Correlation of tri-ponderal mass index with insulin-like growth factor-I and insulin-like growth factor binding protein-3 in children and adolescents

Iee Ho Choi; Sun-Young Kim; Minsun Kim

doi:10.6065/apem.2346158.079

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Choi, Kim, and Kim: Correlation of tri-ponderal mass index with insulin-like growth factor-I and insulin-like growth factor binding protein-3 in children and adolescents

Original Article

Annals of Pediatric Endocrinology & Metabolism 2024;29(4): 258-265.

Published online: August 31, 2024

DOI: https://doi.org/10.6065/apem.2346158.079

Correlation of tri-ponderal mass index with insulin-like growth factor-I and insulin-like growth factor binding protein-3 in children and adolescents

Iee Ho Choi¹

, Sun-Young Kim²

, Minsun Kim^1,²

¹Department of Pediatrics, Jeonbuk National University Medical School, Jeonju, Korea

²Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea

Address for correspondence: Minsun Kim Department of Pediatrics, Jeonbuk National University Hospital, 20 Baekje-daero, Deokkin-gu, Jeonju 54907, Korea Email: children@jbnu.ac.kr, 082kiki@hanmail.net

Received August 2, 2023 Revised November 13, 2023 Accepted December 12, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose

We aimed to investigate the relationship between the tri-ponderal mass index (TMI), a new indirect measure of fat mass, and insulin-like growth factor (IGF)-I/IGF binding protein (IGFBP)-3.

Methods

The study included 1,630 children and adolescents who visited Jeonbuk National University Children's Hospital. Each patient’s medical record was retrospectively reviewed for age, sex, height, weight, body mass index (BMI), TMI, and IGF-1 and IGFBP-3 levels. Study participants were divided by sex and then categorized by age, BMI, and TMI. Finally, the correlations of TMI with IGF-1 level, IGF-1 standard deviation score (SDS), IGFBP-3 level, IGFBP-3 SDS, and IGF-1/IGFBP-3 ratio were investigated.

Results

All participants were <19 years of age. BMI correlated with IGF-1 and IGFBP-3 levels in both sexes; however, the relationship with TMI differed by sex. TMI correlated with IGF-1 and IGFBP-3 SDS in boys and with IGF-1, IGFBP-3, and IGFBP-3 SDS in girls across all ages. In boys, BMI and TMI significantly correlated with IGF-1, IGF-1 SDS, IGFBP-3, IGFBP-3 SDS, and the IGF-1/IGFBP-3 ratio in the normal-weight group. TMI also correlated with IGF-1, IGFBP-3, and IGFBP-3 SDS in the overweight group. In girls, BMI significantly correlated with IGF-1, IGF-1 SDS, IGFBP-3, IGFBP-3 SDS, and the IGF-1/IGFBP-3 ratio in the normal-weight group and with IGFBP-3 and IGFBP-3 SDS in the overweight group, while TMI correlated with IGF-1, IGF-1 SDS, and the IGF-1/IGFBP-3 ratio in the normal-weight group; with IGF-1, IGFBP-3, and IGFBP-3 SDS in the overweight group; and with IGFBP-3 SDS in the obese group.

Conclusions

TMI may more strongly correlate with IGFBP-3 level than BMI in overweight boys and with IGFBP-3 SDS in overweight and obese girls. The correlation of IGFBP-3 SDS with TMI may be helpful for evaluating weight status in adolescent girls.

Keywords: Tri-ponderal mass index, Body mass index, Insulin-like growth factor-1, Insulin-like growth factor binding protein-3

Highlights

· Tri-ponderal mass index is more frequently correlated with insulin-like growth factor (IGF)-1/IGF binding protein (IGFBP)-3 levels than body mass index in overweight and obese adolescents. IGFBP-3 standard deviation score might be helpful clinically to evaluate obesity in girls.

