Metabolic syndrome (MetS) is a cluster of interrelated cardiometabolic risk factors, including central obesity, hypertension, dyslipidaemia, and hyperglycaemia, that collectively elevate the risk of cardiovascular disease and type 2 diabetes mellitus. Globally, the prevalence of MetS among adults is estimated to be between 20 % and 25 %, making it a significant public health concern.1 The marked rise in obesity, sedentary lifestyles, and unhealthy dietary patterns worldwide has contributed strongly to this increasing burden of MetS.2 In low- and middle-income countries, particularly in the Asia-Pacific region, the MetS epidemic is intensifying. Economic modernization, urbanization, and epidemiologic transitions have fuelled this trend, bringing with them a higher prevalence of obesity and metabolic disorders.3 A recent meta-analysis of global data from more than 28 million adults demonstrated that, depending on diagnostic criteria, MetS prevalence can range as high as 31 % in certain regions.4 In India, the situation is equally worrisome. Systematic reviews estimate that around 30 % of Indian adults suffer from MetS, with higher rates reported in urban versus rural populations (32% vs. 22 %).5 Within city populations, studies using the ATP-III criteria have recorded MetS prevalence as high as 31.6 %, with women disproportionately affected compared to men.6 Moreover, in urban slum populations, over 50% of individuals with abdominal obesity were found to meet the criteria for MetS, underscoring the urgent need for screening and intervention in marginalized communities.7 Anthropometric measurements offer a practical, low-cost, and non-invasive way to screen for MetS risk, especially in resource-constrained settings. Traditional indices like Body Mass Index (BMI) are useful, but they may not capture central adiposity reliably. Indicators such as waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) better reflect visceral fat, which is more strongly associated with metabolic abnormalities. In the Indian context—where body fat distribution, lifestyle, and genetic predisposition differ from Western populations—the predictive value of these anthropometric measures may vary. Epidemiological evidence from India also suggests that overweight and obese individuals have significantly higher odds of having MetS.8 Therefore, this cross-sectional study aims to assess metabolic syndrome among obese adults using a broad range of anthropometric measurements and to identify which anthropometric indices are most strongly associated with MetS in this population. By grounding the analysis in both global trends and Indian realities, the study seeks to generate evidence that could inform early detection and public health strategies tailored to high-risk groups. Objective To evaluate the association between anthropometric indicators (such as Body Mass Index, Waist Circumference, Waist-to-Hip Ratio, Waist-to-Height Ratio, and Neck Circumference) and the presence of Metabolic Syndrome among obese adults.

Study design and setting A cross-sectional study will be conducted among adult patients (age ≥18 years) with obesity (BMI ≥30 kg/m²). Study population and eligibility Inclusion criteria: adults (≥18 years) with BMI ≥30 kg/m² who provide written informed consent. Exclusion criteria: pregnancy, known chronic illnesses that markedly alter body composition (e.g., advanced malignancy, Cushing’s syndrome), recent major surgery (past 3 months), or inability to stand for anthropometry. Sample size calculation Objective used for sample-size estimation: to estimate the prevalence of metabolic syndrome among obese adults and to have sufficient precision to examine associations with anthropometric predictors (e.g., waist circumference, waist-to-hip ratio, waist-to-height ratio, neck circumference). We used the standard formula for sample size for estimating a single proportion: n=(Z^2×P(1-P))/d^2 where: Z= 1.96 for 95% confidence, P= expected prevalence of metabolic syndrome among overweight/obese adults, d= absolute precision (margin of error). A recent study of overweight/obese adults reported a prevalence of metabolic syndrome ≈ 41.69 (0.416). Allowable error d= 0.07 (7 percentage points). Calculation (step-by-step) Z^2=〖1.96〗^2=3.8416 P(1-P)=0.416×(1-0.416)=0.416×0.584=0.242944 Numerator = 3.8416×0.242944=0.934(approximately) Denominator d^2=〖0.07〗^2=0.0049 n=0.934/0.0049=190.47→ round up → 191 participants required. Thus, the required sample size is n = 191. Data collection and measurements Data collected using a structured proforma including socio-demographic details, medical history, and medication use. Anthropometric measurements will be taken using standardized procedures: Weight — measured using a calibrated scale, light clothing, no shoes, recorded to nearest 0.1 kg. Height — stadiometer, standing erect, no shoes, nearest 0.1 cm. BMI — calculated as weight (kg) / height (m)^2. Waist circumference (WC) — measured at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest, at end of normal expiration. Hip circumference — at the maximum circumference over the buttocks. Waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) calculated. Neck circumference (NC) — measured below the laryngeal prominence (Adam’s apple) perpendicular to the long axis of the neck. Definition of metabolic syndrome Metabolic syndrome will be defined according to established criteria of IDF (2005) / AHA/NHLBI harmonized criteria — presence of any three of the five components: Elevated waist circumference (ethnicity-specific cut-offs), Elevated triglycerides (≥150 mg/dL) or treatment for raised TG, Reduced HDL cholesterol (<40 mg/dL in men, <50 mg/dL in women) or treatment, Elevated blood pressure (≥130/85 mmHg) or treatment, and Elevated fasting plasma glucose (≥100 mg/dL) or previously diagnosed diabetes. The IDF consensus and ATP III/AHA definitions and harmonized criteria are standard references. Laboratory measurements Fasting blood samples (after 8–12 hour fast) will be collected for fasting plasma glucose, serum triglycerides, and HDL cholesterol. Blood pressure measured following standard technique (average of two readings after 5 minutes rest). Statistical analysis Data were entered into Microsoft Excel and analyzed using SPSS software. Descriptive statistics such as mean, standard deviation, frequencies, and percentages were used to summarize the data. The Chi-square test was applied to find associations between alcohol consumption and cardiovascular risk factors. Student’s t-test or ANOVA was used to compare continuous variables between groups. A p-value of less than 0.05 was considered statistically significant.

