Risk Factors For Heart Disease Research Articles

Triglyceride levels and its association with all-cause mortality and cardiovascular outcomes among patients with heart failure

Remnant cholesterol, identified by triglyceride-rich lipoprotein, is a significant causal risk factor for ischemic heart diseases. The association of triglyceride levels with all-cause and cause-specific outcomes in heart failure (HF) remains unexplored. Using a previously validated territory-wide clinical information registry, all eligible patients diagnosed with HF (N = 127124) from 2000 to 2020 were included. In this population-based cohort (mean age: 71.4 ± 12.2 years, 51.8% male), the association between triglyceride levels and risk of all-cause mortality and cardiovascular disease was a U-shapedḍ curve. High triglyceride levels (≥3.0 mmol/L) were associated with atherosclerotic cardiovascular disease admission or death; conversely, lower triglyceride levels (<1.2 mmol/L) were associated with higher risks of HF readmission or death. The risk of adjusted all-cause mortality reached a nadir between triglyceride levels of 1.2 mmol/L and 3.0 mmol/L. Results were externally validated in BIOSTAT-CHF. Our findings have important implications for defining the role of triglyceride levels in contributing to the diverse outcomes in patients with HF.

New insights into the risk factors for coronary heart disease: A bibliometric analysis

New insights into the risk factors for coronary heart disease: A bibliometric analysis

HBA1C STATUS IN TYPE II DIABETES MELLITUS WITH AND WITHOUT IRON DEFICIENCY ANAEMIA

Diabetes is an endemic global illness that is on the rise in both developed and developing countries. The American Diabetes Association has suggested glycated haemoglobin (HbA1c) as a possible substitute for fasting blood glucose in diagnosing diabetes. HbA1c is an important measure of long-term glycaemic control since it can represent the total glycaemic history of the two to three months before. In addition to providing a reliable measure of chronic hyperglycemia, HbA1c is highly correlated with the risk of long-term complications from diabetes. Elevated HbA1c has also been identified as an independent risk factor for coronary heart disease and stroke in people with or without diabetes. However, recent research has demonstrated that inadequate iron levels and glycemia contribute to high HbA1c.Therefore, decreasing circulating iron levels might be a worrying risk factor for heart attacks and strokes. The HbA1c test is an impartial biomarker for the diagnosis and outlook of diabetes since it offers valuable information with just one test. This studys results have demonstrated the significance of HbA1c in the diagnosis and prognosis of diabetic patients.

2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association.

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2025 AHA Statistical Update is the product of a full year's worth of effort in 2024 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. This year's edition includes a continued focus on health equity across several key domains and enhanced global data that reflect improved methods and incorporation of ≈3000 new data sources since last year's Statistical Update. Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

The Metabolite Indole-3-Acetic Acid of Bacteroides Ovatus Improves Atherosclerosis by Restoring the Polarisation Balance of M1/M2 Macrophages and Inhibiting Inflammation.

Emerging research has highlighted the significant role of the gut microbiota in atherosclerosis (AS), with microbiota-targeted interventions offering promising therapeutic potential. A central component of this process is gut-derived metabolites, which play a crucial role in mediating the distal functioning of the microbiota. In this study, a comprehensive microbiome-metabolite analysis using fecal and serum samples from patients with atherosclerotic cardiovascular disease and volunteers with risk factors for coronary heart disease and culture histology is performed, and identified the core strain Bacteroides ovatus (B. ovatus). Fecal microbiota transplantation experiments further demonstrated that the gut microbiota significantly influences AS progression, with B. ovatus alone exerting effects comparable to volunteer feces from volunteers. Notably, B. ovatus alleviated AS primarily by restoring the intestinal barrier and enhancing bile acid metabolism, particularly through the production of indole-3-acetic acid (IAA), a tryptophan-derived metabolite. IAA inhibited the TLR4/MyD88/NF-κB pathway in M1 macrophages, promoted M2 macrophage polarisation, and restored the M1/M2 polarisation balance, ultimately reducing aortic inflammation. These findings clarify the mechanistic interplay between the gut microbiota and AS, providing the first evidence that B. ovatus, a second-generation probiotic, can improve bile acid metabolism and reduce inflammation, offering a theoretical foundation for future AS therapeutic applications involving this strain.

