HDL subgroups and their paraoxonase-1 activity in the obese, overweight and normal weight subjects

Effects of Seven Weeks of Combined Physical Training on High-Density Lipoprotein Functionality in Overweight/Obese Subjects

Our study aimed to investigate the effects of exercise on HDL composition and functional properties in overweight/obese subjects. Eighteen overweight/obese subjects (nine F and nine M, BMI = 30.3 ± 3 kg/m2) attended supervised training for 7 weeks. The protocol included combined resistance and conditioning training four to five times each week. The activity of the antioxidant enzyme paraoxonase-1 (PON1) associated with HDL was evaluated in all subjects before and after the training intervention. Moreover, myeloperoxidase (MPO) levels and oxidative stress markers (ox-LDLs and total antioxidant capacity) were studied in the serums of the subjects. At the end of the intervention, the activity of PON1 was increased (p < 0.0001), and MPO levels and the MPO/PON1 ratio were decreased (p < 0.0001). In addition, a significant improvement in muscle strength and maximal oxygen uptake (VO2max) (p < 0.0001) and a significant reduction in total and visceral adipose tissue mass (p < 0.001) and waist circumference (p < 0.008), without any significant decrease in body weight, were observed. A significant correlation was established between serum MPO/PON ratios, HDL redox activity and ox-LDLs. In conclusion, our results demonstrate that exercise training, without modifications of dietary habits, improved HDL functionality in overweight/obese adults, without any significant reduction in BMI or modifications of glucose and lipid biochemical parameters.

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

Few predictive studies have been reported on the efficacy of atorvastatin in reducing lipoprotein cholesterol to be qualified after 1-month course of treatment in different individuals. A total of 14,180 community-based residents aged ≥ 65 received health checkup, 1013 of whom had low-density lipoprotein (LDL) higher than 2.6mmol/L so that they were put on 1-month course of treatment with atorvastatin. At its completion, lipoprotein cholesterol was measured again. With < 2.6 mmol/L considered as the treatment standard, 411 individuals were judged as the qualified group, and 602, and as the unqualified group. The basic sociodemographic features covered 57 items. The data were randomly divided into train sets and test ones. The recursive random-forest algorithm was applied to predicting the patients response to atorvastatin, the recursive feature elimination method, to screening all the physical indicators. The overall accuracy, sensitivity and specificity were calculated, respectively, and so were the receiver operator characteristic curve and the area under the curve of the test set. In the prediction model on the efficacy of 1-month treatment of statins for LDL, the sensitivity, 86.86%; and the specificity, 94.83%. In the prediction model on the efficacy of the same treatment for triglyceride, the sensitivity, 71.21%; and the specificity, 73.46%. As to the prediction of total cholesterol, the sensitivity, 94.38%; and the specificity, 96.55%. And in the case of high-density lipoprotein (HDL), the sensitivity, 84.86%; and the specificity, 100%. recursive feature elimination analysis showed that total cholesterol was the most important feature of atorvastatin efficacy of reducing LDL; that HDL was the most important one of its efficacies of reducing triglycerides; that LDL was the most important one of its efficacies of reducing total cholesterol; and that triglyceride was the most important one of its efficacies of reducing HDL. Random-forest can help predict whether atorvastatin efficacy of reducing lipoprotein cholesterol to be qualified after 1-month course of treatment in different individuals.

Prevalence rates of metabolic health and body size phenotypes by different criteria and association with insulin resistance in a Maltese Caucasian population

INTRODUCTION

Hyperinsulinemia and insulin resistance are known to be associated with increased cardiovascular morbidity and mortality. A metabolically unhealthy phenotype is frequently used as a surrogate marker for insulin resistance. The aims of the current study were to compare the prevalence of the body size phenotypes using different definitions of metabolic health and to investigate which one of them is most strongly associated with insulin resistance in men and women.

METHODS

We conducted a cross-sectional study in a middle-aged cohort of Maltese Caucasian non-institutionalized population. Metabolic health was defined using the various currently used definitions.

RESULTS

There were significant differences in the prevalence of body size phenotypes according to the different definitions. We also found significant sex differences in the predictive value of the various definitions of the metabolically unhealthy phenotype to predict insulin resistance. The strongest association was for the definition of having >2 NCEP-ATPIII criteria to characterize the metabolic unhealthy phenotype in women (odds ratio of 19.7). On the other hand, the Aguilar-Salinas et al. definition had the strongest association in men (odds ratio of 18.7).

CONCLUSIONS

We found large differences in the prevalence of the various body size phenotypes when using different definitions, highlighting the need for having standard criteria. Our data also suggest the need for sex-specific definitions of metabolic health.