Analysis of metabolic status and risk factors of small for gestational age children with catch-up growth in East China

Abstract

Purpose

Approximately 85–90% patients with small for gestational age (SGA) experience catch-up growth (CUG) by the age of 2, with their height reaching − 2 standard deviation scores (SDS) or 3% of the height of children of the same age and sex. However, SGA patients with CUG (CUG-SGA) tend to be at a higher risk of developing insulin resistance, obesity, metabolic syndrome, and cardiovascular diseases. This study explored the metabolic conditions of CUG-SGA patients in East China and analyzes the risk factors that may contribute to metabolic issues.

Methods

This multi-center study in East China involved 151 SGA patients aged 2–8 years. Patients were categorized into two groups: CUG-SGA (height not below − 2 SDS among the children of the same age and gender) and NCUG-SGA (SGA patients without CUG, height below − 2 SDS among the children of the same age and gender). Tests for insulin-like growth factor 1 (IGF-1), blood glucose (BG), insulin (INS), triglycerides (TG), total cholesterol (TC), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), and leptin (LEP) were conducted after a 12-hour fast. Body mass index (BMI) and homeostatic model assessment for insulin resistance (HOMA-IR) were calculated.

Results

(1) Out of 151 SGA patients, 82 were girls and 69 were boys. There were 85 patients in CUG-SGA group, including 38 girls and 47 boys. There were 66 patients in NCUG-SGA group, including 44 girls and 22 boys. The height SDS (H-SDS) and weight SDS (W-SDS) of CUG-SGA group were significantly higher than those of NCUG-SGA group (P < 0.05). (2) The level of IGF-1 SDS, ALT, and GGT in the CUG-SGA group were significantly higher than those in the NCUG-SGA group (P < 0.01). The level of W-SDS, LEP, INS, and HOMA-IR in the CUG-SGA group are significantly higher than those of the NCUG-SGA group (P < 0.05). (3) Correlation analysis in SGA patients indicated positive correlations between BG and W-SDS, LEP, and GGT (P < 0.05). INS was positively correlated with IGF-1 SDS, GGT (P < 0.01), and W-SDS (P < 0.05). HOMA-IR was positively correlated with IGF-1 SDS (P < 0.01). TC was positively correlated with LEP (P < 0.05), and TG was negatively correlated with IGF-1 SDS (P < 0.01). (4) After further controlling for confounding factors and performing multiple regression analyses, the results showed that BMI-SDS had a significant positive effect on INS and a significant negative effect on HOMA-IR. IGF-1 had a significant positive effect on INS and HOMA-IR, and a significant negative effect on TG. LEP had a significant positive effect on BG, INS, HOMA-IR, TC, and TG. ALT had a significant negative effect on INS and HOMA-IR, and a significant positive effect on TC and TG. GGT had a significant positive effect on BG, INS, HOMA-IR, TC, and TG.

Conclusion

(1) IGF-1 SDS was significantly positively correlated with fasting INS (FINS) and HOMA-IR, and exerted a significant positive effect on fasting BG. If IGF-1 levels in CUG-SGA infants remain persistently high, careful monitoring of changes in glucose metabolism is warranted. (2) CUG-SGA patients had higher LEP resistance compared to NCUG-SGA patients, potentially contributing to future metabolic issues. (3) Elevated ALT and GGT levels in SGA patients may warrant close monitoring of glucose metabolism, particularly in CUG-SGA patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-025-05868-2.

Introduction

Small for gestational age (SGA) is defined as a birth weight and/or length less than − 2 standard deviation score (SDS) [1]. Longitudinal studies have shown that 85–90% of SGA patients experience catch-up growth (CUG) by the age of 2 years [2]. About 90% of SGA children exhibit catch-up growth during the first year after birth, reaching a height SDS of -2 SDS relative to peers of the same sex and age [3]. CUG may be defined as a period of accelerated height velocity that exceeds the statistical norms for age and/or maturity, following a temporary period of growth inhibition [4]. Furthermore, CUG consistently describes the increase in growth velocity of SGA infants after birth [5]. In this study, SGA patients with CUG (CUG-SGA) patients are defined as those whose height falls within or exceeds 2 SDS compared to children of the same age and gender. Currently, the mechanism of CUG in SGA is still under further research [6].

