This systematic review provides an overview of the available evidence on the inter-individual variability (IIV) in the absorption, distribution, metabolism, and excretion (ADME) of phenolic metabolites and its determinants. Human studies were included investigating the metabolism and bioavailability of (poly)phenols and reporting IIV. One hundred fifty-three studies met the inclusion criteria. Inter-individual differences were mainly related to gut microbiota composition and activity but also to genetic polymorphisms, age, sex, ethnicity, BMI, (patho)physiological status, and physical activity, depending on the (poly)phenol sub-class considered. Most of the IIV has been poorly characterised. Two major types of IIV were observed. One resulted in metabolite gradients that can be further classified into high and low excretors, as seen for all flavonoids, phenolic acids, prenylflavonoids, alkylresorcinols, and hydroxytyrosol. The other type of IIV is based on clusters of individuals defined by qualitative differences (producers vs. non-producers), as for ellagitannins (urolithins), isoflavones (equol and O-DMA), resveratrol (lunularin), and preliminarily for avenanthramides (dihydro-avenanthramides), or by quali-quantitative metabotypes characterized by different proportions of specific metabolites, as for flavan-3-ols, flavanones, and even isoflavones. Future works are needed to shed light on current open issues limiting our understanding of this phenomenon that likely conditions the health effects of dietary (poly)phenols.

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. 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. 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. 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. ClinicalTrials.gov registry (NCT04590989).

Context: Recent studies have outlined the potential role of dietary factors in patients who have survived cancer. Objective: The aim of this study was to summarize the evidence of the relation between dietary intake of phytoestrogens and their blood biomarkers and, overall, cancer-specific mortality and recurrence in patients with cancer. Data Sources: A systematic search of PubMed, EMBASE, and Web of Science databases of studies published up to September 2019 was performed. Databases were searched for prospective and retrospective cohort studies reporting on dietary phytoestrogen intake and/or blood biomarkers and the outcomes investigated. Data extraction: Data were extracted from each identified study using a standardized form. Data analysis: Twenty-eight articles on breast, lung, prostate, and colorectal cancer, and glioma were included for systematic review. Given the availability of studies, a quantitative meta-analysis was performed solely for breast cancer outcomes. A significant inverse association among higher dietary isoflavone intake, higher serum/plasma enterolactone concentrations, and overall mortality and cancer recurrence was found. Among other cancer types, 2 studies reported that higher serum enterolactone and higher intake of lignans were associated with cancer-specific survival for colorectal cancer and glioma, respectively. Conclusions: Dietary phytoestrogens may play a role in survival from breast cancer ; evidence regarding other cancers is too limited to draw any conclusions.

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.

Himanthalia elongata is a brown oceanic seaweed rich in bioactive compounds. It could play an important role in food production because of its antimicrobial and antioxidant properties. Three strains belonging to the Lactobacillus casei group (Lactobacillus casei, Lactobacillus paracasei, and Lactobacillus rhamnosus) and a Bacillus subtilis strain were used for the solid-state fermentation of commercial seaweeds, and bacterial growth was monitored using the plate count method. High-pressure processing (HPP) was also employed (6000 bar, 5 min, 5 °C) before extraction. The antimicrobial activity of the extracts was tested in terms of the main food pathogenic bacteria (Salmonella spp., Listeria monocytogenes, Escherichia coli, Staphylococcus aureus, and Bacillus cereus), and the phenolic content was estimated using the Folin–Ciocalteau method. In addition, targeted UHPLC-MS² methods were used to unravel the profile of phlorotannins. H. elongata allowed the growth of the L. casei group strains and B. subtilis, showing the fermentability of this substrate. Significant antimicrobial activity toward L. monocytogenes was observed in the extracts obtained from unfermented samples, but neither fermentation nor HPP enhanced the natural antimicrobial activity of this seaweed species. The content in the phenolic compounds decreased because of the fermentation process, and the amount of phenolics in both the unfermented and fermented H. elongata extracts was very low. Despite phlorotannins being related to the natural antimicrobial activity of this brown seaweed, these results did not support this association. Even if fermentation and HPP were not proven to be effective tools for enhancing the useful compounds of H. elongata, the seaweed was shown to be a suitable substrate for L. casei group strains as well as for B. subtilis growth, and its extracts exhibited antimicrobial activity toward foodborne pathogens.