Abstract
Background:
The occurrence of acute coronary syndrome (ACS) among individuals under 40 years has increased markedly, yet current atherosclerotic cardiovascular disease risk prediction models are primarily validated in older adults, leading to underrecognition of risk and missed prevention in the young.
Objectives:
The objectives of this study were to evaluate cardiovascular risk factors (CVRFs) in young adults with ACS and to assess the applicability and correlation of QRISK-3 and PREVENT risk prediction scores in this demographic.
Methods:
In this single-center observational study, 250 patients (<40 years of age) with ACS were assessed for the CVRFs; demographic, metabolic, and lifestyle profiles, echocardiographic and coronary angiographic (CAG) findings; and risk scores. The correlation between QRISK-3 and PREVENT models was assessed in the 30–40-year age subgroup.
Results:
The cohort (mean age: 35.1 ± 4.0 years; 93.2% of males) demonstrated a high prevalence of tobacco use (smoking – 40.8% and smokeless tobacco – 76.8%) and diabetes (26.8%). ST-segment-elevated myocardial infarction represented 63.6% of cases, and single-vessel disease predominated on CAG. The mean QRISK-3 10-year and 30-year estimates were 1.53+1.64 and 11.76+7.21, respectively. A strong correlation was observed between QRISK-3 and PREVENT for both 10-year (r = 0.714, P < 0.001) and 30-year (r = 0.868, P < 0.01) risk estimates in the 30–40-year age group. Smokeless tobacco users exhibited shorter sleep duration and lower 30-year QRISK scores compared with nonusers (P < 0.001)
Conclusions:
Premature ACS links to modifiable factors such as tobacco and poor lifestyle. Standard models underpredict short-term risk but align well with long-term; integrating lifetime estimates and region-specific behaviors (e.g., smokeless tobacco) could improve prediction and enable earlier interventions.
Keywords: Acute coronary syndrome, premature coronary artery disease, PREVENT risk model, QRISK-3, young adults
INTRODUCTION
The incidence of acute coronary syndrome (ACS) among young adults is rising globally, posing a growing public health concern.[1,2] Most established atherosclerotic cardiovascular disease (ASCVD) risk prediction models have been developed and validated in individuals over 40 years.[3]Consequently, risk estimation in the younger population remains unreliable, often leading to underrecognition of early cardiovascular risk and delayed medical attention during acute events. Improved characterization of risk profiles in young adults presenting with ACS is critical to guide preventive strategies and optimize early detection. Although QRISK-3 and PREVENT risk assessment tools have been developed to predict risk in the 25–84-year and 30-79-year age groups, respectively, their usefulness in other regions remains unclear.[4,5,6]
This study aims to evaluate the predominant cardiovascular risk factors (CVRFs) among patients under 40 years of age presenting with ACS and to examine the applicability and predictive performance of contemporary risk scoring systems, including QRISK-3 and PREVENT, within this population.
METHODS
Study design and setting
This prospective observational study was conducted from September 2022 to March 2024 at a tertiary care hospital in Central India after obtaining ethical clearance (IEC No./2022/8629-14, September 2022) from the institutional ethical committee of NSCB Medical College, Jabalpur, MP.
Participants
A total of 250 patients under 40 years with ACS were recruited for this study after obtaining written informed consent from all the participants. Patients were evaluated at discharge or at the first follow-up visit (not later than 1 month after discharge) in the outpatient department (OPD) for their clinical data, diagnostic workup, including blood investigations, echocardiography findings, coronary angiography findings, and management. Study design, along with inclusion and exclusion criteria, is illustrated in Figure 1.
Figure 1.
Flow diagram. NSTEACS: Non-ST-elevation acute coronary syndrome, ACS: Acute coronary syndrome, STEMI: ST-elevated myocardial infarction, CKD: Chronic kidney disease, CAD:Coronary artery disease; VHD: Valvular heart disease; PRACS-40 Study : Predicting the Risk of Acute Coronary Syndrome under 40 Study
Variables
The primary variable was the identification of CVRFs among young adults with ACS. Secondary variables included calculated risk scores (QRISK-3 and PREVENT), ACS subtype, significant coronary artery obstruction, occurrence of acute decompensated heart failure (ADHF), and arrhythmias. Risk scores, namely PREVENT and Q-Risk 3, were calculated in the 30–40-year age group for 10-year and 30-year risk estimates. QRISK-3 was calculated for all the patients in this cohort, as it is validated for the 25–84-year age group, while PREVENT is validated only in the 30–79-year age group; therefore, it was calculated in the 30–40-year age group in this cohort.
