TY - JOUR
T1 - Circulating Metabolome and White Matter Hyperintensities in Women and Men
AU - Sliz, Eeva
AU - Shin, Jean
AU - Ahmad, Shahzad
AU - Williams, Dylan M.
AU - Frenzel, Stefan
AU - Gauß, Friederike
AU - Harris, Sarah E.
AU - Henning, Ann Kristin
AU - Hernandez, Maria Valdes
AU - Hu, Yi Han
AU - Jiménez, Beatriz
AU - Sargurupremraj, Muralidharan
AU - Sudre, Carole
AU - Wang, Ruiqi
AU - Wittfeld, Katharina
AU - Yang, Qiong
AU - Wardlaw, Joanna M.
AU - Völzke, Henry
AU - Vernooij, Meike W.
AU - Schott, Jonathan M.
AU - Richards, Marcus
AU - Proitsi, Petroula
AU - Nauck, Matthias
AU - Lewis, Matthew R.
AU - Launer, Lenore
AU - Hosten, Norbert
AU - Grabe, Hans J.
AU - Ghanbari, Mohsen
AU - Deary, Ian J.
AU - Cox, Simon R.
AU - Chaturvedi, Nishi
AU - Barnes, Josephine
AU - Rotter, Jerome I.
AU - Debette, Stephanie
AU - Ikram, M. Arfan
AU - Fornage, Myriam
AU - Paus, Tomas
AU - Seshadri, Sudha
AU - Pausova, Zdenka
N1 - Publisher Copyright:
© 2022 Lippincott Williams and Wilkins. All rights reserved.
PY - 2022/4/5
Y1 - 2022/4/5
N2 - 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.
AB - 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.
KW - brain
KW - glucuronic acid
KW - hydroxyphenylpyruvate
KW - lipid ratios
KW - lipidomics
KW - lipids
KW - lysophosphatidylcholines
KW - metabolomics
KW - sphingomyelins
KW - white matter
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U2 - 10.1161/CIRCULATIONAHA.121.056892
DO - 10.1161/CIRCULATIONAHA.121.056892
M3 - Article
C2 - 35050683
AN - SCOPUS:85127843736
SN - 0009-7322
VL - 145
SP - 1040
EP - 1052
JO - Circulation
JF - Circulation
IS - 14
ER -