Environmental pollution has emerged as a major global health concern (1), with increasing evidence linking air pollution, noise pollution, and heavy

metal exposure to adverse cardiovascular outcomes (2). Long-term exposure to pollutants such as particulate matter (PM2.5), nitrogen dioxide (NO₂), sulphur dioxide (SO₂), and heavy metals has been associated with the development of hypertension, diabetes, dyslipidemia, and other cardiometabolic disorders (3). Mechanistically, chronic inflammation, oxidative stress, endothelial dysfunction, and autonomic dysregulation have been implicated in pollution-induced cardiovascular damage (4).

Urbanization and rapid industrial growth in cities like Bhubaneswar have led to increased environmental pollution levels. The presence of a major medical institution, Hi-Tech Medical College, provided a unique opportunity to conduct an epidemiological study evaluating the long-term cardiovascular and metabolic effects of pollution exposure, as it facilitated diverse participant recruitment, ensured better data collection, and enabled comprehensive long-term follow-up. While previous research has established short-term cardiovascular effects of pollution, this study assessed long-term cardiometabolic risks associated with chronic exposure in urban populations and increase in vehicular traffic in cities like Bhubaneswar.

OBJECTIVES

1. Evaluate the association between environmental pollution (PM2.5, NO₂, SO₂, heavy metals, noise levels) and cardiovascular health in residents of Bhubaneswar, Odisha over a three-year period.
2. Quantify the risk of cardiometabolic diseases (hypertension, diabetes, dyslipidemia) in individuals exposed to varying pollution levels using advanced statistical modelling (multivariate regression, survival analysis).
3. Develop a predictive model for cardiovascular risk based on pollution exposure and demographic factors to aid in early disease prevention and policy recommendations.

Study Design

A prospective cohort study was conducted over three years to assess the impact of environmental pollution on cardiovascular and metabolic health outcomes. This design was chosen as it allowed for direct measurement of exposure levels over time, enabling a more accurate assessment of causality between pollution exposure and cardiometabolic outcomes compared to retrospective or cross-sectional studies.

Study Population and Sample Size

The study included adult residents (aged ≥18 years) of Bhubaneswar, Odisha. Participants were selected from the general population of Bhubaneswar, including individuals from various residential and occupational backgrounds, visiting Hi-Tech Medical College for healthcare services.The final sample size comprised 400 individuals, determined based on power calculations ensuring statistical significance for detecting associations between pollution exposure and cardiometabolic outcomes.

Exposure Assessment

Air pollution exposure was assessed through real-time monitoring of PM2.5, NO₂, SO₂, and heavy metals using environmental sensors installed at key urban locations. Noise pollution levels were recorded using standardized decibel meters across residential and industrial zones. Additionally, biological markers of exposure were evaluated by analyzing blood samples for heavy metal concentrations (lead, cadmium) and oxidative stress markers to quantify physiological effects.

Outcome Assessment

Cardiovascular health parameters were systematically evaluated, including blood pressure, lipid profile, fasting blood glucose, and HbA1c levels. The study tracked the incidence of hypertension, type 2 diabetes, and dyslipidemia over the study period to determine the impact of pollution exposure on disease onset. Additionally, electrocardiographic (ECG) and echocardiographic assessments were performed to detect subclinical cardiovascular changes linked to long-term environmental pollution.

Statistical Analysis

Descriptive statistics summarized continuous variables as Mean ± SD and categorical variables as frequency (%). Multivariate regression models assessed associations between pollution exposure and cardiometabolic risk factors. A predictive risk model was developed using logistic regression and random forest algorithms, integrating pollution data and demographic characteristics for improved accuracy.

1. Air Pollution Levels at Hi-Tech Medical College, Bhubaneswar

Air quality monitoring over three years at Hi-Tech Medical College, Bhubaneswar, which serves as a representative urban location revealed, significant pollution levels exceeding WHO-recommended safety limits (Table 1). The highest PM2.5 concentration (85 ± 15 µg/m³) was recorded during the winter months, suggesting a strong influence of meteorological conditions, vehicular emissions, and industrial activity (Figure 1). NO₂ and SO₂ levels were moderately elevated, particularly in areas with high traffic density and industrial emissions.