Data sources
All patients’ data were recorded in a patient proforma. All the patients with a working diagnosis of ACS were screened for the clinical profile, symptoms, and physical examination records of index events. Baseline investigations of these patients, including complete blood count, renal function test, lipid profile, glycated hemoglobin, electrocardiography, echocardiography, troponin-I report, and coronary angiographic (CAG) report, along with images, were evaluated. These patients were assessed at the time of the follow-up visit to the cardiology OPD for the evaluation of data from the index event till the current visit. The evaluation included assessment for postdischarge events. CVRF were assessed in these patients, and risk scores were computed using standard online calculators.
Statistical methods
Descriptive statistics were utilized to summarize baseline demographic, lifestyle, and clinical variables. Continuous variables (e.g., age, sleep duration, and cardiovascular risk scores) were expressed as mean ± standard deviation, while categorical variables (e.g., gender, diabetes, and smoking status) were reported as counts and percentages.
Comparative analyses between subgroups (e.g., ST-segment-elevated myocardial infarction [STEMI] vs. non-ST-elevation acute coronary syndrome [NSTEACS], smokers vs. nonsmokers, and smokeless tobacco users vs. nonusers) employed the Chi-square or Fisher’s exact test for categorical variables and the independent samples t-test for continuous variables.
Correlation between risk prediction models (PREVENT and QRISK-3) was assessed using Pearson’s correlation coefficient (r) with coefficients of determination (r2) illustrated in scatter plots. Figures representing these correlations included best-fit regression lines and r2 values, verifying model concordance. Risk prediction estimates were also compared among clinical and lifestyle subgroups.
A two-sided P < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS 25 (IBM Corp., Armonk, NY, USA).
RESULTS
Baseline characteristics
Baseline demographic, clinical, and biochemical characteristics of the study cohort comprising 250 participants are summarized in Table 1. The mean age was 35.06 ± 4.03 years, with the majority being male (93.2%). Among them, 26.8% had diabetes, 40.8% were current smokers, and a notably high proportion (76.8%) reported smokeless tobacco use. Most participants (86.4%) followed a nonvegetarian diet, and the average sleep duration was 6.89 ± 1.035 h.
Table 1.
Baseline demographic, clinical, and biochemical characteristics of the study cohort comprising 250 participants and comparison of patients with ST-segment-elevated myocardial infarction (n=159) with non-ST-elevation acute coronary syndrome (n=91)
| Parameters | Baseline (n=250) | STEMI (n=159) | NSTEACS (n=91) | P |
|---|---|---|---|---|
| Age (years) | 35.06±4.03 | 34.77±4.19 | 35.57±3.7 | 0.133 |
| Male, n (%) | 233 (93.2) | 154 (96.8) | 79 (86.8) | 0.002* |
| BMI (kg/m2) | 24.12±2.5 | 23.98±2.6 | 24.3±2.3 | 0.240 |
| Weight (kg) | 65.98±7.3 | 66.1±7.5 | 65.7±6.9 | 0.651 |
| Diabetes, n (%) | 67 (26.8) | 49 (30.8) | 18 (19.7) | 0.058 |
| Current smoker, n (%) | 102 (40.8) | 68 (42.7) | 34 (37.3) | 0.403 |
| Smokeless tobacco use, n (%) | 192 (76.8) | 120 (75.5) | 72 (79.1) | 0.511 |
| Hypertension, n (%) | 29 (11.6) | 19 (11.9) | 10 (10.9) | 0.819 |
| Nonvegetarian diet, n (%) | 216 (86.4) | 137 (86.2) | 79 (86.8) | 0.885 |
