The gut microbiota adapts to age-related changes in host physiology but is also affected by environmental stimuli, like diet. As a source of both pre- and probiotics, dairy and fermented foods modulate the gut microbiota composition, which makes them interesting food groups to use for the investigation of interactions between diet and ageing. Here we present the effects of excluding dairy products and limiting fermented food consumption for 19 days on gut microbiota composition and circulating metabolites of 28 healthy, young (YA) and older (OA) adult men. The intervention affected gut microbial composition in both groups, with significant increases in Akkermansia muciniphila and decreases in bacteria of the Clostridiales order. Lower fasting levels of glucose and insulin, as well as dairy-associated metabolites like lactose and pentadecanoic acid, were observed after the intervention, with no effect of age. The intervention also decreased HDL and LDL cholesterol levels. Dairy fat intake was positively associated with the HDL cholesterol changes but not with the LDL/HDL ratio. In conclusion, restricting the intake of dairy and fermented foods in men modified their gut microbiota and blood metabolites, while the impact of the dietary restrictions on these outcomes was more marked than the effect of age.

The identification and validation of biomarkers of food intake (BFIs) is a promising approach to develop more objective and complementary tools to the traditional dietary assessment methods. Concerning dairy, their evaluation in terms of intake is not simple, given the variety of existing foods, making it difficult to establish the association between specific dairy products consumption and the effects on human health, which is also dependent on the study population. Here, we aimed at identifying BFI of both milk (M) and yogurt (Y) in 14 healthy young (20–35 years) and 14 older (65–80 years). After a 3-week run-in period of dairy exclusion from the diet, the subjects acutely consumed 600 ml of M or Y. Metabolomics analyses were conducted on serum samples during the following 6 h (LC-MS and GC-MS). Several metabolites showing increased iAUC after milk or yogurt intake were considered as potential BFI, including lactose (M > Y, 2-fold), galactitol (M > Y, 1.5-fold), galactonate (M > Y, 1.2-fold), sphingosine-1-phosphate (M > Y from 2.1-fold), as well as an annotated disaccharide (Y > M, 3.6-fold). Delayed serum kinetics were also observed after Y compared to M intake lysine (+22 min), phenylalanine (+45 min), tyrosine (+30min), threonine (+38 min) 3-phenyllactic acid (+30 min), lactose (+30 min), galactitol (+45min) and galactonate (+30 min). The statistical significance of certain discriminant metabolites, such as sphingosine-1-phosphate and several free fatty acids, was not maintained in the older group. This could be related to the physiological modifications induced by aging, like dysregulated lipid metabolism, including delayed appearance of dodecanoic acid (+60 min) or altered postprandial appearance of myristic acid (+70% Cmax), 3-dehydroxycarnitine (−26% Cmin), decanoylcarnitine (−51% Cmin) and dodecanoylcarnitine (−40% Cmin). In conclusion, candidate BFI of milk or yogurt could be identified based on the modified postprandial response resulting from the fermentation of milk to yogurt. Moreover, population specificities (e.g., aging) should also be considered in future studies to obtain more accurate and specific BFI.

Although the composition of the human blood metabolome is influenced both by the health status of the organism and its dietary behavior, the interaction between these two factors has been poorly characterized. This study makes use of a previously published randomized controlled crossover acute intervention to investigate whether the blood metabolome of 15 healthy normal weight (NW) and 17 obese (OB) men having ingested three doses (500, 1000, 1500 kcal) of a high-fat (HF) meal can be used to identify metabolites differentiating these two groups. Among the 1024 features showing a postprandial response, measured between 0 h and 6 h, in the NW group, 135 were dose-dependent. Among these 135 features, 52 had fasting values that were significantly different between NW and OB men, and, strikingly, they were all significantly higher in OB men. A subset of the 52 features was identified as amino acids (e.g., branched-chain amino acids) and amino acid derivatives. As the fasting concentration of most of these metabolites has already been associated with metabolic dysfunction, we propose that challenging normal weight healthy subjects with increasing caloric doses of test meals might allow for the identification of new fasting markers associated with obesity.

Studies examining associations between self-reported dairy intake and health are inconclusive, but biomarkers hold promise for elucidating such relationships by offering objective measures of dietary intake. Previous human intervention studies identified several biomarkers for dairy foods in blood and urine using non-targeted metabolomics. We evaluated the robustness of these biomarkers in a free-living cohort in the Netherlands using both single- and multi-marker approaches. Plasma and urine from 246 participants (54 ± 13 years) who completed a food frequency questionnaire were analyzed using liquid and gas chromatography-mass spectrometry. The targeted metabolite panel included 37 previously-identified candidate biomarkers of milk, cheese, and/or yoghurt consumption. Associations between biomarkers and energy-adjusted dairy food intakes were assessed by a ‘single-marker’ generalized linear model, and stepwise regression was used to select the best ‘multi-marker’ panel. Multi-marker models that also accounted for common covariates better captured the subtle differences for milk (urinary galactose, galactitol; sex, body mass index, age) and cheese (plasma pentadecanoic acid, isoleucine, glutamic acid) over single-marker models. No significant associations were observed for yogurt. Further examination of other facets of validity of these biomarkers may improve estimates of dairy food intake in conjunction with self-reported methods, and help reach a clearer consensus on their health impacts.