Introduction

Childhood obesity has become a global health concern [1]. The prevalence of childhood obesity is increasing annually and increased greatly during the coronavirus disease 2019 (COVID-19) pandemic [2-5]. This highlights the importance of obesity screening. The body mass index (BMI), which was developed based on the discovery that adult body weight is proportional to height squared [6], has been used to evaluate childhood and adolescent obesity [7,8]. However, it has been reported that an individual's weight is not proportional to height squared in childhood, limiting the validity of BMI estimation of obesity in children [9-11].

The tri-ponderal mass index (TMI), defined as weight divided by height cubed, has been proposed as a possible screening index for childhood and adolescent obesity [12]. TMI has been shown to be more accurate in measuring body fat mass and differentiating between overweight and obesity in children of various races [13-15], including Koreans [16,17].

Although TMI has been suggested to be a more accurate obesity index than BMI in children, like BMI, TMI is an indirect method for evaluation of obesity. We propose the additional parameters of insulin-like growth factor (IGF)-1 and IGF binding protein (IGFBP)-3 for more accurate estimation of childhood obesity. IGF-1, a growth hormone mediator, is essential for somatic cell development [18]. Recent in vivo and in vitro studies have suggested that IGF-1 is also involved in adipocyte differentiation [18-21]. Some studies have reported that IGF-1 is associated with dysregulated lipid metabolism [22], cardiovascular disease [23], and nonalcoholic fatty liver disease [24]. IGFBP-3, the most abundant modulatory binding protein, has 2 different roles depending on the binding of IGFs. A role unrelated to the binding of IGFBP-3 has been suggested to impact adipocyte differentiation, obesity, insulin resistance, and type 2 diabetes mellitus [25,26].

Based on previous reports, we assumed possible correlations of IGF-1 and IGFBP-3 with the obesity indices BMI and TMI and hypothesized that different correlations with these levels would be observed for BMI and TMI. In this study, we aimed to investigate the relationships of IGF-1 and IGFBP-3 with each of the 2 obesity indices.

Materials and methods

1. Subjects

Children and adolescents aged <19 years who visited Jeonbuk National University Children's Hospital mainly for growth evaluation and puberty assessment between January 1, 2018, and January 1, 2022, and who underwent a blood examination for IGF-1 and IGFBP-3 levels without underlying disease such as central precocious puberty, endocrine disease, chronic disease, or metabolic disease were selected as study subjects. The study subjects were categorized into 4 age groups: <5 years, 5–9.9 years, 10–14.9 years, and 15–18.9 years. The subjects were also categorized based on BMI and TMI percentiles into underweight (BMI and TMI less than the third percentile), normal-weight (BMI and TMI from the third percentile to the 85th percentile), overweight (BMI and TMI from the 85th percentile to the 95th percentile), and obese (BMI and TMI over the 95th percentile) [16,27].

2. Methods

Clinical characteristics of age, sex, height, weight, BMI, and TMI and laboratory data of serum IGF-1 and IGFBP-3 levels were retrospectively reviewed from medical records. Serum IGF-1 and IGFBP-3 levels were determined using an enzyme-linked immunosorbent assay (Diagnostic Systems Laboratories Inc., Webster, TX, USA). The SDS for BMI was calculated using the age- and sex-specific least mean square method based on 2017 growth reference values for Korean children and adolescents, developed by the Korean Pediatric Society and the Korea Centers for Disease Control and Prevention [27,28]. The SDS values for IGF-1 and IGFBP-3 were calculated using the 2012 reference values for Korean children and adolescents [29], while that for TMI was calculated using the 2020 reference values for Korean children and adolescents aged 10–20 years [16].

3. Statistical analysis

The Mann–Whitney U-test was performed to determine variable independence. The correlation of TMI with IGF-1 or IGFBP-3 level was analyzed by age and weight classification using Spearman correlation analysis. The correlation of BMI with IGF-1 or IGFBP-3 level was analyzed by weight classification using Spearman's correlation analysis. Univariate regression analysis was performed using TMI as the dependent variable and IGF-1 level, IGF-1 SDS, IGFBP-3 level, IGFBP-3 SDS, and IGF-1/IGFBP-3 as independent variables. All statistical analyses were performed using IBM SPSS Statistics ver. 20.0 (IBM Co., Armonk, NY, USA). P<0.05 was considered statistically significant for all clinical and laboratory variables.