| Sleep duration (h) | 6.89±1.035 | 6.87±0.98 | 6.92±1.1 | 0.686 |
| SBP (mmHg) | 112.08±17.69 | 111.39±17.4 | 113.27±18.22 | 0.419 |
| Creatinine (mg/dl) | 1.04±0.41 | 1.06±0.47 | 1.02±0.27 | 0.573 |
| TC (mg/dl) | 173.28±26.84 | 171.42±27.2 | 176.53±25.91 | 0.148 |
| HDL (mg/dl) | 40.92±5.53 | 40.44±5.32 | 41.75±5.8 | 0.071 |
| LDL (mg/dl) | 99.14±25.51 | 97.17±24.57 | 102.58±26.87 | 0.107 |
| TC/HDL | 3.59±0.9 | 3.61±0.89 | 3.55±0.91 | 0.614 |
| QRISK-3 (10 years) | 1.53±1.64 | 1.52±1.54 | 1.55±1.81 | 0.904 |
| QRISK-3 (30 years) | 11.76±7.21 | 11.99±7.31 | 11.36±7.06 | 0.511 |
| LVEF (%) | 49.26±11.06 | 47.23±11.25 | 52.80±9.84 | <0.001* |
| STEMI, n (%) | 159 (63.6) | 159 (100) | 0 | - |
| ADHF, n (%) | 31 (12.4) | 26 (16.3) | 6 (6.6) | 0.016 |
| VT, n (%) | 4 (1.6) | 4 (2.5) | 0 | 0.300 |
| VF, n (%) | 1 (0.4) | 1 (0.6) | 0 | 1.000 |
*P<0.05. ADHF: Acute decompensated heart failure, SBP: Systolic blood pressure, STEMI: ST-elevated myocardial infarction, VT: Ventricular tachycardia, VF: Ventricular fibrillation, NSTEACS: Non-ST-elevation acute coronary syndrome, BMI: Body mass index, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, TC: Total cholesterol, LVEF: Left ventricular ejection fraction
The cardiovascular risk was evaluated using QRISK-3, showing average 10-year and 30-year risk scores of 1.53 ± 1.64 and 11.76 ± 7.21, respectively.
Regarding clinical presentation, 63.6% had STEMI, 30.8% had non-STEMI, and 6% presented with unstable angina.
Comparison of ST-segment-elevated myocardial infarction with non-ST-elevation acute coronary syndrome
The baseline demographic, lifestyle, and clinical parameters of patients presenting with STEMI were compared to those of patients with NSTEACS and are summarized in Table 1. The mean age was comparable between the two groups. The proportion of males was significantly higher in the STEMI cohort (96.9% vs. 86.8%, P = 0.002). There were no significant differences in body mass index (BMI), weight, prevalence of diabetes, hypertension, current smoking, smokeless tobacco use, nonvegetarian diet, or average sleep duration between the groups (P > 0.05).
The assessment of cardiovascular risk using QRISK-3 scores (10-year and 30-year) did not show statistically significant differences. Notably, the proportion of patients presenting with ADHF was higher in the STEMI group (16.4% vs. 6.6%, P = 0.016). Ventricular arrhythmias were seen only in the STEMI group.
Comparison of smokers and nonsmokers
Comparative analysis of baseline characteristics between smokers (n = 102) and nonsmokers (n = 148) among the study cohort is shown in Table 2. A significantly higher proportion of males was found among smokers compared to nonsmokers (98% vs. 89.9%, P = 0.012), and smokeless tobacco use was more common among nonsmokers (43.14% vs. 99.32%, P < 0.001). Smokers had a longer mean sleep duration (7.07 + 1.15 h vs. 6.76 + 0.92 h, P = 0.037). The mean age, BMI, body weight, diabetes prevalence, hypertension, nonvegetarian diet, systolic blood pressure, serum creatinine, lipid profile (total cholesterol [TC], high-density lipoprotein [HDL], low-density lipoprotein, TC/HDL ratio), and clinical presentation (STEMI and NSTEACS) were similar between both the groups (P > 0.05). QRISK-3 (30-year) cardiovascular risk was markedly elevated in smokers (14.22 ± 8.69 vs. 10.07 ± 5.40, P < 0.001), although the 10-year risk, cholesterol values, and left ventricular ejection fraction were comparable.
Table 2.
