The study of the microbiome in broiler chickens holds great promise for the development of strategies for health maintenance and performance improvement. Nutritional strategies aimed at modulating the microbiota—host relationship can improve chickens’ immunological status and metabolic fitness. Here, we present the results of a pilot trial aimed at analyzing the effects of a nutritional strategy involving vitamin B2 supplementation on the ileum, caeca and litter microbiota of Ross 308 broilers, as well as on the metabolic profile of the caecal content. Three groups of chickens were administered control diets and diets supplemented with two different dosages of vitamin B2. Ileum, caeca, and litter samples were obtained from subgroups of birds at three time points along the productive cycle. Sequencing of the 16S rRNA V3–V4 region and NMR metabolomics were used to explore microbiota composition and the concentration of metabolites of interest, including short-chain fatty acids. Vitamin B2 supplementation significantly modulated caeca microbiota, with the highest dosage being more effective in increasing the abundance of health-promoting bacterial groups, including Bifidobacterium, resulting in boosted production of butyrate, a well-known health-promoting metabolite, in the caeca environment.

Food is a complex matter, literally. From production to functionalization, from nutritional quality engineering to predicting effects on health, the interest in finding an efficient physicochemical characterization of food has boomed in recent years. The sheer complexity of characterizing food and its interaction with the human organism has however made the use of data driven approaches in modeling a necessity. High‐throughput techniques, such as nuclear magnetic resonance (NMR) spectroscopy, are well suited for omics data production and, coupled with machine learning, are paving a promising way of modeling food–human interaction. The foodomics approach sets the framework for omic data integration in food studies, in which NMR experiments play a key role. NMR data can be used to assess nutritional qualities of food, helping the design of functional and sustainable sources of nutrients; detect biomarkers of intake and study how they impact the metabolism of different individuals; study the kinetics of compounds in foods or their by‐products to detect pathological conditions; and improve the efficiency of in silico models of the metabolic network.

The addition of frozen curd (FC) during the production process of “Mozzarella di Bufala Campana”, an Italian cheese with Protected Designation of Origin (PDO), is a common fraud not involving modifications of the chemical composition in the final product. Its detection cannot thus be easily obtained by common analytical methods, which are targeted at changes in concentrations of diagnostic chemical species. In this work, the possibility of spotting this fraud by focusing on the modifications of the supramolecular structure of the food matrix, detected by time domain nuclear magnetic resonance (TD-NMR) experiments, was investigated. Cheese samples were manufactured in triplicate, according to the PDO disciplinary of production, except for using variable amounts of FC (i.e., 0, 15, 30, and 50% w/w). Relaxation data were analysed through different approaches: (i) Discrete multi-exponential fitting, (ii) continuous Laplace inverse fitting, and (iii) chemometrics approach. The strategy that lead to best detection results was the chemometrics analysis of raw Carr-Purcell-Meiboom-Gill (CPMG) decays, allowing to discriminate between compliant and adulterated samples, with as low as 15% of FC addition. The strategy is based on the use of machine learning for projection on latent structures of raw CPMG data and classification tasks for fraud detection, using quadratic discriminant analysis. By coupling TD-NMR raw decays with machine learning, this work opens the way to set up a system for detecting common food frauds modifying the matrix structure, for which no official authentication methods are yet available.

Frailty in cirrhosis or advanced chronic liver disease (ACLD) is a relevant prognostic factor. In the present study, we aimed to analyze potential biomarkers associated with frailty and its improvement in patients with ACLD. We analyzed the serum of outpatients with ACLD who participated in a previous study (Román, Hepatol Commun 2024) in which frailty was assessed using the liver frailty index (LFI), and patients who were frail or prefrail were randomized to a multifactorial intervention (home exercise, branched-chain amino acids, and probiotics) or control for 12 months. We determined a biomarker battery of inflammation, bacterial translocation, and liver damage in blood and urine and blood metabolomics by ¹H-NMR. Thirty-seven patients were included. According to the LFI, 32 patients were frail or prefrail, and 5 were robust. At baseline, LFI correlated with LBP, sCD163, mtDNA, FGF-21, urinary NGAL, urinary claudin-3, and the metabolites mannose, ethanol, and isoleucine. During the study, patients in the intervention group showed an improvement in LFI and a decrease in CRP, LBP, sCD163, and ccK18 compared to the control group. Metabolomics showed a decrease in dimethyl sulfone and creatinine and an increase in malonate, ornithine, isoleucine, and valine in the intervention group. We conclude that frailty in patients with ACLD is associated with biomarkers of systemic inflammation, bacterial translocation, and liver damage, and alterations of amino acid and short-chain fatty acid metabolism.

The availability of omics data providing information from different layers of complex biological processes that link nutrition to human health would benefit from the development of integrated approaches combining holistically individual omics data, including those associated with the microbiota that impacts the metabolisation and bioavailability of food components. Microbiota must be considered as a set of populations of interconnected consortia, with compensatory capacities to adapt to different nutritional intake. To study the consortium nature of the microbiome, we must rely on specially designed data analysis tools. The purpose of this work is to propose the construction of a general correlation network-based explorative tool, suitable for nutritional clinical trials, by integrating omics data from faecal microbial taxa, stool metabolome (1H NMR spectra) and GC-MS for stool volatilome. The presented approach exploits a descriptive paradigm necessary for a true multiomics integration of data, which is a powerful tool to investigate the complex physiological effects of nutritional interventions.