Multifactorial determinants of cardiovascular disease: The role of socio-demographic, lifestyle, and morbidity factors

Abstract

BACKGROUND:

Cardiovascular disease (CVD) development was influenced by various factors, including socio-demographic aspects (age, gender, socioeconomic status, education), lifestyle behaviors (diet, physical activity, smoking, alcohol use), health conditions (hypertension, diabetes, obesity), and genetics. The aim was to examine how these factors relate to the likelihood of CVD confirmation through angiography. Understanding one’s CVD risk is essential for several reasons. Predicting CVD risk involves using factors such as age, exercise habits, diabetes, tobacco use, and marital status, along with statistical models. To determine how socio-demographic factors, health conditions, and self-control relate to the likelihood of confirming cardiovascular disease through angiography.

MATERIALS AND METHODS:

An observational cross-sectional study analyzed angiography reports from 274 participants at Krishna Vishwa Vidhypeeth, Karad, Maharashtra, with CVD complaints from January to May 2023. Statistical analysis was done using Microsoft Excel, Instat, and SPSS version 28. The Chi-Square test assessed the link between demographic parameters, Lifestyle, and Morbidity Factors with CVD, while backward logistic regression was used to create a predictive model.

RESULTS:

Angiography revealed several key factors strongly linked to cardiovascular disease (CVD): regular exercise (79.46% of cases), tobacco chewing (69.7%), diabetes (75.45%), and age. The proportion of significant findings increased with age: 20% in the 20–39 age group, 44.7% in the 40–49 group, 65.1% in the 50–59 group, 75.8% in the 60–69 group, 70.3% in the 70–79 group, and 71.4% in the 80–89 group. Backward logistic regression analysis affirmed these factors as the most reliable predictors of CVD, highlighting the importance of lifestyle choices and co-morbidities in the risk of coronary artery disease.

CONCLUSION:

Recognizing the risk factors for cardiovascular disease enables proactive prevention, timely intervention, and efficient management, which improves individual health and reduces the broader societal effects of CVD. Modifiable risk factors like exercise and tobacco use can be adjusted to significantly lower one’s risk of developing the disease.

Introduction

Cardiovascular diseases (CVDs) are conditions that affect the heart and blood vessels, often leading to heart attacks, strokes, or other serious health problems.[1,2,3] The incidence of cardiovascular disease (CVD) was notably higher among users of smokeless tobacco compared to non-users, irrespective of demographic, socioeconomic, lifestyle, and other tobacco-related factors. Furthermore, the regression analysis found higher odds of stroke history among e-cigarette users than traditional smokers [aOR: 1.15; 95% CI: 1.15–1.16].[4] Engaging in physical exercise provides significant health benefits, including the prevention and slowing of cardiovascular disease. There was a well-established link between Diabetes Mellitus (DM) and CVD, with CVD being the leading cause of death and illness in diabetic populations. Hypertension is also common among individuals with both Type I and Type II DM and is a significant risk factor for CVD. These findings underscore the importance of validating personality proxies related to CVD within the Indian cohort.[5] Franklin et al.[5] analogues as The risks of CHD and CVD decrease in association with increasing percentiles of physical activity and cardiorespiratory fitness, corresponding to 30% and 64% in the most active and fit individuals, respectively. Cardiovascular diseases encompass various disorders affecting the heart and blood vessels, such as coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, congenital heart disease, and conditions like deep vein thrombosis and pulmonary embolism, where blood clots can travel to the heart and lungs, Bourke et al.[6] stated as this study estimated the effects of physical activity on high blood pressure (hypertension).

CVD risk factors are divided into two categories: modifiable and non-modifiable. Non-modifiable factors, such as age, ethnicity, and family history, cannot be changed. However, modifiable factors can be managed or reduced by altering behaviors. By making lifestyle changes like quitting smoking, improving diet, and increasing physical activity, individuals can lower their risk of developing cardiovascular disease. Physical inactivity is prominent in the causal constellation for factors predisposing to cardiovascular disease.[1] As in Kohl 3^rd HW.

