Jacopo Troisi

Background: Non-nutritive sweeteners (NNS) are chemical substances developed by the food industry to reduce sugar and calories while preserving sweetness. Epidemiological studies have indicated a potential increased risk of certain cancers, including breast cancer (BC), associated with NNS consumption. However, the evidence has exclusively relied on self-reported dietary information rather than objective measurements of NNSs. We examined the association of urinary concentrations of NNS with BC risk in women from the large Moli-sani Study. Methods: Using a case-cohort design, a sample of 988 middle-aged women (mean age 55 ± 12 y) was randomly selected as a sub-cohort and compared to 272 women with incident BC (38 premenopausal and 234 postmenopausal). A total of 6 commonly used NNSs (i.e., aspartame, acesulfame, saccharin, sucralose, cyclamate, and steviol glycosides) were measured through liquid chromatography–mass spectrometry in spot urine samples (creatinine-adjusted concentrations; µg/mg) collected at baseline (2005–2010). The multivariable hazard ratios (HRs) were estimated using weighted Cox regression models for the case-cohort design, with each NNS modelled as a binary variable (i.e., presence vs. absence). The sum of these values produced a total urinary NNS score, which could range from 0 to 6, and was further categorised as low (0–1) or high (2–5). Results: The predominant NNS detected in urine was saccharin (E954), contributing to 77% of total NNSs, followed by steviol glycosides (E960;11%). In multivariable-adjusted Cox analyses, neither individual NNSs nor total NNS urinary concentrations were associated with overall BC risk (HR = 1.09; 95% CI 0.80–1.48 for high vs. low). However, the presence of sucralose (E955) was directly linked to premenopausal BC (HR = 5.90; 95% CI 1.44–24.20; p = 0.014), although this estimate was based on only 38 cases and was imprecise. Conclusions: Urinary NNS concentrations were not associated with overall BC risk. The observed association between urinary sucralose and premenopausal BC should be considered exploratory and hypothesis-generating and requires independent replication.

Background and Objectives: Maternal nutrition during pregnancy has been associated with fetal development and long-term health trajectories through mechanisms potentially involving epigenetic and metabolic programming. However, the molecular mediators linking dietary quality to fetal biochemical profiles remain poorly characterized. This exploratory pilot study aimed to investigate associations between maternal nutritional adequacy and the amniotic-fluid metabolomic profile during the second trimester. Materials and Methods: In this prospective study, AF samples from 41 pregnant women undergoing second-trimester amniocentesis were analyzed using gas chromatography–mass spectrometry (GC-MS). Nutritional status was assessed via the FIGO Nutrition Checklist. Subjects were divided into two groups based on dietary adequacy (FIGO Score >5 vs. ≤5). Multivariate analysis (PLS-DA, VIP scores, volcano plots) and pathway enrichment were performed to identify discriminatory metabolites. Results: Exploratory metabolomic analysis suggested differences between the two nutritional groups. Several candidate metabolites, including stearic acid, lactic acid, proline, and scyllo-inositol, contributed to the observed differences between groups. These features may provide preliminary hypotheses regarding energy-, amino acid-, and lipid-related biochemical pathways. Conclusions: Maternal dietary quality was associated with differences in the amniotic-fluid metabolomic profile. These preliminary findings support further investigation of amniotic-fluid metabolomics as a potential tool for studying the fetal biochemical environment.

Background: Early cancer detection remains a major unmet clinical need, particularly for malignancies not covered by established screening programs. Metabolomics may capture systemic cancer-associated biochemical alterations, but its translation into multicancer classification requires robust analytical frameworks.Methods: In this retrospective multi-cohort study, 1,983 individuals were analyzed, including 1,110 patients with histologically confirmed solid tumors and 873 healthy controls. Untargeted dried blood spot metabolomic profiling was performed using gas chromatography–mass spectrometry. A hierarchical Double-Weighted Ensemble Machine Learning framework was developed to distinguish cancer from non-cancer samples and progressively refine tumor-type classification across eleven binary nodes. Performance was evaluated in an independent test set using discrimination, calibration, decision curve analysis, and node-specific validation. A reduced metabolite mini-panel was derived from the screening-level layer.Findings: The primary screening node achieved sensitivity of 1.00, specificity of 0.88, and accuracy of 0.938. Performance remained high across most tumor-specific nodes, although lower accuracy was observed in deeper branches. The full-feature screening-level model achieved an AUC of 0.981, while the derived 10-metabolite mini-panel achieved an AUC of 0.970. Recurrent discriminant metabolites mapped to pathways related to purine metabolism, amino-acid turnover, ketone-body metabolism, nitrogen metabolism, and central carbon metabolism.Interpretation: Untargeted dried blood spot metabolomics coupled with hierarchical ensemble learning may capture a shared systemic cancer-associated metabolic signal while enabling tumor-specific refinement. These findings support prospective validation in screening-intended populations but do not establish immediate clinical applicability.

Duchenne muscular dystrophy (DMD) is a progressive, severe muscle-wasting disease caused by mutations in DMD , encoding dystrophin, that leads to loss of muscle function with cardiac/respiratory failure and premature death. Since dystrophic muscles are sensed by infiltrating inflammatory cells, and gut microbial communities can cause immune dysregulation and metabolic syndrome, we sought to investigate whether intestinal bacteria support the muscle immune response in the mdx dystrophic murine model. We highlighted a strong correlation between DMD disease features and the relative abundance of Prevotella . Furthermore, the absence of gut microbes through the generation of mdx germ-free animal model, as well as modulation of the microbial community structure by antibiotic treatment, influenced muscle immunity and fibrosis. Intestinal colonization of mdx mice with eubiotic microbiota was sufficient to reduce inflammation and improve muscle pathology and function. This work identifies a potential role for the gut microbiota in the pathogenesis of DMD.

Organoid models have transformed experimental biology by enabling three-dimensional systems that recapitulate tissue architecture, cellular heterogeneity, and metabolic activity patterns more faithfully than conventional in vitro cultures.

SARS-CoV-2 infection usually has a mild course in childhood, yet few children develop a Multisystem Inflammatory Syndrome (MIS-C). Several metabolic pathways have been found to be dysregulated in adults with COVID-19, yet data are lacking in children. Here we investigate serum metabolomic features of children with COVID-19 in relation to age, sex and both clinical and biochemical severity. We carried out a prospective observational comparative cohort study enrolling 92 children (48 M, mean age 3.69 ± 5.1 years) with acute SARS-CoV-2 infection and 7 with MIS-C along with 41 age- and sex-matched controls. Sera collected at admission, acute phase, discharge and remission were analyzed by Gas Chromatography Mass Spectroscopy. Here we identify a distinct signature featuring inflammation, reactive oxygen species and glicerolipids pathways in children with acute SARS-CoV-2 infection compared to controls (permutation test p = 0.0015). Metabolomic profile changes are associated with age, disease status and disease severity, while a normalization of these changes is observed at disease resolution. MIS-C children showed a unique signature compared to age-/sex-matched COVID-19 patients or controls. Pediatric COVID-19 has a characteristic metabolomic signature featuring glucose and aminoacid metabolism, that varies with age and disease phenotype. Our study supports the value of metabolomics to unveil pathways related to host-viral interaction that may also help identify early predictors of disease evolution.