Utilizing Binary Logistic Regression Models for Predicting the Risk Factors of Heart Disease

With rising yearly death rates, cardiovascular disease (CVD) continues to be a major global health concern. Effective prevention and management of risk factors depend on an understanding of how they interact. Predicting the primary causes of heart disorders, examining the impact of each of these elements, and organizing them according to model choice and classification capabilities were the objectives of this work. To forecast the probability of CVD, a logistic regression model was created. The parameters impacting infection risk were characterized using descriptive analysis. Data were collected from 82 patients at Benghazi Cardiac Centre, comprising 41 infected patients and 41 control subjects selected using the Stephan Thompson Equation. Binary logistic regression analysis, conducted using SPSS 23, identified four significant predictors of cardiac infection: diabetes, BMI, cholesterol level, and blood pressure. The model demonstrated an 89% accuracy in classifying patients. Further research is recommended to investigate the impact of additional factors on cardiac infection risk in this population.

Role of physical activity in cardiovascular disease prevention: impact of epigenetic modifications.

Cardiovascular disease (CVD) remains the leading cause of death worldwide, imposing a major burden on morbidity, quality of life, and societal costs, making prevention of CVD a top public health priority. Extensive research has pointed out that lack of adequate physical activity in life is one of the key risk factors for heart disease. Indeed, moderate exercise is not only beneficial to the heart in healthy populations, but also exerts a protective effect in pathological states. However, the molecular mechanisms underlying the cardioprotective effects of exercise are still not fully understood. An increasing body of research indicates that variations in the epigenetic system-such as DNA methylation, histone modifications, and production of non-coding RNA-are essential for maintaining heart health and preventing heart disease. Exercise is a potent epigenetic modulator that induces direct and long-lasting genetic changes and activates biological signals associated with cardiovascular health. These changes can be influenced by external stimuli such as physical activity and may even be passed on to offspring, thus providing a mechanism for generating genetic effects through behavioral interventions. Therefore, understanding this relationship can help identify potential biomarkers and therapeutic targets associated with CVD. This study aims to provide an overview of the beneficial effects of exercise on heart health. This information may help guide future research efforts and improve our understanding of epigenetics as a therapeutic, prognostic, and diagnostic biomarker for CVD.

Risk factors for coronary atherosclerotic heart disease in postmenopausal women: a meta-analysis.

Coronary atherosclerotic heart disease (coronary heart disease; CHD) is the leading cause of death in women worldwide, and the number of patients and deaths is increasing each year. Approximately 3.8 million women die from CHD every year globally. After menopause, estrogen levels decrease, and the risk of cardiovascular disease increases substantially; however, research on risk factors for CHD in postmenopausal women has been inconclusive. To systematically evaluate the risk factors for CHD in postmenopausal women. The PubMed, Embase, Web of Science, CINAHL, CBM, CNKI, and VIP databases were searched up to February 9, 2024, for studies on risk factors for CHD in postmenopausal women. Two researchers independently screened the literature, extracted data, and evaluated the quality of the included literature. STATA17.0 software was used for meta-analysis. Nine studies involving 29,4103 patients were included. The results of the meta-analysis showed that history of six or more pregnancies (hazard ratio = 1.538, 95% confidence interval: 1.241%-1.906%) was significantly associated with risk of CHD (P < 0.05). Multiple pregnancies are associated with CHD incidence and related mortality in postmenopausal women. In the future, more and higher quality studies are needed to further verify this conclusion.

