{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:46:15Z","timestamp":1787028375696,"version":"build-2736575974"},"reference-count":58,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:00:00Z","timestamp":1760227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2401878"],"award-info":[{"award-number":["2401878"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Background: Cardiovascular disease (CVD) is the leading global cause of death and is shaped by interacting biological, environmental, lifestyle, and social factors. Traditional models often treat risk factors in isolation and may miss dependencies among exposures and biomarkers. Objective: To map interdependencies among environmental, social, behavioral, and biological predictors of CVD risk using Bayesian network models. Methods: A cross-sectional analysis was conducted using NHANES 2017\u20132018 data. After complete-case procedures, the analytic sample included 601 adults and 22 variables: outcomes (systolic\/diastolic blood pressure, total\/LDL\/HDL cholesterol, triglycerides) and predictors (BMI, C-reactive protein (CRP), allostatic load, Dietary Inflammatory Index, income, education, age, gender, race, smoking, alcohol, and serum lead, cadmium, mercury, and PFOA). Spearman\u2019s correlations summarized pairwise associations. Bayesian networks were learned with two approaches: Grow\u2013Shrink (constraint-based) and Hill-Climbing (score-based, Bayesian Gaussian equivalent score). Network size metrics included number of nodes, directed edges, average neighborhood size, and Markov blanket size. Results: Correlation screening reproduced expected patterns, including very high systolic\u2013diastolic concordance (p \u2248 1.00), strong LDL\u2013total cholesterol correlation (p = 0.90), inverse HDL\u2013triglycerides association, and positive BMI\u2013CRP association. The final Hill-Climbing network contained 22 nodes and 44 directed edges, with an average neighborhood size of ~4 and an average Markov blanket size of ~6.1, indicating multiple indirect dependencies. Across both learning algorithms, BMI, CRP, and allostatic load emerged as central nodes. Environmental toxicants (lead, cadmium, mercury, PFOS, PFOA) showed connections to sociodemographic variables (income, education, race) and to inflammatory and lipid markers, suggesting patterned exposure linked to socioeconomic position. Diet and stress measures were positioned upstream of blood pressure and triglycerides in the score-based model, consistent with stress-inflammation\u2013metabolic pathways. Agreement across algorithms on key hubs (BMI, CRP, allostatic load) supported network robustness for central structures. Conclusions: Bayesian network modeling identified interconnected pathways linking obesity, systemic inflammation, chronic stress, and environmental toxicant burden with cardiovascular risk indicators. Findings are consistent with the view that biological dysregulation is linked with CVD and environmental or social stresses.<\/jats:p>","DOI":"10.3390\/ijerph22101551","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:13:27Z","timestamp":1760361207000},"page":"1551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Bayesian Network Modeling of Environmental, Social, and Behavioral Determinants of Cardiovascular Disease Risk"],"prefix":"10.3390","volume":"22","author":[{"given":"Hope","family":"Nyavor","sequence":"first","affiliation":[{"name":"Department of Built Environment, North Carolina A&T State University, Greensboro, NC 27411, USA"},{"name":"Environmental Health and Disease Laboratory, North Carolina A&T State University, Greensboro, NC 27411, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3195-706X","authenticated-orcid":false,"given":"Emmanuel","family":"Obeng-Gyasi","sequence":"additional","affiliation":[{"name":"Department of Built Environment, North Carolina A&T State University, Greensboro, NC 27411, USA"},{"name":"Environmental Health and Disease Laboratory, North Carolina A&T State University, Greensboro, NC 27411, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e254","DOI":"10.1161\/CIR.0000000000000950","article-title":"Heart disease and stroke statistics\u20142021 update: A report from the American Heart Association","volume":"143","author":"Virani","year":"2021","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1056\/NEJMoa1406656","article-title":"Demographic and Epidemiologic Drivers of Global Cardiovascular Mortality","volume":"372","author":"Roth","year":"2015","journal-title":"N. 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