Psoriasis is a chronic immune-mediated inflammatory disorder affecting approximately 3% of the general population [1], with reports in India ranging from 0.44 to 2.8% prevalence in select regions [2]. Characterised by keratinocyte hyperproliferation and an inflammatory milieu driven by Th17/IL-23 and TNF-α pathways, psoriasis is increasingly recognised as a systemic condition with multiple comorbidities [3]. Among these comorbidities, Metabolic Syndrome (MetS) — defined as a cluster of cardiometabolic risk factors including central obesity, elevated triglycerides, low HDL-cholesterol, hypertension and impaired glucose regulation — has emerged as a key concern in persons with psoriasis. The global rise in non-communicable diseases has brought MetS to the forefront of preventive cardiology; concurrently, evidence links psoriasis to a two-fold increased risk of MetS compared to non-psoriatic populations [4]. A large body of evidence confirms an increased prevalence of metabolic syndrome (MetS) among patients with psoriasis worldwide. In a landmark population-based analysis from the United States, approximately 40% of psoriasis patients met the criteria for MetS compared with 23% of controls (adjusted OR = 1.96; 95% CI 1.62–2.37) [5]. Indian data show a similar or even higher burden. The prevalence of MetS among Indian psoriasis patients has been reported to range from 28% to 56.7%, depending on study population and diagnostic criteria [7,8]. In a South Indian hospital-based study, Madanagobalane and Anandan found MetS in 44.1% of psoriasis patients compared with 30% of matched controls, applying South-Asian waist-circumference thresholds recommended by the International Diabetes Federation [8]. These findings align with other regional studies that have consistently demonstrated a positive association between psoriasis severity and the clustering of metabolic risk factors. Together, these results emphasize that psoriasis is not merely a cutaneous disease but a systemic inflammatory disorder predisposing to significant cardiometabolic comorbidities in the Indian context. The biological link between psoriasis and MetS is plausible. Chronic systemic inflammation in psoriasis elevates cytokines such as IL-6 and TNF-α, which may potentiate insulin resistance, endothelial dysfunction, and lipoprotein abnormalities [9]. Conversely, metabolic abnormalities and adiposity may contribute to inflammatory pathways, creating a bidirectional relationship often described as the “psoriatic march” that links skin disease to cardiometabolic risk [10]. Despite growing recognition of this comorbidity, there remain gaps in the Indian literature. Many studies are limited by small sample sizes, variable definitions of MetS (often not using ethnicity-specific waist-circumference thresholds), and heterogeneous psoriasis severity characterization. Fewer studies comprehensively explore the individual components of MetS in Indian psoriasis cohorts, their relative frequencies, and their relationships with disease‐duration and severity. Moreover, regional variations (urban vs rural, South vs North India) in lifestyle, socioeconomic status and healthcare access may affect the prevalence and pattern of MetS in psoriasis — yet are under-explored [11,12]. Given these gaps, we undertook a cross-sectional study at a tertiary care teaching centre in South India to determine the prevalence of MetS and its individual components among adult psoriasis patients, employing South‐Asian waist‐circumference criteria. We also aimed to examine associations between disease-related variables (duration, severity as measured by the Psoriasis Area and Severity Index [PASI]), anthropometric and biochemical factors, and MetS status.

