HDL Subgroups and Their Paraoxonase-1 Activity in the Obese, Overweight and Normal Weight Subjects

Abstract

Background: Obesity and overweight are significant public health problems due to higher risk for coronary artery disease (CAD). It is very important to determine new predictive markers to identify the CAD risk in obese and overweight. To this aim, we analyzed HDL-C subclass and their paraoxonase-1 (PON-1) activity in obese, overweight and normal weight subjects. Method: 71 newly diagnosed obese, 40 overweight and 30 healthy subjects as a control group were enrolled the study. Serum lipids levels were determined with enzymatic colorimetric method. PON-1 activities and HDL-3 levels were determined by spectrophotometric methods. Non-HDL3-C concentrations were calculated with the subtraction of HDL3-C from total HDL-C. Results: The mean serum levels of total HDL-C, HDL3-C, Non-HDL3-C -C and ApoA1 were higher in control group than obese and overweight groups. There were a statistically significant difference between obese and control group in terms of Lp(a), hsCRP and HOMA index. Higher total PON-1, non-HDL3 PON-1 and HDL3 PON-1 activities were found in the control group compared to obese and overweight groups. Total HDL was weakly negative correlated with the HOMA index, BMI and waist circumference. There was a weak negative correlation between non-HDL3-C and waist circumstance. Conclusion: Abnormal HDL-subgroups pattern and decreased PON-1 activities causes increased risk for CVD in obese and overweight individuals. Therefore determination of HDL subgroups and their PON-1 activity improves risk prediction compared with measuring total HDL-C levels and its PON-1 activity alone. Body weight and insulin resistance appear to have a role in the decreased HDL-C levels and PON-1activity in obese.

Machine-learning-based analysis of the sensitivity and specificity on lipid-lowering effect of one-month-administered statins

“…Additionally, body weight appeared to play a role in the decreased HDL-C levels and uric acid impacted the role of HDL-C on carotid atherosclerosis. [20,21]…”

“…Additionally, body weight appeared to play a role in the decreased HDL-C levels and uric acid impacted the role of HDL-C on carotid atherosclerosis. [20,21] In fact, the random-forest algorithm has been widely used to diagnose different diseases and predict the outcome of disease, such as machine-learning that was considered to play a role in the prediction of pathological diagnosis of ovarian cancer from preoperative examinations [22] ; a CVD prediction model that was applied to 3-year risk assessment of CVD to find that its AUC evaluated the predicting ability to be 0.78 [23] ; the prostate cancer prediction based on the random-forest algorithm to take into account the transrectal ultrasound findings, ages, and serum levels of prostate-specific antigen [24] ; COVID-19 predicting model that used the patients' geographical, traveling, physical and demographic data to predict the severity of the case and its possible outcome, recovery or death, the accuracy of which was 94% [25] ; the machine-learning algorithms that improved the prediction of long-term outcomes in ischemic stroke patients [26] ; and the random-forest approach to predict therapeutic efficacy from the data of the failed clinical drug trials so that they could reevaluate the efficacy of the drug. [27] As suggested from the literature, machine-learning has a unique advantage in diagnosis and prediction.…”