While CUG improves the height and nutritional status of SGA patients, previous research indicates that early CUG may increase the risk of obesity, potentially leading to long-term effects [7]. Another study shows that, compared to children appropriate for gestational age (AGA), prepubertal SGA patients experience a 60% decrease in insulin (INS) sensitivity, leading to compensatory hyperinsulinemia [8]. The CUG-SGA patients are at high risk of developing insulin resistance, obesity, metabolic syndrome, cardiovascular diseases, and other related issues in the future [9, 10]. Although these studies suggest that CUG in SGA children may be associated with long-term metabolic risks, there remain relatively few clinical investigations into the metabolic status of 2–8-year-old SGA children in China. The changes in metabolic indicators during catch-up growth, as well as the risk factors potentially leading to unfavorable metabolic outcomes, have not yet been fully elucidated. To fill this research gap in China, the present study classified SGA patients in the East China region into a CUG-SGA group and an NCUG-SGA group, hypothesizing that CUG-SGA children would exhibit significant metabolic differences compared to NCUG-SGA children. We analyzed H-SDS, W-SDS, BMI-SDS, fasting BG, fasting INS, TG, IGF-1, ALT, GGT, and LEP as these indicators provide a comprehensive assessment of growth and metabolic status in SGA children. By comparing the two groups and examining the relationships between various metabolic markers and catch-up growth, we further explored potential risk factors for metabolic disorders in CUG-SGA patients. This not only aids in understanding the role of catch-up growth in metabolic health but also provides a scientific basis for early identification of high-risk children and timely interventions.

Materials and methods

This study was a multi-center research conducted in East China between September 2020 and November 2022. The study included a total of 166 SGA patients aged between 2 and 8 years, 15 of them were excluded because more than 50% of their key variables were missing. Therefore, 151 cases were included in the final analysis, including 69 boys and 82 girls. Data collected for each participant included their names, sex, age, gestational age, birth weight, birth length, and medical history. Height and weight measurements were taken by a pediatric nurse.

The patients were categorized into two groups: the CUG-SGA group, referring to patients whose height was not lower than − 2 SDS compared to children of the same age and gender, and the NCUG-SGA group, consisting of patients with heights lower than − 2 SDS compared to children of the same age and gender. Exclusion criteria involved patients with congenital heart disease, inflammatory bowel disease, genetic disorders, tumors, growth hormone deficiency, or any other conditions or treatments known to impact growth.

Blood samples were collected from the patients after the patients fasted for 12 h. INS-like growth factor 1 (IGF-1), blood glucose (BG), INS, triglycerides (TG), total cholesterol (TC), alanine aminotransferase (ALT), and gamma-glutamyl transferase (GGT) levels were measured using the chemiluminescence immunoassay method. Leptin (LEP) levels were measured using the enzyme-linked immunosorbent assay method. IGF-1 SDS was calculated based on previous research by Cao B [11]. Body mass index (BMI = kg/m2) and homeostatic model assessment for insulin resistance (HOMA-IR = fasting glucose (mmol/L) × fasting INS (µmol/L)/22.5) were calculated for each patient. Statistical analyses included independent-samples t-tests and non-parametric tests to compare the indicators between the CUG-SGA and NCUG-SGA groups. Pearson and Spearman correlation analyses were conducted to explore the relationships between IGF-1, ALT, GGT, LEP, and the indicators of lipid and glucose metabolism in SGA patients, particularly in the CUG-SGA group. The study obtained consent from the children and their parents and was approved by the Ethics Committee of Ruijin Hospital Affiliated with Shanghai Jiao Tong University School of Medicine.

