Cardiovascular diseases are among the leading causes of morbidity and mortality globally. Their increasing burden in low- and middle-income countries has resulted in substantial health-system and socioeconomic consequences. The development of atherosclerotic cardiovascular disease is multifactorial. Important modifiable determinants include elevated blood pressure, diabetes mellitus, abnormal lipid levels, tobacco consumption, unhealthy dietary practices, physical inactivity and excess adiposity. These risk factors frequently cluster within individuals, resulting in a substantially higher cumulative cardiovascular risk.1,2 Primary health-care facilities are ideally positioned for cardiovascular disease prevention because they provide regular contact with apparently healthy as well as symptomatic adults. Screening for cardiovascular risk factors at this level can identify individuals who may otherwise remain undiagnosed. Traditional risk-factor assessment, however, does not always adequately communicate an individual's overall probability of developing cardiovascular disease. Composite cardiovascular risk prediction tools therefore provide an opportunity to integrate multiple risk factors into an estimate of absolute risk. The 2019 ACC/AHA primary prevention guideline recommends assessment of traditional cardiovascular risk factors and estimation of 10-year ASCVD risk in adults aged 40–75 years being evaluated for primary prevention. The Pooled Cohort Equations classify individuals into low, borderline, intermediate and high-risk categories.3–5 The guideline defines low 10-year risk as <5%, borderline risk as 5% to <7.5%, intermediate risk as 7.5% to <20%, and high risk as ≥20%.3–5 Importantly, the PCE has limitations when applied to populations outside the United States, and the ACC/AHA guideline specifically notes that it may overestimate or underestimate risk in non-US populations. This makes evaluation of its application in Indian primary-care settings particularly relevant. India has a large and rapidly changing population with increasing prevalence of hypertension, diabetes, obesity, tobacco use and other cardiovascular risk factors. Early identification of high-risk individuals in primary care may therefore represent an important strategy for prevention.6,7 Despite this, cardiovascular risk assessment in routine primary health-care settings remains suboptimal in many settings. There is consequently a need to assess the burden of risk factors and the distribution of estimated cardiovascular risk among adults attending primary-care facilities. The study was conducted to assess cardiovascular risk factors and estimate 10-year ASCVD risk among adults attending an urban primary health-care facility. The rationale was based on the fact that cardiovascular risk factors such as hypertension, diabetes mellitus, and dyslipidaemia were often asymptomatic and could remain undiagnosed until cardiovascular complications occurred. These risk factors also frequently clustered, with individuals having multiple risk factors such as hypertension, obesity, diabetes, tobacco use, and dyslipidaemia, thereby increasing their overall cardiovascular risk. The urban primary health-care setting provided an opportunity for early identification, counselling, and preventive management before the occurrence of major cardiovascular events such as myocardial infarction or stroke. In accordance with ACC/AHA guidance, cardiovascular risk estimation was used as part of the clinician–patient discussion to guide the intensity of preventive interventions rather than as the sole determinant of treatment. The research question addressed the prevalence of cardiovascular risk factors and the distribution of estimated 10-year ASCVD risk among adults attending the facility. The primary objective was to estimate 10-year ASCVD risk among eligible adults, while the secondary objectives were to determine the prevalence of major cardiovascular risk factors; assess their association with sociodemographic characteristics; assess the association between individual cardiovascular risk factors and estimated 10-year ASCVD risk; determine the proportion of participants in low, borderline, intermediate, and high 10-year ASCVD risk categories; and assess the clustering of cardiovascular risk factors

Study Design and Setting The study was a facility-based cross-sectional study conducted in an urban primary health-care facility attached to a medical college/Urban Health Training Centre in India during the period July 2025 to June 2026. Study Population The study population comprised adults aged 40 years and above attending the selected urban primary health-care facility during the study period. The age threshold was selected because conventional Pooled Cohort Equation (PCE)-based 10-year ASCVD risk assessment is primarily intended for adults aged 40–75 years. Eligibility Criteria Participants aged ≥40 years who attended the selected facility during the study period, were willing to participate, and were able to provide informed consent were included. Participants with a previous