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. 2023 Feb 14;44(7):557-569.
doi: 10.1093/eurheartj/ehac446.

Dietary metabolic signatures and cardiometabolic risk

Ravi V Shah  1 , Lyn M Steffen  2 , Matthew Nayor  3 , Jared P Reis  4 , David R Jacobs  2 , Norrina B Allen  5 , Donald Lloyd-Jones  5 , Katie Meyer  6 , Joanne Cole  7 , Paolo Piaggi  8 , Ramachandran S Vasan  9   10 , Clary B Clish  11 , Venkatesh L Murthy  12
Affiliations

Dietary metabolic signatures and cardiometabolic risk

Ravi V Shah et al. Eur Heart J. .

Abstract

Aims: Observational studies of diet in cardiometabolic-cardiovascular disease (CM-CVD) focus on self-reported consumption of food or dietary pattern, with limited information on individual metabolic responses to dietary intake linked to CM-CVD. Here, machine learning approaches were used to identify individual metabolic patterns related to diet and relation to long-term CM-CVD in early adulthood.

Methods and results: In 2259 White and Black adults (age 32.1 ± 3.6 years, 45% women, 44% Black) in the Coronary Artery Risk Development in Young Adults (CARDIA) study, multivariate models were employed to identify metabolite signatures of food group and composite dietary intake across 17 food groups, 2 nutrient groups, and healthy eating index-2015 (HEI2015) diet quality score. A broad array of metabolites associated with diet were uncovered, reflecting food-related components/catabolites (e.g. fish and long-chain unsaturated triacylglycerols), interactions with host features (microbiome), or pathways broadly implicated in CM-CVD (e.g. ceramide/sphingomyelin lipid metabolism). To integrate diet with metabolism, penalized machine learning models were used to define a metabolite signature linked to a putative CM-CVD-adverse diet (e.g. high in red/processed meat, refined grains), which was subsequently associated with long-term diabetes and CVD risk numerically more strongly than HEI2015 in CARDIA [e.g. diabetes: standardized hazard ratio (HR): 1.62, 95% confidence interval (CI): 1.32-1.97, P < 0.0001; CVD: HR: 1.55, 95% CI: 1.12-2.14, P = 0.008], with associations replicated for diabetes (P < 0.0001) in the Framingham Heart Study.

Conclusion: Metabolic signatures of diet are associated with long-term CM-CVD independent of lifestyle and traditional risk factors. Metabolomics improves precision to identify adverse consequences and pathways of diet-related CM-CVD.

Keywords: CVD; Diet; Metabolism; Metabolomics; Nutrition; Precision medicine.

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Conflict of interest statement

Conflict of interest: V.L.M. owns stock or stock options in General Electric, Cardinal Health, Ionetix, Boston Scientific, Merck, Eli Lilly, Johnson and Johnson, Pfizer, Intel and nVidia. He has received research grants and speaking honoraria from Siemens Medical Imaging and expert testimony fees on behalf of Jubilant Draximage. He has served on medical advisory boards for Ionetix and Curium. R.V.S. has served as a consultant for Myokardia and Best Doctors (concluded), is on a scientific advisory board for Amgen (ongoing) and is a consultant for Cytokinetics (ongoing). He is a co-inventor on a patent for ex-RNAs signatures of cardiac remodelling. He is a co-inventor on a patent for ex-RNAs signatures of cardiac remodelling. K.M. is also supported by a grant from Balchem. The remaining authors have nothing to disclose.

Figures

Structured Graphical Abstract
Structured Graphical Abstract
This figure summarizes the general approach of this work. The bottom portion of this figure (on the CCA analysis) was inspired by Wang et al. The readers are referred to this excellent work for further exposition on the utility of CCA in high-dimensional data analysis.
Figure 1
Figure 1
Precision of the metabolome to identify dietary intake. The proportion of variance explained (R2) by total caloric intake (TCI; black circles) and metabolite levels plus total caloric intake (red circles). As noted in text, this figure represents the results of elastic nets where dietary intake is the dependent variable and the metabolome (with or without TCI) is the independent variable.
Figure 2
Figure 2
Metabolite signatures of dietary intake are more consistently associated with long-term cardiometabolic and cardiovascular disease relative to survey-based dietary intake. Standardized hazard ratios for diabetes (left) and CVD (right) for metabolite scores (red/lower dots, based on elastic net regression of dietary intake as a function of the metabolome) and survey-based dietary intake (black/upper dots) are displayed. Metabolite signatures generally exhibited larger effect sizes than the survey estimates for CM-CVD outcomes. Models presented here are fully adjusted (as specified in the Methods for diabetes and CVD).
Figure 3
Figure 3
Penalized canonical correlation and survival analysis for metabolite signatures of common dietary patterns in CARDIA and FHS. Panel (A) shows a heat map of regression coefficients relating metabolite levels (outcome) to dietary exposures (independent variables), adjusted for total caloric intake, for metabolites selected by the penalized CCA. Weightings for the first canonical variate are shown as marginal bar plots. Panel (B) shows forest plots of standardized hazard ratios for the first metabolomic CCA score for incident diabetes and CVD from Cox models in CARDIA. In general, the CCA-based metabolite score has a larger effect size than HEI for both outcomes. Panel (C) shows corresponding hazard ratios from FHS.
See this image and copyright information in PMC

Comment in

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