Obesity is an important public health problem in its weight implications. According to the World Health Organization (WHO), obesity is a BMI of 30 or greater, a widely used means of measuring body weight in relation to body height. However, throughout various demographics, obesity has been increasing at an alarming rate and has been associated with increased morbidity and mortality triggered by non-communicable diseases (NCD’s) like type 2 diabetes, cardiovascular diseases, and certain cancers [1, 2]. Out of the urban population of the North India nearly 70 percent exhibited abdominal obesity which as strong correlate on metabolic syndrome and cardiovascular risks [2]. Obesity is an extremely serious, disease and its consequences to health are many and complex. Many health conditions linked to excessive fatness, such as hypertension, type 2 diabetes and cardiovascular diseases are risk factors [3]. Like in Tamil Nadu, urban populations are increasing in obesity related health issues too, and require urgent public health response [4]. Dietary and lifestyle factors aren’t the only aspects of obesity, though. Body image and perceptions of health are influenced culture in urban environments which help determine individual behavior related to diet and exercise [5]. Additionally, changes in social perception of body image and health can affect antecedently described individual attempts at regulating diet and exercise, making it important to pay attention the cultural aspects in obesity prevention. But there are essential components for addressing the obesity epidemic too: educational programs designed to change perceptions of body weight, encourage healthier lifestyle choices [6]. Obesity is a complex public health problem for which prevention and management must be multifaceted [7]. To develop effective public health strategies in order to understand the epidemiology of obesity is important through its prevalence, associated health risks, and socioeconomics determinants. This study aims to determine the prevalence of obesity and the associated health problems among adults in selected urban areas.  

This cross-sectional descriptive study was conducted in selected urban areas of Thiruvallur district, Tamil Nadu, India, over a period from April 2024 to June 2024. A total of 150 adults aged 18 years to 59 years were selected using a simple random sampling technique. Ethical approval was obtained from the Institutional Ethics Committee prior to the commencement of the study (002/03/2024/IEC/SMCH).   Inclusion Criteria   Adults aged 18–59 years Permanent residents of the selected area Individuals willing to participate and who provided written informed consent   Exclusion Criteria   Pregnant women Individuals with deformities preventing accurate height/weight measurement Critically ill patients   Sample Size Calculation The sample size was calculated using the formula. n= (Zα/2+Zβ) 2. p. (1-p) d2. Where Zα/2​=1.96 (corresponding to a 95% confidence level), Zβ​ = 0.84 (for 80% statistical power), p = 0.15 (assumed prevalence of obesity), and d = 0.082 (margin of error). Using these values, the required sample size was calculated to be 150.   Study Tools and Data Collection This cross-sectional study, data was collected at a single point in time using two primary tools: a demographic questionnaire and a BMI assessment tool. The demographic questionnaire included variables such as age group, gender, education level, income level, marital status, employment status, physical activity level, smoking status, and alcohol consumption. The BMI tool involved measuring height and weight to calculate Body Mass Index (BMI), which was then used to classify participants according to WHO obesity categories. Data collection procedure: Data for this cross-sectional study was collected using a demographic questionnaire and a BMI assessment tool. Trained investigators conducted face-to-face interviews to record participants’ details on variables such as age group, gender, education level, income, marital and employment status, physical activity, smoking, and alcohol use. Height and weight were measured using a stadiometer and digital scale, respectively, and BMI was calculated using the formula: BMI = weight (kg) / height² (m²). Participants were then classified according to WHO BMI categories.   Statistical Analysis The data were analyzed using SPSS version 26, and the results were presented in terms of frequency and percentage. Descriptive statistics were calculated for categorical variables, and the distributions of the variables were summarized to understand the socio-demographic profile and health status of the study participants.

