Climate change has influenced the transmission of a wide range of vector-borne diseases in Europe, which is a pressing public health challenge for the coming decades. Numerous theories have been developed in order to explain how tick-borne diseases are associated with climate change. These theories include higher proliferation rates, extended transmission season, changes in ecological balances, and climate-related migration of vectors, reservoir hosts, or human populations. Changes of the epidemiological pattern have potentially catastrophic consequences, resulting in increasing prevalence of tick-borne diseases. Thus, investigation of the relationship between climate change and tick-borne diseases is critical. In this regard, climate models that predict the ticks’ geographical distribution changes can be used as a predicting tool. The aim of this review is to provide the current evidence regarding the contribution of the climatic changes to Lyme borreliosis (LB) disease and tick-borne encephalitis (TBE) and to present how computational models will advance our understanding of the relationship between climate change and tick-borne diseases in Europe.

Type 2 diabetes mellitus (T2DM) remaina significant global health challenge, with its increasing prevalence and associated complications contributing to high morbidity and economic burden. Genetic factors play a crucial role in T2DM susceptibility, yet individual responses to dietary interventions vary widely, emphasizing the importance of gene–diet (G × D) interactions. This review synthesizes the current literature on the genetic basis of T2DM and the role of G × D interactions in shaping individual responses to diet. We examine the genetics implication in T2DM risk and modulation by dietary factors, with a focus on the potential of Nutrigenetics in guiding personalized nutrition (PN) strategies. Moreover, the clinical implications of these interactions for the personalized prevention and management of T2DM are explored, highlighting the promise of tailoring dietary recommendations based on genetic profiles. Critical research gaps, including the need for diverse and longitudinal studies, the integration of multi-omic data, and the inclusion of digital health technologies in PN are discussed. Finally, future directions for the field are outlined, advocating for more inclusive, large-scale studies to optimize PN approaches for diverse populations and improve the efficacy of T2DM prevention and management. This review underscores the potential of an individualized, genetically informed dietary approach in modulating the global burden of T2DM.

Climate change (changes in temperature and weather patterns) plays a significant role in the outbreak of tick-borne diseases such as Lyme borreliosis (LB) disease and tick-borne encephalitis (TBE) in Europe. The prevalence of LB disease has increased lately and TBE, one of the most serious infections of the central nervous system, significantly affects public health. Changes in abiotic factors are the main determinants affecting their vectors. The warmer weather during winter has prolonged the extension of the period in which ticks are most active. Therefore, the increased risk of transmission of tick-borne diseases influenced by the climate conditions could be determined by advanced climate models.

Background/Objectives: Obesity, a major risk factor for cardiometabolic traits, is influenced by both genetic and environmental factors. Genetic studies have identified multiple single-nucleotide polymorphisms (SNPs) associated with obesity and related traits. This study aimed to examine the association between genetic risk score (GRS) and obesity-associated traits, while incorporating SNPs with established gene–diet interactions to explore their potential role in precision nutrition (PN) strategies. Methods: A total of 4279 participants were stratified into low- and intermediate-/high-GRS groups based on 18 SNPs linked to obesity and cardiometabolic traits. This study followed a case–control design, where cases included individuals with overweight/obesity, T2DM-positive (+), or CVD-positive (+) individuals and controls, which comprised individuals free of these traits. Logistic regression area under the curve (AUC) models were used to assess the predictive power of the GRS and traditional risk factors on BMI, T2DM and CVD. Results: Individuals in the intermediate-/high-GRS group had higher odds of being overweight or obese (OR = 1.23, CI: 1.03–1.48, p = 0.02), presenting as T2DM+ (OR = 1.56, CI: 1.03–2.49, p = 0.03) and exhibiting CVD-related traits (OR = 1.56, CI: 1.25–1.95, p < 0.0001), compared to the low-GRS group. The GRS was the second most predictive factor after age for BMI (AUC = 0.515; 95% CI: 0.462–0.538). The GRS also demonstrated a predictive power of 0.528 (95% CI: 0.508–0.564) for CVD and 0.548 (95% CI: 0.440–0.605) for T2DM. Conclusions: This study supports the potential utility of the GRS in assessing obesity and cardiometabolic risk, while emphasizing the potential of PN approaches in modulating genetic susceptibility. Incorporating gene–diet interactions provides actionable insights for personalized dietary strategies. Future research should integrate multiple gene–diet and gene–gene interactions to enhance risk prediction and targeted interventions.

Background/Objectives: Type 2 diabetes (T2D) is a multifactorial disorder arising from complex interactions between genetic susceptibility and environmental exposures, including diet. This systematic review aimed to synthesize and critically evaluate evidence on gene–diet interactions in T2D-related phenotypes among adults without diagnosed T2D at baseline. Methods: The review was conducted according to PRISMA guidelines and registered in PROSPERO (CRD420251175793). PubMed and Embase were systematically searched for eligible studies published up to November 2025. Observational studies and intervention trials examining interactions between dietary exposures and genetic variants, haplotypes, or genetic risk scores (GRSs) in relation to incident T2D, insulin resistance, glycemic traits, or β-cell function were included. Risk of bias was assessed using study design-specific tools. Results: A total of 61 reports representing 27 unique studies met the eligibility criteria, comprising 30 reports of randomized trials and 31 reports of non-randomized studies. Across studies, 55 Single Nucleotide Polymorphisms (SNPs) in 40 genes were examined, while 11 studies evaluated GRSs. The most frequently studied loci included TCF7L2, ADIPOQ, FTO, IRS1, GIPR and PPM1K. Reported interactions involved dietary patterns, macronutrient composition, dietary fat quality, fiber/whole-grain intake and specific foods, influencing incident T2D, insulin-related traits and glycemic outcomes. However, findings were heterogeneous, frequently inconsistent, and often lacked independent replication. Most randomized and non-randomized studies were judged at high or serious risk of bias. Conclusions: Genetic variation may contribute to interindividual differences in metabolic responses to diet, although robust and reproducible gene–diet interactions remain limited. Larger, well-powered, standardized studies across diverse populations are needed to establish clinically meaningful applications of Precision Nutrition (PN) in T2D prevention.

Psychiatric disorders affect nearly one billion people worldwide and remain a major therapeutic challenge due to frequent treatment resistance. Pharmacogenetics provides a precision-informed approach by accounting for interindividual variability in drug metabolism and response, and population-specific data offer valuable information for therapeutic considerations. This study analyzed 3011 Greek individuals to assess 24 pharmacogenetic variants across 13 genes. Genotyping was performed using TaqMan OpenArray^® assays, and metabolic phenotypes were predicted based on established genotype-to-phenotype translation guidelines. Allele frequencies were compared with those in European, African, and East Asian populations. Population structure and genetic differentiation were evaluated using Principal Component Analysis (PCA), K-means clustering, fixation index (F_ST), and STRUCTURE analysis. Results indicated that most allele frequencies in Greeks aligned with those in European populations, while several CYP2D6 and CYP2C19 variants differed significantly from those in African and East Asian cohorts. PCA and clustering confirmed strong European affinity, supported by low F_ST values, whereas STRUCTURE revealed minimal non-European admixture. Predicted metabolic phenotypes showed that 36%, 57.7%, and 41.6% of individuals exhibited altered CYP2D6, CYP2C19, and CYP2C9 activity, respectively. These findings highlight clinically actionable variation in the Greek population and emphasize the use of population-specific pharmacogenetic data to inform optimized strategies in precision psychiatry.