significant metabolite, hydroxyphenylpyruvate, explains up to 6% and 14% of variance in WMH volume in the pooled sample and in men, respectively, which is comparatively more than the proportions of variance explained by hypertension (1%), type 2 diabetes (1% to 3%), or smoking (<0.1%).

What Are the Clinical Implications?

• Hydroxyphenylpyruvate is a potentially useful clinical biomarker explaining more variance in WMH volume than the established WMH risk factors.

• The marked sex specificities in the metabolomic associations of WMH volume add to the growing body of research suggesting that sex-specific molecular pathways underlie the associations between risk factors and cerebrovascular outcomes.

• Some associated metabolites support vessel-related pathophysiology of WMH; others suggest the involvement of myelin disruption and neuron injury.

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(Statistics omitted for summary).

 

HPP is hydroxyphenylpyruvate.

 

SM-OH is hydroxy-sphingomyelin.

 

Concluding —