Francisca Serra

Objectives: This study investigated the short- and long-term metabolic effects of two β-sitosterol with propionic acid cocrystals (CCA and CCB) compared with commercial β-sitosterol in male hamsters. Methods: Five experiments were conducted, including three acute and two chronic dietary interventions. Acute experiments evaluated the metabolic response to a single dose of commercial β-sitosterol (S) or its cocrystals (513 mg/kg) administered with a pork-fat load following 40% caloric restriction. Blood samples were collected before and 5 h after the lipid challenge. Long-term studies assessed daily supplementation with CCA (264 mg/kg) or CCB (at two doses, 114 and 342 mg/kg) during exposure to high-fat (HF) or Western diets (WD), respectively, for 21 days. Results: Acutely, CCA attenuated postprandial triglyceride increases in adult animals, while CCB induced a reduced rise in postprandial cholesterol across the three studies compared to S. Young animals treated with CCA and CCB exhibited a reduction in postprandial glucose levels. Long-term, in HF-fed hamsters, CCA lowered plasma cholesterol and triglyceride levels on day 14 and, at the endpoint, decreased liver weight and increased circulating sitosterol levels, unlike its commercial counterpart. In WD-fed animals, CCB supplementation dose-dependently elevated plasma sitosterol levels. At the low dose, CCB treatment reduced body weight gain and cholesterol increase and improved adipocyte morphology, whereas the high dose reduced diet-induced hepatic detrimental outcomes, showing a stronger hepatoprotective effect compared to S-treated animals. Together, these results indicate improved postprandial lipid handling in the short-term and a modulation of metabolic adaptation to obesogenic diets in the long-term. Conclusions: β-sitosterol with propionic acid cocrystals improved metabolic responses more effectively than commercial β-sitosterol, enhancing bioavailability and potential therapeutic value against diet-induced metabolic disturbances.

Prematurity is associated with a higher risk of developing short- and long-term metabolic complications. However, the concrete mechanisms are not yet fully understood. The purpose of this study was to characterize early urinary metabolic adaptations linked to preterm birth. Urine samples collected at one month of age were compared between extremely and very preterm neonates (n = 45) and term newborns (n = 96). Liquid chromatography coupled to mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) techniques were both applied and analyzed independently. Univariate analysis and multivariate analyses were utilized to assess the impact of preterm birth on the metabolites identified. Multiblock analysis was further used to evaluate the effect of prematurity on biological functions. A profound impact of prematurity was observed. Both experimental groups differed in the concentrations of 240 metabolites from the LC-MS dataset and 52 from the NMR one. Multivariate analyses confirmed a significant and important separation between groups. Finally, multiblock analysis identified six major biological outcomes affected by preterm birth: nitrogen metabolism, growth, neurochemical metabolism, microbiota metabolism, cell defense, and metabolic alterations. Most of the observed variations exhibited biological plausibility and were consistent with reported health complications associated with preterm birth. In conclusion, preterm birth is linked to a specific fingerprint in the urinary metabolome, reflecting metabolic adaptations in multiple systems occurring at one month of age.

Deep phenotyping and genetic characterization of individuals are fundamental to assessing the metabolic status and determining nutrition-specific requirements. This study aimed to ascertain the utmost effectiveness of personalized interventions by aligning dietary adjustments with both the genotype and metabolotype of individuals. Therefore, we assessed here the usefulness of a polygenic score (PGS) characterizing a potential pro-inflammatory profile (PGSi) as a nutrigenetic tool to discern individuals from the Danish PREVENTOMICS cohort that could better respond to precision nutrition (PN) plans, specifically targeted at counteracting the low-grade inflammatory profile typically found in obesity. The cohort followed a PN plan to counteract the pro-inflammatory profile (PNi group) or generic dietary recommendations (Control) for 10 weeks. PGSi was applied for genetic stratification (Low/High). The effects of the intervention on anthropometrics and biomarkers related to inflammatory profile and carbohydrate metabolism were assessed. Around 30% of subjects had a high genetic predisposition to pro-inflammatory status (high-PGSi). These individuals demonstrated the most effective response to the dietary plan, experiencing improved body composition, with significant decreases in body weight (∆: -4.84%; p=0.039) and body fat (∆: -4.86%; p=0.007), and beneficial changes in pro- and anti-inflammatory biomarkers, with significant increases in IL-10 (∆: 71.3%; p=0.025) and decreases in TNF-α (∆: -3.0%; p=0.048), CRP (∆: -31.1%), ICAM1 (∆: -5.8%), and MCP1 (∆: -4.2%) circulating levels, compared to low-PGSi individuals. Both phenotypic and genetic stratification contributed to a better understanding of metabolic heterogeneity in response to diet. This approach allows for refinement of the prediction of individual requirements and potentially for better management of obesity.

Periodontal disease is a multifactorial infectious and immunoinflammatory condition that affects the tissues surrounding the teeth, potentially leading to irreversible damage and tooth loss. Early detection and diagnosis are crucial to preventing its progression and associated systemic health risks. Traditional clinical diagnostic methods, such as probing depth and radiographic assessments, have limitations in detecting early-stage disease. Consequently, molecular biomarkers found in saliva have emerged as promising tools for the early diagnosis and monitoring of periodontal disease. These biomarkers include inflammatory mediators, enzymes, bone remodeling markers, and microbial metabolites, providing valuable insights into disease status and progression. Advances in omics technologies, including proteomics and genomics, have further enhanced the identification of potential biomarkers, paving the way for precision medicine in periodontology. This chapter reviews the role of specific salivary biomarkers in periodontal disease assessment, highlighting their diagnostic potential, clinical applications, and future research directions in the field.