4. Ethical statement

This study was approved by the Institutional Review Board (IRB) of the Clinical Research Institute of Jeonbuk National University Hospital (IRB No. CUH 2022-11-035-006). The need for informed consent was waived by the IRB.

Results

1. Clinical characteristics of the study population

This study included 1,630 patients, consisting of 639 boys and 991 girls. Table 1 presents the height, weight, BMI, BMI SDS, TMI, and TMI SDS of the study population as means and standard deviations, categorized by sex and age group. Both the male and female groups experienced significant increases in height and weight with age (P<0.05). Similarly, BMI increased with age in both groups, although the increase in girls aged 15–19 years was not significant. In contrast, TMI decreased with age in both the male and female groups, and there was no significant difference in either boys or girls aged 15–19 years.

2. Laboratory characteristics of IGF-1 and IGFBP-3 in the study population by age group

Table 2 presents the mean and standard deviation of IGF-1 and IGFBP-3 for the study population categorized by sex and age group. In boys, IGF-1 level increased significantly with age (P<0.05), as did IGFBP-3 level and IGFBP-3 SDS level (P<0.05). Among girls, the IGF-1 level showed significant increases in the <5-year-old and 10- to 14.9-year-old groups (P<0.05), but no significant differences were observed in IGF-1 SDS values. The pattern was similar for IGFBP-3 level, with a significant increase until 10–14.9 years old. The IGFBP-3 SDS value, however, experienced a significant increase only in the 5- to 9.9-year-old and 10- to 14.9-year-old groups.

3. Correlations of BMI and TMI with IGF-1, IGFBP-3, and IGF-1/IGFBP-3 by age group

For both boys and girls, in the 5- to 9.9-year-old and 10- to 14.9-year-old groups, BMI showed significant correlations with all variables except the IGF-1/IGFBP-3 ratio, which showed a significant correlation only in the male 5- to 9.9-year-old group (P=0.022) and female 10- to 14.9-year-old group (P=0.007) (Table 3). However, TMI showed significant correlations with IGFBP-3 level (P=0.046 and P=0.001) and IGFBP-3 SDS (P=0.007 and P=0.05) among boys in the 5- to 9.9-year-old group and 10- to 14.9-year-old group. Among girls, TMI showed significant correlations with IGFBP-3 SDS (P=0.01 and P<0.001) in the 5- to 9.9-year-old group and with all variables in the 10- to 14.9-year-old group (Table 3).

4. Correlations of BMI and TMI with IGF-1, IGFBP-3, and IGF-1/IGFBP-3 by weight group in participants ≥10 years of age

The correlations of BMI and TMI with IGF-1 and IGFBP-3 levels were analyzed in boys and girls aged ≥10 years (Table 4). BMI had a significant correlation with all variables (P<0.05) among boys in the normal-weight group. TMI had a significant correlation with IGF-1 (P<0.001 and P=0.029), IGFBP-3 (P<0.001 and P=0.023), and IGFBP-3 SDS (P<0.001 and P=0.020) in the male normal-weight and overweight groups (Table 4). Among girls, BMI had a significant association with all variables (P<0.05) in the normal-weight group, whereas TMI had significant associations with IGF-1 level (P=0.001), IGFBP-3 level (P=0.004), and IGFBP-3 SDS (P=0.011) in the overweight group and with IGFBP-3 SDS (P=0.041) in the obese group and with IGF-I level (P<0.001), IGF-1 SDS (P<0.001), and IGF-1/IGFBP-3 ratio (P<0.001) in the normal-weight group (Table 4).

5. Univariate regression analysis with TMI in participants ≥ 10 years of age

The effects of all variables (IGF-1, IGF-1 SDS, IGFBP-3, and IGFBP-3 SDS) on TMI were investigated through univariate regression analysis in male and female patients aged ≥10 years, as shown in Table 4. The analysis revealed that the most relevant item in girls was IGFBP-3 SDS (P<0.001, ηp²=0.144).

