Obesity and hypertension (HTN) frequently coexist with dysglycemia, yet it remains unclear whether general (BMI) versus central (waist circumference, WC) obesity indices show differential associations with HTN across normoglycemic, prediabetic, and diabetic states. This population-based study aimed to assess this relationship among adults aged 35 to 70 from the Bandare-Kong cohort. HTN was determined based on systolic blood pressure ≥130 mm Hg, diastolic blood pressure ≥80 mm Hg, or self-reported use of antihypertensive medication. Obesity was defined by body mass index (BMI) ≥30 kg/m2 or WC ≥102 cm in men and WC ≥88 cm in women. Participants were stratified as normoglycemic (fasting plasma glucose [FPG] < 100 mg/dL), prediabetic (FPG 100-125 mg/dL), or diabetic (self-reported diabetes or FPG ≥126 mg/dL). Multivariable logistic regression models were used to estimate adjusted odds ratios (aORs) and 95% confidence intervals (CIs). Of 3916 participants (mean age 48.3 years; 42.8% male), 60.1% were normoglycemic. Multivariable analysis indicated that among diabetic individuals, high WC alone (aOR = 3.19, 95% CI: 1.18-5.60, P < .01) and combined obesity (aOR = 3.09, 95% CI: 1.77-5.39, P < .01) were associated with higher odds of HTN. The high-BMI-only category among diabetic participants was not interpreted inferentially because it contained only 2 normotensive individuals, resulting in an unstable estimate. In the prediabetic group, participants with combined obesity had the highest odds of HTN (odds ratio = 2.53, 95% CI: 1.58-4.15, P < .01), followed by those with high WC alone (odds ratio = 1.99, 95% CI: 1.21-3.27, P < .01). Among normoglycemic individuals, combined obesity was associated with higher odds of HTN (aOR = 2.63, 95% CI: 2.01-3.44, P < .01), followed by high WC alone (aOR = 1.44, 95% CI: 1.10-1.86, P < .01). Central obesity, either alone or combined with general obesity, was consistently associated with higher odds of HTN across glycemic strata. These findings support the importance of WC-based obesity assessment in HTN risk evaluation. Longitudinal studies with adequately sized diabetic subgroups are needed to confirm these associations and establish temporality.

Ultra-processed foods (UPFs) are increasingly consumed worldwide, and higher consumption has been linked to higher risk of non-communicable diseases (NCDs). This study examined the nutritional composition and quality, energy density, and hyperpalatability of UPFs available in the Italian food market according to their literature-derived NCD-risk categories. Products were classified into main food groups and subgroups and further categorized according to reported associations with NCD risk: positive, negative, or no reported association. Differences between the main food groups were analyzed in RStudio using non-parametric tests, with results summarized using medians and interquartile ranges. A total of 13,025 UPF products were included in the analysis. UPFs showed substantial variation in nutritional composition, nutritional quality, nutrients-of-concern levels, energy density, and hyperpalatability across the main food groups and literature-derived NCD-risk categories. Snacks represented the largest UPF group and showed the most consistently unfavorable profile, including high median energy, carbohydrate, saturated fat, and sugar contents, a high proportion of Nutri-Score D and E products, and the highest proportion of medium- and high-energy-density products. Overall, 64.3% of UPFs were classified as Nutri-Score D or E, indicating generally lower nutritional quality. However, unfavorable nutritional characteristics were not limited to UPF subgroups reportedly associated with increased NCD risk; some subgroups reportedly associated with lower or no reported NCD risk also contained products with poor nutritional quality, high energy density, or hyperpalatable profiles. These findings suggest that UPFs are not nutritionally uniform and that assessment of UPF-related health relevance should consider food group, subgroup, nutrient profile, energy density, and hyperpalatability in addition to processing classification.

