ArticleLipids in health and disease2020
Modeling the risk factors for dyslipidemia and blood lipid indices: Ravansar cohort study.
Article in Lipids in health and disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 26 citations in OpenAlex.
- A Temporal Validation Study of Diagnostic Prediction Models for the Screening of Elevated Low-Density and Non-High-Density Lipoprotein Cholesterol.Journal of clinical medicine · 2025Article
- Triglyceride-glucose index association with kidney function in adults; a population-based study.BMC endocrine disorders · 2025Article
- Dietary intake of total, animal, and vegetable protein and cardiometabolic risk factors in patients with type 2 diabetes: using iso-energetic substitution models.Journal of diabetes and metabolic disorders · 2025Article
- Determinative sleep traits associated with dyslipidemia in obstructive sleep apnea patients.BMC pulmonary medicine · 2025Article
- Diagnostic prediction model for screening of elevated low-density and non-high-density lipoproteins in young Thai adults between 20 and 40 years of age.BMJ health & care informatics · 2025Article
- Application of Generative Artificial Intelligence in Dyslipidemia Care.Journal of lipid and atherosclerosis · 2025Review
- Article
- Diet-related inflammation is positively associated with atherogenic indices.Scientific reports · 2024Observational
- Dietary amino acid profile and risk of hypertension: findings from the Ravansar cohort study.BMC nutrition · 2024Article
- The association between dietary amino acid profile and the risk of type 2 diabetes: Ravansar non-communicable disease cohort study.BMC public health · 2023Article
- Major dietary patterns and dietary inflammatory index in relation to dyslipidemia using cross-sectional results from the RaNCD cohort study.Scientific reports · 2023Article
- Pro-inflammatory diet, cardio-metabolic risk factors and risk of type 2 diabetes: A cross-sectional analysis using data from RaNCD cohort study.BMC cardiovascular disorders · 2023Article
- Metabolic obesity phenotypes and chronic kidney disease: a cross-sectional study from the RaNCD cohort study.BMC nephrology · 2022Article
- Evaluation of Relationship between Serum Liver Enzymes and Hypertension: A Cross-Sectional Study Based on Data from Rafsanjan Cohort Study.International journal of hypertension · 2022Article
- Influence of Omega-3 Fatty Acid-Rich Fish Oils on Hyperlipidemia: Effect of Eel, Sardine, Trout, and Cod Oils on Hyperlipidemic Mice.Journal of medicinal food · 2021Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundLipid disorder is one of the most important risk factors for chronic diseases. Identifying the factors affecting the development of lipid disorders helps reduce chronic diseases, especially Chronic Heart Disease (CHD). The aim of this study was to model the risk factors for dyslipidemia and blood lipid indices.
methodsThis study was conducted based on the data collected in the initial phase of Ravansar cohort study (2014-16). At the beginning, all the 453 available variables were examined in 33 stages of sensitivity analysis by perceptron Artificial Neural Network (ANN) data mining model. In each stage, the variables that were more important in the diagnosis of dyslipidemia were identified. The relationship among the variables was investigated using stepwise regression. The data obtained were analyzed in SPSS software version 25, at 0.05 level of significance.
resultsForty percent of the subjects were diagnosed with lipid disorder. ANN identified 12 predictor variables for dyslipidemia related to nutrition and physical status. Alkaline phosphatase, Fat Free Mass (FFM) index, and Hemoglobin (HGB) had a significant relationship with all the seven blood lipid markers. The Waist Hip Ratio was the most effective variable that showed a stronger correlation with cholesterol and Low-Density Lipid (LDL). The FFM index had the greatest effect on triglyceride, High-Density Lipid (HDL), cholesterol/HDL, triglyceride/HDL, and LDL/HDL. The greatest coefficients of determination pertained to the triglyceride/HDL (0.203) and cholesterol/HDL (0.188) model with nine variables and the LDL/HDL (0.180) model with eight variables.
conclusionAccording to the results, alkaline phosphatase, FFM index, and HGB were three common predictor variables for all the blood lipid markers. Specialists should focus on controlling these factors in order to gain greater control over blood lipid markers.
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