Discussion

Excessive fat accumulation, a hallmark of overweight and obesity, is linked to the onset of numerous metabolic diseases. IGFs and their binding proteins play intricate roles in fat differentiation and accumulation and in the development of metabolic conditions such as fatty liver disease, diabetes, and hyperlipidemia. In our investigation, we focused on factors associated with fat accumulation in individuals with healthy obesity as well as those with metabolically abnormal obese (MAO).

IGF-1 is a pivotal hormone in carbohydrate and fat metabolism. Studies have reported its association with high fat mass [30] as well as its influence on fat differentiation in individuals with type 2 diabetes [31]. Moreover, IGF-1 has been linked to severe obesity [32], type 2 diabetes mellitus [33], and type 1 diabetes mellitus [34]. In contrast, IGF-2 primarily plays a significant role in fetal development and growth [35]. While recent data on IGF-2 are limited, it has been reported to have associations with BMI and insulin sensitivity [36]. Consequently, previous findings suggest that IGF-1 may be more closely linked to both healthy obesity and metabolically abnormal obesity in children and adolescents compared to IGF-2.

IGFBP-3 is known to be involved in the differentiation of adipocytes and the maintenance of mature adipocytes [26]. It has also been associated with brown adipocytes [26]. In a murine model with overexpression of human IGFBP-3 (hIGFBP-3), fasting hyperglycemia, impaired glucose tolerance, and insulin resistance were observed [37]. Furthermore, IGFBP-4 has been reported to play a role in adipocyte accumulation [38]. Additional binding proteins, including IGFBP-1, IGFBP-2, IGFBP-5, IGFBP-6, and IGFBP-7, have also been suggested to be involved in the context of diabetes and metabolically abnormal obesity. Among these, IGFBP-3, the most prevalent IGFBP, is widely associated with adipocyte development, overweight, normal obesity, and MAO. Therefore, we sought to compare the correlations of IGFBP-3 and IGF-1 with the obesity measures BMI and TMI.

This study revealed variations in the correlation between IGF-1 level, IGFBP-3 level, and obesity index, depending on the specific index and sex. In the overweight group, IGF-1 exhibited a significant correlation solely with TMI in both boys and girls. However, in the obese group, no significant correlation was observed between IGF-1 and any obesity index, regardless of sex. These findings are in accordance with those of a recent study reporting elevated levels of IGF-1 and IGFBP-3 in overweight and obese children and adolescents, irrespective of sex and age [39], and a separate study indicating significant reductions in IGFs and IGFBPs following weight loss in obese children and adolescents [40]. These collective results suggest an association between IGF-1 and the obesity index. Conversely, other reports have identified associations between obesity-related complications and reduced IGF-1 levels [25]. Furthermore, IGF-1 has been negatively correlated with the development of cardiovascular complications in obese children and adolescents [23], and a reduction in IGF-1 has been linked to the development of nonalcoholic fatty liver disease [24]. Hence, it is reasonable to assume that IGF-1 levels do not consistently increase in obese children and adolescents, which may explain the absence of a significant correlation between TMI and IGF-1 in the obese group within our study.

The correlations for IGFBP-3 and IGFBP-3 SDS in the overweight group were significant only for TMI in boys, whereas, in girls, both BMI and TMI showed significant correlations. In the obese group, a significant correlation with TMI was observed for IGFBP-3 SDS only among girls. Meanwhile, while a significant correlation with IGFBP-3 and IGFBP-3 SDS was noted for both TMI and BMI in overweight girls, a correlation with IGFBP-3 and hIGFBP-3 SDS was commonly observed only with TMI in both sexes. Recent studies have documented elevated levels of IGFBP-3 in obese children and adolescents compared to those in normal-weight children and adolescents, which were lowered after weight reduction. Other studies have also reported that levels of IGFBP-3 were higher in obese children and adolescents compared to those in normal-weight children. Furthermore, IGFBP-3 levels have been found to be elevated in pediatric patients with impaired glucose tolerance and type 2 diabetes mellitus. We believe that these findings support an association between IGFBP-3 and obesity indices in overweight and obese groups. However, this correlation displays a sex difference in the obese group, being significant only in girls, which may be related to varying fat mass increases during puberty. The IGF-1/IGFBP-3 ratio showed a significant correlation with both obesity indices only in normal-weight individuals, irrespective of sex. A previous study has suggested usage of the IGF-1/IGFBP-3 ratio in assessing metabolic risk in adults [25]. Our study implies that this analysis did not yield significant results in overweight and obese individuals, possibly due to the intricate and not yet fully understood mechanisms of IGFs and IGFBPs in relation to obesity and metabolic diseases, which may differ between children and adults.