Comparison of the subgroup based on smoking and smokeless tobacco usage, smokers versus nonsmokers, and smokeless tobacco users versus nonusers
| Baseline | Smoker (n=102) | Nonsmoker (n=148) | P | Smokeless tobacco user (n=192) | Smokeless tobacco nonuser (n=58) | P |
|---|---|---|---|---|---|---|
| Age (years) | 34.75±3.99 | 35.28±4.06 | 0.301 | 35.07±4.05 | 35.03±4.01 | 0.301 |
| Male, n (%) | 100 (98.0) | 133 (89.9) | 0.012* | 176 (91.7) | 57 (98.3) | 0.132 |
| BMI (kg/m2) | 23.76±.46 | 24.37±2.59 | 0.062 | 24.17±2.58 | 23.97±2.47 | 0.062 |
| Weight (kg) | 65.87±6.98 | 66.05±7.5 | 0.845 | 65.8±7.32 | 66.57±7.26 | 0.847 |
| Diabetes, n (%) | 19 (18.6) | 48 (32.4) | 0.058 | 52 (27.1) | 15 (25.8) | 0.854 |
| Current smoker, n (%) | 102 (100) | 148 (100) | - | 45 (23.4) | 57 (98.3) | <0.001* |
| Smokeless tobacco use, n (%) | 45 (44.1) | 147 (99.3) | <0.001* | 192 (100) | 58 (100) | - |
| Hypertension, n (%) | 13 (12.7) | 16 (10.8) | 0.639 | 13 (6.7) | 16 (27.6) | 0.899 |
| Nonvegetarian diet, n (%) | 90 (88.2) | 126 (85.1) | 0.482 | 163 (84.9) | 53 (91.4) | 0.207 |
| Sleep duration (h) | 7.07±1.15 | 6.76±0.92 | 0.037* | 6.81±0.99 | 7.16±1.12 | 0.022 |
| SBP (mmHg) | 113.97±20.08 | 110.77±15.77 | 0.160 | 111.46±16.51 | 114.12±21.15 | 0.160 |
| Creatinine (mg/dl) | 1.09±0.55 | 1.01±0.27 | 0.169 | 1.01±0.26 | 1.14±0.71 | 0.169 |
| TC (mg/dl) | 172.91±27.07 | 173.53±26.78 | 0.858 | 171.42±27.27 | 176.53±25.91 | 0.858 |
| HDL (mg/dl) | 40.71±4.9 | 41.06±5.9 | 0.618 | 41.04±5.66 | 40.51±5.11 | 0.618 |
| LDL (mg/dl) | 98.89±28.00 | 99.31±23.74 | 0.899 | 100.85±25.05 | 93.47±26.42 | 0.899 |
| TC/HDL | 3.69±1.02 | 3.52±0.80 | 0.137 | 3.54±0.79 | 3.74±1.19 | 0.137 |
| QRISK-3 (10 years) | 1.70±1.93 | 1.41±1.40 | 0.173 | 1.37±1.3 | 2.06±2.39 | 0.173 |
| QRISK-3 (30 years) | 14.22±8.69 | 10.07±5.40 | <0.001* | 10.70±5.56 | 15.26±10.36 | <0.001* |
| STEMI, n (%) | 68 (66.7) | 91 (61.5) | 0.403 | 120 (62.5) | 39 (67.2) | 0.511 |
| ADHF, n (%) | 17 (16.7) | 14 (9.5) | 0.089 | 22 (11.5) | 9 (15.5) | 0.411 |
| VT, n (%) | 1 (0.9) | 3 (2.0) | 0.517 | 3 (1.6) | 1 (1.7) | 1.000 |
| VF, n (%) | 1 (0.9) | 0 | 1.000 | 0 | 1 (1.7) | 1.000 |
| LVEF (%) | 49.50±9.6 | 49.08±11.9 | 0.759 | 49.24±11.38 | 49.31±10.01 | 0.768 |
*P<0.05. ADHF: Acute decompensated heart failure, LVEF: Left ventricular ejection fraction, SBP: Systolic blood pressure, STEMI: ST-elevated myocardial infarction, VT: Ventricular tachycardia, VF: Ventricular fibrillation, BMI: Body mass index, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, TC: Total cholesterol
Comparison of smokeless tobacco users with nonusers
On comparison of these two groups, as shown in Table 2, it was found that there were no significant differences in age, gender distribution, BMI, body weight, diabetes, hypertension, nonvegetarian diet, systolic blood pressure, creatinine, lipid profile, or clinical presentation (all P > 0.05). However, current smoking was significantly less frequent in smokeless tobacco users compared to nonusers (P < 0.001). Smokeless tobacco users had a significantly shorter mean sleep duration (6.81 ± 0.99 h vs. 7.16 ± 1.12 h, P = 0.022). Notably, the 30-year QRISK-3 cardiovascular risk score was significantly lower in smokeless tobacco users compared to nonusers (10.70 ± 5.51 vs. 15.26 ± 10.36, P < 0.001).