Heavy metals, including lead (0.45 ± 0.08 µg/m³) and cadmium (0.10 ± 0.02 µg/m³), were detected in ambient air samples, raising concerns about long-term exposure risks. Seasonal variations were evident, with pollution levels peaking during winter and declining during monsoon periods (Figure 2).

Further statistical analysis demonstrated that prolonged exposure to PM2.5 and NO₂ significantly increased the risk of cardiovascular diseases, with PM2.5 exposure showing the highest association with hypertension (OR = 2.3, p < 0.01). SO₂ and heavy metals were strongly correlated with metabolic dysfunction, including dyslipidemia and diabetes (Figure 3). The Forest Plot (Figure 3) visually summarizes the strength of these associations, emphasizing the health impact of long-term pollutant exposure.

Table 1: Mean Annual Air Pollution Levels at Hi-Tech Medical College, Bhubaneswar

Figure 1: Monthly Trends of PM2.5 and NO₂ Levels

Figure 2: Boxplot of PM2.5 Levels Across Seasons

2. Association between Pollution Exposure and Cardiovascular Outcomes

Statistical analysis revealed a significant association between air pollution exposure and cardiovascular risk factors. Multivariate regression analysis indicated that PM2.5 exposure was a strong predictor of hypertension (Odds Ratio [OR] = 2.3, 95% CI: 1.8–2.9, p < 0.01) and NO₂ levels were significantly associated with increased odds of dyslipidemia (OR = 1.9, 95% CI: 1.4–2.6, p = 0.03). Additionally, long-term exposure to heavy metals, particularly lead and cadmium, correlated with impaired glucose metabolism and increased risk of type 2 diabetes (p = 0.02) (Table 2).

Figure 3 illustrates the correlation between PM2.5 levels and hypertension prevalence, demonstrating a clear positive association. Notably, individuals residing in high-pollution areas had a 35% higher risk of developing hypertension compared to those in low-exposure zones. These findings underscore the urgent need for targeted interventions to mitigate pollution-related cardiovascular risks.

Table 2: Multivariate Regression Results

Figure 3: Forest Plot Showing Association between Pollution Exposure and Cardiovascular Outcomes (Odds Ratios with 95% Confidence Intervals)

The above figure 3 presents the odds ratios (OR) and 95% confidence intervals (CI) for the association between exposure to major air pollutants (PM2.5, NO₂, SO₂, lead, and cadmium) and the risk of cardiovascular diseases. PM2.5 and NO₂ exhibited the strongest association with hypertension, while SO₂ and heavy metal exposure were significantly linked to dyslipidemia and metabolic dysfunction. The reference line at OR = 1 (red dashed line) indicates no effect. OR values greater than 1 suggest an increased risk associated with exposure.

3. Predictive Modelling and Risk Estimation

To evaluate the predictive capability of pollution exposure on cardiovascular outcomes, machine learning models (logistic regression and random forest) were used. Air pollution levels (PM2.5, NO₂, SO₂, heavy metals), demographic factors, and clinical biomarkers were incorporated as predictor variables.

The random forest model demonstrated superior predictive performance compared to logistic regression, achieving an AUC of 0.85, while logistic regression had an AUC of 0.78 (Table 3). Among the predictors, PM2.5, NO₂, and lead exposure were identified as the strongest contributors to cardiovascular risk. The combined variable importance plot and calibration curve (Figure 4) highlight the most influential factors in the model and the agreement between predicted and actual risk estimates.

Additionally, decision curve analysis showed that using the predictive model resulted in a net clinical benefit by accurately identifying individuals at high risk for pollution-induced cardiovascular diseases, supporting its applicability in early intervention strategies.

Table 3: Model Performance Metrics for Predicting Cardiovascular Risk

AUC = Area under the Curve, Sensitivity = True Positive Rate, Specificity = True Negative Rate

Interpretation of Model Performance

The random forest model outperformed logistic regression across all performance metrics, with a higher AUC (0.85 vs. 0.78), indicating stronger predictive accuracy. Sensitivity and specificity values were higher for random forest, demonstrating its ability to correctly identify high-risk individuals while minimizing false positives. The precision of the random forest model (79.5%) was also superior, making it a more reliable tool for cardiovascular risk prediction based on pollution exposure.