TMI has been proposed to be a precise indirect measure of obesity, particularly in children and adolescents, as it effectively reflects fat mass. Shim's analysis of the correlations between TMI and metabolic parameters in Korean children and adolescents aged 10–20 years suggested the potential use of TMI as a screening tool for childhood obesity [17]. Future studies should explore the correlation between TMI, IGFBP-3, and metabolic parameters. Consequently, IGFBP-3 could serve as an experimental foundation for validating its utility in assessing obesity. Additionally, to our knowledge, this is the first study to examine the correlations of TMI with IGF-1 and IGFBP-3. Previous studies have primarily compared IGF-1 and IGFBP-3 levels between obese and normal children and adolescents using BMI, analyzed correlations between BMI and the IGF-1/IGFBP-3 ratio, or examined associations between metabolic profiles and the IGF-1/IGFBP-3 ratio. However, their findings have been inconsistent, reporting different IGF-1 levels in obese adolescents, with some indicating higher levels, others indicating lower levels, and still others showing no significant difference. Similarly, studies have yielded inconsistent results regarding IGFBP-3 levels, with some reporting higher levels in obese subjects, others indicating normal levels, and some finding no differences. There is also a report of a negative correlation between IGFBP-3 and low-density lipoprotein but no correlation between IGFBP-3 and BMI in overweight children and adolescents. This inconsistency in results from previous studies may be attributed to population variations, a limited understanding of the mechanisms of IGFs and IGFBPs in obesity, or the use of BMI as an obesity measure. As our study presents the first correlation analysis between TMI, IGF-1, and IGFBP-3, we believe that further analysis with larger study groups is essential to validate our findings.

This study has several limitations. First, it is retrospective and was conducted on children and adolescents visiting the hospital for specific purposes, introducing potential selection bias. Second, the recruitment distribution was uneven among the weight groups, with participants primarily concentrated in the normal-weight group. Third, factors such as nutrition and pubertal stage, which can influence IGF-1 and IGFBP-3 levels, were not comprehensively considered. Fourth, actual fat mass was not directly measured or compared with metabolic parameters. Fifth, there is currently no nationwide reference for TMI in Korean children and adolescents. Considering these limitations, future research should focus on investigating the correlations between metabolic parameters and fat mass to establish the validity of TMI and IGFBP-3 as screening tools for overweight and obesity.

We conducted this study to identify associations between obesity indices (BMI, TMI) and IGF-1 and IGFBP-3 levels and to analyze the different associations. In conclusion, both TMI and BMI had similar associations with IGF-1 and IGFBP-3 levels among normal-weight individuals but different correlations in the overweight and obese groups. In the male overweight group, BMI had no correlation while TMI had significant correlation with IGF-1, IGFBP-3, and IGFBP-3 SDS levels. In the female overweight group, while BMI was significantly correlated with only IGFBP-3 SDS, TMI was correlated with IGF-1, IGFBP-3, and IGFBP-3 SDS. In the female obese group, only TMI had a significant correlation with IGFBP-3 SDS. Based on our results, TMI is assumed to be more strongly correlated with IGF-1 and IGFBP-3 levels compared to BMI in overweight and obese individuals. Our findings also suggest that TMI may reflect the fat mass more accurately than BMI. Finally, IGFBP-3 SDS may be particularly helpful in evaluating weight status in girls using TMI.

