Metabolic syndrome is a cluster of interrelated metabolic abnormalities that increase the risk of developing type 2 diabetes mellitus, cardiovascular disease, and other long-term health complications. Main components of metabolic syndrome are: central obesity, increased blood pressure(BP), increased fasting blood glucose(FBG), increased serum triglycerides, and reduced high-density lipoprotein (HDL) cholesterol. The presence of these abnormalities increases the risk of future cardiometabolic disease. The prevalence of metabolic syndrome has increased recently due to changes in dietary habits, reduced physical activity, sedentary lifestyles, increasing obesity, and other environmental and behavioural factors. The development of metabolic abnormalities at an early age is a major public health concern as affected patients may experience prolonged exposure to cardiovascular and metabolic risk factors.1 Young adulthood is an important period for identifying early metabolic abnormalities and implementing preventive measures. Unhealthy food choices, inadequate physical activity, excess screen time, disturbed sleep patterns, and a family history of diabetes or hypertension may contribute to an increased risk of metabolic syndrome. Early detection can help identify subjects who may benefit from lifestyle modification, dietary counselling, regular physical activity, and appropriate medical follow-up. In India, the increasing burden of obesity, diabetes, hypertension, and dyslipidaemia highlights the importance of screening for metabolic syndrome.2 Young adults attending the Medicine Outpatient Department (OPD) represent an important population for evaluating the distribution of metabolic risk factors in a clinical setting. But the prevalence observed among OPD attendees may differ from that in the general community because individuals visiting hospitals may have underlying health concerns. So, the present study was done. The findings may help identify the need for early screening and preventive interventions in this population.3 AIM AND OBJECTIVES: AIM To determine the prevalence of metabolic syndrome among young adults visiting the Medicine Outpatient Department (OPD) of a tertiary care hospital. Objectives • To assess the distribution of individual components of metabolic syndrome, including central obesity, elevated blood pressure, raised fasting blood glucose, elevated triglycerides, and reduced HDL cholesterol. • To assess the association between metabolic syndrome and selected sociodemographic characteristics like age, gender • To know the association with physical activity and dietary habits

Study Design Hospital-based cross-sectional observational study. Study Setting- The study was done in Medicine Outpatient Department of a tertiary care hospital. Study Duration- The study was done for 6 months: February 2026 to July 2026 Study Population The study population included young adults attending the Medicine OPD during the study period who fulfil the eligibility criteria. Sample Size: 80 young adults were included in the study. Sample Size Calculation The sample size was calculated using the formula: n = Z²pq / d² Where, n = Sample size Z = 1.96 at 95% confidence level p = Expected prevalence of metabolic syndrome = 8.7% q = 100 − p = 91.3% d = Absolute precision = 6.2% n = 79.4 So, the sample size was taken as 80 Inclusion Criteria • Young adults aged 18–30 years. • Individuals attending the Medicine OPD during the study period. • Individuals providing written informed consent. • Individuals willing to undergo anthropometric measurements, blood pressure assessment, and relevant biochemical investigations. Exclusion Criteria • Pregnant women. • Individuals with incomplete clinical or biochemical data required to classify metabolic syndrome. Statistical Analysis The collected data were entered into Microsoft Excel and analysed using SPSS 22.0. Categorical variables were expressed as frequencies and percentages. Continuous variables were summarised as mean and standard deviation. Associations between categorical variables were assessed using the chi-square test. A p-value of less than 0.05 was considered statistically significant. Ethical Considerations Written informed consent was obtained from all patients before participation. Confidentiality of patient information was maintained throughout the study.

