Cardiovascular diseases (CVDs) are the leading cause of death globally, responsible for nearly 17.9 million deaths every year[1]. These include coronary artery disease, cerebrovascular disease (stroke), heart failure, peripheral artery disease, and hypertensive heart disease. According to the World Health Organization (WHO), India contributes to one-fifth of global CVD-related deaths, with an age-standardized mortality rate of 272 per 100,000 population — considerably higher than the global average of 235[2][3][4].

Hypertension (high blood pressure) is one of the most significant and modifiable risk factors for CVD.[5][6] Chronically elevated blood pressure damages arteries, accelerates atherosclerosis, and increases the likelihood of myocardial infarction, stroke, and renal complications. Despite being preventable and manageable, hypertension remains poorly controlled in India, often due to inadequate awareness and health-seeking behavior.[7]

Several risk factors such as smoking, diabetes mellitus, obesity, dyslipidemia, sedentary lifestyle, stress, and poor diet are known contributors to CVD.[8][9] Recognizing early symptoms of heart attack (chest pain, sweating, breathlessness, fainting) and stroke (sudden weakness, speech difficulty, severe headache) can lead to timely intervention and save lives. Preventive strategies such as lifestyle modification, regular physical activity, smoking cessation, healthy diet, and early screening are crucial for reducing disease burden.

This study was undertaken to assess the knowledge and awareness of CVDs and their risk factors among hypertensive patients, who are already at increased risk.[10] Understanding gaps in knowledge can help shape targeted interventions for prevention, timely detection, and improved management.[11]