Notes

Conflicts of interest

No potential conflicts of interest related to this article were reported.

Funding

This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

The data supporting the findings of the study are available upon reasonable request from the corresponding author.

Author contribution

Conceptualization: MK; Data curation: IHC, SYK; Formal analysis: IHC, SYK; Methodology: IHC, SYK; Project administration: IHC, SYK; Visualization: IHC, SYK; Writing - original draft: IHC, SYK; Writing - review & editing: IHC, SYK MK

ACKNOWLEDGMENTS

We recognize Young Suk Shim for the first TMI study in Korean children and adolescents.

Table 1.

Clinical characteristic of study population by age group

Age group	Weight (kg)	Height (cm)	BMI (kg/m²)	BMI SDS	TMI (kg/m³)	TMI SDS
Male sex
<5 (n=33)	15.20±4.09 (14.20)^*	97.06±8.20 (97.10)^*	15.74±2.20 (15.44)^*	-0.20±1.63 (-0.39)^†	16.46±2.24 (16.10)^*
5, ≤10 (n=268)	30.25±11.36 (26.85)^*	126.67±11.84 (126.05)^*	18.56±4.01 (17.15)^*	0.43±1.52 (0.10)^§,‡	14.71±2.53 (14.23)^*
10, ≤15 (n=283)	44.92±13.63 (42.50)^*	148.06±10.93 (147.50)^*	20.16±4.66 (19.25)^*	-0.21±4.46 (-0.10)^†	13.56±2.45 (13.14)^§,†	0.22±1.13 (0.25)
15–18 (n=55)	64.94±16.29 (61.10)^*	168.07±9.75 (169.00)^*	22.16±9.19 (21.85)^*	0.18±1.36 (0.10)	13.60±2.86 (13.40)	0.38±1.03 (0.40)
Female sex
<5 (n=42)	13.15±3.33 (13.15)^*	90.14±10.71 (91.70)^*	15.92±1.52 (15.69)^*	0.07±1.17 (0.02)	17.92±2.79 (17.51)^*
5, ≤10 (n=725)	29.93±6.72 (29.30)^*	128.47±8.04 (129.40)^*	17.97±3.30 (17.48)^§,‡	0.46±1.25 (0.40)^‡	13.99±2.08 (13.72)^*
10, ≤15 (n=217)	39.79±12.77 (37.50)^*	143.01±8.51 (142.20)^*	19.06±4.28 (18.42)^§,†	-0.00±1.35 (-0.12)^†	13.32±2.50 (12.91)^§,†	0.38±1.06 (0.38)
15–18 (n=7)	45.95±9.10 (44.20)^*	154.64±5.17 (153.20)^*	19.10±2.55 (18.92)	-0.86±1.14 (-0.84)	12.33±1.43 (12.46)	-0.16±0.85 (-0.22)

Values are presented as mean±standard deviation (median).

TMI SDS are obtained in children aged over 10 years old due to absence of L, M, S reference in children younger than 10 years.

BMI, body mass index; SDS, standard deviation score; TMI, tri-ponderal index.

^* P<0.05, significantly different in terms of sex across age groups.

^§ P<0.05, significantly different in terms of sex across <5-year age group.

^† P<0.05, significantly different in terms of sex across 5- to 10-year age group.

^‡ P<0.05, significantly different in terms of sex across 10- to 15-year age group.

Table 2.