Angiography is a crucial tool in diagnosing and planning the treatment of cardiovascular and other vascular diseases. The images obtained from angiography enable doctors to confirm the presence and severity of vascular conditions. For example, coronary angiography can identify significant narrowing or blockages in the coronary arteries, helping guide further treatment options like angioplasty or surgery.

Materials and Methods

Study design and setting

An observational cross-sectional study conducted in a clinical setting aims to assess the prevalence of diseases, risk factors, or health conditions among patients at a specific point in time.

Study participants and sampling

The study included 274 participants with cardiovascular disease (CVD) symptoms who underwent angiography as recommended by a clinician at Krishna Vishwa Vidyapeeth, Karad, Maharashtra, between January to May 2023. Participants were selected through consecutive sampling.

Data collection tool and technique

Data on cardiovascular disease (CVD) were gathered using a structured questionnaire and an interview technique to obtain relevant information from study variables.

Ethical consideration

Ref. No. KIMSDU/IEC/08/2022, Protocol No.-307/2021-2022.

Statistical analysis

Microsoft Excel was employed for data entry, data cleaning, and data preparation. The Chi-square test was conducted using SPSS version 28 and Instat. To enhance the model, backward logistic regression was utilized, offering a clearer interpretation of the results. The probability of cardiovascular disease (CVD) was calculated for the test data, and the impact of the task on the model’s classification of angiography reports was observed.

Sample size calculation

By Li Yang et al.[7] The overall predictive ability of the statistical model developed to predict CVD and Non-CVD is 78.5%. The model sensitivity (SN) and specificity (SP) is 93.05% and 78%, respectively. Using these findings; the sample size ‘n’ i.e. the subjects required to estimate the sensitivity and specificity of the Statistical Prediction Model that identifying modifiable and non-modifiable risk factors of CVD is determined with assumed proportion of angiographycally confirmed CVD in population of patients attending Cardiology Department with complaints of CVD i.e. P = 75%, with maximum acceptable width of the 95% CI of sensitivity and specificity i.e. ±W = 10% as follows:

For Sensitivity (SN):

TP + FN = (Zα/2)² × SN × (1 − SN) / W²

TP+FN = (1.96)² × 0.9305 × (1-0.9305)/(0.1)²

= 3.8416 × 0.9305 × 0.0695/0.01

= 24.84

n₁ = (TP + FN) / P

n1 = 24.84/0.75

n1 = 33.12

For Specificity (SP):

FP + TN = (Zα/2)² × SP × (1 - SP)/W²

= (1.96)² × 0.78× (1-0.78)/(0.1)²

= 3.8416 × 0.78 × 0.22/0.01

= 65.92

n2= (FP + TN)/ (1 - P)

n2 = 65.92/0.25

n2 = 263.68

n2 > n1, thus n, rounded to the next higher whole number, is 264.

Hence, a minimum of 264 patients attending the Cardiology Department with complaints of CVD were planned to enroll in this study.

Inclusion criteria

1. First-time visitors to the Cardiology OPD aged 20–89 years, for consultation and treatment.

2. Patients undergoing angiography on the clinician’s recommendation.

Exclusion criteria

1. Patients already under treatment for CVD.

Interviews were conducted with all eligible participants who experienced CVD symptoms using a specially designed questionnaire to gather the necessary study data.

Consecutive sampling

Every eligible patient who visits the clinic during the study period was included until the required sample size was reached.

Novelty of the study

1. Comparative Approach: This study’s innovative comparative design—examining both angiographycally confirmed CVD patients and those with normal angiograms—will provide valuable insights into the differential impact of various risk factors. This approach allows for a more nuanced understanding of how certain factors contribute to CVD development.

2. Accurate Risk Assessment: The comparative analysis will enable precise computation of the severity of various risk factors, which is often overlooked in existing studies. This enhanced understanding can lead to better identification of high-risk individuals and targeted interventions.

