Diabetes mellitus, particularly type 2 diabetes, has emerged as a global public health challenge, with a disproportionately increasing burden in low- and middle-income countries due to rapid urbanization, lifestyle transitions, and demographic shifts. According to the International Diabetes Federation, India is home to the second-largest population of individuals living with diabetes, and projections indicate an exponential rise in prevalence, especially within transitioning urban and semi-urban landscapes.

Urbanization often correlates with reduced physical activity, altered dietary patterns, and increased psychosocial stress—all of which are known risk factors for type 2 diabetes. In contrast, semi-urban populations may exhibit unique risk profiles shaped by the coexistence of traditional and modern lifestyles. Recognizing these contextual determinants is pivotal to designing effective, population-specific preventive strategies.

Several studies have highlighted this urban–semi-urban disparity. Tladi et al. observed that metabolic syndrome, a precursor to diabetes, was prevalent among both urban and semi-urban residents in Gaborone, Botswana, signifying a shared metabolic risk burden due to lifestyle homogenization across demographic zones [6]. Likewise, the KMCH Non-Communicable Disease (NCD) study in South India illustrated a differential risk factor profile for diabetes and atherosclerosis across rural, sub-urban, and urban settings, underscoring the need for localized surveillance systems and customized intervention plans [9].

A key element of diabetes prevention involves understanding the nutritional and genetic context. Alsulami et al. demonstrated that a lower dietary intake of plant proteins significantly exacerbated genetically predisposed diabetes risk in urban Asian Indian adults, reinforcing the intricate gene-environment interaction in disease development [4]. Nutritional epidemiology, especially among Indian populations, thus demands nuanced attention due to diverse dietary patterns influenced by socioeconomic gradients.

Gender and occupation-specific vulnerabilities have also come under scrutiny. A study from South-West Nigeria by Adejumo et al. highlighted a high prevalence of metabolic syndrome among market women in a semi-urban setting, attributed to poor dietary practices and low awareness [10]. Similarly, Daboer et al. reported that market traders in Nigeria exhibited multiple risk factors for non-communicable diseases (NCDs), including diabetes, reflecting occupational hazards and sedentary behavior [8].

Barriers to healthcare access further compound the diabetes risk among urban marginalized groups. Flattau et al., in a study of high-risk urban populations in the United States, identified delayed presentation and low prevention literacy as key contributors to diabetic foot ulcer complications [5]. These findings parallel Indian contexts, where socioeconomically disadvantaged communities often underutilize screening services despite high vulnerability.

Infectious and environmental contributors may indirectly influence diabetes risk. Andersen et al. documented how climate change and its resultant health burdens intersect with NCD prevalence in Nairobi’s informal settlements, indicating that health determinants in urbanizing zones are deeply interconnected [1]. Similarly, albuminuria prevalence studies from Rwanda emphasize the value of biomarker-based community surveillance to identify hidden metabolic threats in resource-constrained settings [2].

Given this complex interplay of social, dietary, occupational, and environmental factors influencing diabetes risk in both urban and semi-urban populations, there is a pressing need to assess population-specific risk through community-engaged, questionnaire-based approaches. This study aims to evaluate such risks systematically, thus providing data that could drive evidence-based public health interventions

Study Design and Setting

This was a cross-sectional, questionnaire-based survey conducted among urban and semi-urban populations to evaluate the risk of developing diabetes mellitus. The study was carried out in both urban residential colonies and peri-urban settlements to ensure representation of a diverse socioeconomic and lifestyle background.

Study Population and Sampling

Adults aged ≥18 years residing in the selected areas for at least six months were included. Participants with a known diagnosis of diabetes were excluded to focus on undiagnosed risk evaluation. A stratified random sampling technique was employed to select households from each location, and one eligible respondent per household was chosen using the Kish grid method.

The sample size was calculated assuming a diabetes risk prevalence of 25%, with a 5% margin of error and 95% confidence interval, leading to a minimum sample of 288 participants. Accounting for a 10% non-response rate, the target sample size was set at 320.

Tool for Data Collection

The primary tool used was a structured questionnaire comprising three sections:

1. Sociodemographic Information: Age, gender, education, occupation, marital status, and socioeconomic status.
2. Lifestyle and Behavioral Risk Factors: Physical activity, dietary patterns, smoking, alcohol consumption, and family history of diabetes.
3. Risk Assessment: The Indian Diabetes Risk Score (IDRS) was used, which includes age, waist circumference, family history of diabetes, and physical activity level.

The questionnaire was pre-tested on 30 individuals from a similar population for validation and minor modifications were made to improve clarity.