IGF-I and IGFBP-3 by age group

Age group	IGF-1	IGF-1 SDS	IGFBP3	IGFBP3 SDS
Male sex
<5 (n=33)	109.12±70.26 (97.30)^*	-0.16±0.96 (-0.29)	2891.00±1549.58 (2785.00)^*	1.84±1.66 (1.66)^‡
5, ≤10 (n=268)	170.11±69.63 (164.00)^*	0.11±1.26 (-0.23)^‡	4149.62±2154.52 (4095.00)^*	2.32±0.98 (2.25)^‡
10, ≤15 (n=283)	315.94±134.02 (291.40)^*	-0.04±1.54 (0.00)^†	5040.96±2593.36 (5030.00)^*	2.90±1.85 (2.54)^§,†
15–18 (n=55)	368.38±97.85 (364.00)^*	-0.32±0.86 (-0.46)	5476.28±2724.09 (5370.00)^*	3.70±2.34 (3.57)
Female sex
<5 (n=42)	113.93±66.33 (99.95)^*	-0.15±0.84 (-0.29)	3620.00±1530.00 (3520.00)^*	3.00±2.07 (2.59)
5, ≤10 (n=725)	233.77±116.11 (227.24)^*	0.09±1.06 (0.09)	4607.90±2207.62 (4570.00)^§,‡	2.75±1.30 (2.63)^‡
10, ≤15 (n=217)	400.93±345.84 (353.00)^§,†	0.25±3.79 (0.04)	5329.89±2714.36 (5210.00)^§,†	3.61±2.64 (3.03)^†
15–18 (n=7)	390.01±113.89 (362.00)	-0.36±1.07 (-0.41)	5145.00±2571.54 (5295.00)	2.99±0.97 (2.98)

Values are presented as mean±standard deviation (median).

IGF-1, insulin-like growth factor-1; SDS, standard deviation score; IGFBP-3, IGF binding protein-3.

^* P<0.05, significantly different in terms of sex across age groups.

^§ P<0.05, significantly different in terms of sex across <5-year age group.

^† P<0.05, significantly different in terms of sex across 5- to 10-year age group.

^‡ P<0.05, significantly different in terms of sex across 10- to 15-year age group.

Table 3.

Analysis of Correlation of BMI and TMI with IGF-1, IGFBP-3 by age

Variable	IGF-I		IGF-1 SDS		IGFBP-3		IGFBP-3 SDS		IGF-1/IGFBP-3
Variable	Rho	P-value	Rho	P-value	Rho	P-value	Rho	P-value	Rho	P-value
Correlation of BMI
Age group
Male sex
<5 (n=33)	0.245	0.184	0.280	0.127	0.187	0.429	0.301	0.210	0.122	0.609
5, ≤10 (n=268)	0.326	0.000^*	0.196	0.001^*	0.400	0.000^*	0.205	0.017^*	0.199	0.022^*
10, ≤15 (n=283)	0.212	0.000^*	0.161	0.007^*	0.420	0.000^*	0.347	0.000^*	0.046	0.570
15–18 (n=55)	-0.024	0.875	0.005	0.972	0.223	0.223	0.100	0.621	0.008	0.966
Total (n=639)	0.406	0.000^*	0.211	0.000^*	0.483	0.000^*	0.299	0.000^*	0.269	0.000^*
Female sex
<5 (n=42)	0.166	0.294	0.314	0.043	0.273	0.446	0.273	0.446	0.006	0.987
5, ≤10 (n=725)	0.167	0.000^*	0.168	0.000^*	0.295	0.000^*	0.256	0.000^*	0.082	0.217
10, ≤15 (n=217)	0.322	0.000^*	0.288	0.000^*	0.492	0.000^*	0.490	0.000^*	0.285	0.007^*
15–18 (n=7)	-0.214	0.645	-0.250	0.589	0.086	0.436	0.200	0.287	0.067	0.425
Total (n=991)	0.255	0.000^*	0.206	0.000^*	0.395	0.000^*	0.325	0.000^*	0.216	0.000^*
Correlation of TMI
Age group
Male sex
<5 (n=33)	0.114	0.526	0.214	0.231	-0.108	0.650	0.089	0.710	-0.098	0.682
5, ≤10 (n=268)	0.088	0.153	0.079	0.199	0.173	0.046^*	0.233	0.007^*	0.036	0.678
10, ≤15 (n=283)	-0.002	0.968	0.102	0.088	0.261	0.001^*	0.158	0.050	-0.152	0.061
15–18 (n=55)	-0.018	0.897	-0.032	0.820	0.290	0.091	0.268	0.126	-0.075	0.669
Total (n=639)	-0.176	0.000^*	0.044	0.292	-0.021	0.700	0.114	0.045^*	-0.245	0.000^*
Female sex
<5 (n=42)	-0.502	0.001^*	-0.207	0.189	0.055	0.881	0.055	0.881	-0.527	0.117
5, ≤10 (n=725)	-0.022	0.560	0.049	0.190	0.094	0.149	0.167	0.010^*	-0.094	0.155
10, ≤15 (n=217)	0.153	0.025	0.186	0.006^*	0.446	0.000^*	0.430	0.000^*	0.210	0.046^*
15–18 (n=7)	-0.107	0.819	-0.143	0.760	0.600	0.400	0.257	0.311	0.200	0.352
Total (n=991)	-0.137	0.000^*	0.061	0.058	0.133	0.014^*	0.237	0.000^*	-0.038	0.493