3. Guidance for Lifestyle Modification: By identifying key risk factors and their interactions, the study will provide actionable insights for individuals to modify their lifestyles and reduce CVD risk. This practical application of research findings has the potential to significantly impact public health outcomes.

Results

To compare the study parameters between individuals with CVD and those without CVD. To examine the association between various study parameters and CVDS.

The analysis presented in Table 1 breaks down the percentages and P values of various variables according to angiography criteria, categorized by socio-demographic variables, morbidity and genetic factors, and lifestyle factors. The following conclusions can be drawn from the data:

Table 1.

Distribution of significant variables and predicted angiography report in the trial dataset

Section wise % of trial data (n=274)		Significant variables, P range P<0.05, n (%)		Marginally significant variables, P range 0.05≤P<0.1, n (%)		Non-significant variables, P range P≥0.1, n (%)		Total variables, n (%)
Socio-demographic variables		02 (28.57)		01 (14.28)		04 (57.14)		07 (31.81)
Morbidity and genetic factor		02 (25.00)		01 (12.00)		05 (62.50)		08 (36.36)
Life-style factors		02 (28.57)		01 (14.28)		04 (57.14)		07 (31.81)
Total		06 (27.27)		03 (13.63)		13 (59.09)		22
Trial data (n=274)		P condition		Normal/not significant (%)		Significant (%)		Overall (%)
Predicted angiography report (%)		P<0.05		51.0		82.4		71.2
		P<0.05 and 0.05< P<0.1		46.9		85.2		71.5
		All P range		51.02		87.50		74.45

1. Proportion of Significant Variables: A smaller portion of the variables are statistically significant (P < 0.05). Specifically, socio-demographic variables and lifestyle factors each have 28.57% of their variables in this category, while morbidity and genetic factors have 25.00%. Collectively, significant variables make up 27.27% of the total.

2. Marginally Significant Variables: The category of marginally significant variables (0.05 ≤ P < 0.1) contains the fewest variables in each section: socio-demographic variables (14.28%), morbidity and genetic factors (12.00%), and lifestyle factors (14.28%). Overall, only 13.63% of the variables are marginally significant.

3. Predominance of Non-Significant Variables: Across all categories, the majority of the variables are non-significant (P ≥ 0.1). This is true for socio-demographic variables (57.14%), morbidity and genetic factors (62.50%), and lifestyle factors (57.14%). Overall, 59.09% of the variables fall into this category.

4. Balanced Distribution in Categories: The distribution of significant and non-significant variables is fairly consistent across different categories, suggesting that the likelihood of a variable being significant or non-significant does not vary drastically between socio-demographic, morbidity and genetic, and lifestyle factors.

Overall, the findings emphasize the importance of focusing on the variables that show significant and marginal significance when evaluating factors associated with angiography criteria. Further research may be needed to explore why certain variables show significant associations while others do not.

Integrating Socio-Demographic, Lifestyle, Morbidity, and Genetic Factors to Develop a Practical Health Model for Everyone as below

The following table presents the association between various study variables and angiography findings, categorized as either normal/not significant or significant. The results are as follows:

The distribution of angiography findings across age groups highlights a clear trend: younger individuals (aged 20-39 years) predominantly had normal findings (80%) compared to significant findings (20%), whereas older age groups showed a decreasing proportion of normal findings and increasing proportions of significant findings. For example, in the 60-69 age group, only 24.2% had normal findings, while 75.8% had significant findings, demonstrating a significant association between age and angiography results (χ² = 25.807, P < 0.001).

Regular exercise also showed a distinct impact on angiography findings: individuals who exercised had a higher percentage of normal findings (46.29%) compared to those who did not exercise (20.5%), which had a notably higher proportion of significant findings (79.46%). This underscores a significant association between regular exercise and a reduced likelihood of significant angiography findings (χ² = 19.127, P < 0.001) [Table 2].