myocardial infarction, stroke or transient ischaemic attack, known established ASCVD, critical illness, inability to provide reliable information, pregnancy, or unwillingness to provide informed consent were excluded. Participants with established ASCVD were excluded because the ASCVD risk score is intended for primary prevention and estimates risk among individuals without established ASCVD. Sample Size The sample size for estimating prevalence in the cross-sectional study was calculated using the formula (n=Z2pq/d2), where Z was 1.96 at a 95% confidence level, p was the anticipated prevalence, q was 1−p, and d was the allowable absolute error.8 Assuming an anticipated prevalence of approximately 17% as per a similar previous study9, the calculated sample size was 174, which increased to approximately 300 after allowing for non-response. Accordingly, approximately 300 participants were targeted for the study. Sampling Technique A systematic random sampling technique was used for participant selection. Study Variables The dependent variable was the 10-year ASCVD risk category, classified as low (<5%), borderline (5–<7.5%), intermediate (7.5–<20%), and high (≥20%) according to conventional ACC/AHA PCE categories. Independent variables included sociodemographic characteristics such as age, sex, education, occupation, socioeconomic status, and marital status; behavioural factors including tobacco use, alcohol consumption, physical activity, and dietary habits; anthropometric measures including height, weight, BMI, and waist circumference; clinical factors including blood pressure, diabetes mellitus, hypertension, and family history of premature cardiovascular disease; and biochemical parameters including total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, fasting blood glucose, and HbA1c where available. Operational Definitions Hypertension was defined as previously diagnosed hypertension on treatment or elevated blood pressure according to the predefined diagnostic criteria adopted in the study protocol. Diabetes mellitus was defined as previously diagnosed diabetes on treatment or based on biochemical criteria according to the adopted diagnostic guideline. Tobacco use was defined as current use of smoked or smokeless tobacco within the specified reference period. Physical inactivity was defined as failure to achieve the predefined minimum level of moderate-to-vigorous physical activity according to the selected questionnaire. Overweight and obesity were classified according to the prespecified BMI criteria, with the choice of WHO general or Asian-Indian cut-offs specified in the study protocol. Data Collection Procedure Following institutional ethical approval and informed consent, eligible participants were interviewed using a pretested semi-structured questionnaire. Information regarding age, sex, education, occupation, tobacco and alcohol use, physical activity, dietary habits, history of hypertension, diabetes and dyslipidaemia, family history of cardiovascular disease, and current medication use was collected. Height and weight were measured using calibrated instruments, and BMI was calculated as weight in kilograms divided by height in metres squared. Waist circumference was measured using a non-stretchable measuring tape according to a standardised protocol. Blood pressure was measured after at least five minutes of rest, with two readings taken at an appropriate interval and the average recorded. Laboratory investigations included total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, and fasting blood glucose, with HbA1c additionally measured where available. ASCVD Risk Calculation For participants aged 40–75 years without established ASCVD, the ACC/AHA Pooled Cohort Equation (PCE) was used to estimate 10-year ASCVD risk. The variables incorporated into the conventional PCE included age, sex, total cholesterol, HDL cholesterol, systolic blood pressure, treatment for hypertension, diabetes, and smoking status. The estimated risk was interpreted as a calculated risk estimate rather than a directly validated prediction of cardiovascular events in the Indian population, considering that the PCE was developed predominantly using US cohorts and its applicability to Indian populations may be imperfect. Data Analysis Data were entered into Microsoft Excel and analysed using SPSS version 26.0. Continuous variables were expressed as mean ± standard deviation for normally distributed data and median with interquartile range for skewed data, while categorical variables were presented as frequencies and percentages. The Chi-square test or Fisher’s exact test was used to assess associations between categorical variables. The independent t-test or Mann–Whitney U test was used for comparison of continuous variables between two groups, as appropriate, while ANOVA or Kruskal–Wallis tests were used for comparisons across multiple ASCVD-risk categories. Binary logistic regression was used, where applicable, to identify independent predictors of elevated ASCVD risk. A p-value <0.05 was considered statistically significant.