Table 1. Socio-demographic and clinical characteristics of study participants (n = 191) Variable Frequency (n) Percentage (%) Age group (years) 18–30 30 15.7 31–40 52 27.2 41–50 65 34.0 >50 44 23.1 Sex Male 80 41.9 Female 111 58.1 BMI category Overweight (25–29.9 kg/m²) 69 36.1 Obese (≥30 kg/m²) 122 63.9 Interpretation Most participants were aged 31–50 years (61.2%), with a female predominance (58.1%). Nearly two-thirds (63.9%) were obese, indicating a population at high cardiometabolic risk. Table 2. Prevalence of metabolic syndrome and its components (n = 191) Parameter Frequency(n) Percentage (%) Metabolic syndrome present 91 47.6 Abdominal obesity 159 83.2 Elevated blood pressure 123 64.4 Low HDL cholesterol 108 56.5 Elevated triglycerides 91 47.6 Elevated fasting plasma glucose 74 38.7 Fig 1. Prevalence of metabolic syndrome and its components (n = 191) Interpretation The overall prevalence of metabolic syndrome was 47.6%. Abdominal obesity was the most prevalent component, followed by elevated blood pressure and low HDL cholesterol, reflecting a predominance of central obesity-driven metabolic risk. Table 3. Association between metabolic syndrome and selected variables Variable MetS Present n (%) MetS Absent n (%) χ² p value Sex Male 32 (40.0) 48 (60.0) 4.12 0.042 Female 59 (53.2) 52 (46.8) BMI category Overweight 21 (30.4) 48 (69.6) 20.86 <0.001 Obese 70 (57.4) 52 (42.6) Interpretation Metabolic syndrome was significantly more common among females (p = 0.042) and obese participants (p < 0.001). Obesity showed a strong association with metabolic syndrome. Table 4. Comparison of anthropometric parameters between participants with and without metabolic syndrome Parameter (Mean ± SD) MetS Present (n = 91) MetS Absent (n = 100) t value p value BMI (kg/m²) 33.1 ± 3.8 29.4 ± 3.2 7.12 <0.001 Waist circumference (cm) 105.3 ± 9.0 93.8 ± 8.4 9.01 <0.001 Waist-hip ratio 0.99 ± 0.06 0.91 ± 0.05 9.26 <0.001 Waist-height ratio 0.65 ± 0.05 0.57 ± 0.04 10.14 <0.001 Neck circumference (cm) 39.2 ± 3.0 35.4 ± 2.7 8.32 <0.001 Interpretation All anthropometric measures were significantly higher among individuals with metabolic syndrome (p < 0.001). Central obesity indicators, particularly waist-to-height ratio, showed the strongest association. Table 5. Association between anthropometric cut-offs and metabolic syndrome Anthropometric indicator MetS Present n (%) MetS Absent n (%) χ² p value High waist circumference 83 (52.2) 76 (47.8) 22.41 <0.001 WHR above cut-off 77 (59.2) 53 (40.8) 29.36 <0.001 WHtR ≥0.6 81 (61.8) 50 (38.2) 35.12 <0.001 High neck circumference 73 (60.8) 47 (39.2) 26.85 <0.001 Interpretation All anthropometric cut-offs showed a statistically significant association with metabolic syndrome (p < 0.001). Waist-to-height ratio and neck circumference emerged as strong, practical screening measures.

