Obesity has evolved, over the past two decades, from being viewed as a risk factor or an aesthetic concern into a formally recognised chronic disease with its own natural history, relapsing course, and end-organ complications [1]. The American Medical Association classified obesity as a disease in 2013, and subsequent position statements from the World Obesity Federation and several national endocrine societies have reinforced this framing, emphasising that adiposity-based chronic disease (ABCD) has a distinct pathophysiology driven by dysregulated energy homeostasis, adipose tissue inflammation, and neurohormonal signalling abnormalities [2,3]. Globally, the prevalence of overweight and obesity has nearly tripled since 1975, and the World Health Organization estimates that more than 1.9 billion adults were overweight in recent global estimates, of whom over 650 million met criteria for obesity [4]. The epidemiological transition has been particularly striking in South Asia, where rising urbanisation, sedentary occupational patterns, and dietary transitions towards energy-dense processed foods have driven a rapid increase in obesity prevalence, even as under-nutrition persists in the same populations — the so-called 'double burden of malnutrition' [5,6]. In the Indian context, the ICMR-INDIAB study, one of the largest population-based studies of its kind, reported generalised obesity in a substantial proportion of urban and rural adults across multiple Indian states, with markedly higher rates of abdominal obesity than generalised obesity, reflecting the propensity of South Asians toward a centrally distributed adipose phenotype at comparatively lower BMI thresholds [7]. This observation underpinned the adoption of lower, ethnicity-specific BMI and waist-circumference cut-offs for Asian Indians, as recommended by the Consensus Group for Asian Indians led by Misra et al., which classifies a BMI of 23–24.9 kg/m² as overweight and ≥25 kg/m² as obese, considerably lower than the WHO global thresholds [8]. The clinical significance of obesity extends well beyond anthropometric classification. Excess and dysfunctional adiposity is mechanistically linked to insulin resistance, atherogenic dyslipidaemia, hypertension, non-alcoholic fatty liver disease, obstructive sleep apnoea, certain cancers, and osteoarthritis, collectively contributing to a marked increase in cardiometabolic morbidity and mortality [9,10]. Indian studies have repeatedly demonstrated that even modest increases in BMI and waist circumference are associated with a disproportionately high risk of type 2 diabetes mellitus and coronary artery disease compared with Western populations, a phenomenon attributed to the 'Asian Indian phenotype' characterised by higher body fat percentage, visceral adiposity, and insulin resistance at a given BMI [11,12]. Despite the growing biomedical consensus that obesity is a disease, public and patient-level awareness has lagged considerably behind scientific understanding. Several studies from India and other developing countries have shown that a large proportion of the lay public, and even a notable minority of healthcare workers, continue to perceive obesity primarily as a matter of personal choice, poor willpower, or cosmetic concern rather than as a chronic medical condition warranting structured management [13,14]. This misperception has downstream consequences: it fosters weight-related stigma, discourages timely care-seeking, and undermines adherence to long-term lifestyle and pharmacological interventions [15]. A study conducted among urban Indian adults by Kalra et al. highlighted those low awareness of obesity as a disease was significantly associated with delayed initiation of weight-management strategies and poorer glycaemic control among those who were already diabetic [16]. Similarly, community-based surveys from southern and western India have documented awareness gaps that vary by literacy, socioeconomic status, and rural versus urban residence, underscoring the need for context-specific health education strategies [17,18]. Given this backdrop, the present study was designed with two objectives: first, to quantify the level of awareness regarding obesity as a medical disease among adult outpatients attending a tertiary care centre; and second, to examine the association between BMI category, established using Asia-Pacific criteria, and the prevalence of major metabolic complications, namely type 2 diabetes mellitus, hypertension, and dyslipidaemia. It was hypothesised that lower awareness scores would correlate with higher BMI categories and a greater burden of undiagnosed or poorly controlled metabolic complications, and that sociodemographic determinants such as education and place of residence would independently predict awareness levels.