Study design and setting A hospital-based cross-sectional analytical study was conducted in the Department of Dermatology, of SVS Medical College & Hospital, Mahbubnagar, over a period of 6 months (July 2018–Dec 2018). The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational research. Study population All adult patients (≥18 years) clinically diagnosed with psoriasis vulgaris or its variants by a qualified dermatologist and attending the outpatient department during the study period were screened for eligibility. Inclusion Criteria Patients eligible for inclusion in this study were those with a clinically confirmed diagnosis of psoriasis, encompassing various morphological subtypes such as plaque, guttate, pustular, and erythrodermic psoriasis. The diagnosis was established based on characteristic clinical findings and, where necessary, supported by histopathological confirmation. Participants were required to have a disease duration of at least three months to ensure chronicity and exclude transient or acute psoriasiform eruptions. In addition, only individuals who expressed willingness to provide written informed consent were enrolled, reflecting adherence to ethical standards for voluntary participation. All participants were instructed to fast overnight prior to blood sampling to ensure accuracy in the estimation of biochemical parameters including fasting glucose and lipid profile. Exclusion Criteria Patients meeting any of the following conditions were excluded from the study to eliminate potential confounding factors that could independently influence metabolic parameters. Pregnant and lactating women were excluded due to physiological metabolic alterations associated with pregnancy and lactation. Individuals who had received systemic corticosteroids or retinoids within the preceding eight weeks were excluded because these medications are known to affect glucose and lipid metabolism. Patients with pre-existing metabolic or endocrine disorders—including diabetes mellitus, hypertension, dyslipidaemia, hypothyroidism, and Cushing’s syndrome—diagnosed prior to psoriasis onset were also excluded to ensure that metabolic abnormalities observed were not pre-existing conditions. Furthermore, patients with a history of acute infection, malignancy, or systemic inflammatory diseases other than psoriasis were excluded to avoid potential interference from other inflammatory or immunologic conditions that might skew metabolic or inflammatory markers. Sample size determination The required sample size (n) for estimating the prevalence of metabolic syndrome (MetS) among psoriasis patients was calculated using the formula for a single proportion: n=(Z_(1-α/2)^2×p×(1-p))/d^2 Where: Z_(1-α/2)=1.96for 95% confidence level, p=0.40(expected prevalence of MetS among Indian psoriasis patients based on prior studies [7,8]), and d=0.07(allowable absolute error). n=((1.96)^2×0.40×(1-0.40))/((0.07)^2 )=(3.84×0.24)/(0.0049)≈188.5 After accounting for a 10% non-response or incomplete data rate, the adjusted sample size was: n_adjusted=188.5×1.10=207.3 The calculated value was rounded up to 208. However, to enhance the precision of subgroup analyses (by psoriasis severity and treatment status) and to compensate for any potential data exclusions during biochemical analysis, the study included 216 participants by design. Thus, the final sample size was 216 psoriasis patients, ensuring adequate power for all planned analyses. Study procedure Data collection Each participant underwent a detailed interview, physical examination, and laboratory testing using a standardized proforma. Data collected included: Sociodemographic details: age, sex, occupation, education, residence. Lifestyle habits: smoking, alcohol consumption, dietary pattern, and physical activity (WHO-STEPS questionnaire). Clinical characteristics: type and duration of psoriasis, age at onset, family history, and treatment history (topical, phototherapy, systemic, or biologic agents). Assessment of psoriasis severity Disease severity was quantified using the Psoriasis Area and Severity Index (PASI) score. Patients were categorized as: Mild (<10), Moderate (10–20), or Severe (>20) psoriasis [13]. Anthropometric measurements All measurements were taken in the morning after overnight fasting: Height: measured to the nearest 0.1 cm using a wall-mounted stadiometer. Weight: measured to the nearest 0.1 kg