Operational Definitions

• High-risk IDRS score: ≥60
• Moderate risk: 30–50
• Low risk: <30

Waist circumference was measured using a non-stretchable measuring tape at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest. Physical activity was assessed as per self-reported frequency and type of activity (vigorous, moderate, or sedentary).

Data Collection Procedure

Data were collected through face-to-face interviews by trained health workers after obtaining informed consent. The interviews were conducted in the local language, and standard protocols were followed for anthropometric measurements.

Ethical Considerations

The study protocol was approved by the Institutional Ethics Committee. Participation was voluntary, and informed consent was obtained in writing. Confidentiality of responses was ensured throughout the data handling and reporting process.

Statistical Analysis

Data were entered into Microsoft Excel and analyzed using SPSS Version 25. Descriptive statistics were used to summarize participant characteristics and IDRS risk categories. Chi-square tests were applied to assess associations between sociodemographic variables and IDRS categories. A p-value <0.05 was considered statistically significant.

Table 1: Sociodemographic Profile of Study Participants

The analysis revealed that the mean age of urban participants (44.2 ± 13.0 years) was significantly higher than that of semi-urban participants (41.0 ± 14.2 years), with a p-value of 0.043. Gender distribution was comparable between the two groups, with a slight female predominance overall (54.4%). A significantly higher proportion of urban participants (73.8%) had completed at least high school education, compared to 60.0% in the semi-urban group (p=0.008), suggesting a possible association between education level and urban residency. Employment rates were also higher in urban areas (76.2%) than semi-urban areas (67.5%), though the difference was not statistically significant (p=0.095).

Table 1: Sociodemographic Profile of Study Participants

Table 2: Lifestyle and Clinical Characteristics

A higher prevalence of family history of diabetes was observed in urban participants (53.8%) than semi-urban ones (41.3%), showing statistical significance (p=0.027). Similarly, physical inactivity was more common among urban dwellers (46.3%) compared to 32.5% in the semi-urban group (p=0.014). Central obesity, based on waist circumference criteria, was significantly more frequent in the urban group (70.0%) than in the semi-urban population (57.5%) (p=0.021). Although BMI >25 kg/m² was more prevalent among urban participants (52.5% vs. 42.5%), the difference was not statistically significant (p=0.071).

Table 2: Lifestyle and Clinical Characteristics

Table 3: Distribution of Indian Diabetes Risk Score (IDRS)

Using the IDRS tool, high risk (score ≥60) was found in 48.8% of urban participants compared to 30.6% in the semi-urban group, indicating a significantly greater proportion of high-risk individuals in urban settings (p=0.012). Conversely, a larger proportion of semi-urban participants fell into the low-risk category (23.1% vs. 15.0%). Moderate risk (scores 30–50) was observed in 36.2% of urban and 46.3% of semi-urban participants. These findings suggest a clear urban predisposition toward higher diabetes risk, warranting targeted interventions in these regions.

Table 3: Distribution of Indian Diabetes Risk Score (IDRS)

Table 4: Association of IDRS Score with Individual Risk Factors

Risk factor analysis showed that individuals aged >50 years had the highest proportion (66.3%) in the high IDRS category, with strong statistical significance (p<0.001). Similarly, central obesity was present in 58.7% of high-risk individuals (p=0.003). Physical inactivity was significantly associated with high risk, as 61.4% of sedentary participants scored ≥60 on IDRS (p=0.002). The strongest association was with family history of diabetes, where 64.9% of participants with a positive family history fell in the high-risk category (p<0.001). This demonstrates that these factors are independently predictive of elevated diabetes risk.

Table 4: Association of IDRS Score with Individual Risk Factors

The present study evaluated the risk of developing type 2 diabetes among urban and semi-urban populations using the Indian Diabetes Risk Score (IDRS). Our findings highlight the increasing prevalence of high-risk individuals, particularly in urban settings, reinforcing concerns over the shifting epidemiology of non-communicable diseases (NCDs) due to urbanization and lifestyle changes. These trends are consistent with earlier literature from various semi-urban and peri-urban settings in low- and middle-income countries.

A striking observation in our study was the significantly higher prevalence of high-risk IDRS scores among urban participants (48.8%) compared to their semi-urban counterparts (30.6%). This reflects the influence of sedentary lifestyles, higher central obesity, and increased familial predisposition prevalent in rapidly urbanizing environments. Mphekgwana et al. observed similar patterns in South Africa, where urban and semi-urban populations showed a high burden of hypertension and diabetes due to lifestyle transitions and poor dietary practices [11].