Prevalence of metabolic syndrome: 25% of the patients had metabolic syndrome. Table 1: Prevalence of metabolic syndrome among study participants Metabolic syndrome status Number Percentage Present 20 25.0 Absent 60 75.0 Total 80 100 Table 2: Age group and Metabolic syndrome: 30 patients were aged 26 to 30 years. Among them, 33.3% had metabolic syndrome. Age Present Absent Total 18–21 3 (13.6%) 19 (86.4%) 22 22–25 7 (25.0%) 21 (75.0%) 28 26–30 10 (33.3%) 20 (66.7%) 30 Total 20 (25.0%) 60 (75.0%) 80 Distribution of metabolic components Participants may have more than one component. Reduced HDL cholesterol was the most common component (42.5%), followed by central obesity (35.0%). Raised FBG was the least common component (22.5%). Table 3. Distribution of individual metabolic components Metabolic component Present, n (%) Absent, n (%) Central obesity 28 (35.0) 52 (65.0) Elevated blood pressure 24 (30.0) 56 (70.0) Raised fasting blood glucose 18 (22.5) 62 (77.5) Elevated triglycerides 26 (32.5) 54 (67.5) Reduced HDL cholesterol 34 (42.5) 46 (57.5) Sex: Table 4: Association between sex and metabolic syndrome Sex Present, n (%) Absent, n (%) Total Male 12 (28.6) 30 (71.4) 42 Female 8 (21.1) 30 (78.9) 38 Total 20 (25.0) 60 (75.0) 80 Metabolic syndrome was more prevalent among males (28.6%) than females (21.1%) But chi-square test (chi square value as=0.62),p=0.43) showed no statistically significant association. Association between metabolic syndrome and physical activity Table 5. Association between metabolic syndrome and physical activity (N = 80) Physical activity level Present, n (%) Absent, n (%) Total Low 12 (40.0) 18 (60.0) 30 Moderate 6 (20.0) 24 (80.0) 30 High 2 (10.0) 18 (90.0) 20 Total 20 (25.0) 60 (75.0) 80 The prevalence of metabolic syndrome was highest among participants with low physical activity (40.0%), followed by moderate physical activity (20.0%) and high physical activity (10.0%). Chi-square test (Chi sqaure=6.40), (p=0.041), implied statistically significant association between physical activity and metabolic syndrome. Metabolic syndrome and dietary habits Table 8. Association between Metabolic syndrome and dietary habits (N = 80) There is no significant association between metabolic syndrome and dietary habits. Dietary pattern Present, n (%) Absent, n (%) Total Healthy dietary pattern 4 (13.8%) 25 (86.2%) 29 Mixed dietary pattern 5 (25.0%) 15 (75.0%) 20 Unhealthy dietary pattern 12 (38.7%) 19 (61.3%) 31 Total 21 (26.3%) 59 (73.8%) 80 Pearson chi-square (\(\chi^2\)) = 4.83, p-value = 0.090 Mot statistically significant at the 0.05 level. Graph 1: Association between metabolic syndrome and sex There is no significant difference in metabolic syndrome and sex. Graph 2: Association between metabolic syndrome and physical activity There is no significant difference in metabolic syndrome and physical activity.