A total of 300 participants were included in the study. The mean age of the participants was 54.2 ± 9.8 years. Among the participants, 92 (30.7%) were aged 40–49 years, 101 (33.7%) were aged 50–59 years, 76 (25.3%) were aged 60–69 years, and 31 (10.3%) were aged ≥70 years. Females constituted 168 (56.0%) of the participants, while 132 (44.0%) were males. All participants were urban residents, 276 (92.0%) were married, and 148 (49.3%) were currently employed. Table 1. Sociodemographic characteristics of participants (n=300) Variable n % Age group 40–49 years 92 30.7 50–59 years 101 33.7 60–69 years 76 25.3 ≥70 years 31 10.3 Sex Male 132 44.0 Female 168 56.0 Urban residence 300 100.0 Married 276 92.0 Currently employed 148 49.3 Hypertension was present in 134 (44.7%) participants, while diabetes mellitus was present in 82 (27.3%). Dyslipidaemia was observed in 127 (42.3%) participants. Overweight or obesity was present in 183 (61.0%), and 155 (51.7%) had a high waist circumference. Tobacco use was reported by 54 (18.0%) participants, while 140 (46.7%) were physically inactive. A family history of premature cardiovascular disease was reported by 41 (13.7%) participants. Overall, hypertension, excess body weight, and dyslipidaemia were among the most frequently observed cardiovascular risk factors. Table 2. Distribution of cardiovascular risk factors among participants (n=300) Cardiovascular risk factor n % Hypertension 134 44.7 Diabetes mellitus 82 27.3 Dyslipidaemia 127 42.3 Overweight/obesity 183 61.0 Tobacco use 54 18.0 Physical inactivity 140 46.7 Family history of premature CVD 41 13.7 High waist circumference 155 51.7 Of the 300 participants, 29 (9.7%) had none of the assessed major cardiovascular risk factors, while 72 (24.0%) had one risk factor. Two risk factors were present in 91 (30.3%) participants, three in 69 (23.0%), and four or more in 39 (13.0%). Overall, 199 (66.3%) participants had two or more cardiovascular risk factors, indicating substantial clustering of cardiovascular risk factors in the study population. Table 3. Number of major cardiovascular risk factors among participants (n=300) Number of risk factors n % None 29 9.7 1 72 24.0 2 91 30.3 3 69 23.0 ≥4 39 13.0 Among the 300 participants, 30 were excluded from ASCVD risk calculation because they had established ASCVD or did not meet the eligibility criteria for Pooled Cohort Equation estimation. Therefore, the ASCVD risk analysis included 270 participants. Of these, 119 (44.1%) had low estimated 10-year ASCVD risk, 52 (19.3%) had borderline risk, 68 (25.2%) had intermediate risk, and 31 (11.5%) had high risk. Overall, 99 (36.7%) participants had intermediate or high estimated 10-year ASCVD risk (≥7.5%). Table 4. Distribution of estimated 10-year ASCVD risk among eligible participants (n=270) ASCVD risk category Risk n % Low <5% 119 44.1 Borderline 5–<7.5% 52 19.3 Intermediate 7.5–<20% 68 25.2 High ≥20% 31 11.5 Total 270 100.0 The distribution of ASCVD risk varied significantly across age groups. Intermediate and high-risk categories increased progressively with advancing age. Among participants aged 40–49 years, only 7 participants were classified as having intermediate or high risk, compared with 22 participants aged 50–59 years, 41 participants aged 60–69 years, and 29 participants aged ≥70 years. This association was statistically significant (p<0.001). Table 5. Association between age group and estimated 10-year ASCVD risk (n=270) Age group Low Borderline Intermediate High 40–49 years 48 15 6 1 50–59 years 48 22 19 3 60–69 years 18 12 29 12 ≥70 years 5 3 14 15 Higher-risk ASCVD categories were more frequently observed among males. Among males, 39 participants were classified as having intermediate risk and 18 as having high risk, compared with 29 and 13 females, respectively. The association between sex and estimated ASCVD risk was statistically significant (p<0.001). Table 6. Association between sex and estimated 10-year ASCVD risk (n=270) Sex Low Borderline Intermediate High Male 42 21 39 18 Female 77 31 29 13 Participants with hypertension had substantially greater representation in the intermediate/high ASCVD risk category compared with those without hypertension. Among participants without hypertension, 34 were in the intermediate/high-risk group, compared with 65 participants with hypertension. The association was statistically significant (χ²=28.6; p<0.001). Table 7. Association between hypertension and estimated 10-year ASCVD risk (n=270) Hypertension Low/Borderline Intermediate/High No 111 34 Yes 60 65 Participants with diabetes mellitus had a significantly higher proportion of intermediate/high estimated ASCVD risk compared with those without diabetes. Among participants without diabetes, 42 were classified as intermediate/high risk, whereas 57 participants with diabetes were classified in these categories (χ²=44.1; p<0.001). Table 8. Association between diabetes mellitus and estimated 10-year ASCVD risk (n=270) Diabetes mellitus Low/Borderline Intermediate/High No 146 42 Yes 25 57 Current tobacco users had a significantly higher proportion of intermediate/high estimated ASCVD risk compared with non-users. Among tobacco users, 30 participants were classified as intermediate/high risk compared with 69 among non-users (p<0.001). Table 9. Association between tobacco use and estimated 10-year ASCVD risk (n=270) Tobacco use Low/Borderline Intermediate/High No 157 69 Yes 14 30 Multivariable logistic regression showed that age ≥60 years, male sex, hypertension, diabetes mellitus, current tobacco use, and dyslipidaemia were independently associated with intermediate/high estimated ASCVD risk. Participants aged ≥60 years had 3.42 times higher odds of intermediate/high ASCVD risk (adjusted OR 3.42; 95% CI: 2.01–5.81; p<0.001). Male participants had higher odds compared with females (adjusted OR 1.88; 95% CI: 1.13–3.12; p=0.015). Hypertension (adjusted OR 2.61; 95% CI: 1.55–4.39; p<0.001), diabetes mellitus (adjusted OR 2.74; 95% CI: 1.54–4.86; p<0.001), current tobacco use (adjusted OR 2.19; 95% CI: 1.13–4.24; p=0.019), and dyslipidaemia (adjusted OR 1.79; 95% CI: 1.08–2.98; p=0.024) were also independently associated with higher ASCVD risk. Overweight/obesity was not independently associated with intermediate/high ASCVD risk after adjustment (adjusted OR 1.47; 95% CI: 0.89–2.43; p=0.13). Table 10. Independent predictors of intermediate/high estimated 10-year ASCVD risk Variable Adjusted OR 95% CI p-value Age ≥60 years 3.42 2.01–5.81 <0.001 Male sex 1.88 1.13–3.12 0.015 Hypertension 2.61 1.55–4.39 <0.001 Diabetes mellitus 2.74 1.54–4.86 <0.001 Current tobacco use 2.19 1.13–4.24 0.019 Overweight/obesity 1.47 0.89–2.43 0.130 Dyslipidaemia 1.79 1.08–2.98 0.024

The present study demonstrates a substantial burden of cardiovascular risk factors among adults attending an urban primary health-care facility, with hypertension, excess body weight, dyslipidaemia, diabetes mellitus, physical inactivity and tobacco use being the major contributors. Importantly, cardiovascular risk factors frequently clustered in the study population, with 66.3% of participants having two or more major risk factors. Among the 270 participants eligible for ASCVD risk estimation, 36.7% were classified as having intermediate or high estimated 10-year ASCVD risk. These findings highlight the importance of moving beyond the identification of individual risk factors towards comprehensive cardiovascular risk assessment in primary-care settings. Hypertension was the most frequent clinical cardiovascular risk factor in the present study, affecting 44.7% of participants. This prevalence is higher than that reported in several community-based Indian studies. The ICMR-INDIAB study reported an overall age-standardised prevalence of hypertension of 26.3% among Indian adults, with urban prevalence ranging from approximately 28% to 32% across the studied states and union territory.10 Similarly, the National NCD Monitoring Survey reported raised blood pressure in 34.0% of urban adults.11 The higher prevalence observed in the present study may partly reflect the older age distribution of the study population (mean age 54.2 years) and the fact that participants were recruited from a health-care facility rather than from the general community. The present findings are therefore consistent with the established observation that cardiovascular risk increases substantially with age and that health-care-attending populations may carry a greater burden of cardiometabolic abnormalities. A primary-health-centre-based study from Puducherry provides an additional comparison. Among 324 adults screened opportunistically, hypertension was reported in 17.9%, while 37.7% had prehypertension.12 The markedly lower prevalence compared with the present study may be explained by the younger mean age of that population (47.7 years), differences in participant selection, and differences in the definition and ascertainment of hypertension. Nevertheless, both studies support the usefulness of opportunistic screening at the primary-care level, particularly because hypertension may remain clinically silent for prolonged periods. Diabetes mellitus was present in 27.3% of participants in the present study. This is substantially higher than the 11.4% overall prevalence reported by the large nationally representative ICMR-INDIAB study.13 However, direct comparison should be made cautiously because the present study included adults aged ≥40 years attending a health-care facility, whereas ICMR-INDIAB included adults aged ≥20 years from both urban and rural communities. Diabetes prevalence is strongly age-dependent and is also higher among individuals with coexisting obesity, hypertension and dyslipidaemia. The high prevalence observed in the current study therefore suggests a concentration of metabolic risk within this primary-care population rather than representing the prevalence in the general Indian population. The high prevalence of diabetes in the present study is clinically important because diabetes frequently coexists with other cardiovascular risk factors. In an urban Indian multicity study, Gupta et al. reported that among participants with diabetes, hypertension was present in 73.1%, hypercholesterolaemia in 41.4%, hypertriglyceridaemia in 71.0%, and low HDL cholesterol in 78.5%.14 Although the methodology and population differed from the present study, the findings reinforce the concept that diabetes should be considered within the context of overall cardiometabolic risk rather than as an isolated condition. Dyslipidaemia was observed in 42.3% of participants in the current study. This finding is broadly consistent with previous Indian studies demonstrating a substantial burden of lipid abnormalities, although reported prevalence varies considerably according to the lipid parameters and diagnostic thresholds used. The recently reported national ICMR-INDIAB analysis found dyslipidaemia to be extremely