Table 2.

Association of angiography findings in relation to socio-demographic, lifestyle, and morbidity

All study variables (significant)				Angiography report				Total n (%)		χ2, P
				Normal/Not significant n (%)		Significant n (%)
Age		20-39		12 (80.0)		3 (20.0)		15 (5.47)		25.807, <0.001
		40-49		21 (55.3)		17 (44.7)		38 (13.86)
		50-59		30 (34.9)		56 (65.1)		86 (31.38)
		60-69		22 (24.2)		69 (75.8)		91 (33.21)
		70-79		11 (29.7)		26 (70.3)		37 (13.50)
		80-89		2 (28.6)		5 (71.4)		07 (2.55)
Regular exercise		Yes		75 (46.29)		87 (53.70)		162 (59.1)		19.127, <0.001
		No		23 (20.5)		89 (79.46)		112 (40.9)
Diabetes		Yes		27 (24.54)		83 (75.45)		110 (40.1)		10.072, 0.002
		No		71 (43.29)		93 (56.70)		164 (59.9)
Tobacco chewing		Yes		50 (30.1)		115 (69.7)		165 (60.2)		5.389, 0.020
		No		48 (44.0)		61 (56.0)		109 (39.8)
Marital status		Married		87 (33.9)		170 (66.1)		257		5.332, 0.021
		Unmarried, divorced		11 (64.7)		6 (35.3)		17

Among individuals with diabetes, 24.54% had normal angiography findings, while 75.45% had significant findings (χ² = 10.072, P = 0.002). In contrast, those without diabetes had 43.29% with normal findings and 56.70% with significant findings. This highlights a significant association between diabetes and an increased likelihood of significant angiography findings (P = 0.002) [Table 2].

Tobacco chewing was associated with 30.1% of individuals having normal angiography findings and 69.7% having significant findings (χ² = 5.389, P = 0.020). In comparison, among non-tobacco chewers, 44.0% had normal findings and 56.0% had significant findings. This indicates a notable link between tobacco chewing and an increased likelihood of significant angiography findings (P = 0.020) [Table 2].

Marital status was linked with angiography findings as follows: among married individuals, 33.9% had normal findings and 66.1% had significant findings (χ² = 5.332, P = 0.021). In contrast, among unmarried or divorced individuals, 64.7% had normal findings and 35.3% had significant findings. This points to a significant association between marital status and angiography results, indicating that married individuals are more likely to exhibit significant findings (P = 0.021) [Table 2].

In summary, the critical importance of effectively managing diabetes and avoiding tobacco use, doing daily exercise to reduce the risk of cardiovascular complications identified through angiography.

For further analysis, we examined the multicollinearity of the significant variables in the study as follows:

1. Age: Coefficient is 0.073, showing a positive, statistically significant relationship (P = 0.004) with a VIF of 1.144, indicating no multicollinearity.

2. Diabetic: Coefficient is –0.154, showing a negative, statistically significant relationship (P = 0.006) with a VIF of 1.030, indicating no multicollinearity.

3. Exercise: Coefficient is 0.231, showing a positive, highly significant relationship (P < 0.001) with a VIF of 1.022, indicating no multicollinearity.

4. Tobacco: Coefficient is –0.082, showing a negative, non-significant relationship (P = 0.142) with a VIF of 1.028, indicating no multicollinearity.

5. Marital Status: Coefficient is –0.129, showing a negative, non-significant relationship (P = 0.270) with a VIF of 1.105, indicating no multicollinearity.

6. Collinearity Diagnostics:

   1. Tolerance: Values close to 1 indicate low multicollinearity.

   2. Variance Inflation Factor (VIF): Values less than 10 suggest no significant multicollinearity issues. All VIF values in this table are close to 1, indicating that multicollinearity was not a concern in this model [Table 3].

Table 3.

