Insulin Resistance as a Diagnostic Criterion for Metabolically Healthy Obesity in Children

Mechanisms and risk factors of metabolic syndrome in children and adolescents

Metabolic syndrome (MetS) is a complex disorder characterized by abdominal obesity, elevated blood pressure, hyperlipidemia, and elevated fasting blood glucose levels. The diagnostic criteria for MetS in adults are well-established, but there is currently no consensus on the definition in children and adolescents. The etiology of MetS is believed to involve a complex interplay between genetic predisposition and environmental factors. While genetic predisposition explains only a small part of MetS pathogenesis, modifiable environmental risk factors play a significant role. Factors such as maternal weight during pregnancy, children's lifestyle, sedentariness, high-fat diet, fructose and branched-chain amino acid consumption, vitamin D deficiency, and sleep disturbances contribute to the development of MetS. Early identification and treatment of MetS in children and adolescents is crucial to prevent the development of chronic diseases later in life. In this review we discuss the latest research on factors contributing to the pathogenesis of MetS in children, focusing on non-modifiable and modifiable risk factors, including genetics, dysbiosis and chronic low-grade inflammation.

Diagnostic accuracy of anthropometric indices for metabolically healthy obesity in child and adolescent population

BACKGROUND

A variable percentage of children and adolescents with obesity do not have cardiometabolic comorbidities. A phenotype called metabolically healthy obese (MHO) has emerged to describe this population subgroup. Early identification of this condition may prevent the progression to metabolically unhealthy obesity (MUO).

MATERIAL AND METHODS

A cross-sectional descriptive study of 265 children and adolescents from Cordoba (Spain) conducted in 2018. The outcome variables were MHO, established based on three criteria: International Criterion, HOMA-IR, and a combination of the previous two.

RESULTS

The prevalence of MHO ranged from 9.4% to 12.8% of the study population, between 41% and 55.7% of the sample with obesity. The highest agreement was reached between the HOMA-IR definitions and the combined criteria. The waist-to-height ratio (WHtR) was the indicator with the highest discriminant capacity for MHO in 2 of the three criteria, with its best cut-off point at 0.47 for both.

CONCLUSION

The prevalence of MHO in children and adolescents differed according to the criteria used for diagnosis. The anthropometric variable with the most remarkable discriminating capacity for MHO was WHtR, with the same cut-off point in the three criteria analysed.

IMPACT STATEMENT

This research work defines the existence of metabolically healthy obesity through anthropometric indicators in children and adolescents. Definitions that combine cardiometabolic criteria and insulin resistance are used to identify metabolically healthy obesity, as well as the prediction of this phenomenon through anthropometric variables. The present investigation helps to identify metabolically healthy obesity before metabolic abnormalities begin.

Trends in HOMA-IR values among South Korean adolescents from 2007-2010 to 2019-2020: a sex-, age-, and weight status-specific analysis

BACKGROUND/OBJECTIVES

An increase in obesity prevalence may lead to an increase in the HOMA-IR value. This study aimed to investigate changes in age- and sex-specific homeostasis model assessment of insulin resistance (HOMA-IR) values among South Korean adolescents, using data from the Korean National Health and Nutrition Examination Survey (KNHANES) IV, V, and VIII conducted between 2007-2010 and 2019-2020.

SUBJECTS/METHODS

Overall, 4621 adolescents aged 10-18 years were evaluated, including 3473 from the 2007-2010 dataset and 1148 from the 2019-2020 dataset. The mean HOMA-IR values and percentile curves were evaluated by age, sex, and weight status.

RESULTS

The mean HOMA-IR values peaked at puberty in both sexes and further increased during puberty in the 2019-2020 dataset (boys 5.21, 95% confidence interval [CI] 4.16-6.26; girls 5.21, 95% CI 3.09-7.33) compared with the 2007-2010 dataset (boys 3.25, 95% CI 3.04-3.47; girls 3.58, 95% CI 3.31-3.85). Both groups (with normal-weight and overweight/obesity) exhibited a peak HOMA-IR value during puberty in both sexes and both datasets, although the group with overweight/obesity had a higher and wider peak age range. While the mean HOMA-IR values did not change in adolescents with normal-weight, they increased during puberty and post-puberty in boys with overweight/obesity.