common in Indian adults, with an overall prevalence of 81.2% when the study's comprehensive definition of dyslipidaemia was applied.13 The difference from the present estimate is likely attributable to differences in operational definitions, age structure and the specific lipid abnormalities included. This methodological issue is important when comparing dyslipidaemia prevalence between studies and should be acknowledged rather than interpreting the difference as a true epidemiological discrepancy. Excess body weight was another prominent finding, with 61.0% of participants classified as overweight or obese and 51.7% having a high waist circumference. These observations are consistent with the increasing burden of obesity and abdominal adiposity in India. The ICMR-INDIAB study reported generalised obesity in 28.6% and abdominal obesity in 39.5% of Indian adults.13 More recent national analyses have also demonstrated a particularly high burden of obesity and metabolic abnormalities in urban populations.15 The higher prevalence in the present study may again reflect the older age and health-care-attending nature of the sample. Importantly, the high prevalence of both excess body weight and central adiposity indicates that primary-care cardiovascular prevention should include weight management and lifestyle interventions in addition to pharmacological control of conventional risk factors. Tobacco use was reported by 18.0% of participants. This is comparable to the substantial tobacco burden documented in Indian adults, although estimates differ according to whether smoked and smokeless tobacco are considered together and according to the age and sex distribution of the population. The ICMR India State-Level Disease Burden Initiative has identified tobacco use as an important contributor to cardiovascular disease burden in India, accounting for approximately 6% of total disease burden in 2016.16 In the present study, tobacco use was also significantly associated with higher ASCVD risk, supporting the importance of systematic tobacco assessment and cessation interventions in primary care. Physical inactivity was present in 46.7% of participants. This finding is consistent with the broader transition towards sedentary lifestyles accompanying urbanisation. The recent ICMR-INDIAB dietary and metabolic-risk analysis reported particularly high levels of physical inactivity in urban participants, with approximately 70% classified as physically inactive.15 Differences in questionnaires, thresholds and definitions may explain the lower prevalence observed in the present study. Nevertheless, the relatively high frequency of physical inactivity in the current population, together with the high prevalence of obesity, diabetes and hypertension, suggests that lifestyle modification should form an integral component of cardiovascular prevention. An important finding of this study was the clustering of cardiovascular risk factors. Two or more major risk factors were present in 66.3% of participants, while 13.0% had four or more risk factors. This finding is consistent with national Indian data demonstrating that multiple metabolic and behavioural risk factors commonly coexist. The National NCD Monitoring Survey reported that approximately 40.2% of adults aged 18–69 years had clustering of at least three selected cardiovascular risk factors.11 Although the definitions and study populations differ, both findings demonstrate that risk-factor clustering is an important public-health problem. The coexistence of hypertension, diabetes, dyslipidaemia and obesity has important clinical implications because the cumulative cardiovascular risk associated with multiple abnormalities may be substantially greater than the effect of any single factor considered in isolation. The present findings therefore support the use of integrated cardiovascular risk assessment during routine primary-care encounters rather than relying exclusively on disease-specific screening. Among the 270 participants eligible for ASCVD risk estimation, 44.1% had low risk, 19.3% borderline risk, 25.2% intermediate risk and 11.5% high risk. Overall, 36.7% had intermediate or high estimated 10-year ASCVD risk. The proportion with elevated estimated risk is clinically meaningful because it identifies a sizeable subgroup in whom more intensive assessment and preventive intervention may be warranted. Direct comparison with Indian studies using the same Pooled Cohort Equation is limited because available studies have frequently involved selected populations, such as patients with diabetes and hypertension or individuals attending cardiovascular prevention clinics. A study from Punjab evaluating 201 adults aged ≥40 years with hypertension and/or diabetes using the ASCVD Risk Estimator Plus similarly demonstrated the utility of ASCVD risk stratification in an Indian clinical population.17 However, because that study specifically enrolled patients with established cardiometabolic risk factors, its risk distribution cannot be directly compared with the present broader primary-care population. The NIRAM study from urban India also evaluated ACC/AHA ASCVD risk scores in an urban cohort and demonstrated the applicability of risk-score-based stratification for identifying individuals with elevated predicted cardiovascular risk.18 The present study extends this approach to an urban primary-health-care setting and demonstrates that a considerable proportion of routine attendees