The predictive value of triglyceride-glucose index combined with non–high-density lipoprotein cholesterol in coronary heart disease

Background and aimsTo explore the predictive value of the triglyceride-glucose(TyG)index combined with non–high-density lipoprotein cholesterol (Non-HDL-C) in coronary atherosclerotic heart disease (CHD).Methods and resultsWe retrospectively collected patients who were suspected of CHD and underwent coronary angiography in Yiwu Central Hospital and collected medical history, other serum biochemical evaluation and echocardiography from the enrolled population, Non-HDL-C and TyG indices were calculated, and their correlation with Gensini score was analyzed. Logistic regression analysis was used to analyze the risk factors of coronary heart disease, and ROC curves were plotted to assess the predictive value of CHD in subjects with single or multiple indices. TyG index and Non-HDL-C were higher in patients with CHD than in patients without CHD (P < 0.05), and they were independent risk factors for the development of CHD after logistic regression analysis. Diabetes, Non-HDL-C, TyG index, and Gensini score were positively correlated. The areas under the ROC curves for TyG index and Non-HDL-C for the diagnosis of coronary heart disease were 0.719 (95% CI 0.675–0.763) and 0.652 (95% CI 0.605-0.700), respectively, and the area under the ROC curve plotted with the joint equation of the two was 0.724 (95% CI 0.681–0.768), which can better predict the occurrence of coronary heart disease.ConclusionTyG index and Non-HDL-C are independent risk factors for the occurrence of coronary heart disease, and the combination of the two can better predict the occurrence of coronary heart disease.

The efficacy of dietary approaches to stop hypertension (DASH) diet on lipid profile: A systematic review and meta-analysis of clinical controlled trials.

Dyslipidemia is considered a causal risk factor for coronary heart disease and stroke. Plant-based diets such as dietary approaches to stop hypertension (DASH) have beneficial effects on cardiovascular health. This meta-analysis was conducted to assess the effects of the DASH diet on lipid profiles based on clinical controlled trials. An inclusive online search was performed in medical databases including Web of Science, PubMed, Scopus, and Google Scholar up to October 2021 to identify trials assessing the effect of the DASH diet on lipid profiles. Seventeen studies comprising 2218 individuals were included in this meta-analysis. In comparison to the control group, following the DASH diet resulted in a significant reduction in serum triglycerides (WMD: -5.539 mg/dl; 95% CI: -8.806, -2.272) and low-density lipoprotein cholesterol (WMD: -6.387 mg/dl; 95% CI: -12.272, -0.501). However, DASH diet could not reduce serum total cholesterol (WMD: -5.793 mg/dl; 95% CI: -12.84, 1.254), high-density lipoprotein cholesterol (WMD: 0.631 mg/dl; 95% CI: -0.749, 2.011), and total cholesterol/high-density lipoprotein cholesterol ratio (WMD: -0.11 mg/dl; 95% CI: -0.27, 0.05). Findings of this meta-analysis showed that following the DASH diet had beneficial effects on serum triglycerides and low-density lipoprotein cholesterol, however, it had no effect on serum total cholesterol and high-density lipoprotein cholesterol levels. Based on these results, the DASH diet can be considered a strategy for the prevention and complementary management of dyslipidemia.

Effect of a Ketogenic Diet on Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) Progression: A Randomized Controlled Trial.

Despite the popularity of a ketogenic diet, no randomized, controlled trials have evaluated its efficacy on metabolic dysfunction-associated steatotic liver disease (MASLD) progression. We conducted an 8-week, open-label, randomized controlled trial involving 24 patients with MASLD who were randomly assigned to either the home delivery ketogenic diet or a nutrition education program on adherence to the DASH diet. The primary outcome was a reduction in hepatic steatosis as measured by transient elastography. There were no significant differences between groups in liver stiffness and steatosis measurement after interventions. Subjects on a ketogenic diet had lost more weight than subjects in the control group at 8 weeks (mean change [95% CI], -6.16 [-7.22, -5.10] vs. -2.14 [-4.49, 0.21] kg; p = 0.001). The decrease in waist circumference, systolic blood pressure, fat mass, and visceral fat area was significantly greater among subjects on a ketogenic diet than among those in the control group. Laboratory parameters, including AST, triglyceride, and HDL were also significantly decreased among subjects on a ketogenic diet than among those in the control group. A ketogenic diet produced a significantly greater weight loss (absolute difference, approximately 4%) than did the general lifestyle advice intervention for the first 8 weeks. A ketogenic diet was associated with a greater improvement in some risk factors for coronary heart disease and MASLD. However, a ketogenic diet did not reduce steatosis nor worsen MASLD progression. Longer and larger studies are required to determine the long-term safety and efficacy of the ketogenic diet. Trial Registration: TCTR20220426005.

