Body Weight and Insulin Resistance Indicators Among Children

Introduction

Insulin resistance is characterized by a reduced sensitivity of cells to insulin, which impairs the hormone’s ability to perform its physiological functions [1,2]. Consequently, this leads to an increase in blood glucose levels and a compensatory increase in insulin production by pancreatic β cells and the subsequent development of hyperinsulinemia [1].

When pancreatic β cells secrete more insulin than the body requires, the excess insulin binds to cell membranes, which impairs glucose uptake into the cells and causes glucose to remain in the bloodstream [2]. Insulin resistance and chronic exposure to excess glucose and high levels of free fatty acids ultimately leads to the damage of pancreatic β cells, impairing their ability to synthesize and secrete insulin. This results in β-cell apoptosis and the onset of type 2 diabetes [2].

Adipose tissue functions as a key endocrine organ that secretes numerous biologically active substances, such as leptin, adiponectin, tumor necrosis factor α, and monocyte chemoattractant protein 1, collectively referred to as adipocytokines [3]. As body weight increases in the context of obesity, the size of adipocytes increases as well. Enlarged adipocytes and adipose tissue release free fatty acids, reactive oxygen species, and pro-inflammatory cytokines. With expanding adipose tissue, adipocyte hypertrophy occurs due to insufficient oxygen supply [4]. Macrophages surrounding dead adipocytes form a crown-like structure to remove the dead cells. In this way, lipids from dead adipocytes are taken up by macrophages, which disrupts their normal activity, sustaining local inflammation and metabolic disorders [5].

Chronic overproduction of free fatty acids and excessive lipid accumulation during obesity and overnutrition lead to lipotoxicity in insulin-sensitive tissues, including the skeletal muscle, liver, pancreas, and heart [5]. Insulin resistance arises not only from increased adipose mass but also from inflammation driven by ectopic lipid deposition. Ectopic lipids generate toxic intermediates, such as ceramides and diacylglycerols, which disrupt insulin signaling by activating protein kinase C, c-Jun N-terminal kinase, and the inhibitor of nuclear factor kappa B kinase complex, inducing oxidative stress, endoplasmic reticulum stress, and apoptosis [6]. Ceramides play a central role in lipotoxic insulin resistance, thereby impairing glucose uptake and lipid metabolism. Notably, insulin resistance itself promotes hepatic de novo lipogenesis [7], creating a feed-forward cycle that exacerbates ceramide accumulation and metabolic dysfunction [5].

The incidence of insulin resistance among children has risen markedly in recent years [8,9]. This increase is associated with multiple factors, including higher rates of obesity in children and adolescents, lifestyle changes, and reduced physical activity driven by sedentary behaviors such as prolonged television viewing and excessive use of social media [10,11].

Insulin resistance is a critical precursor to various metabolic disorders. Early recognition and intervention are essential to mitigate the risk of progression to more serious complications [8], including hypertension, hyperlipidemia, cardiovascular disease, and type 2 diabetes [9–11]. Insulin resistance in childhood is associated with cardiometabolic risk [12], which increases the likelihood of developing dyslipidemia [13]. Accurate and early detection of insulin resistance is equally important to delay or prevent the onset of prediabetes and type 2 diabetes [14].

In this study, we aimed to investigate the associations between children’s body weight and 2 selected indicators of insulin resistance, the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) index and the Quantitative Insulin Sensitivity Check Index (QUICKI), stratified by age group and sex.

Material and Methods

STUDY DESIGN:

The research project was based on a cross-sectional clinical observational study conducted at a single center between 2023 and 2024, involving the analysis of children’s medical records, assessment of biochemical indicators and anthropometric measurements. Laboratory test results were collected from patients, including fasting glucose and insulin measurements. The records were analyzed using a convenience sample. The research concept received a positive opinion from the Bioethics Committee (No. KE-0254/14/01/2023) of Medical University of Lublin. Anthropometric measurements, such as body weight and height, were taken using an electronic column scale with a height gauge. Body mass index (BMI) was calculated using the formula: BMI=body weight (kg)/height (m)².

Based on BMI percentile values, 4 subgroups of patients were identified: (1) patients with low body weight (3^rd to 15^th percentile); (2) patients with normal body weight (15^th to 85^th percentile); (3) patients with overweight (85^th to 97^th percentile); and (4) patients with obesity (above the 97^th percentile). Based on the BMI, analysis, 19 children had low body weight, 50 had normal weight, 28 had overweight, and 2 had obesity.

The inclusion criteria were children aged 8 to 14 years and hospitalized children. The exclusion criteria were children with underweight, active inflammation in the body, secondary hypertension, renal defects, renal hypertension, previous cardiac surgery, previous organ transplantation, aortic valve defects, bicuspid aortic valve defects, female sex with Turner syndrome, May-Thurner syndrome, genetic defects, cardiomyopathy, long-term steroid therapy, current oncological treatment, current treatment with beta-blockers, current treatment for hyperthyroidism or hypothyroidism, diabetes, treatment with growth hormone, attention-deficit/hyperactivity disorder, cerebral palsy, myopathies, psychomotor retardation, cardiac arrhythmias visible on routine ECG, second-degree or higher heart block, pacemakers, psychomotor disorders, autism, depression or anxiety, lack of fluency in Polish, residence in an orphanage, and post-traumatic stress disorder.