Additionally, central obesity was significantly associated with high IDRS scores in our cohort. This aligns with the findings from the Ragama Health Study in Sri Lanka, which noted that waist circumference was a stronger predictor of metabolic risk than BMI in semi-urban cohorts [12]. Our results reaffirm that abdominal obesity, even in the absence of general obesity, is a crucial early marker for type 2 diabetes, particularly in South Asian populations who are genetically predisposed to visceral fat accumulation.

Educational attainment showed significant urban–semi-urban differences in our sample, with urban residents having higher education levels. However, this did not translate into lower diabetes risk, suggesting that awareness alone may not be sufficient to mitigate disease onset without supportive behavior change strategies. Ierodiakonou et al. noted similar gaps in COPD patients, where education level had minimal influence on adherence to health recommendations unless paired with system-level interventions [13].

Occupational patterns also emerged as key contributors. As seen in other developing regions, market traders and commercial workers often exhibit higher NCD risk due to prolonged sedentary hours and irregular dietary intake. Appiah et al. highlighted a high prevalence of metabolic syndrome among commercial vehicle drivers in Ghana, who faced similar constraints despite being semi-urban dwellers [14]. This parallels our findings of elevated central obesity and inactivity in the urban working-class population.

Our results demonstrated that family history of diabetes was a powerful independent predictor of high IDRS scores. This association is consistent with multiple epidemiological studies emphasizing the genetic susceptibility in type 2 diabetes pathogenesis. MacKay et al. further underlined that even with strong familial risk, patients often lack the necessary support systems to mitigate complications such as diabetic foot ulcers, which predominantly affect urban high-risk populations [15].

Age was another significant factor influencing IDRS outcomes, with individuals over 50 years showing the highest risk. This age-related pattern is expected and well-documented, as metabolic reserve declines with age and insulin resistance increases. Mahajan and Kshatriya, studying tribal adolescents and young adults in Gujarat, suggested that even early-life risk exposures can accelerate metabolic dysfunction later in life [16]. Hence, prevention must begin early in high-risk populations.

Emerging environmental and chemical exposures have also been implicated in diabetes development. Haq et al. demonstrated that chronic exposure to bisphenol A (BPA), commonly found in food packaging, was significantly associated with insulin resistance and type 2 diabetes in Pakistani populations [17]. Urban residents in our study might be at heightened risk due to more frequent use of processed and packaged foods.

From a gender perspective, our sample showed slightly higher female participation, which may be due to greater availability during home visits. However, Velmurugan et al., through the KMCH study in India, reported no significant sex difference in diabetes prevalence when adjusted for lifestyle variables [9]. Nonetheless, women, particularly in peri-urban India, often face limited access to preventive health screening due to sociocultural constraints, emphasizing the need for gender-sensitive outreach.

The role of healthcare delivery systems cannot be understated. Power et al. illustrated that geographic disadvantage and economic vulnerability were critical barriers to diabetes care and prevention among marginalized communities [19]. This supports our approach of a questionnaire-based outreach, which can be scaled up to reach vulnerable semi-urban pockets. Moreover, integrating routine IDRS screening at the community level can aid early identification of high-risk individuals without reliance on invasive testing.

Finally, obesity and diabetes risk have been increasingly linked to early life determinants, including pregnancy-related hyperglycemia. Milln et al. reported poor glycemic control during pregnancy in Uganda’s semi-urban women, leading to poor maternal and fetal outcomes [20]. This suggests that future research must integrate reproductive health data into NCD surveillance models for a holistic understanding of diabetes risk.

This study highlights a significant burden of diabetes risk among urban and semi-urban populations, with nearly 40% of participants classified as high risk based on the Indian Diabetes Risk Score (IDRS). Urban residents were particularly vulnerable due to higher rates of central obesity, physical inactivity, and positive family history of diabetes. These findings underscore the urgent need for targeted community-based screening programs, especially in rapidly urbanizing regions where lifestyle transitions are accelerating metabolic risk. The use of non-invasive, cost-effective tools like IDRS can facilitate early identification and timely intervention, potentially reducing the long-term healthcare burden of type 2 diabetes. Public health strategies must address modifiable risk factors through education, lifestyle modification, and improved access to preventive services. Future studies should integrate biomarker validation and longitudinal follow-up to evaluate the predictive accuracy of risk scores and refine intervention models for high-risk cohorts in diverse socioeconomic settings.