This review systematically searched publicly available abdominal CT datasets and critically evaluates suitability for artificial intelligence (AI) applications in clinical settings. We examined 45 publicly available abdominal CT datasets (47,049 studies). Across all 45 datasets, we found substantial redundancy (51% case reuse) and a Western/geographic skew (75.3% from North America and Europe). A bias assessment was performed on the 22 datasets with more than 100 cases; within this subset, the most prevalent high-risk categories were racial bias (with a score of 16 out of 22) and selection bias (with a score of 15 out of 22), both of which may undermine model generalizability across diverse healthcare environments-particularly in resource-limited settings. To address these challenges, we propose targeted strategies for dataset improvement, including multi-institutional collaboration, adoption of standardized protocols, and deliberate inclusion of diverse patient populations and imaging technologies. These efforts are crucial in supporting the development of more equitable and clinically robust AI models for abdominal imaging.

β-thalassemia comprises a clinically heterogeneous group of disorders in which anemia severity, transfusion exposure, iron loading, and organ complications vary widely among individuals. This structured narrative review summarizes practical applications of precision medicine in transfusion-dependent thalassemia (TDT) and non-transfusion-dependent thalassemia (NTDT), with explicit attention to which strategies apply to each clinical category. Literature indexed in PubMed and Scopus from 2000 to 2025 was reviewed using terms related to thalassemia, precision medicine, magnetic resonance imaging (MRI), chelation tailoring, next-generation sequencing (NGS), fetal hemoglobin (HbF) modifiers, luspatercept, mitapivat, hepcidin, gene therapy, gene editing, and artificial intelligence (AI). Evidence was synthesized descriptively because interventions, outcomes, and populations were heterogeneous, and no pooled meta-analysis was performed. In TDT, precision care is centered on individualized transfusion planning, extended red-cell antigen matching, MRI-guided cardiac and hepatic iron monitoring, organ-directed chelation intensification, and selection of disease-modifying or curative approaches. In NTDT, precision care emphasizes accurate phenotype classification, MRI liver iron concentration, because serum ferritin may underestimate iron burden, selective chelation, surveillance for NTDT-specific complications, and individualized use of agents that improve anemia. Personalized chelation should include deferiprone, either alone or in combination, when cardiac iron is increased. Comprehensive molecular diagnosis should include HBB together with HBA1 and HBA2 assessment, while secondary and tertiary modifiers help explain phenotypic variability and complication risk. Hepcidin and growth differentiation factor 15 (GDF-15) are discussed as investigational biomarkers; transferrin saturation is not recommended for routine iron-overload assessment in thalassemia. AI currently has its strongest role in screening and diagnosis, whereas risk-stratification models remain exploratory. Equitable implementation requires standardized TDT/NTDT pathways, regional MRI and genomics access, longitudinal registries, and multidisciplinary interpretation.

Hepatitis E virus (HEV) is an emerging zoonotic pathogen in Europe, mainly transmitted via consumption of naturally contaminated food or contact with infected animals. People living in the Balkans have diverse dietary habits, with high pork consumption in some countries, making this region a relevant setting for investigating HEV seroprevalence and its possible determinants. The current study aimed to estimate pooled HEV seroprevalence among adults in the general population and blood donors and to assess factors associated with regional variation. Twenty-eight eligible studies were identified from PubMed, Scopus, and Web of Science following the PRISMA guidelines. Pooled prevalence estimates were calculated using a random-effects meta-analysis of proportions implemented via a generalized linear mixed model (GLMM) with logit transformation. Potential factors associated with HEV seroprevalence, including national pork consumption, serological assay type, population group, year of publication, sex, and country, were evaluated. The pooled anti-HEV seroprevalence was estimated to be 5.68% (95% CI: 3.48-9.12%), with substantial heterogeneity. Country-specific estimates ranged from 1.01% in Greece to 26.66% in Bulgaria. Subgroup analyses showed significant variation according to national pork consumption category, serological assay type, year of publication, and country. However, meta-regression indicated that methodological and temporal factors, particularly serological assay type and year of publication, were the main significant moderators, whereas national pork consumption was not independently associated with seropositivity. Therefore, pork consumption should be interpreted as an exploratory ecological indicator rather than as evidence of a direct association. The methodological differences contribute substantially to the variability in HEV seroprevalence across the Balkans, emphasizing the need for standardized diagnostic approaches within a One Health framework.