Plasma lipid and liporotein biomarkers in LBC1936: Do they predict general cognitive ability and brain structure?

Harris, Sarah E.; Ritchie, Stuart J.; Correia, Gonçalo dos Santos; Jiménez, Beatriz; Fawns-Ritchie, Chloë; Pattie, Alison; Corley, Janie; Maniega, S Muñoz; Hernández, Maria Valdés; Starr, John M.; Hill, Derek L. G.; Wren, Paul; Lewis, Matthew R.; Wardlaw, Joanna M.; Deary, Ian J.

doi:10.1101/2020.07.09.194688

Cited by 2 publications

(1 citation statement)

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“…We used nuclear magnetic resonance-based techniques (Nightingale Health Ltd, National Phenome Center, Bruker Biospin) and mass spectrometry-based technologies (Metabolon, Biocrates Life Sciences AG, Broad Institute) that are commonly used in epidemiologic research and described elsewhere [29][30][31][32][33] (Table S1). Using these platforms, it was possible to measure 2217 different metabolomic measures; of these, 1174 were quantified in ≥2 of the study populations.…”

Section: Metabolomic Quantificationsmentioning

confidence: 99%

Circulating Metabolome and White Matter Hyperintensities in Women and Men

et al. 2022

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Background: White matter hyperintensities (WMH) are identified on T2-weighted magnetic resonance images of the human brain as areas of enhanced brightness; WMH are a major risk factor of stroke, dementia, and death. Currently, there are no large-scale studies testing associations between WMH and circulating metabolites. Methods: We studied up to 9,290 individuals (50.7% females, 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 additionally 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 ( 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 males ( p FDR.full.adj =1.40×10 -7 ) and in both the pooled sample ( p FDR.full.adj =1.66×10 -4 ) and statin-nontreated ( p FDR.full.adj =1.65×10 -6 ) subsample. In males, HPP explained 3-14% of variance in WMH. In males 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 females, the only significant association was with higher level of glucuronate ( p FDR =0.047). Conclusions: Circulating metabolomic measures, including multiple lipid measures (e.g., lysophosphatidylcholines, hydroxysphingomyelins, low-density lipoprotein size and composition) and non-lipid metabolites (e.g., hydroxyphenylpyruvate, glucuronate) associate with WMH in a general population of middle-aged and older adults. Some metabolomic measures show marked sex specificities and explain sizable proportion of WMH variance.

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Section: Metabolomic Quantificationsmentioning

confidence: 99%

Circulating Metabolome and White Matter Hyperintensities in Women and Men

et al. 2022

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Epigenetic predictors of lifestyle traits applied to the blood and brain

Gadd

Stevenson

Hillary

et al. 2020

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Modifiable lifestyle factors influence the risk of developing many neurological diseases. These factors have been extensively linked with blood-based genome-wide DNA methylation (DNAm), but it is unclear if the signatures from blood translate to the target tissue of interest - the brain. To investigate this, we apply blood-derived epigenetic predictors of four lifestyle traits to genome-wide DNAm from five post-mortem brain regions and the last blood sample prior to death in 14 individuals in the Lothian Birth Cohort 1936 (LBC1936). Using these matched samples, we found that correlations between blood and brain DNAm scores for smoking, high density lipoprotein (HDL) cholesterol, alcohol and body mass index (BMI) were highly variable across brain regions. Smoking scores in the dorsolateral prefrontal cortex had the strongest correlations with smoking scores in blood (r=0.5, n=14) and smoking behaviour (r=0.56, n=9). This was also the brain region which exhibited the strongest correlations for DNAm at site cg05575921 - the single strongest correlate of smoking in blood - in relation to blood (r=0.61, n=14) and smoking behaviour (r=-0.65, n=9). This suggested a particular vulnerability to smoking-related differential methylation in this region. Our work contributes to understanding how lifestyle factors affect the brain and suggests that lifestyle-related DNAm is likely to be both brain region dependent and in many cases poorly proxied for by blood. Though these pilot data provide a rarely-available opportunity for the comparison of methylation patterns across multiple brain regions and the blood, due to the limited sample size available our results must be considered as preliminary and should therefore be used as a basis for further investigation.Abstract FigureGraphical abstract 203mm x 127mm (DPI 300)Abbreviated summary [50 words]: We apply blood-derived epigenetic signatures of lifestyle traits to matched blood and brain samples, uncovering variability in how well blood translates across brain regions and a relationship between smoking and the prefrontal cortex. Our preliminary results contribute to understanding how lifestyle-related DNA methylation affects the brain in health and disease.

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Plasma lipid and liporotein biomarkers in LBC1936: Do they predict general cognitive ability and brain structure?

Cited by 2 publications

References 63 publications

Circulating Metabolome and White Matter Hyperintensities in Women and Men

Circulating Metabolome and White Matter Hyperintensities in Women and Men

Epigenetic predictors of lifestyle traits applied to the blood and brain

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