Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies.

Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case.

A Diet Profiling Algorithm (DPA) to Rank Diet Quality Suitable to Implement in Digital Tools-A Test Study in a Cohort of Lactating Women.

Although nutrient profiling systems can empower consumers towards healthier food choices, there is still a need to assess diet quality to obtain an overall perspective. The purpose of this study was to develop a diet profiling algorithm (DPA) to evaluate nutritional diet quality, which gives a final score from 1 to 3 with an associated color (green-yellow-orange). It ranks the total carbohydrate/total fiber ratio, and energy from saturated fats and sodium as potentially negative inputs, while fiber and protein are assumed as positive items. Then, the total fat/total carbohydrate ratio is calculated to evaluate the macronutrient distribution, as well as a food group analysis. To test the DPA performance, diets of a lactating women cohort were analyzed, and a correlation analysis between DPA and breast milk leptin levels was performed. Diets classified as low quality showed a higher intake of negative inputs, along with higher energy and fat intakes. This was reflected in body mass index (BMI) and food groups, indicating that women with the worst scores tended to choose tastier and less satiating foods. In conclusion, the DPA was developed and tested in a sample population. This tool can be easily implemented in digital nutrition platforms, contributing to real-time dietary follow-up of patients and progress monitoring, leading to further dietary adjustment.

Breast Milk MicroRNAs Related to Leptin and Adiponectin Function Can Be Modulated by Maternal Diet and Influence Offspring Phenotype in Rats.

There is evidence of the role of milk components in the metabolic programming of offspring. Here, we aimed to investigate the effects of a diet during lactation on breast milk leptin, adiponectin, and related miRNAs' expression, and their impact on dams and their offspring. Dams were fed a control diet (controls) or a diet enriched with oleic acid, betaine, and leucine (TX) throughout lactation. A TX diet promoted higher leptin at lactation day (LD) five and lower adiponectin on LD15 (vs. controls) in milk, resulting in increased leptin to adiponectin (L/A) ratio throughout lactation. Moreover, TX diet reduced milk levels of miR-27a, miR-103, miR-200a, and miR-222. Concerning TX offspring, higher body fat was early observed and maintained into adult life, accompanied by higher HOMA-IR than controls at three months of age. Offspring body fat content in adulthood correlated positively with milk L/A ratio at LD15 and negatively with miRNAs modulated by the TX diet. In conclusion, maternal diet during lactation can modulate leptin and adiponectin interplay with miRNAs in milk, setting up the metabolic programming of the offspring. Better knowledge about the influence of diet on this process is necessary to promote a healthy adult life in the progeny.

A double-blinded, randomized, parallel intervention to evaluate biomarker-based nutrition plans for weight loss: The PREVENTOMICS study.

BACKGROUND & AIMS: Growing evidence suggests that biomarker-guided dietary interventions can optimize response to treatment. In this study, we evaluated the efficacy of the PREVENTOMCIS platform-which uses metabolomic and genetic information to classify individuals into different 'metabolic clusters' and create personalized dietary plans-for improving health outcomes in subjects with overweight or obesity. METHODS: A 10-week parallel, double-blinded, randomized intervention was conducted in 100 adults (82 completers) aged 18-65 years, with body mass index ≥27 but <40 kg/m2, who were allocated into either a personalized diet group (n = 49) or a control diet group (n = 51). About 60% of all food was provided free-of-charge. No specific instruction to restrict energy intake was given. The primary outcome was change in fat mass from baseline, evaluated by dual energy X-ray absorptiometry. Other endpoints included body weight, waist circumference, lipid profile, glucose homeostasis markers, inflammatory markers, blood pressure, physical activity, stress and eating behavior. RESULTS: There were significant main effects of time (P < 0.01), but no group main effects, or time-by-group interactions, for the change in fat mass (personalized: -2.1 [95% CI -2.9, -1.4] kg; control: -2.0 [95% CI -2.7, -1.3] kg) and body weight (personalized: -3.1 [95% CI -4.1, -2.1] kg; control: -3.3 [95% CI -4.2, -2.4] kg). The difference between groups in fat mass change was -0.1 kg (95% CI -1.2, 0.9 kg, P = 0.77). Both diets resulted in significant improvements in insulin resistance and lipid profile, but there were no significant differences between groups. CONCLUSION: Personalized dietary plans did not result in greater benefits over a generic, but generally healthy diet, in this 10-week clinical trial. Further studies are required to establish the soundness of different precision nutrition approaches, and translate this science into clinically relevant dietary advice to reduce the burden of obesity and its comorbidities. CLINICAL TRIAL REGISTRY: ClinicalTrials.gov registry (NCT04590989).

Absorption, Distribution, Metabolism, and Excretion of the Main Olive Tree Phenols and Polyphenols: A Literature Review.

The effects of olive tree (poly)phenols (OPs) are largely dependent upon their bioavailability and metabolization by humans. Absorption, distribution, metabolism, and excretion (ADME) are fundamental for the nutritional efficacy and toxicological impact of foods containing OPs. This review includes studies on the administration of hydroxytyrosol (HT), oleuropein (Ole), or other OPs and foods, products, or mixtures that contain them. Briefly, data from in vivo studies indicate that OPs are absorbable by intestinal cells. Both absorption and bioavailability depend upon each compound and/or the matrix in which it is contained. OPs metabolism begins in enterocytes and can also continue in the liver. Metabolic phase I mainly consists of the hydrolysis of Ole, which results in an increase in the HT content. Phase II metabolic reactions involve the conjugation of (poly)phenols mainly with glucuronide and sulfate groups. This review offers a complete perspective of the ADME processes of OPs, which could support the future nutritional and/or toxicological studies in this area.