have intermediate or high estimated risk even when they are not necessarily presenting with cardiovascular symptoms. Age showed a strong and statistically significant association with ASCVD risk in the present study. Intermediate/high-risk categories increased progressively across age groups, with only 7 participants aged 40–49 years classified as intermediate/high risk compared with 41 among those aged 60–69 years and 29 among those aged ≥70 years (p<0.001). Multivariable analysis further demonstrated that participants aged ≥60 years had more than threefold higher odds of intermediate/high ASCVD risk (adjusted OR 3.42; 95% CI 2.01–5.81). This finding is biologically plausible and consistent with the structure of the Pooled Cohort Equation, in which age is a major determinant of estimated 10-year ASCVD risk. Similar age-related increases in cardiovascular risk have been documented across Indian epidemiological studies. The strong effect of age in the present study reinforces the importance of systematic cardiovascular risk assessment among adults aged ≥40 years, particularly as the number of coexisting metabolic risk factors also tends to increase with advancing age. Male participants had significantly higher estimated ASCVD risk than females, with 39 males classified as intermediate risk and 18 as high risk compared with 29 and 13 females, respectively. Male sex remained independently associated with intermediate/high ASCVD risk after adjustment (adjusted OR 1.88; 95% CI 1.13–3.12). This finding is consistent with the greater burden of several conventional cardiovascular risk factors, particularly tobacco use and hypertension, historically observed among Indian men. However, the relatively high prevalence of obesity, diabetes and physical inactivity among women in contemporary Indian populations indicates that female cardiovascular risk should not be underestimated. Hypertension was strongly associated with intermediate/high ASCVD risk in the present study. Among participants without hypertension, 34 were classified as intermediate/high risk compared with 65 participants with hypertension (χ²=28.6; p<0.001). Hypertension also remained independently associated with elevated risk after adjustment, with an adjusted OR of 2.61 (95% CI 1.55–4.39). These findings are consistent with the established role of elevated blood pressure as one of the most important modifiable determinants of cardiovascular morbidity and mortality. The ICMR-INDIAB study similarly identified age, male sex, obesity, diabetes and physical inactivity as important factors associated with hypertension.10 Diabetes mellitus demonstrated an even stronger association with intermediate/high ASCVD risk. Participants with diabetes had significantly greater representation in the elevated-risk group, and diabetes remained an independent predictor after multivariable adjustment (adjusted OR 2.74; 95% CI 1.54–4.86). This finding is consistent with the substantial cardiovascular burden associated with diabetes in Indian populations. The coexistence of diabetes with hypertension, dyslipidaemia and obesity further amplifies overall cardiovascular risk, emphasising the need for integrated cardiometabolic management rather than isolated glycaemic control. Current tobacco use was also independently associated with intermediate/high ASCVD risk (adjusted OR 2.19; 95% CI 1.13–4.24). This observation is consistent with the established contribution of tobacco exposure to atherosclerotic cardiovascular disease and with the substantial cardiovascular disease burden attributable to tobacco in India.16 The finding reinforces the importance of incorporating tobacco-use assessment and cessation counselling into routine primary-care cardiovascular prevention. Dyslipidaemia was independently associated with intermediate/high ASCVD risk in the present analysis (adjusted OR 1.79; 95% CI 1.08–2.98). This is consistent with the biological and epidemiological relationship between adverse lipid profiles and atherosclerotic disease. The high prevalence of dyslipidaemia documented in national Indian surveys further supports the need for routine lipid assessment as part of comprehensive cardiovascular risk evaluation.13 Interestingly, overweight/obesity was not independently associated with intermediate/high ASCVD risk after adjustment (adjusted OR 1.47; 95% CI 0.89–2.43; p=0.13). This finding should not be interpreted as evidence that obesity is unimportant in cardiovascular disease. Rather, the absence of statistical significance after adjustment may reflect the relatively small sample size, collinearity between obesity and other metabolic risk factors, or the fact that components such as blood pressure, diabetes and lipid abnormalities mediate part of the association between excess adiposity and cardiovascular risk. This interpretation is supported by national Indian studies demonstrating strong relationships between obesity and metabolic abnormalities, including hypertension, diabetes and dyslipidaemia.13,15 An important consideration in interpreting the present findings is the applicability of the ACC/AHA Pooled Cohort Equation to Indian adults. The PCE was derived predominantly from US cohorts and was not specifically developed or calibrated for the Indian population. Consequently, the estimated percentages in the present study should be regarded as model-derived risk estimates rather than directly observed probabilities of cardiovascular events. This limitation is particularly