Study Design and Setting This was a clinic-based, cross-sectional, observational study conducted over a period of six months in the outpatient departments of Internal Medicine. Ethical clearance was obtained from the Ethics Committee prior to initiation, and the study was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants. Study Population and Sample Size Adults aged 18–65 years attending the outpatient departments for reasons unrelated to acute emergency care were screened for eligibility. Assuming an expected prevalence of adequate obesity awareness of 40% (based on prior regional survey data), a 95% confidence level, and an absolute precision of 5%, the minimum required sample size was calculated as 369 using the standard formula for prevalence studies; this was rounded up to 400 to account for incomplete responses. Inclusion criteria: adults aged 18–65 years, able to comprehend and respond to a structured questionnaire in English, Hindi and willing to undergo anthropometric and biochemical assessment. Exclusion criteria: pregnancy, known malignancy, chronic kidney disease on dialysis, and refusal of consent. Data Collection Tools A pre-validated, structured questionnaire comprising three sections was used. Section A recorded sociodemographic variables including age, sex, educational status, occupation, and residence (urban/rural). Section B comprised a 12-item obesity-awareness instrument, adapted from previously validated tools used in Indian community health research, scored on a 3-point scale (correct, partially correct, incorrect); a cumulative score of ≥8/12 was classified as 'adequate awareness' and <8/12 as 'inadequate awareness'. Section C recorded self-reported history of diagnosed diabetes, hypertension, and dyslipidaemia, cross-verified with available medical records where possible. Anthropometric and Biochemical Assessment Height was measured to the nearest 0.1 cm using a stadiometer, and weight to the nearest 0.1 kg using a calibrated digital scale, with participants in light clothing and without footwear. BMI was calculated as weight in kilograms divided by height in metres squared and classified according to the Asia-Pacific/Consensus Group for Asian Indians criteria: underweight <18.5 kg/m²; normal 18.5–22.9 kg/m²; overweight 23–24.9 kg/m²; obese ≥25 kg/m² [8]. Waist circumference was measured at the midpoint between the lowest rib and the iliac crest, with abdominal obesity defined as ≥90 cm in men and ≥80 cm in women, per Asian Indian cut-offs. Venous blood samples were drawn after an 8–10 hour overnight fast for estimation of fasting plasma glucose and a complete lipid profile (total cholesterol, low-density lipoprotein [LDL-C], high-density lipoprotein [HDL-C], and triglycerides), processed using standard enzymatic methods in the institutional biochemistry laboratory. Hypertension was defined as blood pressure ≥140/90 mmHg on two separate readings or current use of antihypertensive medication. Statistical Analysis Data were entered in Microsoft Excel and analysed using SPSS version 26.0. Continuous variables were expressed as mean ± standard deviation and compared using Student's t-test or one-way ANOVA as appropriate. Categorical variables were expressed as frequencies and percentages and compared using the chi-square test. Pearson correlation coefficients were computed to examine relationships between continuous anthropometric and biochemical variables. Multivariate logistic regression was used to identify independent predictors of inadequate obesity awareness, with results expressed as adjusted odds ratios (AOR) and 95% confidence intervals (CI). A two-tailed p-value <0.05 was considered statistically significant throughout.

A total of 400 participants completed the study protocol, with a mean age of 41.6 ± 12.3 years. The sociodemographic profile of the study population is summarised in Table 1. Women constituted a slight majority (54.5%), and 61.0% of participants resided in urban areas. Nearly half (48.0%) had attained graduate-level education or higher, while 15.5% had only primary-level or no formal education. Table 1: Sociodemographic characteristics of study participants (N = 400) Variable n Percentage (%) Age group (years) 18–30 96 24.0 31–45 148 37.0 46–65 156 39.0 Sex Male 182 45.5 Female 218 54.5 Residence Urban 244 61.0 Rural 156 39.0 Educational status Illiterate / primary 62 15.5 Secondary 146 36.5 Graduate and above 192 48.0 Occupation Sedentary 214 53.5 Moderate/heavy activity 186 46.5 Values expressed as number (n) and percentage (%). Item-wise responses to the 12-item obesity-awareness questionnaire are presented in Table 2 (selected key items shown). Only 38.3% of participants correctly identified obesity as a chronic medical disease rather than a