Statistical analysis

This study used SPSS 26.0 for statistical analysis of the data. For data following a normal distribution, an independent-samples t-test was conducted, with results expressed as mean ± standard deviation (X’ ± s). For data not following a normal distribution, nonparametric tests were used, and results were reported as the median (interquartile range [IQR]). Missing values were imputed using the expectation–maximization (EM) method. Differences in H-SDS, W-SDS, BMI-SDS, BG, INS, TG, IGF-1, ALT, GGT, and LEP between the CUG and NCUG groups were compared using either the independent-samples t-test or nonparametric tests. Pearson and Spearman correlation analyses were performed to assess the relationships of W-SDS, BMI-SDS, IGF-1, ALT, GGT, and LEP with BG, INS and TG. A P-value < 0.05 was considered statistically significant, and P < 0.01 was considered highly significant. To further evaluate the independent effects of each variable on metabolic indicators, multiple linear regression analyses were conducted, treating the metabolic indicators as dependent variables and the various indicators and confounding factors as independent variables. All regression models underwent collinearity diagnostics by checking the variance inflation factor (VIF) to ensure there was no serious multicollinearity. A P-value < 0.05 was considered statistically significant for the regression coefficients.

Results

There were 151 SGA patients, with 82 girls and 69 boys. Among these patients, 85 were in the CUG-SGA group, consisting of 38 girls and 47 boys. The median age of the CUG-SGA group was 5.89 years. Additionally, there were 66 SGA patients in the NCUG-SGA group, with 44 girls and 22 boys. The median age of the NCUG-SGA group was 5.33 years. There was no significant difference in age between the two groups (P = 0.537). There were no differences between the two groups in terms of gestational age, birth weight, or birth length (Table 1).

Table 1.

Comparison of gestational age and birth physical parameters between the CUG and NCUG groups

Variable	CUG	NCUG	T	P
Gestational age (weeks)	38.5 ± 2.2	39.0 ± 1.4	-1.5	0.13
Birth weight (g)	2447.1 ± 357.1	2440.2 ± 380.2	1.1	0.92
Birth length (cm)	47.7 ± 3.1	47.8 ± 2.3	-0.8	0.94

CUG, catch-up growth; NCUG-SGA, small for gestational age without CUG. T represents the independent-samples t-test statistic

The height SDS (H-SDS) and weight SDS (W-SDS) of the CUG-SGA group were significantly higher than those of the NCUG-SGA group (P < 0.05). Although the average BMI-SDS of the CUG-SGA group were higher than those of the NCUG-SGA group, this difference was not found to be statistically significant (P = 0.27)(Table 2).

Table 2.

Comparisons of H-SDS, W-SDS, and BMI-SDS in CUG-SGA group and NCUG-SGA group

Group	CUG	NCUG	Total	P
H-SDS (P50 [P25, P75])	-1.39(-1.79,-0.87)	-2.65(-3.18,-2.22)	-1.93(-2.61,-1.31)	<0.01**
W-SDS (mean ± SD)	-0.82 ± 0.69	-1.67 ± 0.62	-1.19 ± 0.78	<0.05*
BMI-SDS (mean ± SD)	-0.38 ± 0.79	-0.53 ± 0.83	-0.45 ± 0.81	0.27

CUG, catch-up growth; NCUG-SGA, small for gestational age without CUG. * P < 0.05; **P < 0.01

Difference of metabolic indicators between CUG-SGA group and NCUG-SGA group

Based on the analysis of differences between groups, it was found that the levels of IGF-1 SDS, ALT, and GGT were found to be significantly higher in the CUG-SGA group compared to the NCUG-SGA group (P < 0.01). Additionally, the levels of INS, LEP, and HOMA-IR were significantly higher in the CUG-SGA group compared to the NCUG-SGA group (P < 0.05). However, no significant differences were observed in BG and TC levels between the two groups (P > 0.05) (Table 3).