using a calibrated digital scale. Waist circumference: measured midway between the lower costal margin and iliac crest at end-expiration. Hip circumference: measured at the widest portion of the buttocks. Body mass index (BMI): calculated as weight (kg)/height (m²). All measurements were obtained twice and averaged to minimize intra-observer variability. Blood pressure measurement Blood pressure was measured in the right arm using a mercury sphygmomanometer after the participant was seated for at least 5 minutes. Two readings taken 5 minutes apart were averaged. Hypertension was defined as systolic ≥130 mmHg and/or diastolic ≥85 mmHg or current antihypertensive therapy [14]. Laboratory investigations After an overnight fast of 8–12 hours, 5 mL of venous blood was drawn. The following investigations were performed: Fasting plasma glucose (FPG) – by glucose oxidase-peroxidase method. Lipid profile: Triglycerides (TG) – enzymatic colorimetric method. High-density lipoprotein cholesterol (HDL-C) – direct enzymatic method. Total cholesterol and LDL-C – Friedewald’s formula for LDL when TG <400 mg/dL [15]. All assays were performed in the hospital’s NABL-accredited biochemistry laboratory using standardized reagents and internal quality controls. Diagnostic criteria for metabolic syndrome Metabolic Syndrome was diagnosed based on the Joint Interim Statement (JIS) 2009 criteria [16] requiring ≥3 of 5 components: Waist circumference ≥90 cm in men or ≥80 cm in women (South Asian cut-offs) [17]. Triglycerides ≥150 mg/dL or drug treatment for hypertriglyceridemia. HDL-C <40 mg/dL in men or <50 mg/dL in women or treatment for low HDL-C. Blood pressure ≥130/85 mmHg or current antihypertensive therapy. Fasting plasma glucose ≥100 mg/dL or treatment for diabetes. Ethical considerations The study was approved by the Institutional Ethics Committee and the written informed consent was obtained from all participants. Confidentiality and anonymity were maintained throughout the study in accordance with the Declaration of Helsinki (2013 revision). Statistical analysis The statistical analysis for this study was performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA) after data entry in Microsoft Excel 2021. Continuous variables were summarized as mean ± standard deviation (SD) for normally distributed data or median (interquartile range, IQR) for non-normal distributions, while categorical variables were expressed as frequencies and percentages. The overall prevalence of metabolic syndrome (MetS) and its individual components among psoriasis patients was calculated along with 95% confidence intervals (CI). For group comparisons, the Student’s t-test or Mann–Whitney U-test was used for continuous variables, and the Chi-square or Fisher’s exact test was applied for categorical variables. When comparing metabolic parameters across psoriasis severity categories based on Psoriasis Area and Severity Index (PASI), one-way ANOVA or Kruskal–Wallis test was employed depending on data normality. Correlation analysis between PASI scores and metabolic parameters (body mass index, triglycerides, HDL-cholesterol, and fasting plasma glucose) was performed using Pearson’s or Spearman’s correlation coefficients as appropriate. To identify independent predictors of MetS, a binary logistic regression model was constructed incorporating variables such as age, sex, BMI, disease duration, and PASI score, and results were expressed as adjusted odds ratios (aOR) with corresponding 95% CIs. All statistical tests were two-tailed, and a p-value < 0.05 was considered statistically significant. Quality control All investigators underwent inter-observer training for anthropometric and PASI scoring. Laboratory assays were performed by blinded technicians. 10% of data were randomly re-entered to verify accuracy.

