Machine Learning Approach to Classify Cardiovascular Disease in Patients With Nonalcoholic Fatty Liver Disease in the UK Biobank Cohort

Determinants of developing cardiovascular disease risk with emphasis on type-2 diabetes and predictive modeling utilizing machine learning algorithms

This research aims to enhance our comprehensive understanding of the influence of type-2 diabetes on the development of cardiovascular diseases (CVD) risk, its underlying determinants, and to construct precise predictive models capable of accurately assessing CVD risk within the context of Bangladesh. This study combined data from the 2011 and 2017 to 2018 Bangladesh Demographic and Health Surveys, focusing on individuals with hypertension. CVD development followed World Health Organization (WHO) guidelines. Eight machine learning algorithms (Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, K-Nearest Neighbor, Light GBM, and XGBoost) were analyzed and compared using 6 evaluation metrics to assess model performance. The study reveals that individuals aged 35 to 54 years, 55 to 69 years, and ≥ 70 years face higher CVD risk with adjusted odds ratios (AOR) of 2.140, 3.015, and 3.963, respectively, compared to those aged 18 to 34 years. "Rich" respondents show increased CVD risk (AOR = 1.370, P < .01) compared to "poor" individuals. Also, "normal weight" (AOR = 1.489, P < .01) and "overweight/obese" (AOR = 1.871, P < .01) individuals exhibit higher CVD risk than "underweight" individuals. The predictive models achieve impressive performance, with 75.21% accuracy and an 80.79% AUC, with Random Forest (RF) excelling in specificity at 76.96%. This research holds practical implications for targeted interventions based on identified significant factors, utilizing ML models for early detection and risk assessment, enhancing awareness and education, addressing urbanization-related lifestyle changes, improving healthcare infrastructure in rural areas, and implementing workplace interventions to mitigate stress and promote physical activity.

Association of cardiorenal biomarkers with mortality in metabolic syndrome patients: A prospective cohort study from NHANES

Objectives

Approximately 20%-25% of the global adult population is affected by metabolic syndrome (MetS), highlighting its status as a major public health concern. This study aims to investigate the predictive value of cardiorenal biomarkers on mortality among patients with MetS, thus optimizing treatment strategies.

Methods

Utilizing data from the National Health and Nutrition Examination Survey (NHANES) cycles between 1999 and 2004, we conducted a prospective cohort study involving 2369 participants diagnosed with MetS. We evaluated the association of cardiac and renal biomarkers with all-cause and cardiovascular disease (CVD) mortality, employing weighted Cox proportional hazards models. Furthermore, machine learning models were used to predict mortality outcomes based on these biomarkers.

Results

Among 2369 participants in the study cohort, over a median follow-up period of 17.1 years, 774 (32.67%) participants died, including 260 (10.98%) from CVD. The highest quartiles of cardiac biomarkers (N-terminal pro-B-type natriuretic peptide [NT-proBNP]) and renal biomarkers (beta-2 microglobulin, [β2M]) were significantly associated with increased risks of all-cause mortality (hazard ratios [HRs] ranging from 1.94 to 2.06) and CVD mortality (HRs up to 2.86), after adjusting for confounders. Additionally, a U-shaped association was observed between high-sensitivity cardiac troponin T (Hs-cTnT), creatinine (Cr), and all-cause mortality in patients with MetS. Machine learning analyses identified Hs-cTnT, NT-proBNP, and β2M as important predictors of mortality, with the CatBoost model showing superior performance (area under the curve [AUC] = 0.904).

Conclusion

Cardiac and renal biomarkers are significant predictors of mortality in MetS patients, with Hs-cTnT, NT-proBNP, and β2M emerging as crucial indicators. Further research is needed to explore intervention strategies targeting these biomarkers to improve clinical outcomes.

Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions

The integration of machine learning (ML) and artificial intelligence (AI) techniques in life-course epidemiology offers remarkable opportunities to advance our understanding of the complex interplay between biological, social, and environmental factors that shape health trajectories across the lifespan. This perspective summarizes the current applications, discusses future potential and challenges, and provides recommendations for harnessing ML and AI technologies to develop innovative public health solutions. ML and AI have been increasingly applied in epidemiological studies, demonstrating their ability to handle large, complex datasets, identify intricate patterns and associations, integrate multiple and multimodal data types, improve predictive accuracy, and enhance causal inference methods. In life-course epidemiology, these techniques can help identify sensitive periods and critical windows for intervention, model complex interactions between risk factors, predict individual and population-level disease risk trajectories, and strengthen causal inference in observational studies. By leveraging the five principles of life-course research proposed by Elder and Shanahan-lifespan development, agency, time and place, timing, and linked lives-we discuss a framework for applying ML and AI to uncover novel insights and inform targeted interventions. However, the successful integration of these technologies faces challenges related to data quality, model interpretability, bias, privacy, and equity. To fully realize the potential of ML and AI in life-course epidemiology, fostering interdisciplinary collaborations, developing standardized guidelines, advocating for their integration in public health decision-making, prioritizing fairness, and investing in training and capacity building are essential. By responsibly harnessing the power of ML and AI, we can take significant steps towards creating healthier and more equitable futures across the life course.