Coefficients and multicollinearity assessment of predictors for the dependent variable

Constant		Unstandardized coefficients				t		Sig.		Tolerance		VIF
		β		Std. Error
Age		0.073		0.025		2.882		0.004		0.874		1.144
Diabetic		–0.154		0.056		–2.775		0.006		0.970		1.030
Exercise		0.231		0.055		4.176		0.000		0.978		1.022
Tobacco		–0.082		0.056		–1.473		0.142		0.973		1.028
Marital status		–0.129		0.117		–1.106		0.270		0.905		1.105
Constant		1.564		0.232		6.739		0.000		-		-

Overall, Age, Diabetic status, and Exercise are significant predictors of the dependent variable, while Tobacco use and Marital Status are not. The low VIF values indicate that multicollinearity was not an issue in this regression model [Table 3].

The model predicts “Significant” cases with 87.5% accuracy, but its accuracy for “Normal/Not Significant” cases was lower at 42.9%. Overall, it achieves 71.5% accuracy in predicting angiography reports, indicating reasonable effectiveness. The higher accuracy for “Significant” cases suggests the model was better at identifying patients with significant findings than those with normal or not significant results. Using socio-demographic, lifestyle, and co-morbidity factors with a probability cutoff of 0.5, the model correctly predicted angiography outcomes in 71.5% of cases.

However, due to the differing accuracy for normal/not significant and significant cases at the 0.5 cutoff, an ROC curve analysis was performed to balance sensitivity and specificity. The area under the ROC curve (AUC) was 0.679 [Figure 1]. The sensitivity and specificity coordinates indicated a balanced sensitivity of 71% (correctly identifying those with the disease) and specificity of 63.3% (correctly identifying those without the disease) at a cutoff probability of 0.679.

Figure 1.

Receiver operating characteristic (ROC) curve (angiography report and predictive probability)

Table 4 illustrates that the model accurately predicted 72.4% of individuals with normal/not significant angiography results and 68.2% of those with significant angiography findings. The overall accuracy of the model in predicting angiography reports was 69.7%.

Table 4.

Model prediction classification based on socio-demographics, lifestyle, and co-morbidities for angiography (Probability cutoff: 0.5 and 0.679)

Observed angiography report		Probability cutoff -0.5								Probability cutoff -0.679
		Predicted angiography report				Total		% Correct		Predicted angiography report				Total		% Correct
		Normal/Not Significant		Significant						Normal/Not Significant		Significant
Normal/Not significant		42		56		98		42.9		71		27		98		72.4
Significant		22		154		176		87.5		56		120		176		68.2
Overall %						274		71.5						274		69.7

By using the table following Table 5, the Backward Logistic Regression Model target variable “Li” was computed in numerical expression given as:

Table 5.

Statistical model development for angiography using socio-demographics, lifestyle, and co-morbidities

Constants				B		S.E.		Wald		d.f.		Sig.		Exp. (β) 95% CI
Age		20–39		0.867		0.783		1.226		1		0.268		2.379 [0.513–11.033]
		40–49		1.644		0.759		4.692		1		0.030		5.175 [1.169–22.902]
		50–59		2.052		0.758		7.320		1		0.007		7.780 [1.760–34.394]
		60–69		1.789		0.815		4.814		1		0.028		5.982 [1.210–29.571]
		70–79		1.542		1.133		1.853		1		0.173		4.674 [0.508–43.038]
		80–89		Reference
Exercise		Yes		1.163		0.299		15.157		1		<0.001		3.200 [1.782–5.748]
		No		Reference
Diabetic		Yes		0.800		0.298		7.200		1		0.007		2.225 [1.241–3.990]
		No		Reference
Tobacco chewing		Yes		0.381		0.284		1.797		1		0.180		1.464 [0.838–2.557]
		No		Reference
Marital status		Married		0.345		0.671		0.264		1		0.608		1.411 [0.379–5.259]
		Unmarried, divorced		Reference
Constant				–2.269		0.819		7.675		1		0.006		0.103

