Mendelian randomization identifies blood metabolites previously linked to midlife cognition as causal candidates in Alzheimer’s disease

Abstract

There are currently no disease-modifying treatments for Alzheimer’s disease (AD), and an understanding of preclinical causal biomarkers to help target disease pathogenesis in the earliest phases remains elusive. Here, we investigated whether 19 metabolites previously associated with midlife cognition—a preclinical predictor of AD—translate to later clinical risk, using Mendelian randomization (MR) to tease out AD-specific causal relationships. Summary statistics from the largest genome-wide association studies (GWASs) for AD and metabolites were used to perform bidirectional univariable MR. Bayesian model averaging (BMA) was additionally performed to address high correlation between metabolites and identify metabolite combinations that may be on the AD causal pathway. Univariable MR indicated four extra-large high-density lipoproteins (XL.HDL) on the causal pathway to AD: free cholesterol (XL.HDL.FC: 95% CI = 0.78 to 0.94), total lipids (XL.HDL.L: 95% CI = 0.80 to 0.97), phospholipids (XL.HDL.PL: 95% CI = 0.81 to 0.97), and concentration of XL.HDL particles (95% CI = 0.79 to 0.96), significant at an adjusted P < 0.009. MR–BMA corroborated XL.HDL.FC to be among the top three causal metabolites, in addition to total cholesterol in XL.HDL (XL.HDL.C) and glycoprotein acetyls (GP). Both XL.HDL.C and GP demonstrated suggestive univariable evidence of causality (P < 0.05), and GP successfully replicated within an independent dataset. This study offers insight into the causal relationship between metabolites demonstrating association with midlife cognition and AD. It highlights GP in addition to several XL.HDLs—particularly XL.HDL.FC—as causal candidates warranting further investigation. As AD pathology is thought to develop decades prior to symptom onset, expanding on these findings could inform risk reduction strategies.

Effects of genetic liability to Alzheimer’s disease on circulating metabolites across the life course

“…We found evidence that associations of HDL-C and its major constituent apolipoprotein A-1 with higher AD liability were weakly negative. Observationally, and in MR studies, higher HDL-C is associated with lower AD risk 40,41 . Results from a previous prospective cohort study implicated lower plasma apolipoprotein A-1 with elevated risk of AD progression in ε4 carriers 42 .…”

Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers

“…GlycA is a biomarker of chronic inflammation and has been found to predict various endpoints, including types of cardiovascular disease, cancer, and all-cause mortality ( Lawler et al, 2016 ; Connelly et al, 2017 ). Although previous studies of genetically predicted GlycA have been conducted for hypotheses regarding single endpoints ( Lord et al, 2021 ), whether or not circulating GlycA has a causal effect on outcomes from across the disease spectrum has yet to be comprehensively investigated. The role of GlycA is important to establish given the emerging role of inflammation as a pharmacologically modifiable pathway for the prevention and treatment of cardiovascular disease.…”

“…Availability of metabolomics quantified by other platforms (e.g. mass spectroscopy) in large numbers with GWAS genotyping will aid in this effort (Lotta et al, 2021). Furthermore, whilst these data provide an unparalleled sample size compared to predecessors, findings are based on traits derived from whole blood and may therefore not be reflective of molecular signatures identified in other tissue types (Richardson et al, 2020a).…”

“…The human metabolome consists of over 100,000 small molecules and is a rich source of potential risk factors and biomarkers, as well as therapeutic targets (Holmes et al, 2021), for complex traits and disease (Gallois et al, 2019). Many circulating metabolic traits studied to date have a large heritable component as demonstrated by GWAS endeavours (Suhre et al, 2011, Shin et al, 2014, Lotta et al, 2021, suggesting that they have a polygenic architecture.…”

Mendelian randomization in blood metabolites identifies triglycerides and fatty acids saturation level as associated traits linked to pancreatitis risk

“…MR-BMA was used in the subcategory showing large numbers of metabolic traits with statistical significance. Compared with conventional multivariable MR, the MR-BMA method is particularly useful to investigate high-dimensional datasets (i.e., metabolites) with remarkable genetic correlation ( 11 , 12 ). The rationale is that subgroups of metabolites sharing large numbers of genetic variants may act together on the same causal pathway.…”