Obesity has emerged as one of the most critical public health challenges worldwide, with its prevalence tripling over the past four decades¹. It is closely linked to metabolic syndrome (MetS), a cluster of metabolic abnormalities including central obesity, hyperglycemia, hypertension, elevated triglycerides, and reduced high-density lipoprotein cholesterol². MetS significantly increases the risk of cardiovascular disease and type 2 diabetes mellitus³.

Adipose tissue is no longer considered a passive fat storage organ but an active endocrine organ that secretes numerous bioactive substances collectively known as adipokines⁴. In obesity, adipocyte hypertrophy and macrophage infiltration lead to chronic low-grade systemic inflammation⁵. This inflammatory state contributes to insulin resistance and endothelial dysfunction⁶.

High-sensitivity C-reactive protein (hs-CRP) is one of the most widely studied markers of systemic inflammation and is strongly associated with cardiovascular risk⁷. Interleukin-6 (IL-6), produced by adipose tissue and immune cells, stimulates hepatic CRP production and promotes insulin resistance⁸. Tumor necrosis factor-alpha (TNF-α) interferes with insulin signaling pathways and enhances lipolysis⁹.

Recent evidence suggests that inflammatory markers correlate with waist circumference, visceral adiposity, and metabolic parameters¹⁰. Elevated hs-CRP levels have been consistently observed in individuals with MetS¹¹. Furthermore, pro-inflammatory cytokines may serve as predictive markers for progression to diabetes and atherosclerosis¹².

Understanding the role of inflammatory mediators in obesity and MetS may help in early detection and targeted therapeutic interventions. Therefore, the present study aims to evaluate inflammatory markers in obese individuals with metabolic syndrome and compare them with healthy controls.

This was a hospital-based analytical cross-sectional study conducted in the Department of Biochemistry in collaboration with the Departments of Departments of Pharmacology at a tertiary care teaching hospital over a period of 12 months. A total of 200 participants aged between 25 and 60 years were enrolled in the study and divided into two groups: Group I comprised 100 obese individuals diagnosed with metabolic syndrome (MetS), and Group II included 100 non-obese healthy controls. Participants were selected based on predefined eligibility criteria. Individuals aged 25–60 years with a body mass index (BMI) ≥30 kg/m² and fulfilling the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria for metabolic syndrome were included in the obese group. Subjects with acute infections, autoimmune disorders, chronic inflammatory diseases, pregnancy, malignancy, or those currently on anti-inflammatory medications were excluded from the study. Detailed clinical evaluation and data collection were performed for all participants. Anthropometric measurements included height, weight, BMI calculation, waist circumference, and blood pressure assessment using standardized procedures. Biochemical investigations were carried out to assess fasting blood glucose levels, lipid profile parameters (total cholesterol, high-density lipoprotein, low-density lipoprotein, and triglycerides), and inflammatory markers including high-sensitivity C-reactive protein (hs-CRP) measured by the immunoturbidimetric method, and interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) estimated using enzyme-linked immunosorbent assay (ELISA). For laboratory analysis, 8–10 mL of fasting venous blood was collected from each participant after an overnight fasting period of 10–12 hours under aseptic precautions. The collected blood samples were divided into plain tubes for lipid profile and hs-CRP estimation and EDTA tubes for cytokine analysis. The samples were centrifuged at 3000 rpm for 10 minutes, and the separated serum was aliquoted and stored at −80°C until further biochemical analysis. Laboratory Parameters Biochemical parameters were analyzed using standardized laboratory methods. Fasting blood glucose levels were estimated by the glucose oxidase–peroxidase (GOD-POD) method. Serum total cholesterol was measured using the cholesterol oxidase–phenol aminophenazone (CHOD-PAP) method, while triglycerides were determined by the glycerol-3-phosphate oxidase–phenol aminophenazone (GPO-PAP) method. High-density lipoprotein (HDL) cholesterol was assessed by a direct enzymatic method, and low-density lipoprotein (LDL) cholesterol was calculated using the Friedewald formula. High-sensitivity C-reactive protein (hs-CRP) levels were measured using a high-sensitivity immunoturbidimetric assay. Serum interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) concentrations were quantified using the sandwich enzyme-linked immunosorbent assay (ELISA) technique. Internal quality control sera were run daily. External quality assurance was maintained as per NABL guidelines. Assessment of Insulin Resistance Fasting insulin was measured using chemiluminescence immunoassay. Insulin resistance was calculated using: Statistical Analysis Data were analyzed using SPSS version 25. Continuous variables expressed as Mean ± SD. Categorical variables as frequency (%). Independent Student’s t-test for comparison. Chi-square test for categorical data. Pearson’s correlation coefficient for association. Multiple linear regression to assess independent predictors. ROC curve analysis to evaluate diagnostic performance. p < 0.05 considered statistically significant

Table 1: Gender Distribution Among Study Participants

Table 1 shows the the obese + metabolic syndrome group, 54% were males and 46% were females. In the control group, 52% were males and 48% were females. The overall study population consisted of 53% males and 47% females. The p-value (0.76) indicates that there is no statistically significant difference in gender distribution between the two groups.

Table 2: Age Group Distribution Among Study Participants

Table 2 presents the majority of participants in both groups belonged to the 45–54 years age group (34% in obese + MetS vs 33% in controls), followed by the 35–44 years group. The least represented group was 55–60 years. The p-value of 0.92 indicates that there is no statistically significant difference in age distribution between the two groups.

