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Modeling the risk factors for dyslipidemia and blood lipid indices: Ravansar cohort study
Mansour Rezaei1, Negin Fakhri2, Yahya Pasdar3
1Professor of Biostatistics, Biostatistics Department, Social Development and Health Promotion Research Center, Kermanshah University of medical sciences, Kermanshah, Iran.
Insights
Identifying key factors for lipid disorders is crucial for preventing chronic diseases like heart disease. Alkaline phosphatase, Fat Free Mass (FFM) index, and Hemoglobin (HGB) significantly predict blood lipid levels.
Area of Science:
- Biochemistry and clinical diagnostics
- Cardiovascular health and disease prevention
- Data science in healthcare
Background:
- Lipid disorders are significant risk factors for chronic diseases, particularly Chronic Heart Disease (CHD).
- Understanding factors influencing dyslipidemia aids in mitigating chronic disease development.
- This study aimed to model risk factors for dyslipidemia and blood lipid indices.
Purpose of the Study:
- To identify and model key predictor variables for dyslipidemia.
- To investigate the relationship between various factors and blood lipid profiles.
- To provide insights for controlling blood lipid markers and reducing chronic disease risk.
Main Methods:
- Utilized data from the Ravansar cohort study (2014-16).
- Employed Artificial Neural Network (ANN) data mining for sensitivity analysis of 453 variables.
- Used stepwise regression to analyze variable relationships and SPSS software for statistical analysis.
Main Results:
- 40% of participants had lipid disorders.
- ANN identified 12 nutrition and physical status-related predictors for dyslipidemia.
- Alkaline phosphatase, Fat Free Mass (FFM) index, and Hemoglobin (HGB) significantly correlated with all seven blood lipid markers.
- Waist Hip Ratio showed strong correlation with cholesterol and LDL; FFM index most impacted triglyceride and HDL ratios.
Conclusions:
- Alkaline phosphatase, FFM index, and HGB are common predictors for all blood lipid markers.
- Controlling these identified factors is recommended for better management of blood lipid profiles.
- Findings support targeted interventions to reduce chronic disease risk associated with lipid disorders.
Background:
Lipid 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.
Methods:
This 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.
Results:
Forty 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.
Conclusion:
According 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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