During the study, circumstances arose that affected the selection process. For instance, some participants withdrew, some developed inflammation and were ineligible, and in some cases, samples could not be collected due to patient discharge.

PERCENTILE CHARTS USED:

The OLA and OLAF percentile charts were used to assess the physical development of patients. These charts are tools used to assess the physical development of children and adolescents aged 3 to 18 in Poland [15]. The OLAF calculator was also used to calculate accurate percentiles for height, weight, and BMI for children aged 6 to 18 years [15]. This calculator was developed based on data obtained from the nationwide epidemiological study PL0080 OLAF, representative of the national population, conducted and coordinated throughout the Poland by the Department of Public Health of the Children’s Memorial Health Institute [15].

BIOLOGICAL MATERIAL:

Blood samples were drawn from the children’s elbow veins in the morning, during vein puncture for diagnostic testing, and after obtaining a written consent from the parents. Due to the planned fasting glucose measurement, blood samples were taken from the children after a 12-hour overnight fasting.

Glucose levels were measured using the spectrophotometric method with Cobas Pro device. Insulin levels were taken using the Elecsys Insulin test from Roche using the ECLIA method using a Cobas device. The laboratory responsible met the requirements of ISO 9001: 2015 in the field of medical laboratory diagnostic services.

Because of the physiological insulin resistance observed during puberty, interpreting the HOMA-IR index in children and adolescents is not straightforward [16]. In pediatric populations, HOMA-IR values are adjusted for age and sex, as insulin resistance is a normal component of growth and maturation and varies according to developmental stage. It is assumed that insulin resistance in children is indicated by: HOMA-IR value greater than 2.67 in boys and greater than 2.22 in girls before puberty [17,18]. During puberty, which is generally considered to occur between the ages of 12.5 and 15 years for boys and 10.5 and 13 years for girls [18], the normal HOMA-IR is higher and can reach values of greater than 5.22 in boys and greater than 3.82 in girls [17,18]. The HOMA-IR cut-offs used in the present study were derived from research conducted by Kurtoglu et al [17] and have been applied in multiple international pediatric studies for classifying insulin resistance [19–21].

The HOMA-IR index was calculated using the following formula: HOMA=(fasting insulin (μIU/mL)×fasting glucose (mg/dL))/405 [22]. For the HOMA-IR index, higher values indicate greater insulin resistance. The QUICKI was calculated using the following formula: QUICKI=1/(log(fasting insulin μIU/mL)+ log(fasting glucose mg/dL)) [23]. For the QUICKI index, lower values indicate greater insulin resistance, with insulin resistance commonly defined as a QUICKI value below 0.34 [22].

STATISTICAL ANALYSIS:

The dependence of the qualitative variables was assessed using the chi-squared test. The normality of the variable distributions in the studied groups was calculated using the Shapiro-Wilk test. The Mann-Whitney test was used to compare the results between the 2 groups. The Pearson correlation coefficient was used to assess the linear dependence of quantitative variables. Univariate and multivariate logistic regression analysis was used to evaluate the expected relationships. Variable relationships were visualized using scatterplots with locally estimated scatterplot smoothing curves. A significance level of P<0.05 indicated statistically significant differences or relationships. The analyses were performed using Statistica 9.1 (StatSoft, Poland) and PQStat 1.8.2.

Data on lifestyle behaviors, such as diet, physical activity levels, and time spent sitting, were not included in the statistical analyses of this study.

Results

CHARACTERISTICS OF THE RESEARCH GROUP:

The study involved 61 girls (61.62%) and 38 boys (38.38%) aged 8 to 14 years. Of the girls, 70.49% had slim or normal weight, and 29.51% had overweight or obesity. In contrast, 68.42% of the boys had slim or normal weight, and 31.58% had overweight or obesity. The characteristics of the study participants are presented in Table 1.

Table 2 shows the comparison of biochemical parameters and BMI between children with slim and normal weight (group A) and those with overweight and obesity (group B). The results showed differences between insulin levels and BMI (P=0.006) between the HOMA-IR index and BMI (P=0.003 for boys) and between the QUICKI and BMI (P=0.008).

Table 3 summarizes the comparison of the results between patients with and without insulin resistance, classified according to the HOMA-IR and QUICKI indices. The Mann-Whitney test was used for group comparisons.

Table 4 shows the correlations between BMI and insulin resistance as assessed by the HOMA-IR and QUICKI indices. Pearson correlation coefficients were used to determine the strength and direction of these associations. BMI showed a significant correlation with higher HOMA-IR values and lower QUICKI values, indicating reduced insulin sensitivity. BMI also showed a significant correlation with fasting insulin concentrations, whereas no significant correlation was observed between BMI and fasting glucose levels.

