Scope: Untargeted metabolomics may reveal preventive targets in cognitive aging, including within the food metabolome.Methods and results: A case‐control study nested in the prospective Three‐City study includes participants aged ≥65 years and initially free of dementia. A total of 209 cases of cognitive decline and 209 controls (matched for age, gender, education) with slower cognitive decline over up to 12 years are contrasted. Using untargeted metabolomics and bootstrap‐enhanced penalized regression, a baseline serum signature of 22 metabolites associated with subsequent cognitive decline is identified. The signature includes three coffee metabolites, a biomarker of citrus intake, a cocoa metabolite, two metabolites putatively derived from fish and wine, three medium‐chain acylcarnitines, glycodeoxycholic acid, lysoPC(18:3), trimethyllysine, glucose, cortisol, creatinine, and arginine. Adding the 22 metabolites to a reference predictive model for cognitive decline (conditioned on age, gender, education and including ApoE‐ε4, diabetes, BMI, and number of medications) substantially increases the predictive performance: cross‐validated Area Under the Receiver Operating Curve = 75% [95% CI 70–80%] compared to 62% [95% CI 56–67%].Conclusions: The untargeted metabolomics study supports a protective role of specific foods (e.g., coffee, cocoa, fish) and various alterations in the endogenous metabolism responsive to diet in cognitive aging.

The gut microbiome is involved in nutrient metabolism and produces metabolites that, via the gut–brain axis, signal to the brain and influence cognition. Human studies have so far had limited success in identifying early metabolic alterations linked to cognitive aging, likely due to limitations in metabolite coverage or follow-ups. Older persons from the Three-City population-based cohort who had not been diagnosed with dementia at the time of blood sampling were included, and repeated measures of cognition over 12 subsequent years were collected. Using a targeted metabolomics platform, we identified 72 circulating gut-derived metabolites in a case–control study on cognitive decline, nested within the cohort (discovery n = 418; validation n = 420). Higher serum levels of propionic acid, a short-chain fatty acid, were associated with increased odds of cognitive decline (OR for 1 SD = 1.40 (95% CI 1.11, 1.75) for discovery and 1.26 (1.02, 1.55) for validation). Additional analyses suggested mediation by hypercholesterolemia and diabetes. Propionic acid strongly correlated with blood glucose (r = 0.79) and with intakes of meat and cheese (r > 0.15), but not fiber (r = 0.04), suggesting a minor role of prebiotic foods per se, but a possible link to processed foods, in which propionic acid is a common preservative. The adverse impact of propionic acid on metabolism and cognition deserves further investigation.

Fruits (and vegetables) contain cellular structures that are not degraded by human digestive enzymes. Therefore, the structure of the insoluble fraction of swallowed fruits is mostly retained until intestinal microbial fermentation. In vitro fermentation of mango and banana cell structures, which survived in vivo mastication and in vitro gastrointestinal digestion, were incubated with porcine faecal inoculum and showed intensive metabolic activity. This included degradation of cell walls, leading to the release of encapsulated cell contents for further microbial metabolism. Production of cumulative gas, short chain fatty acids and ammonia were greater for mango than for banana. Microscopic and spectroscopic analyses showed this was due to a major fermentation-resistant starch fraction present in banana, that was absent in mango. This study demonstrated distinctive differences in the fermentability of banana and mango, reflecting a preferential degradation of (parenchyma) fleshy cell walls over resistant starch in banana, and the thick cellulosic vascular fibres in mango.