Table 3.

The difference analysis of metabolic indicators of CUG-SGA group and NCUG-SGA group

Variables	CUG	NCUG	T value	P
IGF-1 SDS	-0.61(-1.30, 0.23)	-1.22(-1.80, -0.47)	-3.12#	0.002**
LEP (pg/ml)	149.65(77.19, 427.24)	120.50(56.03, 184.41)	-2.2#	0.028*
BG (mmol/L)	4.93 ± 0.51	4.80 ± 0.42	1.61&	0.109
INS (µIU/L)	4.68 ± 3.74	2.97 ± 1.28	2.40&	0.019*
HOMA-IR	1.07 ± 0.84	0.64 ± 0.31	2.53&	0.015*
TC (mmol/L)	4.17 ± 0.74	4.07 ± 0.49	0.77&	0.441
TG (mmol/L)	0.65 ± 0.14	0.76 ± 0.16	-3.01&	0.004**
ALT (U/L)	14.62 ± 5.03	12.01 ± 3.92	3.10&	0.003**
GGT (U/L)	11.35 ± 2.30	10.24 ± 1.83	2.65&	0.009**

# non-parametric test; & Student’s t test. SDS, standard deviation scores; IGF-1, insulin-like growth factor 1; BG, blood glucose; INS, insulin; TG, triglycerides; TC, total cholesterol; ALT, alanine aminotransferase; GGT, gamma-glutamyl transferase; LEP, leptin; BMI, body mass index; HOMA-IR, homeostatic model assessment for insulin resistance. *P < 0.05; ** P < 0.01

Correlation of W-SDS, BMI-SDS, IGF-1 SDS, LEP, ALT and GGT with glucose and lipid metabolism in SGA patients

BG was positive with W-SDS, LEP, and GGT in SGA patients (P < 0.05). INS showed positive correlations with IGF-1 SDS and GGT (P < 0.01), and with W-SDS (P < 0.05). TG exhibited a negative correlation with IGF-1 SDS (P < 0.01). The heat map illustrating these results is presented in Fig. 1. Additionally, LEP showed positive correlations with both W-SDS (r = 0.44, P < 0.05) and BMI-SDS (r = 0.45, P < 0.05) in SGA patients (Table 4).

Fig. 1.

Heat-map of Spearman correlation analysis in SGA. Blue indicates a positive correlation between the two indicators, with Spearman correlation coefficient > 0. Red indicates a negative correlation between the two indicators, with Spearman correlation coefficient < 0. The darker the color, the stronger the correlation

Table 4.

Correlation of W-SDS, BMI-SDS, IGF-1 SDS, LEP, ALT and GGT with glucose and lipid metabolism

Variables	BG		INS		HOMA-IR		TC		TG
	r	P	r	P	r	P	r	P	r	P
W-SDS	0.20	0.03*	0.32	0.01*	0.26	0.05	0.12	0.234	-0.21	0.09
BMI-SDS	0.11	0.24	0.21	0.12	0.09	0.52	0.13	0.199	-0.11	0.40
IGF-1 SDS	0.12	0.20	0.39	0.002**	0.39	0.003**	0.05	0.603	-0.39	0.001**
LEP (pg/ml)	0.26	0.02*	0.08	0.62	-0.06	0.74	0.30	0.010*	-0.11	0.44
ALT(U/L)	0.10	0.33	-0.09	0.57	-0.02	0.90	0.13	0.237	-0.14	0.29
GGT(U/L)	0.22	0.04*	0.48	0.003**	0.24	0.17	0.21	0.064	0.11	0.47

SDS, standard deviation scores; W-SDS, weight SDS; IGF-1, insulin-like growth factor 1; ALT, alanine aminotransferase; GGT, gamma-glutamyl transferase; LEP, leptin; BMI, body mass index; *P < 0.05; ** P < 0.01