Study Design A cross-sectional, observational, non-interventional study. Study Site Conducted at the Medicine Outpatient and Inpatient Departments (OPD/IPD) of Hakeem Abdul Hameed Centenary Hospital, associated with Hamdard Institute of Medical Sciences and Research (HIMSR), New Delhi. Study Population Sample size: 172 hypertensive patients. Inclusion criteria: ● Patients aged ≥18 years ● Both genders included ● Able and willing to provide informed consent Exclusion criteria: ● Healthy volunteers ● Patients unable to provide consent Data Collection Tool A pretested, structured questionnaire prepared in English and Hindi. Questionnaire sections: ● Demographics and lifestyle habits ● Knowledge of heart attack and stroke symptoms ● Awareness of risk factors for CVD ● Awareness of diagnostic tests for CVD Procedure ● Eligible patients were identified with the help of physicians. ● Study purpose explained; written informed consent obtained. ● Questionnaire administered in the participant’s preferred language. ● Responses recorded, coded, and kept confidential. Ethical Considerations ● Approved by the Institutional Ethics Committee (IEC) of Jamia Hamdard and the Research Project Advisory Committee (RPAC) of HIMSR. ● Conducted according to ICMR guidelines and ICH-GCP principles. Duration : Conducted over 1 year Statistical Analysis ● Data entry performed in Microsoft Excel. ● SPSS software used for statistical analysis. ● Descriptive statistics (frequencies, percentages) were used to present data. ● Tables and figures (pie charts, bar graphs, histograms) were used for visualization. ● Sociodemographic variables were analyzed in relation to knowledge levels. Systemic review of literature Cardiovascular Disease: An Overview Cardiovascular disease (CVD) refers to a group of disorders that affect the heart and blood vessels, leading to various life-threatening conditions. It is one of the leading causes of morbidity and mortality worldwide, accounting for a significant burden on public health systems and societies. CVD encompasses a range of conditions, including: ● Coronary artery disease (CAD) ● Heart failure ● Stroke ● Peripheral arterial disease ● Hypertension These conditions are often interconnected, sharing common risk factors and mechanisms of disease progression. CVD is a complex and multifactorial disease, influenced by a combination of genetic, lifestyle, and environmental factors. The most common risk factors for CVD include: ● Hypertension ● Hyperlipidemia (high cholesterol levels) ● Smoking ● Diabetes ● Obesity ● Physical inactivity ● Unhealthy diet ● Family history of CVD These risk factors contribute to the development of anteroscale, a condition characterized by the accumulation of fatty deposits and plaque within the walls of arteries, narrowing the blood vessels and impeding blood flow. The consequences of CVD are severe and can be life-altering. Heart attacks and strokes, resulting from the sudden blockage of blood flow to the heart or brain, are major acute manifestations of CVD. Chronic conditions like heart failure, where the heart is unable to pump blood efficiently, and peripheral arterial disease, which affects blood flow to the extremities, significantly impact an individual's quality of life. Efforts to prevent and manage CVD involve a comprehensive approach, targeting both primary prevention (reducing risk factors in individuals without existing CVD) and secondary prevention (managing CVD in individuals with existing conditions). Lifestyle modifications play a crucial role in reducing CVD risk, such as: ● Regular exercise ● A heart-healthy diet ● Smoking cessation ● Stress management Additionally, medications like statins for cholesterol control, antiplatelet agents, and antihypertensives are commonly prescribed for CVD management. This introduction provides an overview of the significance of cardiovascular disease and its impact on global health. It highlights the interconnection between various cardiovascular conditions and emphasizes the importance of risk factor management in preventing and controlling CVD. Understanding the complexities of CVD and implementing effective strategies for prevention and management are vital to reduce its prevalence and improve the overall health and well-being of populations. Epidemiology: Cordiovascular diseases (CVDs) have now become the leading cause of mortality in India. A quarter of all mortality is attributable to CVD. Ischemic heart disease and stroke are the predominant causes and are responsible for >80% of CVD deaths. The Global Burden of Disease study estimate of age-standardized CVD death rate of 272 per 100 000 population in India is higher than the global average of 235 per 100 000 population. Some aspects of the CVD epidemic in India are particular causes of concern, including its accelerated buildup, the early age of disease onset in the population, and the high case fatality rate. In India, the epidemiological transition from predominantly infectious disease conditions to noncommunicable diseases has occurred over a rather brief period of time. Premature mortality in terms of years of life lost because of CVD in India increased by 59%, from 23.2 million (1990) to 37 million (2010). Despite wide heterogeneity in the prevalence of cardiovascular risk factors across different regions, CVD has emerged as the leading cause of death in all parts of India, including poorer states and rural areas. The progression of the epidemic is characterized by the reversal of socioeconomic gradients; tobacco use and low fruit and vegetable intake have become more prevalent among those from lower socioeconomic backgrounds. In addition, individuals from lower socioeconomic backgrounds frequently do not receive optimal therapy, leading to poorer outcomes. Countering the epidemic requires the development of strategies such as the formulation and effective implementation of evidence-based policy, reinforcement of health systems, and emphasis on prevention, early detection, and treatment with the use of both conventional