CONCLUSIONS

HOMA-IR values should be interpreted considering sex, weight status, and pubertal stages. In particular, during the pubertal period, insulin resistance (IR) can coexist not only due to weight-related factors but also as a result of the distinct hormonal changes characteristic of puberty. Over the 10-year period, the mean HOMA-IR values increased in the group with overweight/obesity during puberty and post-puberty, highlighting the need for active intervention to prevent metabolic complications in adolescents with overweight/obesity.

Prevalence and clinical characteristics of metabolically healthy obese versus metabolically unhealthy obese school children

INTRODUCTION

Children with obesity in the absence of traditional cardiometabolic risk factors (CRF) have been described as metabolically healthy obese (MHO). Children with MHO phenotype has a favorable metabolic profile with normal glucose metabolism, lipids, and blood pressure compared to children with metabolically unhealthy obese (MUO) phenotype. This study aimed to compare several parameters related to obesity between these two groups and to examine the predictors associated with the MHO phenotype.

METHODS

This study included a cross-sectional baseline data of 193 children with obesity (BMI z-score > +2 SD) aged 8-16 years enrolled in MyBFF@school program, a school-based intervention study conducted between January and December 2014. Metabolic status was defined based on the 2018 consensus-based criteria with MHO children had no CRF (HDL-cholesterol > 1.03 mmol/L, triglycerides ≤ 1.7 mmol/L, systolic and diastolic blood pressure ≤ 90^th percentile, and fasting plasma glucose ≤ 5.6 mmol/L). Those that did not meet one or more of the above criteria were classified as children with MUO phenotype.

RESULTS

The prevalence of MHO was 30.1% (95% CI 23.7 - 37.1) among schoolchildren with obesity and more common in younger and prepubertal children. Compared to MUO, children with MHO phenotype had significantly lower BMI, lower waist circumference, lower uric acid, higher adiponectin, and higher apolipoprotein A-1 levels (p < 0.01). Multivariate logistic regression showed that adiponectin (OR: 1.33, 95% CI 1.05 - 1.68) and apolipoprotein A-1 (OR: 1.02, 95% CI 1.01 - 1.03) were independent predictors for MHO phenotype in this population.

CONCLUSIONS

MHO phenotype was more common in younger and prepubertal children with obesity. Higher serum levels of adiponectin and apolipoprotein A-1 increased the possibility of schoolchildren with obesity to be classified into MHO phenotype.

Degree of Accuracy of the BMI Z-Score to Determine Excess Fat Mass Using DXA in Children and Adolescents

Obesity is caused by fat accumulation. BMI Z-score is used to classify the different degrees of weight status in children and adolescents. However, this parameter does not always express the true percentage of body fat. Our objective was to determine the degree of agreement between the fat mass percentage measured by DXA and the stratification of weight according to BMI Z-score in the pediatric age group. We designed a descriptive cross-sectional study. The patients were classified as underweight/normal weight with Z-scores between −2 and +0.99, overweight from 1 to 1.99, obese from 2 to 2.99, and very obese ≥3. We included 551 patients (47% girls), with a mean age of 11.5 ± 2.8 years (3.7–18 years). Higher BMI Z-scores were associated with a higher percentage of total fat (p < 0.001). However, there were important overlaps between both parameters, such that the BMI Z-score classified patients with the same percentage of total fat mass as having a different nutritional status classification. In conclusion, the stratification of weight status according to BMI Z-score revealed that 46.7% of patients had a fat percentage that did not correspond to their classification. For a more accurate weight assessment in clinical practice, we recommend combining anthropometric indices with diagnostic tools that better correlate with DXA, such as electrical bioimpedance.