Machine Learning Approach for Cardiovascular Death Prediction among Nonalcoholic Steatohepatitis (NASH) Liver Transplant Recipients

Cardiovascular disease is the leading cause of mortality among nonalcoholic steatohepatitis (NASH) patients who undergo liver transplants. In the present study, machine learning algorithms were used to identify important risk factors for cardiovascular death and to develop a prediction model. The Standard Transplant Analysis and Research data were gathered from the Organ Procurement and Transplantation Network. After cleaning and preprocessing, the dataset comprised 10,871 patients and 92 features. Recursive feature elimination (RFE) and select from model (SFM) were applied to select relevant features from the dataset and avoid overfitting. Multiple machine learning algorithms, including logistic regression, random forest, decision tree, and XGBoost, were used with RFE and SFM. Additionally, prediction models were developed using a support vector machine, Gaussian naïve Bayes, K-nearest neighbors, random forest, and XGBoost algorithms. Finally, SHapley Additive exPlanations (SHAP) were used to increase interpretability. The findings showed that the best feature selection method was RFE with a random forest estimator, and the most critical features were recipient and donor blood type, body mass index, recipient and donor state of residence, serum creatinine, and year of transplantation. Furthermore, among all the outcomes, the XGBoost model had the highest performance, with an accuracy value of 0.6909 and an area under the curve value of 0.86. The findings also revealed a predictive relationship between features and cardiovascular death after liver transplant among NASH patients. These insights may assist clinical decision-makers in devising strategies to prevent cardiovascular complications in post-liver transplant NASH patients.

Type 2 diabetes mellitus and cardiometabolic outcomes in metabolic dysfunction-associated steatotic liver disease population

The metabolic syndrome, characterized by type 2 diabetes mellitus (T2DM), hypertension, hyperlipidemia, and obesity, collectively increases the risk of cardiovascular diseases. Nonalcoholic fatty liver disease (NAFLD) is a prominent manifestation, affecting over a third of the global population with a concerning annual increase in prevalence. Nearly 70 % of overweight individuals have NAFLD, and NAFLD-related deaths are predicted to rise, especially among young adults. The association of T2DM and NAFLD has led to the proposal of "metabolic dysfunction-associated steatotic liver disease" (MASLD) terminology, encompassing individuals with T2DM, overweight/obesity, hypertension, hypertriglyceridemia, or low HDL-cholesterol. Patients with MASLD will likely have double the risk of developing T2DM, and the combination of insulin resistance, overweight/obesity, and MASLD significantly elevates the risk of T2DM. Cardiovascular diseases remain the leading cause of mortality in the MASLD and T2DM population, with MASLD directly associated with coronary artery disease, compounded by coexisting insulin resistance and T2DM. Urgency lies in early detection of subclinical cardiovascular diseases among patients with T2DM and MASLD. Novel strategies targeting multiple pathways offer hope for effectively improving cardiometabolic health. Understanding and addressing the intertwined factors contributing to these disorders can pave the way towards better management and prevention of cardiometabolic complications.

Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review

Cardiovascular diseases remain the main cause of death worldwide which makes it essential to better understand, diagnose, and treat atherosclerosis. Artificial intelligence (AI) and novel technological solutions offer us new possibilities and enable the practice of individually tailored medicine. The study was performed using the PRISMA protocol. As of January 10, 2023, the analysis has been based on a review of 457 identified articles in PubMed and MEDLINE databases. The search covered reviews, original articles, meta-analyses, comments, and editorials published in the years 2009-2023. In total, 123 articles met inclusion criteria. The results were divided into the subsections presented in the review (genome-wide association studies, radiomics, and other studies). This paper presents actual knowledge concerning atherosclerosis, in silico, and big data analyses in cardiology that affect the way medicine is practiced in order to create an individual approach and adjust the therapy of atherosclerosis.