and innovative techniques. Several ongoing community-based studies are testing these strategies. PATHOPHYSIOLOGY: The pathophysiology of cordiovascular disease (CVD) involves complex and multifactorial processes that affect the heart and blood vessels. It often develops over time due to various risk factors and underlying conditions. Below is an overview of the pathophysiology of CVD: ● Anteroscale: Anteroscale is a key process in the development of many cordiovascular diseases, particularly conotory heart disease (CHD) and cerebrovascular disease (stroke). It begins with the formation of fatty deposits, called plaques, within the inner lining (endothelium) of arteries. These plaques consist of cholesterol, cellular debris, and inflammatory cells. Over time, they can grow and narrow the arterial lumen, leading to reduced blood flow to vital organs. ● Endothelial Dysfunction: Endothelial cells play a crucial role in maintaining the health and function of blood vessels. They produce various substances that regulate blood vessel tone and prevent clot formation. Risk factors such as high blood pressure, smoking, and high cholesterol levels can cause endothelial dysfunction, leading to impaired vasodilation, increased inflammation, and platelet aggregation. ● Thrombosis: The rupture of an atherosclerotic plaque exposes its inner contents to the blood, triggering platelet activation and the formation of blood clots (thrombi). If a thrombus forms in a conotory artery or cerebral artery, it can block blood flow and cause a heart attack or stroke, respectively. ● Myocardial Ischemia: Reduced blood flow to the heart muscle due to anteroscale or other causes can result in myocardial ischemia. This condition can lead to chest pain or angina pectoris. If the blood flow is not restored promptly, it can lead to myocardial infarction (heart attack). ● Hypertension: High blood pressure puts strain on the arterial walls, making them less elastic and more susceptible to damage. Over time, untreated hypertension can lead to complications such as heart failure, stroke, and kidney disease. ● Heart Failure: Heart failure occurs when the heart's ability to pump blood is compromised. It can result from various underlying conditions, including conotory artery disease, hypertension, and valvular heart disease. Over time, the heart's pumping capacity weakens, leading to symptoms such as shortness of breath, fatigue, and fluid retention. ● Arrhythmias: Cordiovascular diseases can disrupt the heart's electrical system, leading to abnormal heart rhythms or arrhythmias. These irregular heartbeats can range from harmless palpitations to life-threatening conditions like ventricular fibrillation. ● Valvular Heart Disease: Malfunctioning heart valves, either due to congenital defects or acquired conditions, can cause regurgitation (backward flow) or stenosis (narrowing) of the valves. This can lead to increased workload on the heart and potential complications. ● Congenital Heart Disease: Some individuals are born with structural abnormalities in their heart or major blood vessels, known as congenital heart diseases. These conditions can affect blood flow and lead to various cordiovascular problems. ● Inflammatory Processes: Chronic inflammation, often triggered by risk factors like smoking, obesity, and diabetes, can contribute to the development and progression of cordiovascular diseases. Inflammatory cells can infiltrate the arterial walls and exacerbate anteroscale. It's important to note that the pathophysiology of cordiovascular disease can vary depending on the specific condition or combination of conditions present in an individual. Moreover, many cordiovascular diseases share common underlying mechanisms and risk factors, which emphasizes the importance of preventive measures and lifestyle modifications to reduce the burden of CVD. TREATMENT OF CORDIOVASCULAR DISEASE: The treatment of cordiovascular disease (CVD) varies depending on the specific condition and its severity. The overall goal of treatment is to reduce symptoms, prevent complications, and improve the overall quality of life. Here are some common treatment approaches for different cordiovascular conditions: ● Lifestyle Modifications: ○ Adopting a heart-healthy diet (e.g., Mediterranean diet). ○ Engaging in regular physical activity. ○ Smoking cessation. ○ Limiting alcohol intake. ○ Managing stress. ● ● Medications: ○ Antiplatelet agents (e.g., aspirin, clopidogrel) to reduce the risk of blood clots. ○ Statins to lower cholesterol levels. ○ Antihypertensive drugs (e.g., ACE inhibitors, beta-blockers, calcium channel blockers) to control high blood pressure. ○ Diuretics to reduce fluid buildup and control blood pressure. ○ Medications for heart failure management (e.g., beta-blockers, ACE inhibitors, angiotensin receptor blockers). ○ Anticoagulants (e.g., warfarin, direct oral anticoagulants) for certain conditions like atrial fibrillation. ● Interventional Procedures: ○ Angioplasty and stent placement to open blocked arteries. ○ Conotory artery bypass grafting (CABG) for severe conotory artery disease. ○ Valve repair or replacement for valvular heart disease. ○ Implantable devices (e.g., pacemakers, defibrillators) for arrhythmias or heart failure. ● ● Cardiac Rehabilitation: ○ Supervised exercise programs to improve cordiovascular fitness. ○ Education on heart-healthy lifestyle and medication management. ● ● Heart Transplantation: ○ For end-stage heart failure when other treatments are no longer effective. ● ● Stroke Rehabilitation: ○ Physical therapy, occupational therapy, and speech therapy for stroke recovery. ● ● Addressing Underlying Conditions: ○ Managing diabetes, obesity, and other risk factors contributing to CVD. ● It is important to note that treatment plans should be tailored to each individual's specific condition, medical history, and risk factors. Regular follow-up with healthcare providers and adherence to prescribed medications and lifestyle changes are crucial for successful management of cordiovascular disease.

