Polymorphisms in the fatty acid desaturase (FADS) genes influence the arachidonic (AA) and docosahexaenoic (DHA) acid concentrations (crucial in early life). Infants with specific genotypes may require different amounts of these fatty acids (FAs) to maintain an adequate status. The aim of this study was to determine the effect of an infant formula supplemented with AA and DHA on FAs of infants with different FADS genotypes. In total, 176 infants from the COGNIS study were randomly allocated to the Standard Formula (SF; n = 61) or the Experimental Formula (EF; n = 70) group, the latter supplemented with AA and DHA. Breastfed infants were added as a reference group (BF; n = 45). FAs and FADS polymorphisms were analyzed from cheek cells collected at 3 months of age. FADS minor allele carriership in formula fed infants, especially those supplemented, was associated with a declined desaturase activity and lower AA and DHA levels. Breastfed infants were not affected, possibly to the high content of AA and DHA in breast milk. The supplementation increased AA and DHA levels, but mostly in major allele carriers. In conclusion, infant FADS genotype could contribute to narrow the gap of AA and DHA concentrations between breastfed and formula fed infants.

Background/Objectives: Gestational Diabetes Mellitus (GDM) is a global health issue with immediate and long-term maternal–fetal complications. Current diagnostic approaches, such as the Oral Glucose Tolerance Test (OGTT), have limitations in accessibility, sensitivity, and timing. This study aimed to identify key nodes and structural interactions associated with GDM using graph theory and network analysis to improve early predictive strategies. Methods: A literature review inspired by PRISMA guidelines (2004–2025) identified 44 clinically relevant factors. A directed graph was constructed using Python (version 3.10.12), and centrality metrics (closeness, betweenness, eigenvector), k-core decomposition, and a Minimum Dominating Set (MDS) were computed. The MDS, derived using an integer linear programming model, was used to determine the smallest subset of nodes with systemic dominance across the network. Results: The MDS included 20 nodes, with seven showing a high out-degree (≥4), notably Apo A1, vitamin D, vitamin D deficiency, and sedentary lifestyle. Vitamin D exhibited 15 outgoing edges, connecting directly to protective factors like HDL and inversely to risk factors such as smoking and obesity. Sedentary behavior also showed high structural influence. Closeness centrality highlighted triglycerides, insulin resistance, uric acid, fasting plasma glucose, and HDL as nodes with strong predictive potential, based on their high closeness and multiple incoming connections. Conclusions: Vitamin D and sedentary behavior emerged as structurally dominant nodes in the GDM network. Alongside metabolically relevant nodes with high closeness centrality, these findings support the utility of graph-based network analysis for early detection and targeted clinical interventions in maternal health.

Graph-based representations offer a natural framework for capturing structural constraints and dominant interaction patterns in complex biological systems. In this study, we propose a network-based model to investigate the structural organization of tumor extension in unresectable pancreatic cancer (UPC), using clinical data obtained from transabdominal endoscopic ultrasound–guided core needle biopsy (UG-CNB). A descriptive cross-sectional analysis was conducted in a cohort of 23 patients with unresectable pancreatic tumors. Thirty-six clinical, metabolic, morphological, and invasion-related variables were represented as nodes in a weighted network, with edge weights defined by Spearman correlation coefficients to quantify the strength of pairwise associations. To extract the dominant structural backbone of the system, we constructed and analyzed a maximum spanning tree (MST), which highlights the most influential relational pathways while preserving global connectivity. The resulting structure revealed six principal branches organized around tumor margin irregularity, which emerged as the central organizing feature of the network. The identified correlation pathways delineate three major domains of tumor progression: lymphatic dissemination, regional anatomic–vascular extension centered on the hepatoportal axis, and systemic metastatic spread. These domains are consistent with established anatomical routes and clinical patterns of pancreatic cancer progression. Overall, the proposed framework integrates clinical data with structural network analysis to uncover biologically meaningful pathways of tumor extension in unresectable UPC. The findings emphasize tumor margin irregularity as a key structural descriptor linking multiple progression mechanisms and illustrate how graph-based models can serve as complementary tools for elucidating organization principles in complex oncological systems.