Discussion
T2D, METSIM, MEN, HL, cancer, CVD, METabolic Syndrome, diabetes
CVD, CANCER, DISEASE, DIABETES
In this study, a metabolic signature of a predefined HL score was identified in the PREDIMED population using an agnostic machine-learning approach. The metabolic signature was inversely associated with the incidence of diabetes independently of the HL score and to a lesser extent with the risk of CVD. To the best of our knowledge, this is the first study to identify plasma metabolite signatures of a lifestyle score that predict the risk of future T2D and CVD in individuals at high cardiovascular risk, using data at baseline and after 1 year of follow-up.In the last decade, some of the metabolite profiles identified with dietary patterns [However, few studies have analyzed the associations between the metabolomic signatures of healthy lifestyle scores and the risk of future disease. Two studies identified a metabolic signature reflecting a HL pattern that was inversely associated with cancer risk [To the best of our knowledge, only one study has been conducted to identify the plasma metabolite signature of a composite measure of lifestyle, and whether these metabolites can prospectively explain the association between a HL and incident T2D [Our study identified two metabolic signatures of the HL score (categorical or continuous) with 24 and 58 selected metabolites. The performance analysis confirms that the correlation between self-reported and predicted HL scores is quite strong. Selected metabolites may be used as potential biomarkers of lifestyle and might provide a better understanding of the mechanism underlying the benefits of HL. However, this requires confirmation in other studies and populations. Most of the overlapping selected metabolites for both signatures (12 of which were negatively associated with HL and 11 positively) were amino acids and derivatives, lipids, metabolites involved in the intermediate energy metabolism, xenobiotics, and products of bacterial co-metabolism.Most of the metabolites identified had been previously reported to be highly related to the lifestyle factors included in the 2018 WCRF/AICR HL score we used to build our metabolite signatures (healthy weight, physical activity, fiber from plant foods, fast food, and processed foods, red and processed meat, sugar-sweetened beverages, and alcohol consumption) [Concerning the healthy body weight component of the HL score, our results are in line with previous studies that reported associations between some amino acids, lipids, and their derivative metabolites with body weight status, BMI, and fat mass [As far as physical activity is concerned, in 7271 men from the Finnish cohort METabolic Syndrome In MEN (METSIM), increased physical activity was significantly associated with high levels of choline plasmalogens, lysophosphatidyl cholines, polyunsaturated fatty acids, long-chain acylcarnitines, imidazoles, bilirubin, hydroxy acids, indole propionate, and indole lactate [Some metabolites related to HL in our study were previously associated with a plant-based diet [Red meat (RM) and processed meat (PM) are considered in the HL score that we used, and consistent evidence has been reported that high consumption of these food products is associated with an increased risk of diabetes and CVD [A higher intake of sugar-sweetened beverages has been prospectively associated with increased levels of dimethyl guanidino valerate (DMGV) in plasma [Smoking, one of the most widely recognized unhealthy behaviors, has been associated with an elevated risk of T2D and CVD [The loss in the significance of the association between the metabolomic HL signature and CVD incidence at 1-year may be explained by the changes in HL that occurred as a consequence of the intervention as well as a reduced number of participants included in this analysis, in spite of the trend of the associations was in the same direction that in the baseline analysis. Additionally, although the direction of the associations between the metabolomics HL signature and the incidence of T2D was similar between the three arms of the trial, the significance was only observed in both MedDiet groups probably because of the effect of the interventions decreasing the risk of diabetes as was previously reported in PREDIMED [Our study has some limitations. First, data obtained by questionnaires may be susceptible to participant recall bias despite being recorded in face-to-face interviews by trained personnel. Measurement errors must be considered a possibility even though the questionnaires were validated in a population similar to PREDIMED [There are also strengths to our study, smoking habit was included in our HL score because of its importance as a risk factor for T2D and CVD. Furthermore, our analyses were conducted in a large cohort and possible confounding was controlled by several covariates. Finally, the results on the association between the metabolite signatures and the risk of T2D and CVD are still in the same direction when analyzed separately by intervention group. This adds robustness to our findings.In conclusion, a set of metabolites were selected as potential biomarkers of a HL pattern in an elderly Mediterranean population at high risk of CVD. Most of these metabolites are amino acids and derivatives, lipids, xenobiotics, and products of bacterial co-metabolism. This HL metabolomic signature was inversely associated with the risk of incident T2D and to a lesser extent with incident CVD.