Obesity and metabolic syndrome (MetS) are worldwide major health challenges. The Mediterranean diet (MD) is associated with a better cardiometabolic profile, but these beneficial effects may be influenced by genetic variations, modulating the predisposition to obesity or MetS. The aim was to assess whether interaction effects occur between an obesity genetic risk score (obesity-GRS) and the MD on adiposity and MetS in European adolescents. Multiple linear regression models were used to assess the interaction effects of an obesity-GRS and the MD on adiposity and MetS and its components. Interaction effects between the MD on adiposity and MetS were observed in both sex groups (p < 0.05). However, those interaction effects were only expressed in a certain number of adolescents, when a limited number of risk alleles were present. Regarding adiposity, a total of 51.1% males and 98.7% females had lower body mass index (BMI) as a result of higher MD adherence. Concerning MetS, only 9.9% of males with higher MD adherence had lower MetS scores. However, the same effect was observed in 95.2% of females. In conclusion, obesity-related genotypes could modulate the relationship between MD adherence and adiposity and MetS in European adolescents; the interaction effect was higher in females than in males.

Childhood obesity is a worldwide epidemic. Mediterranean diet (MD) is inversely associated with childhood obesity, but the interaction with other environmental factors, such screen time, might influence the health benefits of a high MD adherence in adolescents. The aim of the present study was to assess whether an association between MD and screen time exists in European adolescents. Moreover, we also explored whether sedentary time has a modulatory effect on the association between MD and adiposity. Adherence to the MD (24 h recalls), screen time (questionnaire), pubertal development, body mass index (BMI), fat mass index (FMI) and waist circumference (WC) were evaluated in 2053 adolescents (54.7% females), aged 12.5–17.5 years. In females, MD adherence was associated with lower BMI and FMI only when they were exposed to less than 338 min/day of screen time (81.8% of females); MD adherence was also associated with lower WC only when females were exposed to less than 143 min/day of screen time (31.5% of females). No significant MD-screen time interaction was observed in males. In conclusion, screen-time-based sedentary behaviours had a modulatory effect in the association between MD adherence and adiposity in European female adolescents.

Precision oncology research is challenging outside the contexts of oncogenic addiction and/or targeted therapies. We previously showed that phosphoproteomics is a powerful approach to reveal patient subsets of interest characterized by the activity of a few kinases where the underlying genomics is complex. Here, we conduct a phosphoproteomic screening of samples from HER2-negative female breast cancer receiving neoadjuvant paclitaxel (N = 130), aiming to find candidate biomarkers of paclitaxel sensitivity. Filtering 11 candidate biomarkers through 2 independent patient sets (N = 218) allowed the identification of a subgroup of patients characterized by high levels of CDK4 and filamin-A who had a 90% chance of achieving a pCR in response to paclitaxel. Mechanistically, CDK4 regulates filamin-A transcription, which in turn forms a complex with tubulin and CLIP-170, which elicits increased binding of paclitaxel to microtubules, microtubule acetylation and stabilization, and mitotic catastrophe. Thus, phosphoproteomics allows the identification of explainable factors for predicting response to paclitaxel.

To assess the relationship between body composition indicators and inflammatory biomarkers in children and adolescents of the GENOBOX study. Anthropometry data from 264 subjects from the subsample of Zaragoza (Spain) included: weight, height, waist circumference, body mass index and triponderal index. Body composition was determined by Dual-energy X-ray Absorptiometry (DXA), obtaining visceral adipose tissue, fat mass index and lean mass index. Age and sex specific z-scores were computed. Simple linear regression models were performed with inflammatory biomarkers (hsCRP, IL8, TNF-α, adiponectin, leptin and resistin) as dependent variables, and each of the body composition indices as independent variables. Prepubertal boys had higher IL8 and resistin values and pubertal girls had higher HOMA-IR and leptin values. hsCPR and leptin were associated with fat mass, both in prepubertals and pubertals, independently of lean mass, and regardless of how body composition was measured. All body composition indices were inversely associated with adiponectin, except for fat mass index in pubertals, but none of them were statistically significant. A positive association between hsCRP and leptin with all body fat composition parameters, measured by standard nutritional indicators and DXA, was observed in both sexual stages.

Genetic susceptibility loci are associated with PDAC risk and survival, but the impact of germline HLA region variation remains largely unexplored. This study examined HLA I-II alleles within the PanGenEU study and validated our findings using external datasets (UK Biobank, TCGA, PAN-NGS trial, and Caris trial). HLA-A*02:01and HLA-B*49 alleles were linked to a decreased risk of PDAC, whereas HLA-B*39 , HLA-DPB1*04, and HLA-A*26:01 were directly associated with increased risk. PDAC patients carrying the HLA-A*02:01 allele also showed lower mortality rates, with the effect being more pronounced in those with KRAS ^G12V mutations, pointing to a host*tumor genetic interaction. This research highlights HLA-A*02:01 , found in 20% of Europeans, as a marker for reduced PDAC risk and mortality, especially in KRAS ^G12V mutated tumors. Results from this study could enhance personalized medicine for PDAC by identifying patients who may benefit from regular screenings through tailored risk assessments. Importantly, our findings are crucial for stratifying PDAC patients based on their genetic background and tumor mutational profile, which can guide treatment strategies.

Pancreatic ductal adenocarcinoma (PDAC) presents a significant challenge, with a five-year survival rate of approximately 10%. Tumor heterogeneity contributes to the limited effectiveness of treatments. Several tumor and stroma molecular classifiers have attempted to clarify this heterogeneity with moderate agreement. Recognizing the complexity introduced by this extensive array of taxonomies, this study aims to develop a consensus molecular classifier by including both tumor and stroma features. We integrated gene expression data through Virtual Microdissection and classified the training samples to apply Machine Learning algorithms for each previous classifier. The consensus classifier was then derived using a Markov Clustering Algorithm, and its association with overall survival was assessed. The results indicated that Elastic-Net emerged as the superior model. We identified two classes for tumor components (Consensus Classical and Consensus Non-classical) and stroma components (Consensus Normal-Immune and Consensus Activated-ECM). The consensus Random Forest achieved a balanced accuracy of 96.33% and 98.92%, respectively. While not consistent across retrospective series, the algorithm ( PDAConsensus ) independently predicted overall survival. We developed a robust consensus classifier for PDAC that integrates tumor and stroma features and made it accessible through the R package PDACMOC ( PDACMolecularOmniClassifier , https://github.com/pavillos/PDACMOC ) and a Shiny app ( https://pdacmoc.cnio.es/ ).