Bayesian network model of ethno-racial disparities in cardiometabolic-based chronic disease using NHANES 1999-2018

. 2024 ; 12 () : 1409731. [epub] 20241015

Jazyk angličtina Země Švýcarsko Médium electronic-ecollection

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid39473589

BACKGROUND: Ethno-racial disparities in cardiometabolic diseases are driven by socioeconomic, behavioral, and environmental factors. Bayesian networks offer an approach to analyze the complex interaction of the multi-tiered modifiable factors and non-modifiable demographics that influence the incidence and progression of cardiometabolic disease. METHODS: In this study, we learn the structure and parameters of a Bayesian network based on 20 years of data from the US National Health and Nutrition Examination Survey to explore the pathways mediating associations between ethno-racial group and cardiometabolic outcomes. The impact of different factors on cardiometabolic outcomes by ethno-racial group is analyzed using conditional probability queries. RESULTS: Multiple pathways mediate the indirect association from ethno-racial group to cardiometabolic outcomes: (1) ethno-racial group to education and to behavioral factors (diet); (2) education to behavioral factors (smoking, physical activity, and-via income-to alcohol); (3) and behavioral factors to adiposity-based chronic disease (ABCD) and then other cardiometabolic drivers. Improved diet and physical activity are associated with a larger decrease in probability of ABCD stage 4 among non-Hispanic White (NHW) individuals compared to non-Hispanic Black (NHB) and Hispanic (HI) individuals. CONCLUSION: Education, income, and behavioral factors mediate ethno-racial disparities in cardiometabolic outcomes, but traditional behavioral factors (diet and physical activity) are less influential among NHB or HI individuals compared to NHW individuals. This suggests the greater contribution of unmeasured individual- and/or neighborhood-level structural determinants of health that impact cardiometabolic drivers among NHB and HI individuals. Further study is needed to discover the nature of these unmeasured determinants to guide cardiometabolic care in diverse populations.

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Global Burden of Disease Collaborative Network . Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet. (2020) 396:1204–22. doi: 10.1016/S0140-6736(20)30925-9 PubMed DOI PMC

Palmer RC, Ismond D, Rodriquez EJ, Kaufman JS. Social determinants of health: future directions for health disparities research. Am J Public Health. (2019) 109:S70–1. doi: 10.2105/AJPH.2019.304964, PMID: PubMed DOI PMC

Powell-Wiley TM, Baumer Y, Baah FO, Baez AS, Farmer N, Mahlobo CT, et al. . Social determinants of cardiovascular disease. Circ Res. (2022) 130:782–99. doi: 10.1161/CIRCRESAHA.121.319811, PMID: PubMed DOI PMC

Liu B, Du Y, Wu Y, Snetselaar LG, Wallace RB, Bao W. Trends in obesity and adiposity measures by race or ethnicity among adults in the United States 2011-18: population based study. BMJ. (2021) 372:n365. doi: 10.1136/bmj.n365 PubMed DOI PMC

Cheng YJ, Kanaya AM, Araneta MRG, Saydah SH, Kahn HS, Gregg EW, et al. . Prevalence of diabetes by race and ethnicity in the United States, 2011-2016. JAMA. (2019) 322:2389–98. doi: 10.1001/jama.2019.19365 PubMed DOI PMC

Al Kibria GM. Racial/ethnic disparities in prevalence, treatment, and control of hypertension among US adults following application of the 2017 American College of Cardiology/American Heart Association guideline. Prev Med Rep. (2019) 14:100850. doi: 10.1016/j.pmedr.2019.100850, PMID: PubMed DOI PMC

Frank AT, Zhao B, Jose PO, Azar KM, Fortmann SP, Palaniappan LP. Racial/ethnic differences in dyslipidemia patterns. Circulation. (2014) 129:570–9. doi: 10.1161/CIRCULATIONAHA.113.005757, PMID: PubMed DOI PMC

Post WS, Watson KE, Hansen S, Folsom AR, Szklo M, Shea S, et al. . Racial and ethnic differences in all-cause and cardiovascular disease mortality: the MESA study. Circulation. (2022) 146:229–39. doi: 10.1161/CIRCULATIONAHA.122.059174, PMID: PubMed DOI PMC

van den Houdt SCM, Mommersteeg PMC, Widdershoven J, Kupper N. A network analysis of cardiovascular risk factors in patients with heart disease: the role of socioeconomic status and sex. Psychosom Med. (2023) 85:417–30. doi: 10.1097/PSY.0000000000001196, PMID: PubMed DOI

Ordovas JM, Rios-Insua D, Santos-Lozano A, Lucia A, Torres A, Kosgodagan A, et al. . A Bayesian network model for predicting cardiovascular risk. Comput Methods Prog Biomed. (2023) 231:107405. doi: 10.1016/j.cmpb.2023.107405, PMID: PubMed DOI

Fuster-Parra P, Tauler P, Bennasar-Veny M, Ligęza A, López-González AA, Aguiló A. Bayesian network modeling: a case study of an epidemiologic system analysis of cardiovascular risk. Comput Methods Prog Biomed. (2016) 126:128–42. doi: 10.1016/j.cmpb.2015.12.010, PMID: PubMed DOI

Fuster-Parra P, Yanez AM, Lopez-Gonzalez A, Aguilo A, Bennasar-Veny M. Identifying risk factors of developing type 2 diabetes from an adult population with initial prediabetes using a Bayesian network. Front Public Health. (2022) 10:1035025. doi: 10.3389/fpubh.2022.1035025 PubMed DOI PMC

Pearl J. Probabilistic reasoning in intelligent systems: Networks of plausible inference. Los Altos CA: Morgan Kaufmann Publishers; (1988).

Pearl J. Causality: Models, reasoning and inference. 2nd ed. Cambridge, UK: Cambridge University Press; (2009).

Scutari M, Denis J-B. Bayesian networks with examples in R. Second ed. Boca Raton, Florida, US: CRC Press; (2022).

McComb M, Blair RH, Lysy M, Ramanathan M. Machine learning-guided, big data-enabled, biomarker-based systems pharmacology: modeling the stochasticity of natural history and disease progression. J Pharmacokinet Pharmacodyn. (2022) 49:65–79. doi: 10.1007/s10928-021-09786-5 PubMed DOI

Badawi A, Di Giuseppe G, Gupta A, Poirier A, Arora P. Bayesian network modelling study to identify factors influencing the risk of cardiovascular disease in Canadian adults with hepatitis C virus infection. BMJ Open. (2020) 10:e035867. doi: 10.1136/bmjopen-2019-035867, PMID: PubMed DOI PMC

Loghmanpour NA, Kormos RL, Kanwar MK, Teuteberg JJ, Murali S, Antaki JF. A Bayesian model to predict right ventricular failure following left ventricular assist device therapy. JACC Heart Fail. (2016) 4:711–21. doi: 10.1016/j.jchf.2016.04.004 PubMed DOI PMC

Mechanick JI, Farkouh ME, Newman JD, Garvey WT. Cardiometabolic-based chronic disease, adiposity and Dysglycemia drivers: JACC state-of-the-art review. J Am Coll Cardiol. (2020) 75:525–38. doi: 10.1016/j.jacc.2019.11.044, PMID: PubMed DOI PMC

Correia ETO, Mechanick JI, Jorge AJL, Barbetta L, Rosa MLG, Leite AR, et al. . The hypertension-based chronic disease model in a primary care setting. Int J Cardiol Cardiovasc Risk Prev. (2023) 18:200204. doi: 10.1016/j.ijcrp.2023.200204 PubMed DOI PMC

Mechanick JI, Garber AJ, Grunberger G, Handelsman Y, Garvey WT. Dysglycemia-based chronic disease: an American association of clinical endocrinologists position statement. Endocr Pract. (2018) 24:995–1011. doi: 10.4158/PS-2018-0139 PubMed DOI

Mechanick JI, Hurley DL, Garvey WT. Adiposity-based chronic disease as a new diagnostic term: the American association of clinical endocrinologists and American college of endocrinology position statement. Endocr Pract. (2017) 23:372–8. doi: 10.4158/EP161688.PS PubMed DOI

National Center for Health Statistics . National Health and Nutrition Examination Survey. (2023) Available at: https://www.cdc.gov/nchs/nhanes/index.htm

Russell S, Norvig P. Artificial intelligence: A modern approach. Third Edition ed. Upper Saddle River, New Jersey, US: Prentice Hall; (2009).

Scutari M, Graafland CE, Gutierrez JM. Who learns better Bayesian network structures: accuracy and speed of structure learning algorithms. Int J Approx Reason. (2019) 115:235–53. doi: 10.1016/j.ijar.2019.10.003 DOI

Waddell T, Namburete A, Duckworth P, Fichera A, Telford A, Thomaides-Brears H, et al. . Poor glycaemic control and ectopic fat deposition mediates the increased risk of non-alcoholic steatohepatitis in high-risk populations with type 2 diabetes: insights from Bayesian-network modelling. Front Endocrinol. (2023) 14:1063882. doi: 10.3389/fendo.2023.1063882 PubMed DOI PMC

Friedman N, Goldszmidt M, Wyner A. Data analysis with bayesian networks: A bootstrap approach. Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence. (1999):196–205.