Breast milk provides a wide range of nutritional and bioactive components crucial for infant growth and development during lactation, ultimately influencing future health outcomes. Here, we used untargeted metabolomics to identify differences in the breast milk metabolome related to maternal overweight/obesity and diet, assessing their influence on infant weight gain in the first month of lactation. Anthropometric data from 52 lactating mother–infant pairs were collected at birth and 1 month postpartum, along with breast milk samples. Information about food consumed the day before milk sampling (24‐h questionnaire) was collected, and a food frequency questionnaire was completed. Our findings reveal that mothers with overweight/obesity had greater lactose levels (p = 0.025) and lower levels of orotic acid (p = 0.002), 3‐indoxyl sulfate (p = 0.027), heneicosanoic acid (p = 0.040), and N1‐methylguanosine (p = 0.046) in milk than normal‐weight mothers. Notably, reduced orotic acid levels were associated with a greater infant weight gain during this period. Furthermore, mothers with a low adherence to the Mediterranean diet showed lower levels of citric acid (p = 0.041), N6‐succinyladenosine (p = 0.043), uric acid (p = 0.034), and eicosenoic acid (p = 0.035), and higher levels of acylcarnitine C6:0 (p = 0.041) than mothers with a medium/high adherence. Besides orotic acid, other milk metabolites were related to infant weight gain during this period, with maternal fruit and fish consumption (p = 0.015 and p = 0.017) emerging as the most influential dietary factor for an adequate early infant growth trajectory. None of the metabolites identified remained statistically significant after multiple‐comparisons adjustment. Present results, showing how maternal diet and obesity can affect breast milk metabolites and infant growth, offer insights to improve lactation‐feeding practices and support healthy infant development.

The evidence suggests that diet can modulate endogenous microRNA (miRNA) expression. Changes in miRNA expression may affect metabolic processes and consequently be involved in health status and disease development. The aim of this systematic review was to summarize the evidence of the role of diet and specific food components in the regulation of miRNA expression and discuss its implications for human health and disease development. The PubMed, Embase and Web of Science databases were searched in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for relevant studies. A total of 32 interventional and 5 observational studies performed in adults and evaluating dietary modulation of miRNA expression were included. Energy- and fat-controlled diets along with plant-based foods show substantial evidence of modulating endogenous miRNA levels. Plasma, serum and peripheral blood mononuclear cells (PBMCs) are the main sources used to measure miRNAs. A total of 108 miRNAs modulated by diet were identified. We confirmed that dietary habits are closely associated with the modulation of endogenous miRNAs. Particularly, energy content and fat intake appeared to be key factors influencing miRNA levels. Furthermore, since miRNAs are involved in the regulation of several biological processes, this modulatory process may affect health status and lead to metabolic disorders.

Although preclinical studies have attributed vitamin A (VA) cardiometabolic benefits, these effects are still controversial and not always supported in large human studies. Here, the outcomes associated with VA and its relationship with habitual dietary sources, sex, and genetic background have been studied. To do so, the data from an observational study (n = 455) (64% females, mean age of 36 years) showing that suboptimal VA intake (mainly from retinol rather than carotene) is associated with cardiometabolic risk (CMR) were considered. A higher odds ratio (OR) of suffering ≥ 2 simultaneous CMR factors was observed in men in the low consumption tercile of retinol (OR = 2.04; p = 0.019). In women, however, this relationship was not evident. Then, incubation of peripheral blood mononuclear cells (PBMCs) with VA-related compounds (ex vivo functional assay from 81 men and women) induced specific changes in the activity of genes involved in lipid homeostasis and inflammatory status, which were dependent on the type of compound tested and the sex of the person. In addition, the presence of the genetic variant rs5888 in SCARB1 was identified as having a high influence on VA-related metabolic response. The new evidence derived from this study could be relevant for personalized nutritional advice concerning VA and CMR.

Background: Physical activity (PA) provides health benefits across the lifespan and improves many established cardiovascular risk factors that have a significant impact on overall mortality. However, discrepancies between self-reported and device-based measures of PA make it difficult to obtain consistent results regarding PA and its health effects. Moreover, PA may produce different health effects depending on the type, intensity, duration, and frequency of activities and individual factors such as age, sex, body weight, early life conditions/exposures, etc. Appropriate biomarkers relating the degree of PA level with its effects on health, especially in children and adolescents, are required and missing. The main objective of the INTEGRActiv study is to identify novel useful integrative biomarkers of PA and its effects on the body health in children and adolescents, who represent an important target population to address personalized interventions to improve future metabolic health.Methods/design: The study is structured in two phases. First, biomarkers of PA and health will be identified at baseline in a core cohort of 180 volunteers, distributed into two age groups: prepubertal (n = 90), and postpubertal adolescents (n = 90). Each group will include three subgroups (n = 30) with subjects of normal weight, overweight, and obesity, respectively. Identification of new biomarkers will be achieved by combining physical measures (PA and cardiorespiratory and muscular fitness, anthropometry) and molecular measures (cardiovascular risk factors, endocrine markers, cytokines and circulating miRNA in plasma, gene expression profile in blood cells, and metabolomics profiling in plasma). In the second phase, an educational intervention and its follow-up will be carried out in a subgroup of these subjects (60 volunteers), as a first validation step of the identified biomarkers.Discussion: The INTEGRActiv study is expected to provide the definition of PA and health-related biomarkers (PA-health biomarkers) in childhood and adolescence. It will allow us to relate biomarkers to factors such as age, sex, body weight, sleep behavior, dietary factors, and pubertal status and to identify how these factors quantitatively affect the biomarkers’ responses. Taken together, the INTEGRActiv study approach is expected to help monitor the efficacy of interventions aimed to improve the quality of life of children/adolescents through physical activity. Clinical Trial Registration: ClinicalTrials.gov, Identifier NCT05907785.

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.