Metabolic Age Based on the BBMRI-NL ¹ H-NMR Metabolomics Repository as Biomarker of Age-related Disease

Abstract

Background - The blood metabolome incorporates cues from the environment as well as the host's genetic background, potentially offering a holistic view of an individual's health status. Methods - We have compiled a vast resource of 1 H-NMR metabolomics and phenotypic data encompassing over 25,000 samples derived from 26 community and hospital-based cohorts. Results - Using this resource, we constructed a metabolomics-based age predictor (metaboAge) to calculate an individual's biological age. Exploration in independent cohorts demonstrates that being judged older by one's metabolome, as compared to one's 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. Conclusions - 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.

1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints

“…Both 5-FoldCVs were done such that the original distribution of each clinical variable is maintained as much as possible (using the function createFolds from the R package caret). In the second training-testing procedure, we applied a Leave-One-Biobank-Out-Validation (LOBOV), which consists of holding out one of the biobanks with the considered variable available, which is then used as a test set, while training on the remaining biobanks [16]. Also, in this setting, we applied a 5FCV with 5 repetitions to tune the best model for each training set.…”

“…The underlying motivation is that metabolite concentrations in blood seem to be direct readouts of various biological processes, incorporating cues of the environment as well as the host's genetic background, and hence may be regarded as intermediate phenotypes. Indeed, metabolomics has been shown to capture information on the effect of drug treatments [10], disease status [11]- [14], functional and cognitive decline [15], and aging [16], [17]. In addition, several studies used the blood metabolome to predict single anthropometric measures, i.e.…”

“…BMI [18], or other physiological characteristics, i.e. sex [19] or age [16]. However, it remains unclear whether the blood metabolome captured by 1 H-NMR could represent phenotypic information over a wider set of conventional clinical variables.…”

1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints

“…Indeed, metabolomics has been shown to capture information on the effect of drug treatments, 9 disease status, 10À13 functional and cognitive decline, 14 and ageing. 15,16 In addition, several studies used the blood metabolome to predict single anthropometric measures, i.e. BMI, 17 or other physiological characteristics, i.e.…”

Comparing the genetic and environmental architecture of blood count, blood biochemistry and urine biochemistry biological ages with machine learning

“…Participants whose biological age is higher than their chronological age are called accelerated agers. Biological age predictors have been developed from diverse data modalities 4 , including blood [5][6][7][8][9][10][11] and urine 12 biochemistry biomarkers.…”