Sex-specific cardiovascular risk factors in the UK Biobank

The lack of sex-specific cardiovascular disease criteria contributes to the underdiagnosis of women compared to that of men. For more than half a century, the Framingham Risk Score has been the gold standard to estimate an individual's risk of developing cardiovascular disease based on the age, sex, cholesterol levels, blood pressure, diabetes status, and the smoking status. Now, machine learning can offer a much more nuanced insight into predicting the risk of cardiovascular diseases. The UK Biobank is a large database that includes traditional risk factors and tests related to the cardiovascular system: magnetic resonance imaging, pulse wave analysis, electrocardiograms, and carotid ultrasounds. Here, we leverage 20,542 datasets from the UK Biobank to build more accurate cardiovascular risk models than the Framingham Risk Score and quantify the underdiagnosis of women compared to that of men. Strikingly, for a first-degree atrioventricular block and dilated cardiomyopathy, two conditions with non-sex-specific diagnostic criteria, our study shows that women are under-diagnosed 2× and 1.4× more than men. Similarly, our results demonstrate the need for sex-specific criteria in essential primary hypertension and hypertrophic cardiomyopathy. Our feature importance analysis reveals that out of the top 10 features across three sexes and four disease categories, traditional Framingham factors made up between 40% and 50%; electrocardiogram, 30%-33%; pulse wave analysis, 13%-23%; and magnetic resonance imaging and carotid ultrasound, 0%-10%. Improving the Framingham Risk Score by leveraging big data and machine learning allows us to incorporate a wider range of biomedical data and prediction features, enhance personalization and accuracy, and continuously integrate new data and knowledge, with the ultimate goal to improve accurate prediction, early detection, and early intervention in cardiovascular disease management. Our analysis pipeline and trained classifiers are freely available at https://github.com/LivingMatterLab/CardiovascularDiseaseClassification.

Performance of the Pooled Cohort Equations in non-alcoholic fatty liver disease: The Multi-Ethnic Study of Atherosclerosis

BACKGROUND AND AIMS

Non-alcoholic fatty liver disease (NAFLD) is associated with a high risk of cardiovascular disease. Whether risk scores developed in the general population accurately assess cardiovascular risk in the NAFLD population is unknown. This study aimed to evaluate the performance of the Pooled Cohort Equations (PCE) in NAFLD.

METHODS

Individuals in the Multi-Ethnic Study of Atherosclerosis with baseline non-contrast cardiac computed tomography scans with sufficient data to determine the presence of hepatic steatosis were identified and assessed for the development of incident 10-year atherosclerotic cardiovascular disease. The discrimination and calibration of the PCE were evaluated, and the observed and expected events by risk category (<5%, 5-<7.5%, 7.5-<20%, ≥20%) were determined. Risk reclassification with the addition of NAFLD to the PCE was assessed.

RESULTS

Of 4014 participants included, 698 (17.4%) with NAFLD were identified, including 247 (35.3%) with moderate-to-severe steatosis. Discrimination of the PCE was suboptimal in NAFLD (c-statistic 0.69), particularly moderate-to-severe steatosis (0.65), and calibration was overall poor. While risk was overestimated in non-NAFLD, it was underestimated in NAFLD in lower/intermediate risk categories, predominantly in women (5-<7.5% observed/expected ratio = 1.67). The addition of NAFLD to the PCE improved risk classification in women.

CONCLUSIONS

The PCE overall performed suboptimally in cardiovascular risk assessment in NAFLD, particularly in women and individuals with moderate-to-severe steatosis in clinically relevant risk categories. Primary prevention may need to be considered at a lower risk threshold in these groups, and further work is needed to improve risk stratification in this growing high-risk population.

Classification Comparison of Machine Learning Algorithms Using Two Independent CAD Datasets

In the last few decades, statistical methods and machine learning (ML) algorithms have become efficient in medical decision-making. Coronary artery disease (CAD) is a common type of cardiovascular disease that causes many deaths each year. In this study, two CAD datasets from different countries (TRNC and Iran) are tested to understand the classification efficiency of different supervised machine learning algorithms. The Z-Alizadeh Sani dataset contained 303 individuals (216 patient, 87 control), while the Near East University (NEU) Hospital dataset contained 475 individuals (305 patients, 170 control). This study was conducted in three stages: (1) Each dataset, as well as their merged version, was subject to review separately with a random sampling method to obtain train-test subsets. (2) The NEU Hospital dataset was assigned as the training data, while the Z-Alizadeh Sani dataset was the test data. (3) The Z-Alizadeh Sani dataset was assigned as the training data, while the NEU hospital dataset was the test data. Among all ML algorithms, the Random Forest showed successful results for its classification performance at each stage. The least successful ML method was kNN which underperformed at all pitches. Other methods, including logistic regression, have varying classification performances at every step.