Cardiovascular risk assessment and risk scores
QRISK-3 risk score was calculated for all 250 patients. However, another risk score, i.e., PREVENT score, can assess the risk in patients under 40, but it is valid only for patients above 30 and up to 79 years of age. Therefore, QRISK-3 and PREVENT scores were calculated for patients between the 30 and 40-year age groups and compared across four groups based on smoking and smokeless tobacco usage, as shown in Figure 2. In this comparison, for the 10-year risk estimates (PREVENT and QRISK-3), all categories show relatively low risk, with smokeless tobacco nonusers demonstrating marginally higher risk than the other groups. In both 30-year estimates (PREVENT and QRISK-3), risk values are substantially higher for all the groups, with smokeless tobacco nonusers showing the highest predicted risk, followed by smokers and smokeless tobacco users, while nonsmokers show the least risk. Subgroup analysis of 223 participants aged between 30 and 40 years is shown in Table 3.
Figure 2.
Comparison of estimated 10-year and 30-year risk estimates for atherosclerotic cardiovascular disease among smokers, nonsmokers, smokeless tobacco users, and smokeless tobacco nonusers using PREVENT and QRISK-3 models
Table 3.
Subgroup analysis of patients (n=223) and the comparison of patients with ST-elevated myocardial infarction (n=138) with non-ST-elevation acute coronary syndrome (n=85) in the 30–40-year age group
| Parameters | Baseline (n=223) | STEMI (n=138) | NSTEACS (n=85) | P |
|---|---|---|---|---|
| Age (years) | 36.08±2.9 | 34.77±4.19 | 35.57±3.7 | 0.133 |
| Male, n (%) | 206 (92.4) | 133 (96.3) | 73 (85.9) | 0.004* |
| BMI (kg/m2) | 24.14±2.53 | 23.98±2.6 | 24.3±2.3 | 0.240 |
| Weight (kg) | 65.82±7.18 | 66.1±7.5 | 65.7±6.9 | 0.651 |
| Diabetes, n (%) | 63 (28.3) | 45 (32.6) | 18 (21.2) | 0.066 |
| Current smoker, n (%) | 91 (40.8) | 58 (42.1) | 33 (38.8) | 0.636 |
| Smokeless tobacco use, n (%) | 171 (76.7) | 105 (76.1) | 66 (77.6) | 0.789 |
| Hypertension, n (%) | 28 (12.6) | 19 (13.8) | 9 (10.6) | 0.486 |
| Nonvegetarian diet, n (%) | 193 (86.5) | 119 (86.2) | 74 (87.1) | 0.860 |
| Sleep duration (h) | 6.91±1.01 | 6.93±0.97 | 6.87±1.07 | 0.647 |
| SBP (mmHg) | 112.09±18.05 | 11.9±18.02 | 112.3±18.2 | 0.885 |
| Creatinine (mg/dl) | 1.05±0.43 | 1.08±0.507 | 1.02±0.27 | 0.373 |
| TC (mg/dl) | 173.08±27.26 | 170.61±27.8 | 177.10±26.01 | 0.084 |
| HDL (mg/dl) | 41.0±5.46 | 40.46±5.09 | 41.89±5.94 | 0.058 |
| LDL (mg/dl) | 99.5±25.88 | 97.14±24.72 | 103.32±27.38 | 0.084 |
| TC/HDL | 3.5±0.92 | 3.57±0.92 | 3.57±0.93 | 0.970 |
| QRISK-3 (10 years) | 1.68±1.67 | 1.71±1.57 | 1.64±1.83 | 0.789 |
| QRISK-3 (30 years) | 12.4±7.2 | 12.75±7.3 | 11.85±7.03 | 0.363 |
| PREVENT 10 | 1.73±2.80 | 1.81±2.69 | 1.61±2.99 | 0.611 |
| PREVENT 30 | 8.76±7.63 | 9.05±7.72 | 8.29±7.52 | 0.472 |
| STEMI, n (%) | 138 (61.9) | 138 (100) | 85 (100) | - |
| ADHF, n (%) | 28 (12.6) | 23 (16.7) | 5 (5.9) | 0.018* |