Li = 0.867*(Age: 20–39 years)

+ 1.644*(Age: 40–49 years)

+ 2.052*(Age: 50–59 years)

+ 1.789*(Age: 60–69 years)

+ 1.542*(Age: 70–79 years)

+ 1.163*(Performs Exercise)

+ 0.800*(Diabetic)

+ 0.381*(Tobacco User)

+ 0.345*(Married) – 2.269

These commands, the coefficients to compute the predicted log odds. Which are stored in the new variable “Y”. The value of Y is then used to compute the predicted probability by [1/1 + Exp (–Y)] of the target event and store that probability in the new variable by choosing a cut-off value <0.678 as 1 and else.

The logistic regression model demonstrated an overall accuracy of 91.97% on the test data (n = 137) for predicting angiography findings. It accurately identified 85.71% of “Normal/Not Significant” cases (42 out of 49) and 95.45% of “Significant” cases (84 out of 88). However, it misclassified 7 normal cases as significant and 4 significant cases as normal. While the model performs well in both categories, there is still potential for improvement in minimizing misclassifications.

F1 Score: The harmonic mean of precision and recall, providing a single measure that balances both.

= (2 * 85.71 * 91.30)/(85.71 + 91.30)

= 88.42%

An F1 score of 88.42% indicates that the model has a good balance between precision and recall. The F1 score is useful when you need a balance between precision and recall, especially in scenarios where the cost of false positives and false negatives is significant. A high F1 score, like 88.42% suggests that the model was reliable and effective for the task it was designed for.

Discussion

Age wise angiographic report

Roth et al.[8] analogous to cardiovascular diseases, remain the leading cause of disease burden in the world. CVD burden continues its decades-long rise. Participants with complaints of CVD symptoms were found in the age range of 20-80 years. Similar to the findings of a study carried out by the American Heart Association (AHA),[7,8] the maximum participants belonged to 60-69 years age group, 91 (33.21%), while 44 (15.6%) belonged to the ≥70 years age. There was not a single subject with an angiographically significant report in the younger age group (20-29). Age of angiographically normal or not significant subjects ranged between 21 to 84 years, with a mean age of 54.3 years and SD 12.7 years. Similarly, the age of angiographically significant subjects ranged between 36 to 84 years, with a mean age of 60.5 years and SD 9.5 years. The comparison of the mean age of angiographically normal or not significant and angiographically significant revealed significantly longer period taken (6.2 years, t = 4.603, P < 0.001) for the existence of CVD. However, 69.7% of participants with angiographycally significant reports were below 50 years of age. This indicates that Indians are at an increased risk of angiography at a younger age.[9,10] The prevalence of CVD has also been shown to increase with age, in both men and women.[8,9,10,11]

Distribution of angiography report according to the exercise of study participants

A statistically significant association between exercise and angiography outcomes is evident (P < 0.001). Regular exercise has been shown to slow the progression of cardiovascular disease.[12] Jeyalakshmi et al.[6] 80% (n = 287) of study participants had moderate adherence to diet, and only 37.8% (n = 136) of them practiced physical exercise. Mohebbi et al.[9] as from 60 articles, 35 studies met the inclusion criteria. Most interventions improved at least some educational, including model constructs (20%), clinical (14.2%), and practical (88.5%) outcomes related to CVDs.

Distribution of angiography report in relation to co-morbidities as diabetes, with medicine of study variables

The presence of diabetes greatly increases the likelihood of developing macrovascular complications, including coronary artery disease (CAD) and stroke.[13] According to the findings of the interstoke study, there is a significant correlation between diabetes and an elevated risk of stroke.[13] As a result, the risk of cardiovascular events in individuals with diabetes is significantly elevated; Research has shown that people with diabetes are two to four times more likely to get CVD than people without diabetes also the WHO risk chart was 2.5% (95% CI: 2.4–2.6), ranging from 2.3% (95% CI: 2.2–2.4).[10] This higher risk is caused by several things, such as insulin resistance, high blood pressure, and systemic inflammation.