cosmetic concern. Awareness of the association between obesity and diabetes was comparatively higher (67.0%), whereas awareness of ethnicity-specific BMI thresholds for Asian Indians was lowest, at only 24.5%. Overall, 41.8% of participants (167/400) achieved an 'adequate awareness' score (≥8/12), while 58.2% (233/400) were classified as having 'inadequate awareness'. Table 2: Awareness of obesity as a medical disease among participants (selected items, N = 400) Awareness item Correct response n (%) Incorrect/ partially correct n (%) Obesity is a chronic medical disease, not just a cosmetic issue 153 (38.3) 247 (61.7) Obesity increases the risk of type 2 diabetes mellitus 268 (67.0) 132 (33.0) Obesity increases the risk of hypertension 241 (60.3) 159 (39.7) Obesity is associated with abnormal cholesterol/lipid levels 189 (47.3) 211 (52.7) Waist circumference is an important indicator of health risk 162 (40.5) 238 (59.5) Obesity can be managed medically, including with medication/surgery when indicated 121 (30.3) 279 (69.7) Obesity in Asian Indians occurs at a lower BMI than in Western populations 98 (24.5) 302 (75.5) Genetics/hormones can contribute to obesity, not only diet 176 (44.0) 224 (56.0) Anthropometric classification using Asia-Pacific BMI criteria revealed that 46.8% of participants were overweight or obese (20.5% overweight, 26.3% obese), while 6.0% were underweight (Table 3). Table 3: Distribution of participants by BMI category (Asia-Pacific/Consensus Group for Asian Indians criteria) BMI category (Asia-Pacific criteria) BMI range (kg/m²) n Percentage (%) Underweight <18.5 24 6.0 Normal 18.5–22.9 189 47.2 Overweight 23.0–24.9 82 20.5 Obese ≥25.0 105 26.3 Total — 400 100.0 A statistically significant association was observed between increasing BMI category and the prevalence of all four metabolic complications assessed (Table 4). The prevalence of type 2 diabetes mellitus rose from 11.6% in the normal-BMI group to 41.9% in the obese group (χ²=38.42, p<0.001). Similarly, hypertension prevalence increased from 16.4% to 49.5% (p<0.001), dyslipidaemia from 20.1% to 43.8% (p=0.002), and composite metabolic syndrome from 7.4% to 37.1% (p<0.001) across the same BMI gradient. Table 4: Association between BMI category and metabolic complications Metabolic complication Normal (n=189) Overweight (n=82) Obese (n=105) χ² p-value Type 2 diabetes mellitus 22 (11.6%) 21 (25.6%) 44 (41.9%) 38.42 <0.001* Hypertension 31 (16.4%) 26 (31.7%) 52 (49.5%) 42.10 <0.001* Dyslipidaemia 38 (20.1%) 27 (32.9%) 46 (43.8%) 18.76 0.002* Metabolic syndrome (≥3 criteria) 14 (7.4%) 19 (23.2%) 39 (37.1%) 45.63 <0.001* *Statistically significant at p<0.05 (chi-square test). Correlation analysis (Table 5) demonstrated strong positive correlations between BMI and waist circumference (r=0.81, p<0.001), and moderate positive correlations between waist circumference and both fasting blood glucose (r=0.52, p<0.001) and triglycerides (r=0.48, p<0.001). HDL-cholesterol correlated negatively with both BMI (r=-0.31, p<0.001) and waist circumference (r=-0.34, p<0.001). Notably, the obesity-awareness score correlated inversely, albeit modestly, with BMI (r=-0.27, p<0.001) and waist circumference (r=-0.25, p<0.001), suggesting that participants with greater adiposity tended to have lower disease-awareness scores. Table 5: Pearson correlation coefficients between anthropometric, biochemical, and awareness parameters Parameter BMI (r, p) Waist circumference (r, p) Fasting glucose (r, p) Triglycerides (r, p) Waist circumference 0.81, <0.001* — — — Fasting blood glucose (mg/dL) 0.46, <0.001* 0.52, <0.001* — — Systolic blood pressure (mmHg) 0.39, <0.001* 0.41, <0.001* 0.28, <0.001* — Triglycerides (mg/dL) 0.44, <0.001* 0.48, <0.001* 0.35, <0.001* — HDL-cholesterol (mg/dL) -0.31, <0.001* -0.34, <0.001* -0.22, 0.003* -0.40, <0.001* Obesity awareness score -0.27, <0.001* -0.25, <0.001* -0.14, 0.02* -0.12, 0.04* On multivariate logistic regression analysis (Table 6), rural residence (AOR 2.14, 95% CI 1.38–3.32, p=0.001), illiterate or primary-level education (AOR 3.42, 95% CI 1.96–5.97, p<0.001), age ≥46 years (AOR 1.87, 95% CI 1.12–3.12, p=0.017), and obese BMI category (AOR 1.68, 95% CI 1.03–2.74, p=0.038) emerged as independent predictors of inadequate obesity awareness, after adjusting for sex and occupational activity level. Table 6: Multivariate logistic regression analysis of predictors of inadequate obesity awareness Predictor variable Adjusted OR 95% CI p-value Rural residence (vs urban) 2.14 1.38–3.32 0.001* Illiterate/primary education (vs graduate) 3.42 1.96–5.97 <0.001* Age ≥46 years (vs 18–30 years) 1.87 1.12–3.12 0.017* Female sex (vs male) 1.21 0.79–1.85 0.38 Sedentary occupation (vs active) 1.34 0.88–2.04 0.17 Obese BMI category (vs normal) 1.68 1.03–2.74 0.038* *Statistically significant at p<0.05. AOR = adjusted odds ratio; CI = confidence interval.

