Aims To characterize serum metabolic signatures associated with atherosclerosis in the coronary or carotid arteries and subsequently their association with incident cardiovascular disease (CVD). Methods and results We used untargeted one-dimensional (1D) serum metabolic profiling by proton nuclear magnetic resonance spectroscopy (1H NMR) among 3867 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), with replication among 3569 participants from the Rotterdam and LOLIPOP studies. Atherosclerosis was assessed by coronary artery calcium (CAC) and carotid intima-media thickness (IMT). We used multivariable linear regression to evaluate associations between NMR features and atherosclerosis accounting for multiplicity of comparisons. We then examined associations between metabolites associated with atherosclerosis and incident CVD available in MESA and Rotterdam and explored molecular networks through bioinformatics analyses. Overall, 30 1H NMR measured metabolites were associated with CAC and/or IMT, P = 1.3 × 10−14 to 1.0 × 10−6 (discovery) and P = 5.6 × 10−10 to 1.1 × 10−2 (replication). These associations were substantially attenuated after adjustment for conventional cardiovascular risk factors. Metabolites associated with atherosclerosis revealed disturbances in lipid and carbohydrate metabolism, branched chain, and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analyses of incident CVD events showed inverse associations with creatine, creatinine, and phenylalanine, and direct associations with mannose, acetaminophen-glucuronide, and lactate as well as apolipoprotein B (P < 0.05). Conclusion Metabolites associated with atherosclerosis were largely consistent between the two vascular beds (coronary and carotid arteries) and predominantly tag pathways that overlap with the known cardiovascular risk factors. We present an integrated systems network that highlights a series of inter-connected pathways underlying atherosclerosis.
ABSTRACT:Large scale metabolomics studies involving thousands of samples present multiple challenges in data analysis, particularly when an untargeted platform is used. Studies with multiple cohorts and analysis platforms exacerbate existing problems such as peak alignment and drift correction. Therefore there is a need for robust processing pipelines which can ensure reliable, quality controlled data for statistical analysis. The COMBI-BIO project is aimed at detection of metabolic markers of pre-clinical atherosclerosis, and incorporates plasma from 8000 individuals, in 3 cohorts, profiled by 6 assays in 2 phases using both NMR and UPLC-MS. Here we present the COMBI-BIO NMR analysis pipeline and demonstrate its fitness for purpose through statistical analysis of identical representative quality control (QC) samples interleaved with study samples throughout the analytical run. Standard 1-dimensional 1 H-NMR spectra were aligned using the Recursive Segment-wise Peak Alignment algorithm and normalized using the Probabilistic Quotient method. After removing interfering signals, outliers identified using Hotelling's T 2 were removed and a cohort/phase adjustment was applied, resulting in two NMR data sets for each sample. A number of quality assessment metrics were computed to assess the developed pipeline. Alignment of the NMR data was shown to increase the correlation-based aq0.02 quality measure from 0.319 to 0.391 for CPMG and 0.536 to 0.586 for NOESY data, showing that the improvement was present across both large and small peaks. End-to-end quality assessment of the pipeline was achieved by examining the distribution of Hotelling's T 2 values across both pooled QC and biological samples. For CPMG spectra, the interquartile range decreased from 1.425 in raw QC data to 0.679 in processed spectra, while the corresponding change for NOESY spectra was 0.795 to 0.636 indicating a substantial improvement in precision following processing. PCA indicated that gross phase and cohort differences were no longer present in the final data sets. Taken together, these results illustrate that the developed pipeline produces robust and reproducible data across thousands of samples, successfully addressing the challenges of this large multi-faceted study.
Background Child eating behaviors are highly heterogeneous and their longitudinal impact on childhood weight is unclear. The objective of this study was to characterize eating behaviors during the first ten years of life and evaluate associations with BMI at age 11 years. Method Data were parental reports of eating behaviors from 15 months to age 10 years (n=12,048) and standardized body mass index (zBMI) at age 11 years (n=4884) from the Avon Longitudinal Study of Parents and Children. Latent class growth analysis was used to derive latent classes of over-, under-, and fussy eating. Linear regression models for zBMI at 11 years on each set of classes were fitted to assess associations with eating behavior trajectories. Results We identified four classes of overeating; “low stable” (70%), “low transient” (15%), “late increasing” (11%), and “early increasing” (6%). The “early increasing” class was associated with higher zBMI (boys: β=0.83, 95%CI:0.65, 1.02; girls: β=1.1; 0.92, 1.28) compared to “low stable”. Six classes were found for undereating; “low stable” (25%), “low transient” (37%), “low decreasing” (21%), “high transient” (11%), “high decreasing” (4%), and “high stable” (2%). The latter was associated with lower zBMI (boys: β=-0.79; -1.15, -0.42; girls: β=-0.76; -1.06, -0.45). Six classes were found for fussy eating; “low stable” (23%), “low transient” (15%), “low increasing” (28%), “high decreasing” (14%), “low increasing” (13%), “high stable” (8%). The “high stable”class was associated with lower zBMI(boys: β =-0.49; -0.68 -0.30; girls: β =-0.35;-0.52, -0.18). Conclusions Early increasing overeating during childhoodis associated with higher zBMI at age 11. High persistent levels of undereating and fussy eating are associated with lower zBMI. Longitudinal trajectories of eating behaviors may help identify children potentially at risk of adverse weight outcomes.
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