The present study was aimed to determine the prevalence of metabolic syndrome and assess its association with age, sex, physical activity, and dietary habits. The findings are discussed below in comparison with previously published studies indexed in PubMed. In the present study, metabolic syndrome was identified in 20 out of 80 participants, giving a prevalence of 25%. This finding indicates that metabolic syndrome may be an important health concern even among young adults attending the Medicine OPD. In comparison, Manjunath et al. studied 473 urban Indian young adults aged 18–25 years and reported prevalence estimates of 3.6% using the NCEP ATP III criteria, 6.6% using the International Diabetes Federation criteria, and 8.7% using the Indian consensus criteria. The prevalence varied according to the diagnostic definition used. 10 Krishnamoorthy et al.4, in a systematic review and meta-analysis of Indian adults, reported a pooled prevalence of metabolic syndrome of 30%, with an estimated prevalence of 13% among adults aged 18–29 years. In the present study, 42 participants (52.5%) were male and 38 (47.5%) were female. The largest proportion of participants belonged to the 26–30-year age group, comprising 30 participants (37.5%), followed by 28 participants (35%) aged 22–25 years and 22 participants (27.5%) aged 18–21 years. The distribution suggests that the study included a slightly more proportion of males and participants in the older age group. These characteristics are relevant when interpreting the prevalence of metabolic syndrome because metabolic risk factors may vary with age and sex. In the present study, the prevalence of metabolic syndrome showed an increasing trend with age. Among participants aged 18–21 years, 3 out of 22 participants had metabolic syndrome, corresponding to 13.6%. In the 22–25-year age group, 7 out of 28 participants (25%) had metabolic syndrome, while 10 out of 30 participants (33.3%) in the 26–30-year age group were affected. These findings suggest that metabolic syndrome was more frequent among participants in the older age group within the study population. This observation is consistent with the findings of Krishnamoorthy et al.5, who reported an increasing burden of metabolic syndrome across age groups in India. Their meta-analysis found a pooled prevalence of 13% among adults aged 18–29 years, increasing to 32% among those aged 30–39 years and 41% among those aged 40–49 years. The increasing prevalence seen in the present study may reflect the accumulation of metabolic risk factors with age, including changes in body composition, physical activity, and lifestyle habits. But as the present study was cross-sectional, it cannot establish that increasing age directly caused metabolic syndrome. Statistical testing is also required to determine whether the observed age-group differences are significant. Metabolic syndrome was present in 12 of 42 male participants (28.6%) and 8 of 38 female participants (21.1%). The proportion was more among males than females in this dataset. The systematic review and meta-analysis by Krishnamoorthy et al. reported a pooled prevalence of metabolic syndrome of 35% among Indian women and 26% among Indian men. These estimates were based on a broad adult population and may not directly represent young adults attending a Medicine OPD. The difference between the present findings and the pooled Indian estimates may be related to age distribution, clinical setting, regional differences, and diagnostic criteria. The sex-specific findings from the current study should be interpreted only after confirming the actual participant data and conducting appropriate statistical analysis.4 Physical-activity pattern suggests an inverse relationship between physical activity and metabolic syndrome, with more proportion of affected participants in the low-activity group. López-Martínez et al.7 investigated physical activity, fitness, and metabolic syndrome among 275 young adults aged 18–30 years. Their study examined the relationship between physical activity and metabolic risk factors, supporting the relevance of physical activity assessment in young adults. Regular physical activity may help improve insulin sensitivity, regulate body weight, reduce abdominal fat, and improve blood pressure and lipid profiles. The present study's findings can establish an association only if the physical-activity categories and metabolic syndrome status are confirmed from the actual data. The cross-sectional design cannot establish causality. Liu et al.8 did a cross-sectional study on 442 young adults aged 18–35 years in China. They reported associations between certain dietary patterns and metabolic syndrome. An egg-and-vegetable dietary pattern was associated with lower odds of metabolic syndrome (OR 0.26, 95% CI 0.10–0.70), whereas a legume-and-nut dietary pattern was associated with higher odds (OR 2.63, 95% CI 1.08–6.37). The authors also reported that physical activity modified Deshmukh examined dietary patterns among 995 young adults aged 19–39 years. The prudent dietary pattern, characterised by foods like whole grains, legumes, vegetables, and fruits, was inversely associated with several metabolic risk markers and the occurrence of metabolic syndrome. 9 Manjunath et al. reported sex-related differences in individual metabolic risk factors among urban Indian young adults, with males having more waist circumference, systolic blood pressure, fasting blood glucose, and triglyceride concentrations. Their findings on individual components should not be interpreted as proof that metabolic syndrome is more prevalent in males. 10 The findings from these studies show the importance of examining overall dietary patterns rather than individual foods in isolation. The relationship between dietary habits and metabolic syndrome can be affected by total energy intake, portion sizes, physical activity, body composition, and other confounding factors. Early identification of these abnormalities may support timely lifestyle counselling, assessment of cardiovascular risk, and appropriate clinical follow-up. Because the present study is based on a relatively small, hospital-based sample, its findings should not be considered representative of all young adults in the general population.

Metabolic syndrome is a major concern among young adults and that its prevalence varies according to population characteristics, study setting, and diagnostic criteria. Larger studies using standardised measurements are needed to confirm these associations and guide preventive strategies.

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