Demographic Variables A total of 56.7 percent of the sample were between 30-49 years, of which the majority of participants in the study. There were more females (53.3%) than males. Regarding education, most people had attained secondary (33.3%) or higher secondary education (30%) and almost half fell in the middle-income group (₹10,000-₹50,000/month). Most were married (60%) and full time employed (53.3%). Over one third of participants reported a sedentary lifestyle (40%), and 60% of them had never smoked (60%) or consumed alcohol (46.7%). This overview describes the socio demographic profile of study population and may help to understand health behaviors and risks (Table, Figure- 1).   Table 1: Frequency and Percentage of Socio-Demographic Variables Demographic Variable Categories Frequency (n) Percentage Age Group 18-29 years 30 20.0 30-39 years 45 30.0 40-49 years 40 26.7 50-59 years 35 23.3 Gender Male 70 46.7 Female 80 53.3 Education Level No formal education 10 6.7 Primary school 25 16.7 Secondary school 50 33.3 Higher secondary 45 30.0 Graduate and above 20 13.3 Income Level Low (< ₹10,000/month) 40 26.7 Middle (₹10,000-₹50,000) 70 46.7 High (> ₹50,000/month) 40 26.7 Marital Status Single 40 26.7 Married 90 60.0 Widowed/Divorced 20 13.3 Employment Status Unemployed 30 20.0 Employed (Full-time) 80 53.3 Employed (Part-time) 20 13.3 Retired 15 10.0 Student 5 3.3 Physical Activity Level Sedentary 60 40.0 Moderate activity 55 36.7 Active 35 23.3 Smoking Status Never smoked 90 60.0 Former smoker 20 13.3 Current smoker 40 26.7 Alcohol Consumption Non-drinker 70 46.7 Occasional drinker 50 33.3 Regular drinker 30 20.0     Figure 1: Distribution of Socio-Demographic and Lifestyle Characteristics of Study Participants   Prevalence of Obesity The prevalence of obesity among the participants is shown in Table 2 according to their Body Mass Index (BMI). 40.0% of participants had BMI within the normal weight range (18.5–24.9 kg/m²), whereas 26.7% of participants were considered to be at an overweight (BMI 25.0–29.9 kg/m²). Of these subjects, 13.3 % were as Obese Class I (BMI 30.0 - 34.9 kg/m²), 6.7 % were as Obese Class II (BMI 35.0 - 39.9 kg/m²), and 3.3 % were as Obese Class III (BMI ≥40.0 kg/m²). Only 10.0% of the participants were underweight according to BMI < 18.5 kg/m². These findings support a finding on the prevalence of excess body weight in the sample, for nearly half of the sample (49.7%) is recorded as overweight or obese in Figure 2.   Table 2: Prevalence of Obesity among the Adults BMI Category BMI Range (kg/m²) Frequency (n) Percentage Underweight < 18.5 15 10.0 Normal Weight 18.5 - 24.9 60 40.0 Overweight 25.0 - 29.9 40 26.7 Obese (Class I) 30.0 - 34.9 20 13.3 Obese (Class II) 35.0 - 39.9 10 6.7 Obese (Class III) ≥ 40.0 5 3.3     Figure 2: BMI Classification of Study Participants   Associated Health Problems with Obesity The prevalence of health problems associated with obesity among study participants is shown in Table 3. A total sample prevalence of 34.3% for obesity, and 13.3% diabetes. Overall, 16.7% presented with hypertension and 42.9% among the obese. The participants had joint pain (10.0%), or osteoarthritis (28.6%) among them was obese. Overall, rates for cardiovascular disease, sleep apnea, high cholesterol and respiratory issues were less than 3.3 to 8.0%. However, these health issues were more common among obese individuals: 22.9 percent had cardiovascular disease, 11.4 percent had sleep apnea, 25.7 percent had high cholesterol and 14.3 percent had respiratory problems in Figure 3. This finding whether obesity is a disease deserves to be taken very seriously and address through obesity because of the increased risk of various health problems among obese persons.   The pie chart illustrates the percentage distribution of key variables collected in the study, including age groups, gender, education level, income level, marital status, employment status, physical activity level, smoking status, and alcohol consumption patterns. Each segment represents the proportion of participants within each category, providing an overview of the demographic and behavioral profile of the study population (Figure 1).   The pie chart shows the proportion of participants across different BMI categories, including underweight (10%), normal weight (40%), overweight (27%), obese class I (13%), obese class II (7%), and obese class III (3%). This distribution provides an overview of the weight status profile within the study population (Figure 2).   The pie chart illustrates the prevalence of major health conditions associated with obesity, including diabetes mellitus, hypertension, cardiovascular disease, joint pain/osteoarthritis, sleep apnea, high cholesterol, and respiratory issues. Each segment represents the proportion of participants reporting these comorbidities, providing an overview of the burden of obesity-linked health complications in the study population (Figure 3).   Table 3: Associated Health Problems with Obesity among the Adults Health Problem Frequency (n) Percentage Among Obese Individuals (n = 35) Percentage Among Obese Diabetes 20 13.3 12 34.3 Hypertension 25 16.7 15 42.9 Cardiovascular Disease 10 6.7 8 22.9 Joint Pain/Osteoarthritis 15 10.0 10 28.6 Sleep Apnea 5 3.3 4 11.4 High Cholesterol 12 8.0 9 25.7 Respiratory Issues 7 4.7 5 14.3     Figure 3: Distribution of Obesity-Related Comorbidities among Study Participants