In order to further adjust for confounding factors, we conducted multiple linear regression analyses with the metabolic indicators (FBG, FINS, HOMA-IR, cholesterol, and triglycerides) as dependent variables, and weight SDS, BMI-SDS, IGF-1-SDS, Leptin, ALT, and GGT as independent variables. Height SDS, IGFBP3, Ghrelin, GLP-1, Irisin, Nesfatin, Osteocalcin, LDL, HDL, Thyroid function test, Complete blood count, Liver function tests and Renal function tests and Serum electrolyte test were treated as confounding factors. Weight SDS was excluded due to multicollinearity (VIF > 10) (Table 5). 。Residual analysis indicated that all five regression models met the assumptions of linear regression. Specifically, for the BG model, the standardized residuals passed the Shapiro–Wilk test for normality (P = 0.73), and the Durbin–Watson (DW) statistic was 1.98. Similarly, the INS model (P = 0.49, DW = 2.06), HOMA-IR model (P = 0.18, DW = 2.03), TC model (P = 0.10, DW = 1.85), and TG model (P = 0.32, DW = 2.00) all showed no evidence of residual non-normality or autocorrelation, indicating that the models were statistically appropriate and reliable.

Table 5.

Multiple linear regression analysis of metabolic indicators

	BG			INS			HOMA-IR			TC			TG
	R2		aR2	R2		aR2	R2		aR2	R2		aR2	R2		aR2
	0.73		0.64	0.92		0.90	0.94		0.90	0.79		0.73	0.77		0.70
	β	VIF	P	β	VIF	P	β	VIF	P	β	VIF	P	β	VIF	P
BMI-SDs	0.01	3.95	0.85	0.13	2.27	0.001**	0.08	4.24	0.02*	0.08	2.24	0.22	0.06	4.08	0.38
IGF-1 SDS	0.09	6.19	0.47	0.13	6.22	< 0.001**	0.32	6.22	< 0.001**	-0.16	6.09	0.13	-0.39	5.54	< 0.001**
LEP(pg/ml)	0.33	4.78	0.003**	0.29	5.16	< 0.001**	0.47	5.16	< 0.001**	0.37	4.49	< 0.001**	3.34	4.66	0.001**
ALT(U/L)	-0.02	4.14	0.84	-0.23	4.14	< 0.001**	-0.19	4.14	< 0.001**	0.32	3.66	< 0.001**	0.25	3.86	0.01*
GGT(U/L)	0.20	2.36	< 0.01**	0.20	2.50	< 0.001**	0.11	2.50	0.003**	0.28	2.12	< 0.001**	0.23	2.26	0.001**

*P < 0.05; ** P < 0.01

β represents the standardized coefficient

SDS, standard deviation scores; W-SDS, weight SDS; IGF-1, insulin-like growth factor 1; ALT, alanine aminotransferase; GGT, gamma-glutamyl transferase; LEP, leptin; BMI, body mass index

Discussion

Most SGA patients could achieve their CUG before 2 years old, while about 13% of SGA patients do not experience CUG, leading to short stature in their adult life. In such cases, CUG is imperative for SGA patients. However, metabolic disorders often accompany with CUG. This study randomly sampled SGA patients in Southern China, categorizing them into the CUG-SGA group and the NCUG-SGA group based on their height-SDS. Difference analysis and correlation were used to clarify the metabolic condition of CUG-SGA patients and the plasma indicators that may impact the glucolipid metabolism ability of SGA patients.

