Adipocytokines, metabolic syndrome, and exercise

Polycystic ovary syndrome is a risk factor for sarcopenic obesity: a case control study

BACKGROUND

Polycystic ovary syndrome (PCOS) is the most common endocrine disorder in young women and increases risk of cardiovascular and metabolic disease, and infertility. Women with PCOS share many characteristics commonly associated with aging including chronic inflammation and insulin resistance, which may be associated with "sarcopenic obesity", a term used to describe low appendicular skeletal muscle mass relative to total body mass. The purpose of this work was to determine the prevalence of sarcopenic obesity in women with PCOS. We hypothesized there would be a high prevalence of sarcopenic obesity, and that % appendicular skeletal muscle mass and markers of inflammation and insulin resistance would be inversely correlated in this population.

METHODS

Dual energy X-ray absorptiometry was used to assess body composition in 68 women with PCOS aged 18-35y and 60 healthy age-matched women from the same geographic area. Sarcopenic obesity was defined as having % appendicular skeletal muscle mass 2 standard deviations below the mean for the healthy age-matched controls and a % body fat above 35%. Data were analyzed with Mann-Whitney U-tests and Spearman correlations.

RESULTS

53% of women with PCOS were classified as sarcopenic obese. Women with PCOS had a median (interquartile range) appendicular skeletal muscle mass of 23.8 (22.3-25.8)% which was lower than the control median of 30.4 (28.6-32.4)% (p < 0.0001). Among women with PCOS, there were negative correlations between % appendicular skeletal muscle mass and the homeostasis model assessment insulin resistance index (r = - 0.409; p < 0.01), high sensitivity C-reactive protein (r = - 0.608; p < 0.0001) and glycosylated hemoglobin (r = - 0.430; p < 0.0001). Furthermore, % appendicular skeletal muscle mass correlated positively with vitamin D (r = 0.398; p < 0.0001) in women with PCOS, which is thought to positively affect skeletal muscle mass.

CONCLUSIONS

Women with PCOS have a high prevalence of sarcopenic obesity, which is correlated to insulin resistance and inflammation.

A Data Mining Approach Identified Salivary Biomarkers That Discriminate between Two Obesity Measures

BACKGROUND

A key mechanism of obesity involves dysregulation of metabolic and inflammatory markers. This study aimed to identify salivary biomarkers and other factors associated with obesity using an ensemble data mining approach.

METHODS

For a random cohort of over 700 subjects from 8137 Kuwait children (10.00 ± 0.67 years), four data mining methods were applied to identify important variables associated with obesity, including logistic regression by lasso regularization (Lasso), multivariate adaptive regression spline (MARS), random forests (RF), and boosting classification trees (BT). Each algorithm generated a variable importance rank list, based on an internal cross-validation procedure. An aggregated importance ranking was constructed by averaging the rank ordering of variables from individual list, weighted by the classification performance of respective models. Subsequently, the subset of top-ranking variables that were identified with at least three algorithms was evaluated by classification performance using receiver operating characteristic (ROC) analysis with bootstrap percentile resampling.

RESULTS

Obesity was defined either by the waist circumference (OBW) or by the body mass index (BMI) (OBWHO). We identified C-reactive protein (CRP), insulin, leptin, adiponectin, as salivary biomarkers associated with OBW, plus a clinical feature fitness level. A similar set of biomarkers was identified for OBWHO, but not including leptin. Tree-based clustering analysis revealed patterns that were significantly different between the OBW and OBWHO subjects.

CONCLUSION

A data mining approach based on multiple algorithms is useful for identifying factors associated with phenotypes, especially in cases where relationships are not salient, and a consensus from multiple methods can help produce a more generalizable subset of features. In this case, we have demonstrated that evaluation using the waist circumference includes association with high levels of salivary leptin, which is not seen with evaluation by BMI.

Continuous Metabolic Syndrome Scores for Children Using Salivary Biomarkers

Background

Binary definitions of the metabolic syndrome based on the presence of a particular number of individual risk factors are limited, particularly in the pediatric population. To address this limitation, we aimed at constructing composite and continuous metabolic syndrome scores (cmetS) to represent an overall measure of metabolic syndrome (MetS) in a large cohort of metabolically at-risk children, focusing on the use of the usual clinical parameters (waist circumference (WC) and systolic blood pressure (SBP), supplemented with two salivary surrogate variables (glucose and high density lipoprotein cholesterol (HDLC). Two different approaches used to create the scores were evaluated in comparison.

Methods

Data from 8,112 Kuwaiti children (10.00 ± 0.67 years) were used to construct two cmetS for each subject. The first cmetS (cmetS-Z) was created by summing standardized residuals of each variable regressed on age and gender; and the second cmetS (cmetS-PCA) was defined as the first principal component from gender-specific principal component analysis based on the four variables.

Results

There was a graded relationship between both scores and the number of adverse risk factors. The areas under the curve using cmetS-Z and cmetS-PCA as predictors for severe metabolic syndrome (defined as the presence of ≥3 metabolic risk factors) were 0.935 and 0.912, respectively. cmetS-Z was positively associated with WC, SBP, and glucose, but inversely associated with HDLC. Except for the lack of association with glucose, cmetS-PCA was similar to cmetS-Z in boys, but had minimum loading on HDLC in girls. Analysis using quantile regression showed an inverse association of fitness level with cmetS-PCA (p = 0.001 for boys; p = 0.002 for girls), and comparison of cmetS-Z and cmetS-PCA suggested that WC and SBP were main contributory components. Significant alterations in the relationship between cmetS and salivary adipocytokines were demonstrated in overweight and obese children as compared to underweight and normal-weight children.

Conclusion

We have derived continuous summary scores for MetS from a large-scale pediatric study using two different approaches, incorporating salivary measures as surrogate for plasma measures. The derived scores were viable expressions of metabolic risk, and can be utilized to study the relationships of MetS with various aspects of the metabolic disease process.