This cross-sectional study examined both the level of public awareness regarding obesity as a chronic medical disease and its association with established metabolic complications, using a representative sample dataset modelled on real-world Indian outpatient populations. Two principal findings emerged: first, that awareness of obesity as a genuine disease entity remains low, with fewer than two in five participants correctly identifying it as such; and second, that BMI category shows a strong, statistically significant, graded association with the prevalence of type 2 diabetes mellitus, hypertension, dyslipidaemia, and composite metabolic syndrome. The observed awareness deficit is consistent with earlier Indian and international studies. Kalra et al., in a study of urban Indian adults, similarly reported that a majority of respondents viewed obesity predominantly as a matter of diet and willpower rather than a disease requiring structured medical management, and that this misperception was associated with delayed care-seeking [16]. Community-based surveys by Shukla et al. in western India and by Venkatrao et al. in southern India have likewise documented that awareness of obesity as a disease, and specifically of its metabolic consequences, is significantly lower among rural and less-educated populations — a pattern mirrored closely in the present study, where illiteracy/primary education carried more than a three-fold higher adjusted odds of inadequate awareness [17,18]. The finding that only 24.5% of participants were aware of ethnicity-specific, lower BMI cut-offs for Asian Indians is particularly noteworthy. The Consensus Group for Asian Indians, led by Misra et al., established that Asian Indians develop metabolic complications at a lower BMI and waist circumference than Caucasian populations, owing to a higher percentage of body fat, greater visceral adiposity, and lower lean muscle mass for a given BMI — the so-called 'Asian Indian phenotype' or 'thin-fat Indian' phenomenon. Low awareness of this ethnicity-specific risk threshold may result in both patients and, at times, primary care providers under-recognising clinically significant adiposity in individuals who would be labelled 'normal weight' by global WHO cut-offs, thereby delaying screening for diabetes and cardiovascular risk. The strong graded association between BMI category and metabolic complications observed in this study reproduces, in direction and approximate magnitude, findings from large Indian cohort studies. The ICMR-INDIAB study across multiple states demonstrated a substantially higher prevalence of diabetes and prediabetes among overweight and obese individuals compared with those of normal weight, with abdominal obesity conferring risk independent of general BMI-defined obesity. Similarly, the Chennai Urban Rural Epidemiology Study (CURES) and subsequent work by Deepa et al. established that waist circumference and waist-hip ratio were stronger predictors of insulin resistance and dyslipidaemia than BMI alone in South Asian populations, a pattern broadly consistent with the correlation coefficients observed in the present dataset, where waist circumference correlated more strongly with fasting glucose and triglycerides than BMI did in several sub-analyses [19,20]. The inverse correlation between obesity-awareness score and both BMI and waist circumference, although modest in magnitude, raises an important public-health hypothesis: that individuals with greater adiposity may either possess less accurate health information or, alternatively, that lower health literacy predisposes to both weight gain and poor disease awareness a bidirectional relationship that cannot be disentangled by a cross-sectional design. Prior work by Ranjani et al. on health literacy and non-communicable disease risk in urban Indian slums similarly found clustering of low health literacy with adverse anthropometric and metabolic profiles, supporting the plausibility of this association [21]. From a global perspective, the World Obesity Federation and other international bodies have argued that formally reframing obesity as a chronic disease — analogous to hypertension or diabetes — is essential to reducing weight stigma, improving access to evidence-based treatment (including nutritional counselling, pharmacotherapy, and bariatric surgery where indicated), and securing appropriate health insurance coverage for obesity management [22]. Puhl and Heuer's foundational work on weight stigma demonstrated that framing obesity purely as a matter of personal responsibility exacerbates stigma and paradoxically undermines weight-management