Using big data to predict young adult ischemic vs. non-ischemic heart disease risk factors: An artificial intelligence based model

Using big data to predict young adult ischemic vs. non-ischemic heart disease risk factors: An artificial intelligence based model

Supporting General Practitioners and people with hypertension to maximise medication use to control blood pressure: the contribution of Collective Intelligence to the development of the ‘Maximising Adherence, Minimising Inertia’ (MIAMI) intervention

ABSTRACT Background: Hypertension remains one of the most important modifiable risk factors for stroke and heart disease. Anti-hypertensive medications are effective, but are often not used to maximum benefit. Sub-optimal dosing by prescribers and challenges with medication-taking for patients remain barriers to effective blood pressure control. Objectives: We aimed to systematically develop a theory-based complex intervention to support General Practitioners (GPs) and people with hypertension to maximise medication use to control blood pressure. Methods: We used the three-phase Behaviour Change Wheel (BCW) as the overarching intervention development framework. Collective Intelligence methodology was used to operationalise the stakeholder input to Phases 2 and 3 of the BCW. This took the form of a Collective Intelligence workshop with 19 stakeholders from diverse backgrounds including lived experience, general practice, nursing, pharmacy and health psychology. Techniques such as barrier identification, idea-writing and scenario-based design were used to generate possible intervention options. Intervention options were then selected and refined using the Acceptability, Practicability, Effectiveness, Affordability, Side-effects and Equity (APEASE) criteria and guidance from the MIAMI Public and Patient Involvement Panel. Results: The finalised MIAMI intervention consists of both GP and patient supports. GP supports include a 30-minute online training, information booklet and consultation guide (drop-down menu) embedded within the patient electronic health system. Patient supports include a pre-consultation plan, website, and a structured GP consultation with results from an Ambulatory Blood Pressure Monitor and urine chemical adherence test. The intervention components have been mapped to the intervention functions of the BCW and Behaviour Change Technique Ontology. Conclusion: Collective Intelligence offered a novel method to operationalise stakeholder input to Phases 2 and 3 of the BCW. The MIAMI intervention is now at pilot evaluation stage.

Predicting Heart Disease Risk Using Machine Learning

Heart disease is considered the world leader in mortality rates among people. A project was created with the goal of deploying a machine learning model for the prediction of cardiac disease using publicly available datasets from the University of California, Irvine Machine Learning Repository. Early detection through prompt care may improve death rates. In this respect, several machine learning algorithms: decision trees, logistic regression, random forests, and XGBoost, were used to identify heart disease patterns and risk factors. These above-mentioned models will be evaluated against the following key performance metrics: precision, accuracy, recall, and F1-score. Of all the algorithms, the XGBoost model performed the best, giving a precision of 89% and an F1-score of 0.87, which was one of the best in predicting heart diseases. These findings emphasize the crucial role of machine learning in further improving the prediction of cardiovascular diseases, possibly allowing for early diagnosis. Such predictive tools will allow healthcare providers to move toward more personalized and preventive treatments in patient care and outcomes.

A study to assess the effectiveness of community health nurse led educational module on modifiable risk factors of cardiovascular diseases among adults residing at selected urban areas, Chennai

Knowledge is an important pre-requisite for implementing both primary as well as secondary preventive strategies for cardiovascular disease (CVD). There are no estimates of the level of knowledge of risk factor of heart disease in patients with CVD. Therefore, educating the public with relevant information related to cardiac health will help reduce the spread of non-communicable diseases (NCDs). One of the community-based strategy is to educate participants about cardiac health and CVD. A nurse-led intervention was carried out to bring about a change in the behaviour participants related to modifiable and non-modifiable risk factors related to CVD. The present study indicates the value of educating the public about risk factors associated with CVD.