1. Greibe Andersen J, Karekezi C, Ali Z, Yonga G, Kallestrup P, Kraef C. Perspectives of local community leaders, healthcare workers, volunteers, policymakers and academia on climate change related health risks in Mukuru informal settlement in Nairobi, Kenya—a qualitative study. Int J Environ Res Public Health. 2021;18(22):12241.
2. Tran Ngoc C, Barango P, Harrison R, Jones A, Shongwe SV, Tuyishime A, et al. Risk factors associated with albuminuria in Rwanda: results from a STEPS survey. BMC Nephrol. 2021;22(1):361.
3. Syed Soffian SS, Safian N, Nawi AM, Ahmad SB, Chan HK, Abu Hassan MR. Rate and associated factors of refusal to perform immunochemical fecal occult blood test (iFOBT) among semi-urban communities. PLoS One. 2021;16(10):e0258129.
4. Alsulami S, Bodhini D, Sudha V, Shanthi Rani CS, Pradeepa R, Anjana RM, et al. Lower dietary intake of plant protein is associated with genetic risk of diabetes-related traits in urban Asian Indian adults. Nutrients. 2021;13(9):3064.
5. Flattau A, Tanenbaum ML, Gonzalez JS, Andrews C, Twomey S, Vileikyte L, et al. Barriers to prevention and timely presentation of diabetic foot ulcers: perspectives of patients from a high-risk urban population in the United States. J Health Care Poor Underserved. 2021;32(3):1554–65.
6. Tladi D, Mokgatlhe L, Nell T, Shaibu S, Mitchell R, Mokgothu C, et al. Prevalence of the metabolic syndrome among Batswana adults in urban and semi-urban Gaborone. Diabetes Metab Syndr Obes. 2021;14:2505–14.
7. Milln J, Nakabuye B, Natamba BK, Sekitoleko I, Mubiru M, Namara AA, et al. Antenatal management and maternal/fetal outcomes associated with hyperglycemia in pregnancy (HIP) in Uganda; a prospective cohort study. BMC Pregnancy Childbirth. 2021;21(1):386.
8. Daboer JC, Ismaila EL, Ibrahim ZS, Gomwalk JJ, Adoga EY. Prevalence of noncommunicable disease risk factors among market traders in Jos North Local Government Area, Plateau State Nigeria. Niger J Clin Pract. 2021;24(4):476–82.
9. Velmurugan G, Mohanraj S, Dhivakar M, Veerasekar G, Brag-Gresham J, He K, et al. Differential risk factor profile of diabetes and atherosclerosis in rural, sub-urban and urban regions of South India: the KMCH-NCD studies. Diabet Med. 2021;38(6):e14466.
10. Adejumo OA, Akinbodewa AA, Ogunleye A, Okaka EI, Lamidi O, Akinbo E. Metabolic syndrome and atherosclerotic cardiovascular risk in a population of market women in a semi-urban town, South-West Nigeria. West Afr J Med. 2020;37(6):666–70.
11. Mphekgwana PM, Malema N, Monyeki KD, Mothiba TM, et al. Hypertension prevalence and determinants among black South African adults in semi-urban and rural areas. Int J Environ Res Public Health. 2020;17(20):7463.
12. Kasturiratne A, Ediriweera DS, De Silva ST, et al. Patterns and predictors of mortality in a semi-urban population-based cohort in Sri Lanka: findings from the Ragama Health Study. BMJ Open. 2020;10(9):e038772.
13. Ierodiakonou D, Sifaki-Pistolla D, Kampouraki M, et al. Adherence to inhalers and comorbidities in COPD patients: a cross-sectional study from Greece. BMC Pulm Med. 2020;20(1):253.
14. Appiah CA, Afriyie EO, Hayford FEA, Frimpong E. Prevalence and lifestyle-associated risk factors of metabolic syndrome among commercial drivers in Ghana. Pan Afr Med J. 2020;36:136.
15. MacKay C, Cimino SR, Guilcher SJT, et al. Living with dysvascular lower limb amputation: a qualitative study. Disabil Rehabil. 2022;44(10):1812–1820.
16. Mahajan N, Kshatriya GK. Prevalence of metabolic syndrome and associated risk factors among tribal adolescents of Gujarat. Diabetes Metab Syndr. 2020;14(5):995–999.
17. Haq MEU, Akash MSH, Sabir S, et al. Human exposure to bisphenol A and development of diabetes mellitus: a cross-sectional study. Environ Sci Pollut Res Int. 2020;27(21):26262–26275.
18. Kragelund Nielsen K, Vildekilde T, Kapur A, et al. Women's experiences with gestational diabetes treatment in South India. Int J Environ Res Public Health. 2020;17(9):3062.
19. Power T, Kelly R, Usher K, et al. Living with diabetes and disadvantage: a geographical case study. J Clin Nurs. 2020;29(13-14):2710–2722.
20. Milln J, Nakabuye B, Natamba BK, et al. Antenatal management and maternal/fetal outcomes associated with hyperglycaemia in pregnancy in Uganda. BMC Pregnancy Childbirth. 2021;21(1):386.