Table 1 presents the demographic and clinical profile of the study participants. A total of 216 patients with psoriasis were included in the final analysis (Figure 1). The mean ± SD age was 44.3 ± 12.7 years, with a male-to-female ratio of 1.8:1. The majority of participants (61.6%) were between 31–50 years of age. The mean duration of psoriasis was 7.4 ± 5.9 years, and chronic plaque psoriasis was the predominant clinical type (78.2%). Table 1. Demographic and Clinical Characteristics of Study Participants (n = 216) Variable Category Frequency (%) / Mean ± SD Age (years) Mean ± SD 44.3 ± 12.7 18–30 34 (15.7%) 31–50 133 (61.6%) >50 49 (22.7%) Gender Male 139 (64.4%) Female 77 (35.6%) Residence Urban 127 (58.8%) Rural 89 (41.2%) Type of psoriasis Chronic plaque 169 (78.2%) Guttate 23 (10.6%) Pustular 14 (6.5%) Erythrodermic 10 (4.6%) Duration of disease (years) Mean ± SD 7.4 ± 5.9 PASI score Mean ± SD 12.6 ± 6.8 Severity Mild (<10) 91 (42.1%) Moderate (10–20) 78 (36.1%) Severe (>20) 47 (21.8%) Family history Present 43 (19.9%) Absent 173 (80.1%) Table 2 summarizes the prevalence of MetS and its components among psoriasis patients. Metabolic syndrome (MetS) was identified in 88 patients (40.7%) based on JIS 2009 criteria. The most frequent MetS components were low HDL-C (61.6%) and abdominal obesity (56.5%), followed by hypertriglyceridemia (45.8%), hypertension (42.6%), and impaired fasting glucose (31.0%). Table 2. Prevalence of Metabolic Syndrome Components among Psoriasis Patients (n = 216) Parameter Definition (JIS 2009) Frequency (%) Abdominal obesity Waist ≥ 90 cm (men) or ≥ 80 cm (women) 122 (56.5%) Triglycerides ≥ 150 mg/dL or on treatment 99 (45.8%) Low HDL-C < 40 mg/dL (men) / < 50 mg/dL (women) 133 (61.6%) Blood pressure ≥ 130/85 mmHg or on antihypertensives 92 (42.6%) Fasting plasma glucose ≥ 100 mg/dL or on antidiabetic medication 67 (31.0%) Metabolic Syndrome (≥3 components) — 88 (40.7%) Table 3 presents a comparative analysis of clinical and metabolic parameters between psoriasis patients with and without metabolic syndrome (MetS). The findings demonstrate that patients with MetS exhibited significantly higher mean values across most variables, indicating a clear metabolic and cardiovascular risk clustering in this subgroup. Specifically, psoriasis patients with MetS were older (49.7 ± 11.3 years vs. 40.2 ± 12.8 years; p < 0.001) and had a longer disease duration (9.3 ± 6.2 years vs. 6.1 ± 5.3 years; p = 0.002), suggesting a cumulative risk with advancing age and chronicity of psoriasis. Anthropometric parameters including BMI (27.3 ± 3.6 vs. 23.8 ± 3.2 kg/m²; p < 0.001) and waist circumference (95.8 ± 10.9 vs. 84.3 ± 8.7 cm; p < 0.001) were significantly elevated in the MetS group, reflecting central obesity as a key driver of metabolic risk. Disease severity, measured by the Psoriasis Area and Severity Index (PASI), was also significantly higher among patients with MetS (15.1 ± 6.4 vs. 10.8 ± 6.6; p = 0.001), indicating that more severe psoriasis is associated with a higher likelihood of metabolic abnormalities. Similarly, systolic blood pressure (133.9 ± 12.8 vs. 122.7 ± 11.9 mmHg; p < 0.001), fasting glucose (103.9 ± 16.2 vs. 92.4 ± 13.8 mg/dL; p < 0.001), and triglyceride levels (168.7 ± 55.1 vs. 134.5 ± 41.2 mg/dL; p < 0.001) were significantly higher in the MetS group. Conversely, HDL-cholesterol was markedly lower (37.6 ± 7.8 vs. 46.2 ± 8.1 mg/dL; p < 0.001), consistent with an atherogenic lipid profile. Table 3. Comparison of Clinical and Metabolic Parameters between Psoriasis Patients with and without Metabolic Syndrome Parameter With MetS (n = 88) Without MetS (n = 128) p-value Age (years) 49.7 ± 11.3 40.2 ± 12.8 < 0.001 * Duration of psoriasis (years) 9.3 ± 6.2 6.1 ± 5.3 0.002 * BMI (kg/m²) 27.3 ± 3.6 23.8 ± 3.2 < 0.001 * Waist circumference (cm) 95.8 ± 10.9 84.3 ± 8.7 < 0.001 * PASI score 15.1 ± 6.4 10.8 ± 6.6 0.001 * Systolic BP (mmHg) 133.9 ± 12.8 122.7 ± 11.9 < 0.001 * Fasting glucose (mg/dL) 103.9 ± 16.2 92.4 ± 13.8 < 0.001 * Triglycerides (mg/dL) 168.7 ± 55.1 134.5 ± 41.2 < 0.001 * HDL-C (mg/dL) 37.6 ± 7.8 46.2 ± 8.1 < 0.001 * *Significant at p < 0.05 (Student’s t-test). Table 4 demonstrates the correlation between psoriasis severity (PASI score) and key metabolic parameters using Pearson’s correlation coefficients (r). The results reveal a consistent and statistically significant relationship between increasing psoriasis severity and worsening metabolic profile. Specifically, the PASI score showed a moderate positive correlation with BMI (r = 0.38, p < 0.001) and waist circumference (r = 0.41, p < 0.001), indicating that patients with more severe psoriasis tend to have greater overall and central adiposity. Similarly, triglyceride levels exhibited a significant positive correlation with PASI (r = 0.33, p < 0.001), while fasting plasma glucose (r = 0.28, p = 0.004) and systolic blood pressure (r = 0.23, p = 0.011) also increased proportionally with psoriasis severity. In contrast, HDL-cholesterol