Scutari M, Nagarajan R. Identifying significant edges in graphical models of molecular networks. Artif Intell Med. (2013) 57:207–17. doi: 10.1016/j.artmed.2012.12.006, PMID: PubMed DOI PMC

de Brey C, Musu L, Mcfarland J, Wilkinson-Flicker S, Diliberti M, Zhang A, et al. . Status and trends in the education of racial and ethnic groups, NCES 2019-038 (2019). US Department of education. Washington, DC: National Center for education statistics. 2019–2038.

Tao MH, Liu JL, Nguyen UDT. Trends in diet quality by race/ethnicity among adults in the United States for 2011-2018. Nutrients. (2022) 14:4178. doi: 10.3390/nu14194178, PMID: PubMed DOI PMC

Bennett G, Bardon LA, Gibney ER. A comparison of dietary patterns and factors influencing food choice among ethnic groups living in one locality: a systematic review. Nutrients. (2022) 14:941. doi: 10.3390/nu14050941, PMID: PubMed DOI PMC

Kubota Y, Heiss G, MacLehose RF, Roetker NS, Folsom AR. Association of Educational Attainment with Lifetime Risk of cardiovascular disease: the atherosclerosis risk in communities study. JAMA Intern Med. (2017) 177:1165–72. doi: 10.1001/jamainternmed.2017.1877, PMID: PubMed DOI PMC

Azizi Fard N, De Francisci MG, Mejova Y, Schifanella R. On the interplay between educational attainment and nutrition: a spatially-aware perspective. EPJ Data Sci. (2021) 10:18. doi: 10.1140/epjds/s13688-021-00273-y DOI

Magnani JW, Mujahid MS, Aronow HD, Cené CW, Dickson VV, Havranek E, et al. . Health literacy and cardiovascular disease: fundamental relevance to primary and secondary prevention: a scientific statement from the American Heart Association. Circulation. (2018) 138:e48–74. doi: 10.1161/CIR.0000000000000579, PMID: PubMed DOI PMC

Rosoff DB, Clarke TK, Adams MJ, McIntosh AM, Davey Smith G, Jung J, et al. . Educational attainment impacts drinking behaviors and risk for alcohol dependence: results from a two-sample Mendelian randomization study with ~780,000 participants. Mol Psychiatry. (2021) 26:1119–32. doi: 10.1038/s41380-019-0535-9, PMID: PubMed DOI PMC

Archundia Herrera MC, Subhan FB, Chan CB. Dietary patterns and cardiovascular disease risk in people with type 2 diabetes. Curr Obes Rep. (2017) 6:405–13. doi: 10.1007/s13679-017-0284-5 PubMed DOI

Aune D, Norat T, Leitzmann M, Tonstad S, Vatten LJ. Physical activity and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis. Eur J Epidemiol. (2015) 30:529–42. doi: 10.1007/s10654-015-0056-z PubMed DOI

Ozemek C, Laddu DR, Arena R, Lavie CJ. The role of diet for prevention and management of hypertension. Curr Opin Cardiol. (2018) 33:388–93. doi: 10.1097/HCO.0000000000000532 PubMed DOI

Chaudhry SI, Herrin J, Phillips C, Butler J, Mukerjhee S, Murillo J, et al. . Racial disparities in health literacy and access to care among patients with heart failure. J Card Fail. (2011) 17:122–7. doi: 10.1016/j.cardfail.2010.09.016, PMID: PubMed DOI PMC

Fusaro VA, Levy HG, Shaefer HL. Racial and ethnic disparities in the lifetime prevalence of homelessness in the United States. Demography. (2018) 55:2119–28. doi: 10.1007/s13524-018-0717-0, PMID: PubMed DOI PMC

Jbaily A, Zhou X, Liu J, Lee TH, Kamareddine L, Verguet S, et al. . Air pollution exposure disparities across US population and income groups. Nature. (2022) 601:228–33. doi: 10.1038/s41586-021-04190-y, PMID: PubMed DOI PMC

Yang Y, Cho A, Nguyen Q, Nsoesie EO. Association of Neighborhood Racial and Ethnic Composition and historical redlining with built environment indicators derived from street view images in the US. JAMA Netw Open. (2023) 6:e2251201. doi: 10.1001/jamanetworkopen.2022.51201, PMID: PubMed DOI PMC

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