Deelen, J. | Search bioRxiv

bioRxiv2019

Predicting biological age based on the BBMRI-NL 1H-NMR metabolomics repository

The blood metabolome incorporates cues from the environment as well as the hosts genetic background, potentially offering a holistic view of an individuals health status. We have compiled a vast resource of 1H-NMR metabolomics and phenotypic data encompassing over 25,000 samples derived from 26 community and hospital-based cohorts. Using this resource, we constructed a metabolomics-based age predictor (metaboAge) to calculate an individuals biological age. Exploration in independent cohorts demonstrates that being judged older by ones metabolome, as compared to ones chronological age, confers an increased risk on future cardiovascular disease, mortality and functionality in older individuals. A web-based tool for calculating metaboAge (metaboage.researchlumc.nl) allows easy incorporation in other epidemiological studies. Access to data can be requested at bbmri.nl/samples-images-data. In summary, we present a vast resource of metabolomics data and illustrate its merit by constructing a metabolomics-based score for biological age that captures aspects of current and future cardio-metabolic health.

epidemiology↗

bioRxiv2019

Genome-wide association study of plasma lipids

There is continuous interest in the genetic determinants of plasma triglycerides (TGs) and phospholipids and their role in the etiology of cardiovascular disease (CVD). Here, we report the results of a Dutch genome wide association study (GWAS) of an in-house developed lipidomics platform, focusing on 90 plasma lipids. Lipids were assessed by liquid chromatography mass spectrometry in participants from the Leiden Longevity Study, the Netherlands Twin Register and the Erasmus Rucphen Family (ERF) study and meta-analysed, resulting in a sample size of 5537 participants. In addition, we performed genetic correlation analyses between the 90 plasma lipids and markers of metabolic health, as well as vascular pathology and CVD combining our GWAS results with publicly available GWAS outputs. We replicated previously known associations between 34 lipids and 10 lipid quantitative trait loci (lipQTL) (GCKR, APOA1, FADS1, SGPP1,TMEM229B, LIPC, PDXDC1, CETP, CERS4 and SPTLC3) with metabolome-wide (P < 1.61 x 10-9) significance. Moreover, we report 6 novel phospholipid-related and 5 triglyceride (TG)-related loci: SGGP1 (SM21:0), SPTLC3 (SM21:0 and SM25:1), FADS1 (LPCO16:1, PC38:2, PEO36:5, PEO38:5, TG56:5, TG56:6, and TG56:7), TMEM229 (LPCO16:1), GCKR (TG50:2), and APOA1 (TG54:4). In addition, we report suggestively significant (P < 5 x 10-8) associations mapping to eleven novel lipid quantitative trait loci (lipQTLs), three of which are supported by mining previous GWAS data: MAU (PC34:4), LDLR (SM16:0), and MLXIPL (TG48:1 and TG50:1)). Genetic correlation analysis indicates that one specific specific sphingomyelin, SM22:0, shares common genetic background with CVD. Levels of SM22:0 also positively associate with carotid artery intima-media thickness in the ERF study, and this observation is independent of LDL-C level. Our findings yield higher resolution of plasma lipid species and new insights in the biology of circulating phosholipids and their relation to CVD risk.

epidemiology↗