Dataset Information

Proteomic and Metabolomic Correlates of Healthy Dietary Patterns: The Framingham Heart Study.

ABSTRACT: Data on proteomic and metabolomic signatures of healthy dietary patterns are limited. We evaluated the cross-sectional association of serum proteomic and metabolomic markers with three dietary patterns: the Alternative Healthy Eating Index (AHEI), the Dietary Approaches to Stop Hypertension (DASH) diet; and a Mediterranean-style (MDS) diet. We examined participants from the Framingham Offspring Study (mean age; 55 years; 52% women) who had complete proteomic (n = 1713) and metabolomic (n = 2284) data; using food frequency questionnaires to derive dietary pattern indices. Proteins and metabolites were quantified using the SomaScan platform and liquid chromatography/tandem mass spectrometry; respectively. We used multivariable-adjusted linear regression models to relate each dietary pattern index (independent variables) to each proteomic and metabolomic marker (dependent variables). Of the 1373 proteins; 103 were associated with at least one dietary pattern (48 with AHEI; 83 with DASH; and 8 with MDS; all false discovery rate [FDR] ? 0.05). We identified unique associations between dietary patterns and proteins (17 with AHEI; 52 with DASH; and 3 with MDS; all FDR ? 0.05). Significant proteins enriched biological pathways involved in cellular metabolism/proliferation and immune response/inflammation. Of the 216 metabolites; 65 were associated with at least one dietary pattern (38 with AHEI; 43 with DASH; and 50 with MDS; all FDR ? 0.05). All three dietary patterns were associated with a common signature of 24 metabolites (63% lipids). Proteins and metabolites associated with dietary patterns may help characterize intermediate phenotypes that provide insights into the molecular mechanisms mediating diet-related disease. Our findings warrant replication in independent populations.

SUBMITTER: Walker ME

PROVIDER: S-EPMC7284467 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Proteomic and Metabolomic Correlates of Healthy Dietary Patterns: The Framingham Heart Study.

Walker Maura E ME Song Rebecca J RJ Xu Xiang X Gerszten Robert E RE Ngo Debby D Clish Clary B CB Corlin Laura L Ma Jiantao J Xanthakis Vanessa V Jacques Paul F PF Vasan Ramachandran S RS

Nutrients 20200519 5

Data on proteomic and metabolomic signatures of healthy dietary patterns are limited. We evaluated the cross-sectional association of serum proteomic and metabolomic markers with three dietary patterns: the Alternative Healthy Eating Index (AHEI), the Dietary Approaches to Stop Hypertension (DASH) diet; and a Mediterranean-style (MDS) diet. We examined participants from the Framingham Offspring Study (mean age; 55 years; 52% women) who had complete proteomic (<i>n</i> = 1713) and metabolomic (<i ...[more]

PMID: 32438708

Similar Datasets

Project description:BackgroundIn individuals with chronic kidney disease (CKD), healthy dietary patterns are inversely associated with CKD progression. Metabolomics, an approach that measures many small molecules in biofluids, can identify biomarkers of healthy dietary patterns.ObjectivesWe aimed to identify known metabolites associated with greater adherence to 4 healthy dietary patterns in CKD patients.MethodsWe examined associations between 486 known plasma metabolites and Healthy Eating Index (HEI)-2015, Alternative Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension (DASH) diet, and alternate Mediterranean diet (aMED) in 1056 participants (aged 21-74 y at baseline) in the Chronic Renal Insufficiency Cohort (CRIC) Study. Usual dietary intake was assessed using a semiquantitative FFQ. We conducted multivariable linear regression models to study associations between healthy dietary patterns and individual plasma metabolites, adjusting for sociodemographic characteristics, health behaviors, and clinical factors. We used principal component analysis to identify groups of metabolites associated with individual food components within healthy dietary patterns.ResultsAfter Bonferroni correction, we identified 266 statistically significant diet-metabolite associations (HEI: n = 60; AHEI: n = 78; DASH: n = 77; aMED: n = 51); 78 metabolites were associated with >1 dietary pattern. Lipids with a longer acyl chain length and double bonds (unsaturated) were positively associated with all 4 dietary patterns. A metabolite pattern low in saturated diacylglycerols and triacylglycerols, and a pattern high in unsaturated triacylglycerols was positively associated with intake of healthy food components. Plasmalogens were negatively associated with the consumption of nuts and legumes and healthy fat, and positively associated with the intake of red and processed meat.ConclusionsWe identified many metabolites associated with healthy dietary patterns, indicative of food consumption. If replicated, these metabolites may be considered biomarkers of healthy dietary patterns in patients with CKD.