Prevalence, Patterns and Factors Associated with Dyslipidemia Among Adult Hypertensive Patients

Dyslipidemia is a major risk factor for coronary heart disease and is responsible for an estimated 2.6 million deaths annually. Hypertensive patients often suffer from dyslipidemia, which can lead to complications such as ischemic heart disease and stroke. This study examined the prevalence and factors associated with dyslipidemia among adult hypertensive patients admitted to Lira Regional Referral Hospital (LRRH) in Uganda. This cross-sectional study, conducted over three months at Lira Regional Referral Hospital (LRRH), involved 431 patients. Medical examinations and histories were recorded, and blood samples were analyzed to measure high-density lipoprotein (HDL), low-density lipoprotein (LDL-c), total cholesterol (TC), and triglycerides (TG). Dyslipidemia was defined by any of the following: TC ≥200 mg/dL, TG ≥150 mg/dL, HDL-C &lt; 40 mg/dL, or LDL-C ≥100 mg/dL. The data were analyzed using binary logistic regression in SPSS version 26 to identify associated factors. A total of 431 patients were included in the study, with the majority being females over 65 years of age (mean age = 64.7, SD = 8.8 years). The prevalence of dyslipidemia was 48.3% (208) (95% CI = 43.2-52.9%). High LDL levels were observed in 24.4% (105), high TC in 22.5% (97), high TG in 20.0% (86), and low HDL in 20.0% (86 patients). Multivariate analysis revealed that patients with elevated blood pressure, stage 1 hypertension, and stage 2 hypertension had 1.350 (aOR = 1.350, CI = 1.194-1.525, P &lt; 0.001), 1.290 (aOR = 1.290, CI = 1.123-1.482, P &lt; 0.001), and 1.302 (aOR = 1.302, CI = 1.077-1.576, P = 0.007) times higher odds of having dyslipidemia, respectively. Additionally, dyslipidemia was 1.414 times more common (aOR = 1.414, CI = 1.280-1.561, P &lt; 0.001) among current or former smokers and 1.493 times more common (aOR = 1.493, CI = 1.372-1.625, P &lt; 0.001) in patients with diabetes mellitus. The findings highlight a high prevalence of dyslipidemia, emphasizing the need for targeted interventions. Enhancing patient education on antihypertensive therapy adherence and increasing efforts to discourage smoking are crucial steps to reduce the burden of dyslipidemia in hypertensive populations.

Marital Status and Its Association with Leading Cardiovascular Risk Factors of Ischemic Heart Disease

The aim. To establish a relationship between marital status and frequency of cardiovascular risk factors: type II diabetes, hypertension, overweight, smoking, and hypodynamia. Materials and methods. The study included patients with coronary artery disease (CAD) who underwent off-pump coronary bypass surgery. The total number of people in the sample was 3674, the mean age of the patients was 60.6 ± 0.8 years. Medical history, case records and results of general clinical and laboratory examinations provided for inpatient treatment became the material for analysis. The study design was based on the patient’s age according to the WHO age classification. The relationship between marital status and leading cardiovascular risk factors was calculated using the odds ratio and relative risk. Results. As a result of studying the relationship between marital status as a leading factor in a person’s social status, it was established that the presence of a partner relationship was associated with a 1.50-fold increased risk of overweight (p = 0.0003, χ2 = 21.59), 1.07-fold increased risk of hypertension (p = 0.005, χ2 = 7.60) and 1.26-fold increased risk of type II diabetes (p = 0.04, χ2 = 4.28). Taking into account the age of the patients, it was found that senile married people had an increased risk of overweight (p = 0.0001, χ2 = 20.51) and elderly patients had an increased risk of hypertension (p = 0.002, χ2 = 9.73). It was found that loneliness in patients with CAD was associated with a 4.97-fold increase in the frequency of obese people (p = 0.08, χ2 = 3.02) and smokers (p = 0.0001, χ2 = 182.60). It was also determined that young single patients were significantly more likely to smoke (p = 0.0001, χ2 = 102.19). Conclusions. As a result of the study, the associations of loneliness with leading cardiovascular factors regarding the risk of developing coronary heart disease were clarified. It was found that single persons, as a group with an unfavorable socio-economic status, had increased cardiovascular risks: obesity (p = 0.08, χ2 = 3.02) and smoking (p = 0.0001, χ2 = 121.16). It was also established that young single patients were significantly more likely to smoke (p = 0.0001, χ2 = 102.19), and elderly family patients had a significantly higher risk of overweight (p = 0.0001, χ2 = 20.51), and hypertension (p = 0.002, χ2 = 9.73).