| VT, n (%) | 4 (1.8) | 4 (2.9) | 0 | 1.000 |
| VF, n (%) | 1 (0.4) | 1 (0.7) | 0 | 1.000 |
| LVEF (%) | 49.26±11.1 | 47.21±11.36 | 52.58±10.07 | <0.001* |
*P<0.05. ADHF: Acute decompensated heart failure, SBP: Systolic blood pressure, STEMI: ST-elevated myocardial infarction, VT: Ventricular tachycardia, VF: Ventricular fibrillation, BMI: Body mass index, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, TC: Total cholesterol, LVEF: Left ventricular ejection fraction, NSTEACS: Non-ST-elevation acute coronary syndrome
Correlation of QRISK-3 and PREVENT risk score
The correlation of 10-year and 30-year risk estimates using QRISK-3 and PREVENT risk score is shown in Figure 3a and b. The Pearson’s correlation coefficient (r) is 0.714 (P < 0.001) for 10-year risk estimates (10-year-QRISK-3 and 10-year-PREVENT) with a coefficient of determination (r2) of 0.510, indicating a strong positive correlation and a moderately strong linear relationship between the two risk scores. This correlation for 30-year risk estimates showed an even stronger correlation (r = 0.868, P < 0.01) and coefficient of determination (r2 = 0.754), reflecting a robust positive association between the two models.
Figure 3.
The correlation of 10-year (a) and 30-year risk (b) estimates using QRISK-3 and PREVENT risk score in the 30–40-year age group (n = 223). Angiographic findings among 250 patients under 40 years (c). LAD: Left anterior descending, LCX: Left circumflex, RCA: Right coronary artery
Angiographic findings
Single-vessel disease was the predominant finding, followed by non-obstructive coronary artery disease (CAD). CAG findings are shown in Figure 3c. Thrombus-containing lesions were 94, while slow flow was seen in 17 patients. Myocardial bridge was observed in 26. A significantly visible calcified lesion was not seen in any of the cases.
DISCUSSION
The present study provides important insights into the clinical profile, lifestyle risk factors, and predictive utility of existing cardiovascular risk scores among young individuals presenting with ACS. The findings highlight the distinctive risk profile of this population, the predominance of modifiable lifestyle factors such as tobacco use, and the limited predictive performance of traditional long-term cardiovascular risk models in anticipating early-onset coronary events.
The study observed that ACS predominantly affected men in this cohort of under 40 years, accounting for more than 90% of the patients. This gender disparity aligns with prior Indian and global data, which consistently report a higher prevalence of premature coronary artery disease in males, likely due to differences in hormone-related protection, lifestyle patterns, and health-seeking behavior.[7,8,9,10] The mean age of ~35 years indicates an alarming trend of premature onset, emphasizing the need for early identification and intervention strategies targeting this age group.
