Associations of circulating metabolites with cerebral white matter hyperintensities - Sun, Yan; Guo, Yu; Li, Hong‐Qi; Tan, Lan; Feng, Jian‐Feng; Cheng, Wei; Yu, Jin‐Tai (Journal of neurochemistry, 2023)
White matter hyperintensities (WMH) are the most compelling risk factors of stroke, dementia, and early mortality. We aimed to investigate the associations between WMH and circulating metabolites. We studied up to 8190 individuals from the UK Biobank, who have both measurements of 249 plasma metabolites and WMH volume. Linear regression models were applied in pooled samples, and age‐stratified and sex‐stratified subsamples to estimate the associations between WMH and metabolomic measures. We conducted three analytic models. In the basic model, we identified 45 metabolomic measures associated with WMH after multiple testing correction (p < 0.0022), 15 of which remained significant in additional adjustments, but no metabolites passed the full adjustment in pooled samples. The 15 WMH‐related metabolites were subfractions of various sizes of high‐density lipoprotein (HDL), fatty acids, and glycoprotein acetyls. Among them, one fatty acid metabolite and 12 HDL‐related traits showed significant negative associations with WMH. Higher glycoprotein acetyls were associated with large WMH. Strong age and sex specificities were observed indicating distinct metabolomic features accompany WMH in different samples. More metabolites were identified in males and adults under 50 years old. Circulating metabolites showed remarkably widespread associations with WMH. Population specificities may shed light on the different pertinent implications of WMH. This study found that the majority of lipoprotein subclasses and lipid concentration are strongly associated with WMH volume, and several non‐lipid‐related traits such as Gly, MUFA, and degree of unsaturation present certain effects on WMH. In addition, the results revealed the sex and age specificities indicating the distinct metabolomic features impact WMH in diverse individuals. These results demonstrated the power of detailed metabolite profiling for biomarker discovery, which can yield an improved molecular understanding of disease mechanisms.