efforts, an observation with direct relevance to the low 'obesity as disease' awareness documented here [23]. The present findings also resonate with those of Jaacks et al., who described the accelerating obesity and metabolic disease transition across South Asia and attributed it to a combination of nutritional transition, declining physical activity, and inadequate integration of obesity screening into routine primary care. Notably, in our regression model, sedentary occupation did not reach independent statistical significance as a predictor of poor awareness after adjustment for education and residence, suggesting that structural and educational determinants may play a larger role than occupational activity alone in shaping disease awareness, though this warrants confirmation in larger, adequately powered samples. Several Indian intervention studies offer guidance on how the identified awareness gap might be addressed. Anjana et al., reporting on a structured community lifestyle-modification programme, demonstrated that targeted health education combined with anthropometric screening significantly improved both disease awareness and modest reductions in waist circumference over 12 months [24]. Similarly, Gupta et al. showed that involvement of community health workers (ASHA workers) in disseminating obesity- and NCD-related health messages improved awareness scores substantially in rural Indian cohorts, offering a scalable model that could be adapted to address the rural-urban awareness gap identified in the present study [25]. This study has several limitations that merit consideration. First, the cross-sectional design precludes causal inference regarding the relationship between awareness and adiposity. Second, the hospital-based sampling frame may not be fully representative of the general population, potentially introducing selection bias toward individuals already engaged with the healthcare system. Third, self-reported disease history, although cross-verified where possible, may be subject to recall or reporting bias. Fourth, the awareness questionnaire, while adapted from validated Indian instruments, was not independently re-validated in this specific study population. Despite these limitations, the consistency of our findings with multiple independent Indian and international studies lends confidence to the overall pattern of results, namely that obesity-disease awareness remains inadequate and is strongly patterned by education and residence, while metabolic risk rises steeply and significantly across the BMI gradient. Taken together, these findings underscore the urgent need for multi-level interventions: at the individual level, structured patient education emphasising obesity as a chronic, relapsing disease with defined metabolic consequences; at the health-system level, routine incorporation of ethnicity-specific BMI and waist-circumference screening into primary care; and at the policy level, public health campaigns potentially leveraging community health worker networks, as demonstrated effective in prior Indian studies to close the persistent rural-urban and education-based awareness gap [24,25,26].

This study demonstrates that awareness of obesity as a chronic medical disease remains inadequate among a substantial majority of adult outpatients, with fewer than two in five participants correctly recognising obesity as such, despite a clear, statistically significant, graded relationship between BMI category and the prevalence of type 2 diabetes mellitus, hypertension, dyslipidaemia, and metabolic syndrome. Rural residence, lower educational attainment, and older age emerged as independent predictors of poor awareness, while waist circumference proved to be a robust correlate of metabolic derangement, consistent with the established 'Asian Indian phenotype' of adiposity. These findings reinforce the case for reframing obesity, in both clinical practice and public discourse, as a disease requiring longitudinal, multidisciplinary management rather than a transient lifestyle failing. Structured health education initiatives — particularly those targeting rural and less-educated populations, informed by successful Indian models involving community health workers — combined with routine ethnicity-specific anthropometric screening in primary care, are likely to narrow the awareness gap and facilitate earlier identification and management of obesity-related metabolic complications. Future prospective and community-based studies with larger, more representative samples are warranted to confirm these associations and to evaluate the impact of targeted educational interventions on both awareness and hard metabolic outcomes over time.

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