relevant because previous Indian studies have raised concerns regarding the performance of the PCE in South Asian populations. In a North Indian study comparing several cardiovascular risk scores with measures of subclinical atherosclerosis, the ACC/AHA Pooled Cohort Equation underestimated cardiovascular risk compared with the Framingham and Joint British Societies' scores and showed weaker correlations with carotid intima-media thickness and coronary calcium score.19 Similarly, a study comparing cardiovascular risk scores in Indian patients presenting with myocardial infarction found considerable differences in risk classification between the ACC/AHA PCE, Framingham, WHO and Joint British Societies' models.20 More recent evidence from a large South Asian cohort also suggests that the PCE may underestimate observed ASCVD events, particularly among individuals categorised as low risk. In that study, the PCE predicted a 10-year risk of 1.8% in the low-risk group compared with an observed risk of 4.9%.21 These findings support the caution expressed in the present study that PCE-derived estimates should not be interpreted as definitive measures of actual 10-year cardiovascular event probability in Indian adults. Nevertheless, despite these limitations, the PCE may still have practical value as a structured framework for identifying individuals with a greater concentration of conventional risk factors and facilitating clinician–patient discussions regarding preventive interventions. The present study therefore supports its use as an adjunct to clinical assessment rather than as a stand-alone determinant of treatment decisions. The findings have important implications for primary-care practice. A substantial proportion of adults attending the urban health-care facility had multiple modifiable cardiovascular risk factors, and more than one-third of eligible participants were classified as having intermediate or high estimated ASCVD risk. These findings suggest that primary-care facilities can serve as important points for opportunistic cardiovascular risk detection. A practical approach would involve routine measurement of blood pressure, BMI and waist circumference; screening for diabetes and lipid abnormalities according to clinical indications; assessment of tobacco use and physical activity; and calculation of overall cardiovascular risk among eligible adults. Individuals with elevated estimated risk could subsequently receive intensified lifestyle counselling, appropriate pharmacological management, and scheduled follow-up. The findings also support integration of cardiovascular risk assessment into existing non-communicable disease services rather than establishing separate vertical programmes. Such integration could improve early identification of high-risk individuals and facilitate longitudinal monitoring of blood pressure, glycaemic status, lipid levels, weight and behavioural risk factors. The major strengths of the study include simultaneous assessment of multiple behavioural, anthropometric, clinical and biochemical cardiovascular risk factors and the use of an absolute risk estimation approach rather than relying solely on individual risk factors. The facility-based design also provides clinically relevant information regarding the burden of cardiovascular risk among individuals who actually interact with primary-care services. However, several limitations should be considered. First, the cross-sectional design prevents determination of temporal or causal relationships between risk factors and estimated ASCVD risk. Second, the facility-based sample may not be representative of the wider urban population because individuals attending health-care facilities may differ systematically from those who do not seek care. Third, behavioural variables such as tobacco use, alcohol consumption, physical activity and dietary practices were partly self-reported and may therefore be affected by recall and social-desirability bias. Finally, the PCE has limited validation in Indian populations, and the study estimated predicted risk rather than observing actual cardiovascular events during a 10-year follow-up period.

Overall, the present study demonstrates a high burden and substantial clustering of modifiable cardiovascular risk factors among adults attending an urban primary health-care facility. The coexistence of hypertension, diabetes, dyslipidaemia, excess body weight, tobacco use and physical inactivity translated into a considerable proportion of participants being classified as having intermediate or high estimated 10-year ASCVD risk. The results are broadly consistent with the growing body of Indian evidence demonstrating a high and increasing burden of cardiometabolic risk, although differences in age, setting, definitions and sampling methods account for variation in prevalence estimates between studies. The findings support a shift from isolated risk-factor screening towards integrated cardiovascular risk assessment in primary care. At the same time, because currently available risk-prediction equations were largely developed outside India and may misclassify risk in South Asian populations, their estimates should be interpreted alongside clinical judgement and other risk-enhancing information. Future prospective studies involving larger and more representative Indian populations are required to validate existing risk equations and develop population-specific cardiovascular risk-prediction models.