A Preliminary Screening of Single Nucleotide Polymorphism (SNP) of A1 and A2 Beta-casein Alleles in Dairy Cattle

Milk containing A1 variants in the beta-casein gene is considered to be a risk factor for coronary heart disease (CHD), type I diabetes (DM-I), autism, and schizophrenia due to the release of beta-casomorphin 7 (BCM-7). The objectives of this study were to screen for polymorphism of beta-casein in dairy cattle and to examine the allelic and genotypic frequencies of A1 and A2 variants. A total of 290 cattle blood samples were collected from two states and the samples were extracted. This was followed by PCR amplification for exon 7 of the beta-casein gene (CSN2). The PCR products were then subjected to DNA sequencing. The sequencing results were aligned and the variants were screened. In Pahang, 125 samples are of the A2A2 genotype, followed by A1A2 (n=97) and A1A1 (n=28). While in Melaka, the highest genotype was A2A2 (n=20) as compared to A1A2 (n=18) and A1A1 (n=2). Meanwhile, the frequency of the A2 allele was higher than A1 in both states. The high genotypic frequencies of A2A2 in these cattle populations could be influenced by the cattle breed as well as the rate of crossbreeding on the farm. Thus, screening of dairy cattle for A1 and A2 beta-casein could be used in selecting the preferred animals for breeding.

Pattern and Frequency of Congenital Heart Defects Among Infants of Diabetic Mothers.

Background Maternal diabetes mellitus (DM) is a known risk factor for congenital heart diseases (CHDs), which are of significant concern to infants born to diabetic mothers. Compared to newborns born to non-diabetic mothers, infants born to diabetic mothers had a higher overall risk of developing congenital malformations. This association has a complex pathophysiology that includes genetic predispositions, metabolic abnormalities, and environmental factors during key stages of fetal development. By developing screening strategies for neonates born to diabetic mothers, it will be imperative to reduce preventable neonatal mortality by healthcare providers. Purpose The primary objective of this study was to explore the spectrum of congenital heart defects (infants of diabetic mothers, IDMs). Methods This exploratory study was conducted at the maternity and children's hospital in AlAhsa, Saudi Arabia, from 2022 to 2023. The study included 401 neonates delivered at our institution. Within the first seven days of life, an expert pediatric cardiologist from the same institute performed echocardiography on all patients. Results A total of 401 infants born to diabetic mothers were selected, with 293 meeting the inclusion criteria. In total, 144 (49.1%) were boys and 149 (50.9%) were girls. Nearly more than half of mothers (189, 64.5%) had gestational diabetes, while 104 (35.5%) had pre-gestational diabetes. Out of 293 infants born to diabetic mothers, 200 (68.3%) had various CHDs, while the remaining 93 (31.7%) were found to be normal after echocardiography. The most commonly reported CHD is patent ductus arteriosus (PDA) (71.5%), followed by hypertrophic cardiomyopathy (36.5%) and ventricular septal defect (VSD) (11%), and only one patient has a complex congenital heart disease. Conclusion More than half of the infants born to diabetic mothers had congenital heart defects, according to the current study, which examined the various types of congenital heart diseases in neonates of diabetic mothers. It thus emphasizes the necessity of a thorough evaluation and the strong recommendation for an early diagnosis of CHD in this high-risk group. In our population, prenatal CHD screening programs need to be developed.

Exploring Predictive factors for Heart Disease: A Comprehensive Analysis Using Logistic Regression

This study investigates the predictive factors for heart disease by applying logistic regression analysis on a dataset containing various health indicators. By using a backward elimination approach and Akaike Information Criterion (AIC) for model selection, significant predictors such as sex, chest pain type, blood pressure, cholesterol level were identified. Assumption checks confirmed the model’s validity. Visualizations and statistical summaries provided insights into the relationships between heart disease occurrence and key predictors. The final logistic regression model, validated by ROC curve and a confusion matrix, demonstrates a robust predictive capability. The primary contribution of this study lies in demonstrating logistic regression techniques and model validation through tools like ROC curves and confusion matrices. This research thus serves as a tutorial on statistical modeling of heart disease risk factors.