Table 3: Baseline Characteristics

Table 3 presents the mean Body Mass Index (BMI) in the obese + MetS group was 32.5 ± 2.8 kg/m², which was significantly higher compared to the control group (22.4 ± 1.9 kg/m²) with a p-value <0.001. Waist circumference, a marker of central obesity and visceral adiposity, was markedly elevated in the case group (102.6 ± 8.4 cm) compared to controls (78.2 ± 6.3 cm) (p <0.001). Fasting blood glucose levels were also significantly higher in the obese group (118 ± 22 mg/dL) compared to controls (89 ± 10 mg/dL) (p <0.001). This indicates impaired glucose metabolism and insulin resistance in the MetS group.

Table 4: Lipid Profile

Triglyceride levels were significantly elevated in the obese + MetS group (192 ± 35 mg/dL) compared to controls (110 ± 22 mg/dL) with a p-value <0.001. High-density lipoprotein (HDL) cholesterol levels were significantly lower in the obese group (38 ± 6 mg/dL) compared to controls (52 ± 8 mg/dL) (p <0.001). Low-density lipoprotein (LDL) cholesterol was also significantly higher in obese individuals (142 ± 28 mg/dL) compared to controls (98 ± 18 mg/dL) (p <0.001). The highly significant p-values (<0.001) across all lipid parameters indicate a strong association between obesity with metabolic syndrome and dyslipidemia.

Table 5: Inflammatory Markers

Table 5 presents the High-sensitivity C-reactive protein (hs-CRP) levels were significantly elevated in obese individuals (5.8 ± 1.2 mg/L) compared to controls (1.2 ± 0.5 mg/L) (p <0.001). Interleukin-6 (IL-6) concentrations were also significantly higher in cases (8.4 ± 2.1 pg/mL) compared to controls (2.9 ± 1.1 pg/mL) (p <0.001). Tumor necrosis factor-alpha (TNF-α) levels were substantially increased in obese participants (12.6 ± 3.4 pg/mL) versus controls (4.3 ± 1.5 pg/mL) (p <0.001).

Table 4: Correlation with Waist Circumference

Table 4 demonstrates the strong positive correlation was observed between waist circumference and hs-CRP (r = 0.72, p < 0.001). This indicates that as waist circumference increases, hs-CRP levels also increase significantly. The correlation coefficient (r = 0.72) suggests a strong linear relationship, implying that central obesity is closely linked with systemic inflammation Similarly, IL-6 showed a significant positive correlation with waist circumference (r = 0.65, p < 0.001). TNF-α also demonstrated a moderate-to-strong positive correlation with waist circumference (r = 0.61, p < 0.001).

Table 5: Correlation with Triglycerides

Table 5 presents a significant positive correlation was observed between hs-CRP and triglycerides (r = 0.68, p < 0.001). IL-6 also showed a significant positive correlation with triglyceride levels (r = 0.59, p < 0.001).

Table 6: Insulin Resistance (HOMA-IR)

Table 6 compares the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) value in the obese + MetS group was 3.8 ± 0.9, whereas in the control group it was 1.6 ± 0.4. The difference between the two groups was statistically highly significant (p < 0.001). A HOMA-IR value above 2.5–3.0 is generally considered indicative of insulin resistance. The highly significant p-value (<0.001) confirms that the observed difference is not due to random variation but reflects a true metabolic distinction between the groups.

The present study demonstrates significantly elevated inflammatory markers in obese individuals with metabolic syndrome. These findings are consistent with previous studies showing chronic low-grade inflammation as a central mechanism linking obesity to metabolic disturbances¹³.

In this study the High-sensitivity C-reactive protein (hs-CRP) levels were significantly elevated in obese individuals (5.8 ± 1.2 mg/L) compared to controls (1.2 ± 0.5 mg/L) (p <0.001). Interleukin-6 (IL-6) concentrations were also significantly higher in cases (8.4 ± 2.1 pg/mL) compared to controls (2.9 ± 1.1 pg/mL) (p <0.001). Tumor necrosis factor-alpha (TNF-α) levels were substantially increased in obese participants (12.6 ± 3.4 pg/mL) versus controls (4.3 ± 1.5 pg/mL) (p <0.001). Adipose tissue macrophage infiltration promotes secretion of TNF-α and IL-6, which interfere with insulin signaling pathways¹⁴. Elevated hs-CRP observed in this study aligns with reports that CRP levels correlate with central obesity and cardiovascular risk¹⁵.

In this study strong positive correlation was observed between waist circumference and hs-CRP (r = 0.72, p < 0.001). This indicates that as waist circumference increases, hs-CRP levels also increase significantly. The correlation coefficient (r = 0.72) suggests a strong linear relationship, implying that central obesity is closely linked with systemic inflammation Similarly, IL-6 showed a significant positive correlation with waist circumference (r = 0.65, p < 0.001). The strong correlation between waist circumference and hs-CRP supports the hypothesis that visceral adiposity drives systemic inflammation¹⁶. Previous large cohort studies have shown that IL-6 predicts development of type 2 diabetes independently of BMI¹⁷.

In our study, TNF-α also demonstrated a moderate-to-strong positive correlation with waist circumference (r = 0.61, p < 0.001). TNF-α plays a role in endothelial dysfunction and promotes atherogenesis¹⁸. Elevated cytokine levels in MetS patients have been linked to increased oxidative stress and vascular inflammation¹⁹.

Our findings reinforce the concept that inflammatory markers may serve as early predictors of cardiometabolic risk. Lifestyle interventions targeting weight reduction have been shown to significantly reduce CRP and IL-6 levels²⁰.

Thus, inflammatory biomarkers may not only reflect disease burden but also provide therapeutic targets in obesity and metabolic syndrome.

Inflammatory markers such as hs-CRP, IL-6, and TNF-α are significantly elevated in obese individuals with metabolic syndrome. These markers correlate positively with central obesity and insulin resistance. Monitoring inflammatory markers may aid in early detection and prevention of cardiometabolic complications.

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