Figure 4: Variable Importance Plot and Calibration Curve for Predictive Model Performance

The above figure4 presents the odds ratios (OR) and 95% confidence intervals (CI) for the association between exposure to major air pollutants (PM2.5, NO₂, SO₂, lead, and cadmium) and the risk of cardiovascular diseases. PM2.5 and NO₂ exhibited the strongest association with hypertension, while SO₂ and heavy metal exposure (lead, cadmium) were significantly linked to dyslipidemia and metabolic dysfunction. The reference line at OR = 1 (solid vertical line) indicates no effect. OR values greater than 1 suggest an increased risk associated with exposure, while those below 1 indicate potential protective effects.

The findings of this study demonstrate a significant association between long-term exposure to air pollution and adverse cardiovascular outcomes. Our results align with previous epidemiological studies linking particulate matter (PM2.5), nitrogen dioxide (NO₂), and heavy metals to an increased risk of hypertension, dyslipidemia, and metabolic disorders (5,6). The mechanisms underlying these associations likely involve oxidative stress, systemic inflammation, and endothelial dysfunction, as suggested by Miller and Shaw (7).

Air Pollution and Cardiovascular Risk

The strong correlation between PM2.5 exposure and hypertension observed in this study supports previous work by Pope et al. (5), who reported a dose-dependent increase in cardiovascular mortality with rising PM2.5 concentrations. Similarly, Cohen et al. (10) demonstrated that ambient air pollution is a major contributor to the global burden of disease, particularly through its effects on cardiometabolic health. The elevated NO₂ levels in high-traffic zones and their association with metabolic dysfunctions correspond to findings from Landrigan et al. (11), who highlighted urban pollution as a key driver of non-communicable diseases.

Noise Pollution and Cardiovascular Health

Although air pollution is widely recognized as a cardiovascular risk factor, emerging evidence suggests that noise pollution also plays a critical role. The significant association between traffic-related noise and hypertension in our cohort is consistent with the systematic review by Hansell and Ghosh (13), which established noise as an independent risk factor for cardiovascular disease. Münzel et al. (6) further emphasized the combined impact of noise and air pollution, demonstrating their synergistic effects on vascular health.

Heavy Metal Exposure and Metabolic Disorders

The presence of heavy metals, particularly lead and cadmium, in the study environment raises serious health concerns. Our results indicate a strong association between lead exposure and impaired glucose metabolism, similar to findings by Brook et al. (12), who identified heavy metals as potent disruptors of metabolic homeostasis. Cadmium exposure, linked to increased dyslipidemia risk in our study, has also been implicated in metabolic dysfunction by Newby et al. (8), emphasizing the need for stricter environmental regulations.

Comparative Strengths and Limitations

While this study provides robust evidence of pollution-related cardiometabolic risks, certain limitations should be acknowledged. Unlike large-scale multi-regional studies such as those by Cohen et al. (10), which incorporate extensive geographic data, our research was conducted at a single institution. Although this may limit direct generalizability, the selection of participants from diverse residential and occupational backgrounds across Bhubaneswar helps mitigate this concern. Additionally, high-resolution environmental monitoring and comprehensive clinical assessments strengthen the internal validity of our findings. Compared to previous epidemiological studies, which primarily focused on short-term cardiovascular responses to pollution exposure (6,7), this study extends the understanding of long-term cardiometabolic risks in an urban Indian setting. Future research should consider multi-site air quality monitoring and prospective interventions to enhance the generalizability and applicability of findings, as recommended by Brook et al. (9).

Implications for Public Health

Given the substantial burden of pollution-related diseases, policy-level interventions are essential. Recommendations from the Lancet Commission on Pollution and Health (11) emphasize the need for stricter emissions regulations and urban planning reforms. Additionally, community-based strategies, including air quality awareness campaigns and noise mitigation policies, may offer practical approaches to reducing exposure and improving cardiovascular health.

This study provides compelling evidence linking environmental pollution to long-term cardiovascular and metabolic health risks, reinforcing existing research while offering novel insights specific to an urban Indian context. The findings align with global research trends and highlight the urgent need for policy interventions to reduce pollution exposure. Future efforts should focus on refining predictive models, expanding exposure assessments, and developing targeted public health strategies to mitigate pollution-related health risks. By adopting a multidisciplinary approach integrating epidemiology, machine learning, and environmental science, policymakers can implement data-driven strategies to improve urban health outcomes.

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