Cardiovascular disease (CVD) remains a leading cause of morbidity worldwide, posing significant challenges to global public health systems. Despite advancements in medical science and healthcare delivery, the need for more effective prevention, early detection, and management strategies continues to rise. Biomarkers are crucial in identifying and monitoring CVD, offering valuable insights into disease pathophysiology, prognosis, and potential therapeutic targets. Integrating biomarkers into predictive models enhances risk assessment and facilitates personalized healthcare interventions, ultimately improving patient outcomes.
This paper uses data from a 20-year cohort study (NHANES) to explore the relationships among biomarkers, risk factors, and CVD mortality. We focus on identifying key biomarkers linked to CVD mortality and developing predictive models to enhance risk assessment. We analyze a range of biomarkers, including lipid profiles, inflammatory markers, and genetic factors, to determine their associations with CVD mortality. Using advanced statistical and machine learning techniques, we construct and validate predictive models that integrate demographic, lifestyle, and biomarker data to improve accuracy and reliability.
The results of this study offer important implications for identifying individuals at high risk for CVD mortality and improving early intervention strategies, ultimately enhancing patient outcomes.
This study used public domain data from the NHANES conducted by the National Center for Health Statistics (NCHS) from 1999 to 2000, 2001–2002, and 2003–2004. This study was approved by the NCHS Research Ethics Review Board (ERB) to protect human participants with an exempt review. Each NHANES included a nationally representative, stratified, multistage, probability sample of the U.S. population. The survey procedures included a home interview and an examination in the mobile examination center (MEC). The examination component included anthropometry and a dietary interview. Details on the above data can be found on the NHANES website.
In this study, we defined death resulting from heart disease as cardiovascular mortality (seen in Table S1). The follow-up time was calculated using person months from the mobile examination center date to the date of death or the end of the mortality period (December 31, 2019).
The following criteria were used for selecting participants or data points relevant to biomarker analysis. The inclusion of specific biomarkers and CVD-related variables was based on their relevance to CVD-related mortality:
Participants were excluded based on the following criteria:
This study investigates the association between a diverse array of biomarkers and the onset of cardiovascular disease (CVD) mortality. The biomarkers assessed include lipid profiles (e.g., total cholesterol, LDL, HDL, triglycerides), inflammatory markers (e.g., C-reactive protein (CRP), interleukin-6), and cardiac biomarker (e.g., NT-proBNP, a biomarker indicative of cardiac stress).
We employed a longitudinal cohort design, collecting baseline biomarker data from participants and following them over time to assess the incidence of CVD mortality. Lipid profiles and inflammatory markers were measured using standardized laboratory assays, and NT-proBNP was quantified via immunoassay.
Multiple variables were integrated into the predictive models to enhance their accuracy. These variables included demographic factors (e.g., age, race, gender), lifestyle variables (e.g., smoking status), and biomarker measurements (e.g., lipid profiles, inflammatory markers).
The efficacy of different models in predicting CVD-related mortality was compared and contrasted. This comparison allowed us to identify the most effective models for risk assessment and intervention. Potential confounding variables including gender, age, race, race, marriage status, education, history of hypertension, central obesity, atherosclerotic cardiovascular disease (ASCVD), Congestive heart failure (CHF), smoking status, drinking status, systolic blood pressure, CRP, uric acid, blood urea nitrogen (BUN), High-sensitivity troponins (including hs-troponin T and I), NT-proBNP, body mass index (BMI), total cholesterol, triglycerides, glucose, direct HDL-Cholesterol, Albumin, Globulin, protein, creatinine, bilirubin, homocysteine, AST/ALT, Glycohemoglobin, Calcium, serum sodium (NA), potassium, Chloride.