BMI, body mass index; TMI, tri-ponderal mass index; IGF-1, insulin-like growth factor-1; SDS, standard deviation score; IGFBP-3, IGF factor binding protein-3; SDS, standard deviation score.

^* P<0.05, statistically significant correlation.

Table 4.

Analysis of Correlation of BMI, TMI with IGF-1, IGFBP3 in each sex aged over 10 years old by weight group

Weight group	IGF-1		IGF-1 SDS		IGFBP-3		IGFBP-3 SDS		IGF-I/IGFBP-3
Weight group	Rho	P-value	Rho	P-value	Rho	P-value	Rho	P-value	Rho	P-value
Male sex
By BMI
Underweight (n=15)	0.399	0.141	-0.016	0.957	-0.464	0.294	-0.049	0.873	0.429	0.337
Normal weight (n=229)	0.338	0.000^*	0.219	0.002^*	0.338	0.000^*	0.262	0.005^*	0.187	0.035^*
Overweight (n=30)	0.253	0.177	-0.220	0.269	0.177	0.498	0.464	0.081	0.217	0.403
Obesity (n=38)	0.091	0.586	-0.125	0.494	0.083	0.707	0.236	0.316	0.323	0.132
By TMI
Underweight (n=5)	0.400	0.505	0.800	0.200	0.500	0.667	0.332	0.602	0.209	0.247
Normal weight (n=255)	0.316	0.000^*	0.249	0.000^*	0.381	0.000^*	0.324	0.000^*	0.175	0.044^*
Overweight (n=49)	0.318	0.029^*	0.108	0.511	0.421	0.023^*	0.464	0.020^*	0.276	0.148
Obesity (n=29)	0.248	0.212	0.152	0.467	0.291	0.274	0.289	0.296	0.435	0.092
Female sex
By BMI
Underweight (n=8)	0.643	0.086	0.179	0.702	0.200	0.800	0.200	0.800	0.400	0.600
Normal weight (n=174)	0.387	0.000^*	0.331	0.000^*	0.373	0.002^*	0.352	0.004^*	0.423	0.000^*
Overweight (n=22)	0.191	0.393	0.143	0.526	0.727	0.007	0.776	0.003^*	-0.308	0.331
Obesity (n=20)	0.056	0.819	-0.134	0.574	0.150	0.700	0.367	0.332	-0.167	0.668
By TMI
Underweight (n=3)	-0.261	0.234	0.122	0.369	0.067	0.432	0.083	0.416	-0.417	0.132
Normal weight (n=168)	0.397	0.000^*	0.335	0.000^*	0.228	0.070	0.206	0.115	0.430	0.000^*
Overweight (n=28)	0.587	0.001^*	0.374	0.054	0.673	0.004^*	0.614	0.011^*	0.468	0.068
Obesity (n=25)	-0.033	0.878	-0.195	0.362	0.214	0.482	0.571	0.041^*	-0.077	0.803

BMI, body mass index; TMI, tri-ponderal mass index; IGF-1, insulin-like growth factor-I; SDS, standard deviation score; IGFBP-3, insulin-like growth factor binding protein-3.

^* P<0.05, statistically significant correlation.

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