The present study demonstrated a high prevalence of metabolic syndrome (47.6%) among overweight and obese adults, indicating a substantial cardiometabolic risk burden in this population. This finding is consistent with reports from similar adult cohorts worldwide, where the prevalence of metabolic syndrome among overweight or obese individuals ranges between 35% and 50%, depending on diagnostic criteria and population characteristics.1,4 The observed prevalence is considerably higher than estimates reported for the general adult population, reaffirming obesity as a major driver of metabolic syndrome. In the current study, metabolic syndrome was significantly more prevalent among obese participants compared to overweight individuals, highlighting a dose–response relationship between increasing adiposity and metabolic risk. This observation aligns with previous studies demonstrating that excess adipose tissue, particularly visceral fat, plays a central role in insulin resistance, dyslipidaemia, and hypertension.10 The progressive increase in metabolic abnormalities with higher BMI categories has been well documented across diverse ethnic populations.11 Gender-wise analysis revealed a higher prevalence of metabolic syndrome among females, which is comparable to findings reported in several Asian and Indian studies.6 Hormonal factors, post-menopausal changes, and higher rates of central obesity among women may partly explain this gender disparity. Sociocultural factors such as reduced physical activity and differential access to healthcare may further contribute to the increased metabolic risk observed among women in developing countries. Among the individual components of metabolic syndrome, abdominal obesity was the most prevalent abnormality, followed by elevated blood pressure and low HDL cholesterol. Similar patterns have been reported in earlier studies, emphasizing the dominant role of central adiposity in the pathogenesis of metabolic syndrome.12 Visceral fat accumulation is known to promote chronic low-grade inflammation and altered adipokine secretion, which contribute to cardiometabolic dysfunction. Anthropometric analysis in this study showed that waist circumference, waist-to-hip ratio, waist-to-height ratio, and neck circumference were significantly higher among individuals with metabolic syndrome. Central obesity-related indices demonstrated stronger associations with metabolic syndrome than BMI alone, supporting previous evidence that BMI may not adequately reflect fat distribution or metabolic risk.13 Waist-to-height ratio, in particular, emerged as a robust indicator, reinforcing recommendations for its use as a simple and effective screening tool in clinical and community settings. The significant association observed between neck circumference and metabolic syndrome is noteworthy. Neck circumference reflects upper-body subcutaneous fat and has been increasingly recognized as a surrogate marker for insulin resistance and cardiometabolic risk. Several studies have reported its independent association with metabolic syndrome and its components, making it a practical alternative in situations where waist measurements may be difficult to obtain.14 Overall, the findings of this study emphasize the high burden of metabolic syndrome among overweight and obese adults and highlight the importance of incorporating simple anthropometric measures for early identification of high-risk individuals. Early detection and targeted lifestyle interventions focusing on weight reduction, dietary modification, and physical activity are essential to mitigate the growing burden of cardiometabolic diseases in this vulnerable population.

The study revealed a high prevalence of metabolic syndrome (47.6%) among overweight and obese adults, highlighting the urgent need for early identification and intervention in this high-risk group. Central obesity-related anthropometric indicators showed strong associations with metabolic syndrome, with waist-to-height ratio emerging as a particularly useful screening tool. Incorporation of simple anthropometric measurements into routine clinical practice can facilitate early detection and help prevent future cardiometabolic complications.

1. Ranasinghe P, Mathangasinghe Y, Jayawardena R, Hills AP, Misra A. Prevalence and trends of metabolic syndrome among adults in the Asia-Pacific region: a systematic review. BMC Public Health. 2017;17(1):101. 2. Kalandarova M, Ahmad I, Aung TN, Moolphate S, Shirayama Y, Okamoto M, Aung MN. Association Between Dietary Habits and Type 2 Diabetes Mellitus in Thai Adults: A Case-Control Study. Diabetes Metab Syndr Obes. 2024; 17:1143-1155. 3. World Health Organization. Metabolic syndrome. WHO EMRO Med J. 2015;22(4):414-9. 4. Noubiap JJ, Nansseu JR, Lontchi-Yimagou E, Nkeck JR, Nyaga UF, Ngouo AT, Tounouga DN, Tianyi FL, Foka AJ, Ndoadoumgue AL, Bigna JJ. Geographic distribution of metabolic syndrome and its components in the general adult population: a meta-analysis of global data from 28 million individuals. Diabetes Res Clin Pract. 2022; 188:109924. 5. Krishnamoorthy Y, Rajaa S, Murali S, Rehman T, Sahoo J, Kar SS. Prevalence of metabolic syndrome among adult population in India: a systematic review and meta-analysis. PLoS One. 2020;15(10): e0240971.