showed a significant negative correlation with PASI (r = −0.35, p < 0.001), suggesting that as disease severity increases, protective lipid levels decline, contributing to an atherogenic metabolic profile. Table 4. Correlation of PASI Score with Metabolic Parameters (Pearson’s r) Parameter Correlation coefficient (r) p-value BMI (kg/m²) 0.38 < 0.001 * Waist circumference (cm) 0.41 < 0.001 * Triglycerides (mg/dL) 0.33 < 0.001 * HDL-C (mg/dL) −0.35 < 0.001 * Fasting plasma glucose (mg/dL) 0.28 0.004 * Systolic BP (mmHg) 0.23 0.011 * *Statistically significant correlations at p < 0.05. Figure 1 depicts the correlation between psoriasis severity (PASI score) and two key metabolic parameters—body mass index (BMI) and serum triglyceride levels—in psoriasis patients. In the left panel, a positive linear correlation is observed between PASI score and BMI (r = 0.46, p < 0.001), indicating that patients with more severe psoriasis tend to have higher body mass index values. This suggests that obesity and psoriasis severity are interlinked, possibly due to shared inflammatory pathways involving adipokines and cytokines. In the right panel, serum triglyceride levels also show a significant positive correlation with PASI score (r = 0.42, p < 0.001). As psoriasis severity increases, triglyceride levels rise correspondingly, reflecting underlying dyslipidaemia associated with systemic inflammation. A disease duration exceeding 5 years was also a significant determinant (aOR = 2.36; 95% CI 1.22–4.55; p = 0.011), suggesting a cumulative metabolic risk with chronic psoriasis. Similarly, moderate-to-severe disease (PASI > 10) was independently associated with a 2.7-fold increased likelihood of MetS (aOR = 2.71; 95% CI 1.42–5.15; p = 0.002). Conversely, male sex, smoking, and alcohol use did not show significant associations in the adjusted model (p > 0.05). Model diagnostics indicated a good fit (Hosmer–Lemeshow χ² = 6.21; p = 0.62), and the overall model explained 34% of the variance (Nagelkerke R² = 0.34) Table 5. Multivariable Logistic Regression for Predictors of Metabolic Syndrome (n = 216) Predictor Variable Adjusted OR (95% CI) p-value Age > 40 years 2.14 (1.12–4.08) 0.021 * Male sex 1.28 (0.67–2.42) 0.45 BMI ≥ 25 kg/m² 3.97 (2.09–7.54) < 0.001 * Duration > 5 years 2.36 (1.22–4.55) 0.011 * PASI > 10 (moderate-severe) 2.71 (1.42–5.15) 0.002 * Current smoker 1.53 (0.79–2.96) 0.21 Alcohol use 1.41 (0.72–2.77) 0.31 Hosmer–Lemeshow χ² = 6.21; p = 0.62 (good fit). Nagelkerke R² = 0.34, *Statistically significant correlations at p < 0.05. Figure 2 presents the multivariate logistic regression analysis showing independent predictors of metabolic syndrome (MetS) among psoriasis patients. The forest plot displays adjusted odds ratios (aORs) with 95% confidence intervals (CIs) on a logarithmic scale for key demographic and disease-related variables. The model identified age > 40 years (aOR ≈ 2.1, 95% CI 1.1–4.1), BMI ≥ 25 kg/m² (aOR ≈ 4.0, 95% CI 2.1–7.5), disease duration > 5 years (aOR ≈ 2.4, 95% CI 1.2–4.5), and PASI > 10 (aOR ≈ 2.7, 95% CI 1.4–5.1) as statistically significant predictors of MetS. Conversely, male sex, smoking, and alcohol use were not found to have a significant association after adjusting for confounders. The figure demonstrates that higher age, elevated BMI, longer disease duration, and greater psoriasis severity substantially increase the likelihood of metabolic syndrome, reinforcing the systemic inflammatory nature of psoriasis and its strong interplay with metabolic risk factors. Distribution of Metabolic Syndrome Across Psoriasis Severity Figure 3 illustrates the upward trend of MetS prevalence with increasing PASI categories. The distribution of metabolic syndrome (MetS) across varying grades of psoriasis severity demonstrated a significant positive association. Among patients with mild psoriasis (PASI < 10), the prevalence of MetS was 24.2% (22 of 91 patients). In contrast, the prevalence increased to 46.2% (36 of 78 patients) in those with moderate disease (PASI 10–20), and further rose to 63.8% (30 of 47 patients) among individuals with severe psoriasis (PASI > 20). This stepwise escalation in MetS prevalence with increasing disease severity was statistically significant (χ² = 22.15; p < 0.001), indicating that patients with more extensive and severe psoriatic involvement are considerably more likely to exhibit metabolic abnormalities.

