Background/Objectives: Metabolic syndrome (MetS) is a combination of conditions including central obesity, high blood pressure, high glucose levels, and abnormal triglycerides and cholesterol, which together increase the chances of heart disease, diabetes, and even death. The rates of MetS are different around the world, with 20–30% in Europe and 21.8–23.9% in Kazakhstan. Because MetS changes over time, it is important to study the dynamics of their components to improve prevention and treatments. This work aims to obtain the incidence of MetS and to evaluate the specific components associated with the emergence of new MetS cases in this population. Methods: This is a longitudinal study with a 10-year follow-up in Turkestan city between 2012 and 2024. Information was collected through physical exams, blood tests, and anthropometric measurements. Logistic regression and ROC curve analysis were used to find which factors increase the incidence of MetS. Results: Among 434 participants analyzed (78.8% women, and mean age 40.87, 66% < 2 MetS components, 20% had MetS blood pressure, and 65.9% had MetS waist circumference), the incidence of MetS at follow-up was 40%. The key risk factors for newly diagnosed MetS included elevated blood pressure and increased waist circumference. Multivariate analyses highlighted these components as the strongest predictors of MetS, with significant associations observed for participants with two or more MetS components at baseline. Conclusions: Elevated blood pressure and central obesity were identified as pivotal contributors to MetS progression. Given the rising prevalence of Mets and its implications, these results show the need to start treatment and check these risks early to prevent serious health problems.

Cardiovascular risk factors aggregate in determined individuals. Patients with Type 2 diabetes mellitus (T2DM) have higher cardiovascular This study aimed to investigate insulinresistance (IR) and β-cell function using the homeostasis model assessment (HOMA) indexes in a general Kazakh population and determine the effect he effect that cardiovascular factors may have on those indexes. We conducted a cross-sectional study among employees of the Khoja Akhmet Yassawi International Kazakh-Turkish University (Turkistan, Kazakhstan) aged between 27 and 69 years. Sociodemographic variables, anthropometric measurements (body mass, height, waist circumference, hip circumference), and blood pressure were obtained. Fasting blood samples were collected to measure insulin, glucose, total cholesterol (TC), triglycerides (TG), and high- (HDL) andlow-density lipoprotein (LDL) levels. Oral glucose tolerance tests were performed. Hierarchical and K-means cluster analyses were obtained. The final sample was composed of 427 participants. Spearmen correlation analysis showed that cardiovascular parameters were statistically associated with HOMA-β (p < 0.001) and not with HOMA IR. Participants were aggregated into the three clusters where the cluster with a higher age and cardiovascular risk revealed deficient β-cell functioning, but not IR (p < 0.000 and p = 0.982). Common and easy to obtain biochemical and anthropometric measurements capturing relevant cardiovascular risk factors have been demonstrated to be associated with significant deficiency in insulin secretion. Although further longitudinal studies of the incidence of T2DM are needed, this study highlights that cardiovascular profiling has a significant role not just for risk stratification of patients for cardiovascular prevention but also for targeted vigilant glucose monitoring.

Background/Objectives: Prediabetes (PreDM) is a heterogeneous condition, impacting hundreds of millions worldwide, associated with a substantially high risk of Type 2 Diabetes Mellitus (T2DM) and cardiovascular complications. Early identification of subgroups within the PreDM population may support tailored prevention strategies. Methods: We conducted a cross-sectional study using data from annual health check-ups of 419 university staff (aged 27–69) in Kazakhstan. Latent Class Analysis (LCA) was applied to identify subgroups of individuals with PreDM based on cardiovascular risk factors. Differences in glucose metabolism markers (fasting glucose, OGTT, HOMA-IR, HOMA-β) were compared across identified classes. Results: PreDM prevalence was 43.4%. LCA revealed four distinct classes: Class 1: healthy, low-risk individuals; Class 2: overweight with moderate metabolic risk; Class 3: older, overweight individuals with high cardio-metabolic risk; and Class 4: obese, middle-aged to older individuals with very high cardio-metabolic risk. Significant differences were found in glucose metabolism profiles across the classes. IFG predominated in Class 1 (95%), while Classes 3 and 4 had higher rates of β-cell dysfunction and combined IFG/IGT patterns. HOMA-β differed significantly between classes (p < 0.001), while HOMA-IR did not. Conclusions: PreDM is highly prevalent in this working-age Kazakh population and demonstrates marked heterogeneity. Based on easily obtainable cardiovascular risk factors, we have identified four subgroups with distinct glucose profiles that may inform personalized interventions. These distinct subgroups may require differentiated prevention strategies, moving beyond a one-size-fits-all approach.