Project description:BackgroundThere have been conflicting reported associations between dietary factors and incident atrial fibrillation (AF).ObjectiveWe evaluated associations between consumption of alcohol, caffeine, fiber, and polyunsaturated fatty acids (PUFAs) and incident AF in the Framingham Heart Study.DesignParticipants without AF (n = 4526; 9640 examinations; mean age: 62 y; 56% women) from the original and offspring cohorts completed food-frequency questionnaires and were followed prospectively for 4 y. We examined the associations between dietary exposures and AF with Cox proportional hazards regression.ResultsA total of 296 individuals developed AF (177 men, 119 women). In multivariable analyses, there were no significant associations between examined dietary exposures and AF risk. Hazard ratios (HRs) for increasing quartiles of dietary factors were as follows: for alcohol, 0.73 (95% CI: 0.5, 1.05), 0.85 (95% CI: 0.61, 1.18), and 1.12 (95% CI: 0.83, 1.51) (P for trend = 0.48); for caffeine, 0.84 (95% CI: 0.62, 1.15), 0.87 (95% CI: 0.64, 1.2), and 0.98 (95% CI: 0.7, 1.39) (P for trend = 0.84); for total fiber, 0.86 (95% CI: 0.61, 1.2), 0.64 (95% CI: 0.44, 0.92), and 0.81 (95% CI: 0.54, 1.2) (P for trend = 0.16); and for n-3 (omega-3) PUFAs, 1.11 (95% CI: 0.81, 1.54), 0.92 (95% CI: 0.65, 1.29), and 1.18 (95% CI: 0.85, 1.64) (P for trend = 0.57; quartile 1 was the reference group). In exploratory analyses, consumption of >4 servings of dark fish/wk (5 cases and 21 individuals at risk) was significantly associated with AF risk compared with the consumption of <1 serving of dark fish/wk (HR: 6.53; 95% CI: 2.65, 16.06; P < 0.0001).ConclusionsConsumption of alcohol, caffeine, fiber, and fish-derived PUFAs was not significantly associated with AF risk. The observed adverse association between the consumption of dark fish and AF merits further investigation. Our findings suggest that the dietary exposures examined convey limited attributable risk of AF in the general population.

Project description:OBJECTIVES:Traditional clinical risk factors are associated with inflammation cross-sectionally, but associations of longitudinal variation in inflammatory biomarkers with corresponding changes in clinical risk factors are incompletely described. We sought to analyze clinical factors associated with change in inflammation in the community. METHODS:We studied 3013 Framingham Offspring (n = 2735) and Omni Cohort (n = 278) participants (mean age 59 years, 55% women, 9% ethnic/racial minority) who attended two consecutive examination cycles (mean 6.7 years apart). We selected ten inflammatory biomarkers representing distinctive biological functions: C-reactive protein (CRP), intercellular adhesion molecule-1, interleukin-6, isoprostanes, lipoprotein-associated phospholipase-2 (Lp-PLA2) activity, Lp-PLA2-mass, monocyte chemoattractant protein-1, osteoprotegerin, P-selectin, and tumor necrosis factor receptor II (TNFRII). We constructed multivariable-adjusted regression models to assess the relations of baseline, follow-up and change in clinical risk factors with change in biomarker concentrations over time. RESULTS:Baseline, follow-up and change in clinical risk factors explain a moderate amount of the variation in biomarker concentrations across 2 consecutive examinations (ranging from r(2) = 0.28 [TNFRII] up to 0.52 [Lp-PLA2-mass]). In multivariable models, increasing body-mass index, smoking initiation, worsening lipid profile, and increasing waist size were associated with increasing concentrations of several biomarkers. Conversely, hypercholesterolemia therapy and hormone replacement cessation were associated with decreasing concentrations of biomarkers such as CRP, Lp-PLA2-mass and activity. CONCLUSION:Cardiovascular risk factors have different patterns of association with longitudinal change in inflammatory biomarkers and explain modest amounts of variability in biomarker concentrations. Nevertheless, a substantial proportion of longitudinal change in inflammatory markers is not explained by traditional risk factors.

Dataset Information

Proteomic and Metabolomic Correlates of Healthy Dietary Patterns: The Framingham Heart Study.

Publications

Proteomic and Metabolomic Correlates of Healthy Dietary Patterns: The Framingham Heart Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets