Machine Learning Approach to Classify Cardiovascular Disease in Patients With Nonalcoholic Fatty Liver Disease in the UK Biobank Cohort
Open accessAbstract
Background: Nonalcoholic fatty liver disease (NAFLD) is the most prevalent liver disease worldwide. Cardiovascular disease (CVD) is the leading cause of mortality among patients with NAFLD. The aim of our study was to develop a machine learning algorithm integrating clinical, lifestyle, and genetic risk factors to identify CVD in patients with NAFLD. Methods and Results: We created a cohort of patients with NAFLD from the UK Biobank, diagnosed according to proton density fat fraction from magnetic resonance imaging data sets. A total of 400 patients with NAFLD with subclinical atherosclerosis or clinical CVD, defined by disease codes, constituted cases and 446 NAFLD cases with no CVD constituted controls. We evaluated 7 different supervised machine learning approaches on clinical, lifestyle, and genetic variables for identifying CVD in patients with NAFLD. The most significant clinical and lifestyle variables observed by the predictive modeling were age (59 years [54.00–63.00 years]), hypertension (145 mm Hg [134.0–156.0 mm Hg] and 85 mm Hg [79.00–93.00 mm Hg]), waist circumference (98 cm [95.00–105.00 cm]), and sedentary lifestyle, defined as time spent watching TV >4 h/d. In the genetic data, single‐nucleotide polymorphisms in IL16 and ANKLE1 gene were most significant. Our proposed ensemble‐based integrative machine learning model achieved an area under the curve of 0.849 using the random forest modeling for CVD prediction. Conclusions: We propose a machine learning algorithm that identifies CVD in patients with NAFLD through integration of significant clinical, lifestyle, and genetic risk factors. These patients with NAFLD at higher risk of CVD should be flagged for screening and aggressive treatment of their cardiometabolic risk factors to prevent cardiovascular morbidity and mortality.
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ML models integrating clinical, genetic, and lifestyle factors have been developed to predict an individual's risk of developing cardiovascular disease.
“Researchers have developed ML models that integrate clinical, genetic, and lifestyle factors to predict an individual’s risk of developing cardiovascular disease [4–7].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [4]
At the population level, ML and AI can identify high-risk subgroups and distinct disease trajectories associated with specific combinations of early life exposures, socioeconomic factors, and health behaviors.
“At the population level, ML and AI can identify high-risk subgroups and distinct disease trajectories associated with specific combinations of early life exposures, socioeconomic factors, and health behaviors [4, 9, 16, 30, 31].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [4]
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- Authors
- Sharma, D., Gotlieb, N., Farkouh, M. E., Patel, K., Xu, W., & Bhat, M.
- Affiliations
- University Health Network (CA)
- University of Toronto (CA)
- Toronto General Hospital (CA)
- Corresponding author
- Xu, Wei
- Bhat, Mamatha
- Venue
- Journal of the American Heart Association
- Published
- 2022-01-04
- Volume
- 11
- Issue
- 1
- Pages
- e022576-e022576
- ISSN
- 2047-9980 (ISSN-L)
- PMC
- PMC9075189
- OpenAlex
- W4200328492
- Cited by
- 36
- References
- 77
- Citations by year
- 2026: 9 · 2025: 9 · 2024: 10 · 2023: 4 · 2022: 4
- FWCI
- 3.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- Journal of the American Heart Association (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- British Heart Foundation (CH/1996001/9454)
- Medical Research Council (MC_PC_17228)
- Topics
- Health Sciences › Medicine › Epidemiology › Liver Disease Diagnosis and Treatment
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- non-alcoholic fatty liver disease, cardiovascular disease, cardiovascular risk prediction, subclinical atherosclerosis, random forest, single nucleotide polymorphisms, interleukin-16, sedentary lifestyle, waist circumference, hypertension, proton density fat fraction, cardiometabolic risk factors, UK Biobank
- MeSH terms
- Machine Learning, Cardiovascular Diseases, Endonucleases, United Kingdom, Humans, Middle Aged, Risk Factors, Biological Specimen Banks, Non-alcoholic Fatty Liver Disease
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