Table 5 shows the relationship between insulin resistance, sex, and BMI categories. The chi-squared analysis revealed significant differences across sex and BMI categories for both the HOMA-IR and QUICKI indices. Statistical significance was observed for both sexes (HOMA-IR: P=0.007; QUICKI: P=0.014) and BMI (HOMA-IR: P=0.008; QUICKI: P=0.003).

Table 6 shows the results of univariable and multivariable logistic regression analyses for the likelihood of insulin resistance, assessed using the HOMA-IR. In both models, female sex and overweight and obesity were significantly associated with higher odds of insulin resistance. In the univariable model, girls had an odds ratio (OR) of 3.30 (95% CI, 1.37–7.96; P=0.008) compared with boys, and children with overweight and obesity had an OR of 3.24 (95% CI, 1.33–7.91; P=0.010) compared with children with thinness and normal weight. In the multivariable model, adjusting for both factors simultaneously, the associations remained significant, with an OR of 3.81 (95% CI, 1.49–9.73; P=0.005) for girls and 3.78 (95% CI, 1.45–9.84; P=0.006) for children with overweight and obesity. These results indicate that female sex and excess body weight are independent predictors of insulin resistance in the studied pediatric population.

Table 7 shows the distribution of insulin resistance, assessed by HOMA-IR, according to sex and BMI categories. The chi-square analysis demonstrated statistically significant differences across sex and BMI categories (chi-square, 16.315; P=0.00098), highlighting that overweight and obesity are strongly associated with insulin resistance in both girls and boys, with girls showing a slightly higher prevalence in the overweight obesity group compared with boys.

Relationship between the analyzed variables is further illustrated in Figure 1 using a scatterplot with both a fitted linear regression line and a locally estimated scatterplot smoothing curve for visualization.

Discussion

LIMITATIONS OF THE STUDY:

There are several important limitations of this study that should be acknowledged. First, the observational, cross-sectional design precludes drawing conclusions about the causal relationship between body weight and insulin resistance. While cross-sectional studies provide valuable population-specific pediatric data, they cannot capture the temporal dynamics of insulin resistance development over time, as longitudinal studies would. Additionally, the absence of direct pubertal stage assessment (eg, Tanner staging) is a notable limitation, given that puberty profoundly influences insulin sensitivity and metabolic parameters. To better elucidate the determinants of pediatric insulin resistance, future studies should incorporate longitudinal designs and more precise measures of body composition and pubertal staging.

Furthermore, the BMI, which is used as a general indicator of adiposity, does not accurately reflect body composition or distinguish between visceral and subcutaneous fat. This limits the precision with which the relationship between fat distribution and metabolic risk can be assessed. Additionally, the small number of children with overweight and obesity in the study sample restricted the possibility of performing more advanced statistical analyses and limits the generalizability of the findings to a broader pediatric population.

Furthermore, national percentile charts (OLA and OLAF) were used to accurately classify physical development within the Polish pediatric population, which can limit direct international comparisons. However, our primary objective was to ensure accurate classification of body weight and physical development within the studied Polish pediatric population. Local growth references are recommended for population-based studies, as they better reflect regional anthropometric characteristics and reduce the risk of misclassification that can occur when international standards are applied.

Although the HOMA-IR cut-offs applied in this study serve as a useful reference, their diagnostic performance can differ across populations, and individual risk estimates should be interpreted with caution. Additionally, the lack of lifestyle data is a significant limitation of the study that can influence the interpretation of the observed associations.

FUTURE RESEARCH DIRECTIONS:

Future research on the development of insulin resistance and overweight in children should focus on the following: (1) a longitudinal study design to monitor glucose and insulin changes over time; (2) precise metabolic measurements, including inflammatory markers, advanced body composition assessments, and detailed evaluations of insulin sensitivity to elucidate underlying mechanisms; (3) hormonal and pubertal evaluation to examine hormonal profiles, epigenetic influences, and standardized assessment of pubertal stage (eg, Tanner staging), as puberty significantly affects insulin sensitivity and may contribute to sex-specific differences; and (4) the validation of diagnostic tools, because although HOMA-IR and QUICKI showed high concordance in the present analysis, formal assessment of their diagnostic agreement and clinical implications in pediatric populations remains important.

Conclusions

This cross-sectional study demonstrated that higher BMI was significantly associated with insulin resistance in children and adolescents, consistent with findings from previous research. Although the study design does not allow for causal inference, the findings highlight the importance of early metabolic screening in pediatric populations with excess body weight. The results also underscore the need for longitudinal studies to better understand the development of insulin resistance over time. Finally, the comparable performance of HOMA-IR and QUICKI supports their usefulness as practical screening tools for identifying insulin resistance in children and adolescents.