Modeling the risk factors for dyslipidemia and blood lipid indices: Ravansar cohort study

Abstract

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.

Influence of Omega-3 Fatty Acid-Rich Fish Oils on Hyperlipidemia: Effect of Eel, Sardine, Trout, and Cod Oils on Hyperlipidemic Mice

“…Studies with the Skh mouse have shown that approximately equal levels of 18:3 FA were present in the plasma and epidermis of animals fed either omega-3 or omega-6 FA diets, suggesting that Δ 6 -desaturase activity was not inhibited by omega-3 FA. 25 There were differences, however, in the levels of 20:3 and 20:4 FA, with threefold higher levels in animals fed an omega-6 FA diet.…”

“… 2 , 4 Its prominent role in the etiopathogenesis of atherosclerosis and other symptoms connected to CVD is well known. 5 , 6 There are three distinct types of dyslipidemia: hypercholesterolemia (high cholesterol concentration), hypertriglyceridemia (high triglyceride [TG] concentration), and mixed hyperlipidemia (high cholesterol and TGs). 7 …”

Evaluation of Relationship between Serum Liver Enzymes and Hypertension: A Cross-Sectional Study Based on Data from Rafsanjan Cohort Study

“…Socioeconomic status was also determined using the wealth score index (WSI). The WSI was calculated by multiple correspondence analysis (MCA) of the subjects' economic and social variables [ 16 ]. According to WSI, the studied population was divided into four groups: low class, low-middle class, middle-high class, and high class.…”

“…To evaluate the intensity of physical activity, metabolic equivalent of task (MET) was used. Physical activity was assessed according to the 24 h physical activity and a 22- item questionnaire and was categorized as low (≤35.29 MET-hours per week), moderate (35.30–40.32 MET-hours per week), and heavy (≥40.32 MET-hours per week), respectively, based on the 25th and 75th percentile [ 16 ]. Questionnaires were validated by the PERSIAN [ 14 ].…”

Metabolic obesity phenotypes and chronic kidney disease: a cross-sectional study from the RaNCD cohort study

“…Hypertension was defined as SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg and/or those who were using antihypertensive drugs [24]. Dyslipidaemia was defined as a disorder in lipid profile and/or a history of consuming medication for lipid disorders [25]. In addition, Type 2 diabetes mellitus (T2DM) was defined as Fasting Blood Sugar (FBS) > = 126 mg/dl and/or history of using medication for T2DM treatment [26].…”