ArticleJMIR public health and surveillance2021
Using Machine Learning Techniques to Predict Factors Contributing to the Incidence of Metabolic Syndrome in Tehran: Cohort Study.
Article in JMIR public health and surveillance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 15 citations in OpenAlex.
- A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein.Scientific reports · 2025Article
- Metabolic syndrome, small airway dysfunction and the mediating role of inflammation.Scientific reports · 2025Article
- Associations between Global Diet Quality Score and Risk of Metabolic Syndrome and Its Components: Tehran Lipid and Glucose Study.Journal of obesity & metabolic syndrome · 2024Article
- Prediction of hypercholesterolemia using machine learning techniques.Journal of diabetes and metabolic disorders · 2023Article
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Authors and funding
7 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMetabolic syndrome (MetS), a major contributor to cardiovascular disease and diabetes, is considered to be among the most common public health problems worldwide.
objectiveWe aimed to identify and rank the most important nutritional and nonnutritional factors contributing to the development of MetS using a data-mining method.
methodsThis prospective study was performed on 3048 adults (aged ≥20 years) who participated in the fifth follow-up examination of the Tehran Lipid and Glucose Study, who were followed for 3 years. MetS was defined according to the modified definition of the National Cholesterol Education Program/Adult Treatment Panel III. The importance of variables was obtained by the training set using the random forest model for determining factors with the greatest contribution to developing MetS.
resultsAmong the 3048 participants, 701 (22.9%) developed MetS during the study period. The mean age of the participants was 44.3 years (SD 11.8). The total incidence rate of MetS was 229.9 (95% CI 278.6-322.9) per 1000 person-years and the mean follow-up time was 40.5 months (SD 7.3). The incidence of MetS was significantly (P<.001) higher in men than in women (27% vs 20%). Those affected by MetS were older, married, had diabetes, with lower levels of education, and had a higher BMI (P<.001). The percentage of hospitalized patients was higher among those with MetS than among healthy people, although this difference was only statistically significant in women (P=.02). Based on the variable importance and multiple logistic regression analyses, the most important determinants of MetS were identified as history of diabetes (odds ratio [OR] 6.3, 95% CI 3.9-10.2, P<.001), BMI (OR 1.2, 95% CI 1.0-1.2, P<.001), age (OR 1.0, 95% CI 1.0-1.03, P<.001), female gender (OR 0.5, 95% CI 0.38-0.63, P<.001), and dietary monounsaturated fatty acid (OR 0.97, 95% CI 0.94-0.99, P=.04).
conclusionsBased on our findings, the incidence rate of MetS was significantly higher in men than in women in Tehran. The most important determinants of MetS were history of diabetes, high BMI, older age, male gender, and low dietary monounsaturated fatty acid intake.
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