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.

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.

Background: Metabolic syndrome (MetS) is influenced by behavioral and genetic factors, yet evidence on eating behavior patterns and related genetic polymorphisms in Central Asian populations remains limited. Aim: The aim of this study was to assess eating behaviors among adults with and without MetS and evaluate their associations with clinical indicators and ADIPOQ rs266729 and MC4R rs17782313 variants. Methods: A cross-sectional study of 200 adults (115 non-MetS, 85 MetS) was conducted using Dutch Eating Behavior Questionnaire (DEBQ), standardized clinical measurements, and PCR-RFLP genotyping. Results: Participants with MetS were older than non-MetS adults (52 vs. 47 years; p = 0.004) and had substantially higher systolic blood pressure (126 vs. 114 mmHg; p < 0.001), diastolic blood pressure (83 vs. 74 mmHg; p < 0.001), and BMI (32.2 vs. 25.9 kg/m²; p < 0.001). Waist circumference, hip circumference, triglycerides, total cholesterol, and LDL were also significantly higher, while HDL was lower (1.13 ± 0.40 vs. 1.58 ± 1.50 mmol/L; p = 0.008). DEBQ restrained, emotional, and external eating scores showed no differences between groups (all p > 0.05). Eating behavior distribution was similar (p = 0.291). ADIPOQ genotypes (CC/CG/GG) did not differ by MetS status (p = 0.227), nor did MC4R variants (p = 0.679). Among MetS participants, clinical indicators did not vary across eating behavior categories, and no associations were observed between eating behavior and either polymorphism. Conclusions: Despite clear clinical and metabolic differences between MetS and non-MetS groups, neither eating behavior patterns nor ADIPOQ and MC4R variants were associated with metabolic measures among MetS group.