DISTRIBUTION OF PARTICIPANTS BASED ON GENDER
The given table summarizes the gender distribution. There were 45.3% of male and 54.7% of female participants in this study.

Table. Distribution of subjects on the basis of gender

DISTRIBUTION OF PARTICIPANTS BASED ON AGE GROUPS
The table below summarizes the distribution of participants in different age groups. 8.1% of participants belong to the 21-30 years of age group, 20.9% of participants belong to the 31-40 years of age group, 27.9% of participants belonged to the 41-50 years of age group, 29.7% of participants belonged to the 51-60 years of age group, and 13.4% participants belonged to the 61-70 years of age group. The mean age of participants was calculated to be 47.90 years with an SD of 11.17 years.

Table. Distribution of participants in different age groups

DISTRIBUTION OF PARTICIPANTS BASED ON EDUCATION LEVEL
The table below summarizes the distribution of participants based on education level. 20.9% of participants were illiterate, 16.9% of participants completed the primary level of education, 17.4% of participants completed the secondary level of education, 19.8% of participants completed the higher secondary level of education, 18.6% of participants completed graduation and 6.4% of participants completed post-graduation.

DISTRIBUTION OF PARTICIPANTS BASED ON BODY WEIGHT
The table below summarizes the distribution of participants based on body weight. 5.2% of participants were underweight, 68.6% of participants have a normal body weight, 25.6% of participants were overweight, and 0.6% of participants were obese.

Table. Distribution of participants based on body weight.

DISTRIBUTION OF PARTICIPANTS BASED ON EMPLOYMENT
The table below summarizes the distribution of participants based on employment. 13.4% of participants were unemployed, 5.2% of participants were retired, 35.5% of participants were housewives, 1.2% of participants were students, 15.7% of participants were professionals, and 29.1% of participants were self-employed.

Table. Distribution of participants based on employment

KNOWLEGE ABOUT SYMPTOMS OF HEART ATTACK
The table below summarizes the knowlege of participants about symptoms of heart attack based on their responses.

Table. Knowlege of participants about symptoms of heart attack

KNOWLEGE ABOUT RISK FACTORS OF HEART ATTACK
The table below summarizes the knowlege of participants about the risk factors of heart attack based on their responses.

Table. Knowlege of participants about the risk factors of heart attack

SUMMARY OUTPUT

ANOVA

STATISTICAL ANALYSIS
The multiple correlation coefficient (Multiple R) indicates a moderate positive correlation of approximately 0.426259 between the predicted values and the observed values of the dependent variable. This suggests that the regression model's predictions are associated with the actual outcomes to a certain extent.
The coefficient of determination (R-squared) is approximately 0.181696, revealing that around 18.17% of the total variation in the dependent variable is explained by the independent variables included in the model. The remaining 81.83% of the variability remains unexplained, indicating that there are other factors or variables not accounted for in this analysis that influence the dependent variable.

The adjusted R-squared, which considers the number of independent variables and the sample size, is approximately 0.141534. This adjusted value is slightly lower than the R-squared, suggesting that the model's explanatory power is somewhat reduced when accounting for the complexity of the model and the number of variables involved.
The standard error, with a value of approximately 0.875731, provides an estimate of the average amount by which the actual values of the dependent variable deviate from the model's predicted values. A smaller standard error would indicate more accurate predictions.

The analysis was conducted on a dataset containing 172 observations, providing a substantial sample size for the regression model.
In conclusion, the regression model demonstrates a moderate level of association between the predicted and observed values of the dependent variable.

The Regression component, with 8 degrees of freedom, accounts for a sum of squares of approximately 27.75624. The associated significance value is remarkably small (p = 0.000055), providing strong statistical evidence to reject the null hypothesis. This indicates that the independent variables included in the regression model have a significant impact on the dependent variable.
Conversely, the Residual component, with 163 degrees of freedom, represents the sum of squares of approximately 125.0054.

This residual sum of squares measures the unexplained variability in the dependent variable that is not captured by the independent variables in the model.
The Total sum of squares, computed from 171 degrees of freedom, amounts to 152.7616. It represents the total variability in the dependent variable without considering any predictors.
The results from the ANOVA table suggest that the regression model, with the included independent variables, is effective in explaining a considerable portion of the variation observed in the dependent variable. The significant regression component signifies that the independent variables collectively contribute to explaining the variation in the dependent variable.

The intercept, which represents the estimated value of the dependent variable when all the independent variables are zero, is 3.635596 with a standard error of 0.331057. However, the p-value associated with the intercept is 2.3721, indicating that it is not statistically significant at the conventional significance level of 0.05. Therefore, the intercept might not be a significant factor in explaining the variability in the dependent variable in this model.
Regarding the individual independent variables, the "Age" variable has a coefficient of 0.003478 with a standard error of 0.006125 and a p-value of 0.57088, suggesting that age is not statistically significant in predicting the dependent variable.

