Obesity and Insulin Resistance Screening Tools in American Adolescents: National Health and Nutrition Examination Survey (NHANES) 1999 to 2010

Association between serum uric acid-to-high-density lipoprotein cholesterol ratio and insulin resistance in an American population: A population-based analysis

INTRODUCTION

Previous studies have demonstrated a correlation between the serum uric acid-to-high-density lipoprotein cholesterol ratio (UHR) and insulin resistance (IR) in individuals with type 2 diabetes mellitus. However, no existing studies have investigated the relationship between IR and UHR in the general population. Therefore, the primary objective of this study was to investigate the correlation between UHR and IR in the general American population.

METHODS

A sample of 8,817 participants was selected from the 2013 to 2020 National Health and Nutrition Examination Survey (NHANES). Homeostatic model assessment of insulin resistance (HOMA-IR) was used to assess insulin resistance. Multiple logistic regression, generalized smooth curve fitting, and subgroup analysis were used to assess the association between IR and UHR.

RESULTS

Multiple logistic regression analysis indicated a significant correlation between insulin resistance and UHR, with odds ratios (OR) of 1.07 (95% CI = 1.03-1.11) in males and 1.18 (95% CI = 1.13-1.25) in females. A non-linear relationship and saturation effect between IR risk and UHR were observed, characterized by an inverted L-shaped curve and a critical inflection point at 8.82. It was found that the area under the ROC curve (AUC) of UHR was significantly larger (AUC = 0.703 for males and 0.747 for females, all P < 0.01) compared with the use of UA or HDL-C alone. Subgroup analysis showed that this independent association remain consistent regardless of race, age, BMI, diabetes, moderate activities, education level, alcohol drinking, and gender.

CONCLUSION

Elevated UHR demonstrates a significant correlation with insulin resistance, so it can be used as a potential indicator of insulin resistance within the American population.

Social jetlag is associated with adverse cardiometabolic latent traits in early adolescence: an observational study

Introduction

Adolescence is marked by physiological and social changes, such as puberty, increased responsibilities and earlier school start times. This often leads to insufficient sleep on school nights and the need to compensate for lost sleep on weekends, causing a misalignment between biological and social times, which has been termed social jetlag (SJL). SJL triggers stress responses and is associated with several negative health outcomes, including higher cardiometabolic risk in adults. In adolescence, however, SJL has only been consistently related to increases in adiposity but its association with other cardiometabolic indicators are unclear.

Method

In a sample of 278 healthy early adolescents (9-15 years of age; 168 girls) we investigated: 1) whether self-reported SJL is associated (using path analyses) with a cardiometabolic status latent factor obtained by testing the best fitting model via confirmatory factor analyses from an initial set of eight indicators [body mass index (BMI), waist/height ratio, triglyceride concentration, diastolic and systolic blood pressure, glycated hemoglobin, total cholesterol/high-density lipoprotein ratio (chol/HDL), and % body fat]; and 2) whether age and/or pubertal status influence the association between SJL and cardiometabolic status.

Result

We found that, for girls, higher SJL was associated with more adverse cardiometabolic latent scores (the shared variance of BMI, waist/height ratio, chol/HDL and systolic blood pressure, which had acceptable model fit indices). However, the role of age and pubertal status in this association was unclear for both sexes.

Discussion

SJL was associated with adverse cardiometabolic latent traits beyond increases in adiposity in this observational study in early female adolescents. Because disruptions of circadian rhythms are believed to lead to dysregulated energy homeostasis and not vice-versa, our findings highlight the need for sleep interventions in adolescence to help reduce the global burden of cardiometabolic ill health, especially in girls.

Tri-ponderal mass index as a tool for insulin resistance prediction in overweight adolescents: A cross-sectional study

OBJECTIVE

The aims of this study were to assess the capacity of tri-ponderal mass index (TMI) to screen and predict insulin resistance (IR) in overweight Brazilian adolescents, comparing it with body mass index (BMI) predictive capacity; and to verify the IR predictive capacity of other assessment tools such as waist circumference (WC), and waist-to-height ratio (WHR).