This study’s findings are aligned with national and South Asian research, including the ICMR-INDIAB data and studies from North and South India, which report rising trends in urban obesity. The prevalence of overweight and obesity (49.7%) in this study highlights the nutrition transition occurring in urban Tamil Nadu due to sedentary lifestyles and high-calorie diets [8]. The classification of obesity among participants, with 26.7% categorized as overweight and 23.3% as obese, underscores the urgent need for public health interventions aimed at weight management and obesity prevention [9]. The breakdown of obesity classes reveals that 13.3% were classified as Class I, 6.7% as Class II, and 3.3% as Class III, indicating a spectrum of obesity severity that is associated with increased health risks [10]. Health problems associated with obesity were prevalent among participants, with 34.3% reporting obesity-related issues. Specifically, 13.3% were diagnosed with diabetes, and 16.7% with hypertension, with a significant proportion of these individuals being obese. This correlation is well-documented in the literature, which consistently shows that obesity is a major risk factor for the development of type 2 diabetes and hypertension [11]. The presence of osteoarthritis in 28.6% of participants further illustrates the musculoskeletal complications that can arise from excessive body weight, emphasizing the multifaceted health challenges posed by obesity [12]. Interestingly, while rates of cardiovascular disease, sleep apnea, high cholesterol, and respiratory issues were reported to be low overall, these conditions were significantly more prevalent in obese participants. For instance, 22.9% of obese individuals reported cardiovascular disease, and 25.7% had high cholesterol, which is consistent with findings from other studies that link obesity to increased cardiovascular morbidity [13]. The relationship between obesity and respiratory problems, with 14.3% of obese participants affected, highlights the respiratory complications that can arise from excess weight, including obstructive sleep apnea and chronic obstructive pulmonary disease (COPD) [14]. These findings collectively underscore the serious health risks associated with obesity, necessitating comprehensive public health strategies to address this growing epidemic. The evidence suggests that obesity is not merely a personal health issue but a significant public health challenge that requires coordinated efforts across various sectors, including healthcare, education, and community planning. [15] The recognition of obesity as a chronic disease by health organizations further emphasizes the need for sustained interventions aimed at prevention and management [16].   Recommendations According to the study findings, the body should inquire from nearby healthcare professionals to include lifestyle interventions in their practice to assist in weight management and improve lipid profile. The growing public health concern of obesity should be addressed by the priority of interventions directed at obesity prevention and management. Future work should also investigate the longer-term effects on weight management and lipid profiles of lifestyle interventions as a means to identify additional sustainable approaches to obesity prevention.

This study concluded a substantial prevalence of overweight and obesity among adults in selected urban areas of Thiruvallur District, with nearly half of the population exhibiting excess body weight and a significant proportion experiencing associated health problems such as diabetes, hypertension, joint pain, high cholesterol, sleep apnea, and respiratory issues. The strong link between obesity and these comorbidities underscores the urgent need for targeted community-based interventions that promote healthier lifestyles, regular physical activity, and improved nutritional awareness. The findings emphasize obesity as a growing public health concern that demands coordinated efforts from healthcare providers, public health nurses, and policymakers to implement effective prevention and management strategies. Future longitudinal studies are warranted to better understand causal relationships and to develop sustainable, evidence-based approaches for obesity reduction and health promotion in urban populations.   Limitations This study is limited by its cross-sectional design, which restricts the ability to determine causal relationships between obesity and associated health problems. The use of self-reported data introduces potential recall and response biases.