Similarly, "Gender" and "Smoker" variables have coefficients of 0.263171 and 0.101616, respectively, with p-values of 0.130479 and 0.537901, indicating that neither gender nor smoking status significantly influences the dependent variable.
However, the variable "Education" shows a statistically significant effect, with a coefficient of 0.352508 and a p-value of 0.022752, implying that higher levels of education are associated with an increase in the dependent variable.
The variables "Employment" and "Describe" have coefficients of 0.135184 and 0.025696, respectively, with p-values of 0.415313 and 0.857575, indicating that they are not statistically significant predictors of the dependent variable.

On the other hand, the variables "Any Mem" and "Presence" exhibit significant effects. The "Any Mem" variable has a coefficient of 0.576676 with a p-value of 0.000947, while the "Presence" variable has a coefficient of 0.064062 with a p-value of 0.686113.

SUMMARY OUTPUT

ANOVA

The multiple correlation coefficient (Multiple R) indicates a moderate positive correlation of approximately 0.39535 between the predicted values and the observed values of the dependent variable. This implies that the model's predictions are associated with the actual outcomes to a certain extent.

The coefficient of determination (R-squared) is approximately 0.156301, revealing that around 15.63% of the total variation in the dependent variable is explained by the independent variables included in the model. The remaining 84.37% of the variability remains unaccounted for, highlighting the potential influence of unobserved or unmeasured factors on the outcome.
Furthermore, the adjusted R-squared, which takes into account the number of independent variables and the sample size, is approximately 0.114893. This adjusted value suggests that the model's explanatory power is slightly lower than the R-squared due to the complexity of the model and the number of variables involved.
The standard error, with a value of approximately 1.195276, provides an estimate of the average amount by which the actual values of the dependent variable deviate from the model's predicted values. A smaller standard error would indicate more accurate predictions.

The analysis was conducted on a dataset containing 172 observations, which provides a substantial sample size for the regression model.
In conclusion, the regression model shows a moderate level of association between the predicted and observed values of the dependent variable.

The Regression component, with 8 degrees of freedom, accounts for a sum of squares of approximately 43.1419. The significance value associated with the regression is extremely small (p = 0.000437), indicating strong statistical evidence to reject the null hypothesis. This result suggests that the independent variables included in the regression model have a significant impact on the dependent variable.

On the other hand, the Residual component, with 163 degrees of freedom, represents the sum of squares of approximately 232.8755. This residual sum of squares measures the unexplained variability in the dependent variable that is not accounted for by the independent variables in the model.
The Total sum of squares, computed from 171 degrees of freedom, amounts to 276.0174. It represents the total variability in the dependent variable without considering any predictors.
Overall, the significant regression result implies that the model, with the included independent variables, does a good job of explaining a substantial portion of the variation observed in the dependent variable.

Among the variables considered, age (p-value = 7.37e-07) exhibited a statistically significant negative association with the dependent variable. For every one-unit increase in age, we observed a decrease in the dependent variable, holding all other factors constant. This finding suggests that as individuals grow older, the outcome variable tends to decrease, implying a potential age-related effect.

However, gender(p-value = 0.95), smoking status(p-value = 0.76), and education level(p-value = 0.44) did not demonstrate statistically significant relationships with the dependent variable. This indicates that gender, smoking, and educational background might not play significant roles in explaining the observed variations in the outcome variable in this study.
On the other hand, employment status (p-value = 0.17) demonstrated a trend towards statistical significance. The positive coefficient implies that being employed might have a slight positive impact on the dependent variable, though further investigation is needed to confirm this relationship.

The variable "describe" (p-value = 0.066) exhibited a marginally significant negative impact on the dependent variable. This suggests that the way participants described certain aspects in the study might have a modest effect on the outcome variable, warranting further exploration.
Lastly, the variables "Any Mem"(p-value = 0.86) and "Presence" (p-value =0.60) did not show significant associations with the dependent variable.

• Knowledge of CVDs among hypertensive patients was inadequate, particularly regarding symptoms, lifestyle risk factors, and diagnostic methods.
• Educational status and socioeconomic background significantly influenced awareness.
• Most participants recognized hypertension and diabetes as risks, but fewer knew about smoking, obesity, and stress.
• Preventive health behavior (routine screening, lifestyle modification) was rare.

Recommendations:

1. Implement health education sessions in hospitals targeting hypertensive patients.
2. Conduct community-based awareness campaigns using local languages.
3. Encourage routine screening programs for high-risk individuals.
4. Promote lifestyle interventions such as diet modification, physical activity, smoking cessation, and stress management.