In our study of 216 psoriasis patients attending a tertiary care centre in South India, we observed a metabolic syndrome (MetS) prevalence of 40.7%. This finding aligns with global evidence indicating that psoriasis patients carry a significantly higher burden of MetS compared to the general population. In a landmark U.S. cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES, 2003–2006), Love et al. reported that 40% of psoriasis patients met criteria for MetS, compared with 23% of controls (adjusted OR = 1.96; 95% CI 1.62–2.37) [5]. Similarly, a large meta-analysis of 63 observational studies by Armstrong et al. demonstrated that psoriasis patients had more than twice the odds of having MetS compared with non-psoriatic individuals (pooled OR = 2.26; 95% CI 1.70–3.01) [18]. Another Meta-Analysis by Buquicchio et al. reinforced this association, highlighting that chronic systemic inflammation in psoriasis contributes to metabolic dysfunction through shared inflammatory pathways [19]. Indian data also support a high coexistence of MetS in psoriasis. Specifically in South India, a hospital-based study by Madanagobalane & Anandan reported MetS prevalence of ~44.1 % in psoriasis patients vs. 30 % in controls, using South Asian–modified criteria [8]. Another study in a coastal South Indian population observed ~60 % among psoriasis patients, although no statistically significant difference vis-à-vis controls was found—likely due to sample size limitations [7]. Our finding of 40.7 % therefore lies within the established range seen in both international and Indian data, supporting the external validity of our results. In our cohort the most frequent MetS components were low HDL-cholesterol (61.6 %) and abdominal obesity (56.5 %), followed by elevated triglycerides (45.8 %), hypertension (42.6 %) and impaired fasting glucose (31.0 %). This pattern echoes earlier observations: in the U.S. NHANES-based analysis the most common component among psoriasis patients was abdominal obesity (~63 %), followed by hypertriglyceridaemia and low HDL-C [5]. Another Indian report found elevated triglycerides, hypertension and low HDL-C to be disproportionately represented in psoriasis-associated MetS [7]. The high prevalence of dyslipidaemia (especially low HDL-C) and central adiposity in our cohort underlines the need for targeted lipid and obesity management in psoriasis care. We found that psoriasis patients with metabolic syndrome (MetS) were significantly older, had longer disease duration, higher BMI, and higher mean PASI scores compared to those without MetS. In multivariable logistic regression, age > 40 years, BMI ≥ 25 kg/m², disease duration > 5 years, and PASI > 10 emerged as independent predictors of MetS, supporting a “dose–response” relationship in which increasing psoriasis severity and chronicity escalate cardiometabolic risk. This is consistent with earlier evidence: for example, a population-based U.S. study found a 40% prevalence of MetS among psoriasis patients vs 23% in controls (adjusted OR 1.96) [5]; and another meta-analysis reported that the odds of MetS in psoriasis increased with greater disease burden [18]. However, some studies in India reported no statistically significant association between psoriasis duration/severity and MetS—for instance, Madanagobalane & Anandan found 44.1% MetS prevalence in psoriasis but no relation with severity or duration [8]. These discrepancies likely reflect heterogeneity in study populations, definitions of MetS, and cut-offs for severity. Our findings add clarity by demonstrating that beyond the mere presence of psoriasis, the extent and duration of disease independently influence metabolic risk. The observed strong overlap between psoriasis and metabolic syndrome (MetS) is biologically plausible, as both share chronic systemic inflammation and immune–metabolic dysregulation as central pathogenic mechanisms. Psoriasis is characterized by upregulated Th17/IL-17 and TNF-α pathways, which play pivotal roles in promoting insulin resistance, endothelial dysfunction, and dyslipidaemia [20]. Elevated IL-17A and TNF-α levels in psoriasis not only drive keratinocyte hyperproliferation but also induce vascular inflammation and impair glucose homeostasis, linking cutaneous and metabolic inflammation [21]. Adipokines such as leptin, resistin, and adiponectin, along with pro-inflammatory cytokines like IL-6, secreted from visceral adipose tissue, have been shown to mediate cross-talk between adiposity and psoriatic inflammation [22,23]. Leptin and resistin act as pro-inflammatory mediators enhancing Th1/Th17 polarization, while low adiponectin levels reduce anti-inflammatory control, thereby worsening both metabolic and psoriatic pathology [24]. Emerging evidence also implicates oxidative stress, endoplasmic reticulum (ER) stress, and gut-microbiota dysbiosis as shared pathophysiologic mechanisms linking these disorders. Gut microbial imbalance and intestinal permeability can enhance systemic inflammation through lipopolysaccharide translocation, further amplifying IL-17 and TNF-α