1. Cardiovascular diseases (CVDs) [Internet]. [cited 2026 Aug 30]. Available from: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) 2. Flora GD, Nayak MK. A Brief Review of Cardiovascular Diseases, Associated Risk Factors and Current Treatment Regimes. CPD. 2019 Dec 17;25(38):4063–84. doi:10.2174/1381612825666190925163827 3. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, et al. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019 Sep 10;140(11). doi:10.1161/CIR.0000000000000677 4. Correction to: 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019 Sep 10;140(11). doi:10.1161/CIR.0000000000000724 5. Correction to: 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2020 Apr 21;141(16). doi:10.1161/CIR.0000000000000770 6. Jadhav UM, Nair T, Abdullakutty J, Garg R, Unni TG, Shrivastava S, et al. A National Hypertension Awareness and Early Screening Initiative: “Act at 30 – It’s Time for Heart Check.” J of Clinical Hypertension. 2026 Apr;28(4):e70203. doi:10.1111/jch.70203 7. Jain A, Ambade M, Balla S, Pal A, Rajpal S, Kim R, et al. Measuring changes in prevalence of hypertension and diabetes across 720 districts in India using cross-sectional data from 2016 to 2021. bmjph. 2025 Jul;3(2):e002653. doi:10.1136/bmjph-2025-002653 8. Sample size calculation in medical studies - PMC [Internet]. [cited 2026 Sep 1]. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC4017493/ 9. Sheth P, Mullasari A, Ahire P, Khandhedia C, Markandeywar N, Mane A, et al. Assessment of atherosclerotic cardiovascular disease (ASCVD) risk using ASCVD risk estimator plus in indian patients with type 2 diabetes - a subgroup analysis from real-world, retrospective study. EJEA. 2025 May 9. doi:10.1530/endoabs.110.EP54 10. Bhansali A, Dhandania VK, Deepa M, Anjana RM, Joshi SR, Joshi PP, et al. Prevalence of and risk factors for hypertension in urban and rural India: the ICMR–INDIAB study. J Hum Hypertens. 2015 Mar;29(3):204–9. doi:10.1038/jhh.2014.57 11. Mathur P, Kulothungan V, Leburu S, Krishnan A, Chaturvedi HK, Salve HR, et al. National noncommunicable disease monitoring survey (NNMS) in India: Estimating risk factor prevalence in adult population. Wehrmeister FC, editor. PLoS ONE. 2021 Mar 2;16(3):e0246712. doi:10.1371/journal.pone.0246712 12. Majumdar A, Chinnakali P, Vinayagamoorthy V, Daya PA, Shidam UG, Roy G. Opportunistic Screening for Hypertension and Selected Cardiovascular Risk Factors among Adults Attending a Primary Health Center in Puducherry, India. Int J Prev Med. 2014 Dec;5(12):1616–20. PubMed PMID: 25709799; PubMed Central PMCID: PMC4336994. 13. Anjana RM, Unnikrishnan R, Deepa M, Pradeepa R, Tandon N, Das AK, et al. Metabolic non-communicable disease health report of India: the ICMR-INDIAB national cross-sectional study (ICMR-INDIAB-17). The Lancet Diabetes & Endocrinology. 2023 Jul;11(7):474–89. doi:10.1016/S2213-8587(23)00119-5 14. Gupta A, Gupta R, Sharma KK, Lodha S, Achari V, Asirvatham AJ, et al. Prevalence of diabetes and cardiovascular risk factors in middle-class urban participants in India. BMJ Open Diab Res Care. 2014 Dec;2(1):e000048. doi:10.1136/bmjdrc-2014-000048 15. Anjana RM, Sudha V, Abirami K, Gayathri R, Manasa VS, Deepa M, et al. Dietary profiles and associated metabolic risk factors in India from the ICMR–INDIAB survey-21. Nat Med. 2025 Nov;31(11):3813–24. doi:10.1038/s41591-025-03949-4 16. Hay SI, Abdulkader RS, Ashkan Afshin, Agarwal SK, Aggarwal AN, Aggarwal R, et al. India: Health of the Nation’s States - The India State-level Disease Burden Initiative [Internet]. 2017. doi:10.13140/RG.2.2.26119.88489 17. Osoro I, Kumar R, Sharma A. Ten-year risk assessment for cardiovascular diseases using ASCVD risk estimator plus: outcomes from hypertension and diabetes patients. Diabetol Metab Syndr. 2023 Oct 27;15(1):216. doi:10.1186/s13098-023-01170-2 18. Menon VP, Edathadathil F, Sathyapalan D, Moni M, Don A, Balachandran S, et al. Assessment of 2013 AHA/ACC ASCVD risk scores with behavioral characteristics of an urban cohort in India: Preliminary analysis of Noncommunicable disease Initiatives and Research at AMrita (NIRAM) study. Medicine. 2016 Dec;95(49):e5542. doi:10.1097/MD.0000000000005542 19. Bansal M, Kasliwal RR, Trehan N. Relationship between different cardiovascular risk scores and measures of subclinical atherosclerosis in an Indian population. Indian Heart Journal. 2015 Jul;67(4):332–40. doi:10.1016/j.ihj.2015.04.017 20. Bansal M, Kasliwal RR, Trehan N. Comparative accuracy of different risk scores in assessing cardiovascular risk in Indians: A study in patients with first myocardial infarction. Indian Heart Journal. 2014 Nov;66(6):580–6. doi:10.1016/j.ihj.2014.10.399 21. Mantri NM, Merchant M, Rana JS, Go AS, Pursnani SK. Performance of the pooled cohort equation in South Asians: insights from a large integrated healthcare delivery system. BMC Cardiovasc Disord. 2022 Dec 23;22(1):566. doi:10.1186/s12872-022-02993-z