Metabolomic Profile of Different Dietary Patterns and Their Association with Frailty Index in Community-Dwelling Older Men and Women

Abstract

Diet quality has been associated with slower rates of aging; however, the mechanisms underlying the role of a healthy diet in aging are not fully understood. To address this question, we aimed to identify plasma metabolomic biomarkers of dietary patterns and explored whether these metabolites mediate the relationship between diet and healthy aging, as assessed by the frailty index (FI) in 806 participants of the Baltimore Longitudinal Study of Aging. Adherence to different dietary patterns was evaluated using the Mediterranean diet score (MDS), Mediterranean–DASH Diet Intervention for Neurodegenerative Delay (MIND) score, and Alternate Healthy Eating Index-2010 (AHEI). Associations between diet, FI, and metabolites were assessed using linear regression models. Higher adherence to these dietary patterns was associated with lower FI. We found 236, 218, and 278 metabolites associated with the MDS, MIND, and AHEI, respectively, with 127 common metabolites, which included lipids, tri/di-glycerides, lyso/phosphatidylcholine, amino acids, bile acids, ceramides, cholesterol esters, fatty acids and acylcarnitines, indoles, and sphingomyelins. Metabolomic signatures of diet explained 28%, 37%, and 38% of the variance of the MDS, MIND, and AHEI, respectively. Signatures of MIND and AHEI mediated 55% and 61% of the association between each dietary pattern with FI, while the mediating effect of MDS signature was not statistically significant. The high number of metabolites associated with the different dietary patterns supports the notion of common mechanisms that underly the relationship between diet and frailty. The identification of multiple metabolite classes suggests that the effect of diet is complex and not mediated by any specific biomarkers. Furthermore, these metabolites may serve as biomarkers for poor diet quality to identify individuals for targeted dietary interventions.

Dietary patterns, metabolomics and frailty in a large cohort of 120 000 participants

“…Second, we estimated the associations between dietary patterns (in continuous variable) and 168 metabolites (log-transformed and then z -standardized) using multivariable linear regression adjusting for covariates in the aforementioned Model 2, eGFR 8 and the use of lipid-lowering drugs. 46 All yielded P values were corrected for false discovery rate (FDR) using Bonferroni corrections.…”

“…Dietary patterns reflect the overall diet quality through the combination and frequency of food consumption. 7 To date, several studies have shown significant positive associations between multiple healthy dietary patterns and frailty, [8][9][10][11][12][13][14] while there are also studies suggesting no associations. 15,16 Moreover, whether and to what extent the recommended food score (RFS), an important dietary pattern, is associated with frailty remain unclear.…”

“…Previous studies have shown that people with different dietary patterns have different metabolomic biomarkers (e.g., triglycerides, quinic acid, 2,6-dimethoxy-4-propylphenol and 1-methylhistamine) that may be influenced by diet. 8,[17][18][19][20] Recent evidence also highlights that several metabolites (e.g., glycoprotein acetyls, creatinine and several lipoprotein lipids) are associated with frailty. 21 Thus, the question of whether metabolomics partially mediate the associations between dietary patterns and frailty is of great interest but remains unexplored.…”

“…, triglycerides, quinic acid, 2,6-dimethoxy-4-propylphenol and 1-methylhistamine) that may be influenced by diet. 8,17–20 Recent evidence also highlights that several metabolites ( e.g. , glycoprotein acetyls, creatinine and several lipoprotein lipids) are associated with frailty.…”

Blood metabolite profiles linking dietary patterns with health—Toward precision nutrition

“…Understanding determinants of individual variation in dietary responses and modeling such variations is challenging but could also improve the understanding of how specific foods affect key metabolites and their effects on disease risk factors. Important steps have already been taken [96][97][98][99] and more are currently ongoing. Interdisciplinary collaborations between experts in nutrition, medicine, and bioinformatics incorporating various omics and computational modeling-for example, machine learning and analysis of big data-are necessary to accomplish this task.…”