Many studies have observed abnormalities in glucolipid metabolism among SGA patients over the past decades. Hofman et al. [12] found that pre-pubertal SGA children exhibit decreased INS sensitivity compared to AGA children. Similarly, Goedegebuure et al. [13] discovered that TC, TG, and low-density lipoprotein levels in SGA adults were significantly elevated compared to those in AGA adults, while high-density lipoprotein levels were lower in SGA adults. In this study, BG, INS, and HOMA-IR were used to assess the glucose metabolism of SGA patients, while TC and TG were used to evaluate their lipid metabolism. The results indicate that the INS and HOMA-IR levels in the CUG-SGA group were significantly higher than those in the NCUG-SGA group (P < 0.05), consistent with the findings of Hofman’s study.

IGF-1 shares nearly 50% amino acid sequence homology with INS and can elicit a hypoglycemic response similar to that of INS. Friedrich et al. [14] found a high prevalence of metabolic syndrome and type II diabetes in growth hormone deficiency patients with low levels of IGF-1 and acromegaly patients with high levels of IGF-1. In this case, they proposed a U-shaped correlation between the levels of IGF-1 and INS, indicating that both low and high IGF-1 levels were associated with INS resistance. In this study, we compared the IGF-1 SDS between the CUG-SGA group and the NCUG-SGA group. The difference analysis revealed that the IGF-1 SDS of the CUG-SGA group was significantly higher than that of the NCUG-SGA group (p < 0.05). Correlation analysis in the SGA patients showed that IGF-1 SDS was significantly positively correlated with INS (r = 0.388, P = 0.002) and HOMA-IR (r = 0.394, P = 0.003), and significantly negatively correlated with TG (r = -0.387, P = 0.001). This demonstrates a preliminary relationship between IGF-1 and metabolic indices. After controlling for confounding factors, the regression analysis indicated the same conclusion. If CUG-SGA children maintain persistently high serum IGF-1 levels, close monitoring of changes in glucose metabolism is warranted.

Previous research indicates that LEP can regulate the gene expression of pancreatic β-cells, leading to a reduction in INS secretion [15]. Furthermore, INS can stimulate adipose tissue to secrete LEP, establishing a hormone regulatory feedback loop known as the adipose-INS axis [16]. Consequently, an increase in LEP levels can decrease INS secretion, potentially elevating BG levels. Additionally, obese individuals often exhibit high levels of LEP expression in adipose tissue, increasing the likelihood of concurrent INS resistance and LEP resistance [17]. This study compared circulating LEP levels in children between the CUG-SGA and NCUG-SGA groups and found that the median circulating LEP level in the CUG-SGA group was significantly higher than that in the NCUG-SGA group. Among the SGA patients, LEP showed a significant positive correlation with W-SDs (r = 0.441, P < 0.05) and BMI-SDs (r = 0.452, P < 0.05). In addition, LEP was also positively correlated with fasting BG (r = 0.26, P = 0.02). After controlling for confounding factors, regression analysis indicated the same conclusion We speculate that the relatively high circulating LEP levels in CUG-SGA children may be related to their increased risk of developing glucose and lipid metabolism problems in adulthood.

ALT plays an important role in gluconeogenesis and amino acid metabolism. ALT is released when the membrane integrity of liver cell is disturbed during glycogen accumulation [18]. Previous studies have linked ALT levels to central obesity, type II diabetes, hypertension, and lipid metabolism disorders [19]. In this study, the ALT levels in the CUG-SGA group were found to be significantly higher than those in the NCUG-SGA group (p < 0.05). Furthermore, multiple linear regression analysis demonstrated that ALT was independently associated with several key metabolic indicators. Specifically, ALT levels were negatively associated with insulin (β = -0.23, P < 0.001) and HOMA-IR (β = -0.19, P < 0.001), suggesting a potential inverse relationship with insulin resistance. In contrast, ALT showed significant positive associations with total TC (β = 0.32, P < 0.001) and TG (β = 0.25, P = 0.01). These findings support the hypothesis that elevated ALT levels may not only reflect liver injury but also serve as a marker of metabolic dysfunction, particularly in lipid metabolism and insulin sensitivity, among children born SGA with catch-up growth. However, the causal relationship between ALT elevation and metabolic alterations in CUG-SGA individuals remains unclear. It is yet to be determined whether elevated ALT contributes to the development of metabolic disorders. Further studies need to be performed to understand the relationship.