Knee osteoarthritis (KOA) is a degenerative joint disease that leads to pain, stiffness, and disability. While established risk factors such as age, obesity, and joint injury are well-documented, the role of cultural practices remains underexplored. This study investigates the association between traditional cultural practices and KOA prevalence in Central Asia. A case-control study was conducted between January and March 2025 in Turkestan, Kazakhstan, involving 337 KOA cases and 685 matched controls. Participants completed structured questionnaires assessing demographic characteristics, lifestyle habits, and cultural practices. Unconditional logistic regression analyses identified associations between cultural factors and KOA, adjusting for age, sex, BMI, education level, physical workload, and type of residence. Frequent floor sitting (OR = 4.25, 95% CI: 3.02-5.99), squat toilet use (OR = 2.32, 95% CI: 1.66-3.23), and rural residence (OR = 7.57, 95% CI: 4.99-11.48) were strongly associated with KOA. Daily prayer postures did not significantly differ between cases and controls. Traditional cultural practices involving prolonged knee flexion were associated with KOA. Public health initiatives should consider culturally appropriate strategies that may help mitigate knee strain. Further longitudinal research is needed to establish causality.

Background/Objectives: Epilepsy is a long-term condition that affects the brain and has a big impact on a person’s daily life, especially in areas where people do not have a lot of money or access to good healthcare. This study aimed to evaluate the relationship between medication adherence and QoL and to assess the role of seizure severity in the association between them among patients with epilepsy. Methods: A cross-sectional study of 1100 adult patients with epilepsy was conducted using registry data and structured interviews. The main outcomes that were assessed are quality of life (QoL), medication adherence, and seizure severity. Results: Reduced QoL was observed in 62% of patients. Low medication adherence was significantly associated with reduced QoL (OR = 4.33 [3.24–5.79] unadjusted; 3.90 [3.07–5.80] fully adjusted). Seizure severity was also associated with reduced QoL (OR = 1.62, p = 0.002; OR = 2.05, p < 0.001). Cognitive impairment showed the strongest association with reduced QoL, with ORs of 14.6 for mild and 80.8 for moderate-severe impairment in unadjusted models, remaining significant after adjustment. Medication adherence was significantly associated with seizure severity (OR = 1.18, p = 0.002), and attenuation of its effect after adjustment suggests that these variables are interrelated, although causality cannot be determined in this study. Additional factors associated with reduced QoL included lower education, longer disease duration, polytherapy, structural brain abnormalities, and comorbidities. Conclusions: Reduced QoL in epilepsy is strongly influenced by cognitive impairment and medication nonadherence, with seizure severity potentially contributing to this association, although causality cannot be inferred. These findings support integrated care strategies targeting adherence, cognition, and seizure control to improve patient outcomes.

Background: Rare pediatric neurological diseases (RPND) often remain undiagnosed for years, creating prolonged and costly diagnostic odysseys. Combining Human Phenotype Ontology (HPO)-based deep phenotyping with exome sequencing (ES) and reverse phenotyping offers the potential to improve diagnostic yield, accelerate diagnosis, and support sustainable healthcare in resource-limited settings. Objectives: To evaluate the diagnostic yield and clinical impact of an integrated approach combining deep phenotyping, ES, and reverse phenotyping in children with suspected RPNDs. Methods: In this multicenter observational study, eighty-one children from eleven hospitals in South Kazakhstan were recruited via the Central Asian and Transcaucasian Rare Pediatric Neurological Diseases Consortium. All patients underwent standardized HPO-based phenotyping and ES, with variant interpretation following ACMG guidelines. Reverse phenotyping and interdisciplinary discussions were used to refine clinical interpretation. Results: A molecular diagnosis was established in 34 of 81 patients (42%) based on pathogenic or likely pathogenic variants. Variants of uncertain significance (VUS) were identified in an additional 9 patients (11%), but were reported separately and not included in the diagnostic yield. Reverse phenotyping clarified or expanded clinical features in one-third of genetically diagnosed cases and provided supportive evidence in most VUS cases, although their classification remained unchanged. Conclusions: Integrating deep phenotyping, ES, and reverse phenotyping substantially improved diagnostic outcomes and shortened the diagnostic odyssey. This model reduces unnecessary procedures, minimizes delays, and provides a scalable framework for advancing equitable access to genomic diagnostics in resource-constrained healthcare systems.