Traditional CVRFs were predominant in this cohort. Smokeless tobacco use (76.8%) was the most common risk factor, followed by smoking (40.8%), suggesting that tobacco exposure remains a major contributor to early atherosclerotic disease in this region. Despite relatively low rates of diabetes (26.8%) and hypertension, the clustering of lifestyle factors such as poor dietary habits, inadequate sleep, and combined use of tobacco products may amplify vascular injury even in the absence of classic metabolic abnormalities. The predominance of a nonvegetarian diet (86.4%) also reflects dietary influences previously linked with higher cardiovascular risk in South Asian population.[10]
A majority (63.6%) of patients presented with STEMI, which is consistent with previous reports suggesting that young ACS patients more often experience complete coronary occlusion rather than plaque erosion or microvascular disease.[11,12,13,14] The relatively low incidence of multivessel disease and calcified lesions, along with high thrombus burden and occasional slow flow phenomenon, parallels earlier studies describing similar angiographic features.[15,16,17,18]
Although baseline parameters and blood investigations did not differ significantly between STEMI and NSTEACS groups, the higher incidence of ADHF and ventricular arrhythmias in the STEMI group was consistent with previous studies.[16,19,20,21]
Comparative analysis between smokers and nonsmokers revealed that while the baseline metabolic parameters and lipid profiles were similar, long-term (30-year) cardiovascular risk estimated by QRISK-3 was significantly higher among smokers. This finding indicates the cumulative vascular damage induced by smoking, not always reflected in short-term risk estimations but evident over extended horizons. Interestingly, smokeless tobacco users had a lower calculated 30-year QRISK-3 score compared to nonusers, which may be due to risk score limitations rather than a true lower risk. Smokeless tobacco use was also linked to shorter sleep duration, potentially compounding cardiovascular risk. These results emphasize that both forms of tobacco use substantially contribute to early coronary artery disease, though current risk prediction tools may underestimate their impact. Both smoking and smokeless tobacco use are established risk factors for ASCVD; however, most of the existing risk scores do not incorporate smokeless tobacco use in their equation, leading to an underestimation of risk among smokeless tobacco users.[3,21,22,23]
One of the commonly used risk prediction tools, i.e., the ASCVD risk score, was developed mainly for adults above 40 years of age.[3,24] However, certain other tools were developed for even younger population, such as QRISK-3, validated for 25–84 years age group, and PREVENT risk scores for the 30–79 years age group.[4,5,6,24,25] Both QRISK-3 and PREVENT risk scores were used to estimate the risk in the 30–40-year age subgroup (n = 223) of this cohort. Both 10-year QRISK-3 and 10-year PREVENT risk estimates were low across all categories, reflecting the inability of these models to predict risk in younger adults. However, the 30-year risk estimates were considerably higher, indicating that lifetime risk models may be more relevant in this demographic. The robust correlation between QRISK-3 and PREVENT (r = 0.868 for 30-year estimates) suggests that both tools show consistent directional prediction, though their ability to predict near-term events remains limited. Although these scores are recommended for region-specific general population and not for patients with established CAD, in this study, they were used in patients under 40 years with ACS and no prior history of established CAD or a similar episode.
These findings highlight a critical gap: existing scoring systems, validated primarily in population over 40 years, underestimate risk in younger individuals who subsequently present with acute coronary events. The strong correlation between QRISK-3 and PREVENT suggests comparability, but both fail to account for early-onset risk driven by behavioral and genetic factors unique to the younger population in India. Interrogating these risk prediction models and the inclusion of smokeless tobacco use seems an unmet need. Hence, developing or recalibrating predictive models that give greater weight to smoking, smokeless tobacco, dietary habits, sleep deprivation, and regional epidemiological patterns is warranted.
CONCLUSIONS
This study demonstrates that young Indians presenting with ACS often exhibit prominent lifestyle-related risk factors, particularly tobacco use, yet their predictive cardiovascular risk by conventional models remains deceptively low. Thus, ACS among young adults under 40 years represents a distinct but preventable subset of the broader cardiovascular disease spectrum. Our results demonstrate that smoking and smokeless tobacco use dominate the risk landscape, while metabolic and lifestyle factors compound long-term susceptibility. Risk prediction tools validated in older individuals, though correlated, underestimate near-term event probability in young adults.
Integrating lifetime risk assessment using tools such as PREVENT and QRISK-3, along with aggressive primary prevention targeting modifiable exposures, will be key to mitigating the rising burden of premature coronary events. Future research should prioritize the development of age- and region-specific predictive algorithms and longitudinal surveillance of young ACS survivors to refine prevention paradigms tailored to early-life cardiovascular risk trajectories.
Ethics declarations (ethics approval and consent to participate)
This study was conducted after obtaining ethical clearance (IEC No./2022/8629-14, September 2022) from the institutional ethical committee of NSCB Medical College, Jabalpur, MP, and all patients gave written informed consent before participating in this study.
Conflicts of interest
There are no conflicts of interest.
Funding Statement
Nil.
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