Metabolic correlates of late midlife cognitive function: findings from the 1946 British Birth Cohort

Background: Investigating associations between metabolites and late midlife cognitive function could reveal potential markers and mechanistic insights relevant to early dementia. Here, we aimed to identify the metabolic underpinnings of cognitive outcomes in late midlife by exploring and integrating associations of single metabolites, metabolic pathways, and networks. We further aimed to untangle the influence of life course factors on these relationships; a previously unexplored avenue using a systems biology approach. Methods and Findings: Levels of 1019 metabolites were detected by liquid chromatography-mass spectrometry (Metabolon Inc) and quantified at age 60-64 among participants of the British 1946 Birth Cohort (N=1740). Cognitive outcomes were assessed at the same age and 5-9 years later, and included short-term memory (age 60-64, 69 and change), delayed memory (age 60-64), processing speed (age 60-64, 69 and change) and Addenbrooke's Cognitive Examination III (age 69). Using a combination of linear regression analysis, quantitative pathway analysis and weighted gene correlation network analysis, we evaluated relationships between metabolite measures (single-metabolites, pathways and network modules) and cognitive outcomes. Single-metabolite and network analyses were sequentially adjusted for life course factors across four models, including: sex and blood clinic information (model 1); model 1 + BMI and lipid medication (model 2); model 2 + childhood cognition, education and socioeconomic position (model 3); model 3 + smoking, exercise, alcohol intake, blood pressure and diet (model 4). After correcting for multiple tests, we identified 155 metabolites, 10 pathways and 5 modules to show relationships with cognitive outcomes. Thirty-five metabolites were influential in their module and identified in single-metabolite analyses. Notably, we report independent relationships between a module comprised of acylcarnitines and processing speed, revealing palmitoylcarnitine (C16) as a key driver of associations (model 4: {beta} = -0.10, 95%CI = -0.15 to -0.052). Two modules demonstrated associations with several cognitive outcomes that were partly explained by life course factors: one enriched in modified nucleosides and amino acids ({beta} range (model 1) = -0.12 to -0.09, attenuation (model 4)= 39.2 to 55.5%), and another in vitamin A and C metabolites ({beta} range (model 1) = 0.11 to 0.23, attenuation (model 4) = 68.6 to 92.6%). Our other findings, including a module enriched in sphingolipid pathways ({beta} range (model 1) = 0.085 to 0.10, attenuation (model 4) = 87.0 to 116%), were entirely explained by life course factors - particularly childhood cognition and education. The limitations of this study include those commonly seen with population-based cohorts, such as possible residual confounding and generalisability to other populations, as well as a lack of longitudinal metabolite data. Conclusions: Using a large birth cohort study with information across the life course, we highlighted potential metabolic mechanisms underlying cognitive function in late midlife, suggesting marker candidates and life course relationships for further study.