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.

Metabolic disorders such as obesity, insulin resistance, hypertension and dyslipidemia increase the risk of cardiovascular diseases as well as cognitive and psychosomatic disorders. With the increasing proportion of the elderly population, age-related cognitive decline, defined as a gradual decline in cognitive abilities during the aging process, has emerged as an important public health problem. Genetic determinants of cognitive and psychosomatic disorders in individuals with metabolic syndrome include a large number of genes involved in the regulation of inflammation, metabolism, neuroplasticity and stress. Studies confirm that cognitive impairment in the elderly population is mostly associated with various factors such as environment, lifestyle, metal exposure, some genetic polymorphisms and diseases. The influence of genetic factors in the mechanism of development of cognitive and psychosomatic disorders in metabolic syndrome may help to understand the underlying mechanisms of the disease by identifying genetic biomarkers that indicate susceptibility to the disease. It will also provide the opportunity to select patients for monitoring and follow-up of treatment progress. It may therefore help address the challenges of early diagnosis, screening and prognosis assessment in patients with cognitive impairment with metabolic syndrome.

Background: Metabolic syndrome (MetS), a cluster of cardiometabolic abnormalities including elevated blood pressure, impaired glucose regulation, dyslipidemia, and increased waist circumference is increasingly recognized as a condition linked to both physical and psychological health risks. This study aims to investigate genotype-specific differences in psychological distress between healthy individuals and those with metabolic disorders, as well as to examine potential gene metabolic status interactions. Methods: This study is a cross-sectional analysis conducted in Turkistan city in the Southern region of Kazakhstan. Participants (healthy and those with metabolic syndrome) were invited to take part in the study by random sampling from the Khoja Akhmet Yassawi Kazakh-Turkish International University Medical Center. Consenting individuals provided a genetic analysis. Psychosomatic indicators were assessed using the Perceived Stress Questionnaire (PSQ) and the Depression, Anxiety, and Stress Scale (DASS-21). Results: A total of 200 individuals participated, with an approximately 3:1 ratio of women to men. The mean age in years was 50.4 ± 9.5 and 48.8 ± 7.7 for men and women, respectively. Preliminary analyses showed variations in cognitive and psychosomatic measures among individuals with metabolic syndrome, but no associations with genetic variants, and no significant group differences across key psychosomatic indicators when stratified by metabolic or genetic factors. However, a significant difference in LPL-Anxiety between genotypes GA-GG (p < 0.05) was found. Conclusions: Variations in metabolic and genetic factors within the studied population were not associated with measurable differences in stress or depressive symptoms.