signaling [25]. From a clinical perspective, this shared inflammatory and metabolic milieu implies that effective control of psoriatic inflammation—particularly via biologic therapies targeting TNF-α or IL-17A—may favorably modulate cardiometabolic parameters. Conversely, metabolic dysfunction such as obesity and insulin resistance can exacerbate psoriatic disease activity by fueling systemic cytokine production [16]. Clinical Implications Given the relatively high prevalence of metabolic syndrome (MetS) found in our cohort, and its independent association with age, BMI, disease duration, and severity, several clinical implications can be drawn. First, routine screening for MetS components should be integrated into the regular management of psoriasis. Dermatologists and physicians should actively assess waist circumference, lipid profile, blood pressure, and fasting glucose during follow-up visits. Early detection of abdominal obesity, dyslipidaemia, or hypertension can facilitate timely interventions and help reduce the long-term cardiovascular risk burden among these patients. Second, the findings emphasize the need for multidisciplinary management. Patients presenting with both psoriasis and metabolic syndrome require coordinated care between dermatologists, endocrinologists, cardiologists, and lifestyle medicine specialists. Interventions focusing on dietary modification, regular physical activity, and weight reduction have shown measurable improvements in both psoriasis severity and metabolic outcomes. Third, treatment selection in psoriasis should consider existing metabolic comorbidities. Certain systemic agents such as cyclosporine and acitretin may exacerbate hypertension, dyslipidaemia, or hepatic dysfunction, whereas newer biologic agents targeting TNF-α, IL-17, or IL-23 may exert a more favorable cardiometabolic effect. Thus, clinicians should individualize therapy choices based on the metabolic profile and ensure appropriate monitoring for adverse metabolic effects during treatment. Finally, addressing obesity and visceral adiposity holds special relevance in the Indian population, where central obesity often manifests at lower BMI thresholds compared to Western cohorts. Lifestyle counseling and patient education should therefore form a core component of psoriasis management in Indian settings, aiming to mitigate both dermatologic and systemic disease progression. Public health relevance In India, the escalating burden of non-communicable diseases renders the psoriasis–MetS nexus particularly important. Given that psoriasis often presents in younger adults (as in our cohort: mean age 44 yrs) and the observed prevalence of MetS in this group is ~40 %, the window for preventive action is substantial. Early detection and management may avert downstream cardiovascular disease, type 2 diabetes and renal complications. As one Indian study noted, psoriasis patients under 50 yrs may already carry elevated cardiometabolic risk [3]. Integration of dermatology, NCD programmes (e.g., under the National Health Mission) and lifestyle-intervention initiatives could enhance outcomes. Strengths and limitations Strengths of our study include use of consecutive sampling in a tertiary reference centre, use of South-Asian waist-circumference cut-offs for relevance to Indian populations, and robust multivariable analysis of predictors. Limitations include the cross-sectional design (precluding causal inferences), potential referral/tertiary-centre bias (possibly over-representing moderate/severe psoriasis), and absence of a matched control group from the same population. Further, we did not assess psoriatic arthritis status or inflammatory (CRP/IL-17) biomarkers which might have added mechanistic insight. Future research directions Prospective cohort studies are needed to ascertain the temporal sequence between psoriasis onset/severity and development of MetS components, and whether aggressive psoriasis control attenuates metabolic risk. Interventional trials examining weight-loss/physical-activity programmes specifically in psoriasis populations are warranted. Additionally, inclusion of biomarker (e.g., IL-17, adipokines) and imaging (e.g., visceral fat MRI) sub-studies may illuminate mechanistic pathways. Given the Indian context, research addressing socio-economic, dietary, rural–urban and genetic modifiers of the psoriasis-MetS link would be valuable.

In conclusion, our study confirms a high burden of metabolic syndrome among psoriasis patients in South India, with dyslipidaemia and abdominal obesity being particularly prevalent. The independent association of MetS with age, BMI, duration and PASI score underscores the importance of prognostic stratification. These findings highlight the need for integrated screening and management of cardiometabolic risk in psoriasis care—particularly in the Indian context where NCD risk is rapidly increasing. Addressing this link proactively could substantially attenuate long-term cardiovascular morbidity in psoriasis populations.

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