In the present study, it was found that following module-based intervention, there was significant improvement in the mean duration of physical activity in order to control hypertension and Diabetes mellitus.[14]

Distribution of angiography findings associated with the impact of tobacco chewing

Although substantial evidence strongly links tobacco chewing with cardiovascular disease (CVD), the relationship between tobacco use and CVD is complex. Tobacco products impact various forms of CVD as well as their key risk factors. This relationship is further complicated by the numerous cardiovascular effects that can result from smoking and possibly from the use of other tobacco products. Overall, even occasional smoking significantly increases the risk of CVD, with a relative risk (RR) of 1.5 (95% CI: 1.0–2.3), which was relatively similar to Exp. (β = 1.46), [CI = (0.838–2.557)].[3] Baykal et al.[15] Similar results show that Healthy lifestyle behaviors of nursing students are related to the cardiovascular risk factors’ knowledge level and obsession symptoms. Similar to smoking (OR = 3.0; 95% CI = 1.4–6.5), (OR = 5.6; 95% CI = 1.1–28.7) were associated with increased risk in women.[16]

Distribution of angiography report with marital status

When considering marital status, there were about 2/3^rd (66.1%) amongst married, while about 1/3^rd (35.3%) amongst single, with angiographically significant report (χ² = 5.332, P = 0.021). The odds ratio, when compared angiographically significantly reported amongst married about single individuals, at 3.582 with a 95% CI of 1.28–10.01. It was comparatively small with a narrow range of CI in a study of Wong et al.[2] (OR 1.42; 95% CI 1.00-2.01). Wang et al.[13] A similar statement stated that, compared with married individuals, being unmarried was significantly associated with all-cause, cancer, CVD, and coronary heart disease mortalities for both sexes.[17]

Limitations

The study has certain limitations:

1. Social Desirability Bias: One of the limitations can be Recall Bias, as behavioral and self-control factors often rely on self-reported data, which can be influenced by recall bias or social desirability bias.

2. Single-Center Study: Since the study was conducted at a single hospital, the findings may not be generalizable to other regions or healthcare settings.

3. Limited Time Frame: The data collection was conducted over a limited timeframe, specifically from January to May 2022, covering late winter to early summer months. This may not fully capture seasonal variations that could influence cardiovascular conditions.

4. Short Study Duration: The study’s short duration of five months may be insufficient for observing long-term trends or changes in patient profiles.

5. Another limitation, especially with regards to age, edu, work type and family type and exercise which are dependence on the reliability of the interviewee’s report.

6. Study Weakness: In addition, there has been no study on a CVD risk prediction model based on a large cohort population in western Maharashtra, India.

Strengths

This cross-sectional analytical study individually estimated the effect of demographic parameters, heredity, lifestyle, Exercise, COVID-19, and habits with respect to the angiography report. Identifying risk factors of CVDs and receiving appropriate treatment can prevent premature deaths.

Conclusion

Based on health policy benchmarks, addressing modifiable risk factors such as physical inactivity and tobacco use is essential for reducing the burden of cardiovascular disease (CVD) at both individual and population levels. Public health initiatives should prioritize promoting physical activity and implementing stringent tobacco control measures to mitigate CVD risk. Furthermore, policies aimed at enhancing self-regulation and behavioral interventions can contribute to better cardiovascular outcomes. By integrating these strategies into preventive healthcare frameworks, policymakers can improve overall public health while reducing healthcare costs associated with CVD management.

Transferability

Generalizability depends on demographic, clinical, and lifestyle factors. The applicability was higher in populations with similar socio-demographic profiles, risk factors, and healthcare access.

Future prospective studies on CVD progression and intervention outcomes

Conduct long-term follow-up studies to assess the progression of cardiovascular disease (CVD) and the effectiveness of different interventions over time.

Conflicts of interest

Nil.

Funding Statement

Nil.