METHODS

A cross-sectional study was carried out with 217 overweight adolescents, from both sexes, between 12 and 18 y of age. The participants were classified as having IR according to a previous established cutoff point of ≤3.16. IR was determined by the homeostasis model assessment of insulin resistance. The other assessment tools were measured with standard protocols.

RESULTS

There was no difference between TMI and BMI to explain the presence of IR. In girls, BMI presented a slightly better predictive capacity to explain IR than TMI. Moreover, WC was reported to be the most effective IR screening methods for girls. Receiver operating characteristic curves showed that TMI and BMI presented similar values of sensibility and specificity for boys. Nevertheless, BMI had a better sensibility and TMI had a better specificity for girls. Interestingly, WC demonstrated a strong sensibility for both sexes.

CONCLUSIONS

TMI did not present a superior predictive accuracy for IR screening in overweight Brazilian adolescents when compared with BMI. TMI and BMI presented similar values of sensitivity and specificity for boys and WC a slightly superior IR predictive capacity for girls.

Relationship between DXA measured metrics of adiposity and glucose homeostasis; An analysis of the NHANES data

Introduction

Obesity is associated with insulin resistance and type 2 diabetes. Dual-energy X-ray absorptiometry (DXA) is a means of determining body composition and body fat distribution at different sites including whole body and trunk–locations where there tends to be high correlation at an individual level.

Methods

We performed an analysis of DXA-derived metrics of adiposity (truncal fat %,subtotal fat % and total fat %) from the NHANES database and then correlated the findings with markers of insulin resistance. We analyzed the data from DXA scans in NHANES 1999–2004. Homeostatic model assessment-insulin resistance and HOMA-β (beta-cell function) were estimated. Spearman correlation coefficients were calculated (ρ) between HOMA-IR,HOMA-β and different measures of obesity (Waist circumference(in cm), Body Mass Index (kg/m2), truncal fat %, subtotal fat % as well as total fat %) to gauge the relationship between markers of glucose homeostasis and DXA derived metrics of obesity. We also performed logarithmic transformation of HOMA-IR as well as HOMA-β to ensure normality of distribution and to meet the criteria for regression analysis. A forward selection model (by outcome and gender) was performed to predict log transformed insulin resistance (log HOMA-IR) as well as log transformed HOMA-β (log HOMA-β,measure of beta cell function) from age, serum triglycerides, HDL, trunk fat % and the SBP (in both males and females separately), after reviewing the spearman correlation coefficients.

Results

There were a total of 6147 men and 6369 women who were part of the study cohort. There was a positive correlation between markers of adiposity and log HOMA-IR and log HOMA-β in both males and females.Truncal fat % had the highest nonparametric correlation coefficent with log HOMA-IR among the DXA derived fat% (0.54 in males and 048 in females). In the multivariate analysis, truncal fat % was an independent predictor of logHOMA-IR as well as logHOMA-β. In males, the significant predictors of log HOMA-IR were; age, truncal fat % and HDL cholesterol (Adjusted R square of 0.325 (±0.66), F(3,207) = 34.63, p < .01). In females, the significant predictors of log HOMA-IR were; age, truncal fat %, SBP, Serum triglyceride and HDL cholesterol (Adjusted R square of 0.307 (±0.65),F(5,198) = 18.9, p < .01). In both males and females, the significant predictors of log HOMA-β were; age, and truncal fat % (Males; adjusted R square of 0.25 (±0.63), F (2,208) = 36.4, p < .01, Females; adjusted R square of 0.27 (±0.62), F (2,201) = 38.4, p < .01).

Conclusions

Body fat % on DXA is an imaging biomarker for insulin resistance. Incorporating this important information into DXA acquisitions and reporting frameworks may allow for this information to be available to providers who refer patients for these imaging studies.