Research on lipid metabolism has shown a positive correlation between GGT levels and blood lipid levels [20, 21]. Additionally, other studies have demonstrated a positive correlation between GGT and INS levels, both in fasting and postprandial states [22]. In this study, the GGT levels in the CUG-SGA group were found to be higher than those in the NCUG-SGA group. Among all SGA patients, GGT was positively correlated with BG and INS levels. After controlling for confounding factors, regression analysis indicated that GGT had a significant, positive effect on BG, INS, HOMA-IR, TC and TG. These findings suggest that GGT may be related to the susceptibility of metabolic disorders in CUG-SGA patients. Therefore, continuous monitoring of GGT levels in CUG-SGA patients is crucial, as elevated GGT levels may indicate a need for close attention to their BG levels and INS sensitivity.

In this study, we found several statistically significant correlations between metabolic indicators and growth and development indices. However, some correlation coefficients were relatively small, indicating weak correlations (approximately 0.2). For example, BG was correlated with W-SDS (r = 0.198, P = 0.03), and IGF-1 SDS was correlated with INS (r = 0.388, P = 0.002). Such relationships suggest that although an association exists, it may not imply a strong direct relationship. This could be related to the multifactorial nature of metabolic regulation, where many variables (including genetic, environmental, and lifestyle factors) can influence the metabolic outcomes of children born SGA, highlighting the complexity of metabolic factors in CUG among SGA children.

This study also has certain limitations. Although there was no significant age difference between the two groups and the SGA children in this study were from the East China region (where dietary habits are relatively similar), factors such as economic status, parental education level, and urban vs. rural residence were not fully controlled. These uncontrolled factors might have had some impact on the study results. Moreover, some correlation coefficients between metabolic and growth indices were weak, indicating that the strength of the association is insufficient to establish a direct causal relationship. Therefore, caution should be exercised when interpreting these weak correlations, recognizing that other unmeasured factors may influence the observed differences. Future research with larger sample sizes and more comprehensive data may help clarify these relationships and identify factors that substantially affect metabolic disorders in CUG-SGA children.

Despite previous studies providing evidence for the metabolic risks in children born SGA, this study makes a unique contribution in several respects. First, it systematically compared the metabolic status of CUG-SGA and NCUG-SGA children aged 2–8 years in the East China region, filling a gap in metabolic health research in this locale. Second, it explored differences in metabolic indicators such as IGF-1, LEP, ALT, and GGT between CUG-SGA and NCUG-SGA groups, revealing their associations with insulin resistance and other metabolic disturbances, and offering important information for future early intervention strategies. Lastly, through multiple regression analyses, this study further confirmed the independent influence of metabolic factors on the metabolic risks in CUG-SGA children. Hence, it not only expands our understanding of metabolic health in SGA patients but also provides a scientific basis for the early identification of high-risk children and the implementation of targeted interventions in clinical practice. Additionally, the proposal to strengthen follow-up monitoring of glucose and lipid metabolism in CUG-SGA patients with higher IGF-1, ALT, and GGT levels broadens the direction for future CUG research. However, this study also has certain shortcomings: it lacks an AGA (appropriate-for-gestational-age) control group, and because the enrolled children are relatively young, many potential glucose and lipid metabolism disorders have not yet reached clinical diagnostic criteria, necessitating long-term follow-up.

Electronic supplementary material

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Abbreviations

AGA

Appropriate for Gestational Age

ALT

Alanine Aminotransferase

BG

Blood Glucose

BMI

Body Mass Index

CUG

Catch-Up Growth

CUG: SGA

Catch-Up Growth Small for Gestational Age

GGT

Gamma-Glutamyl Transferase

HOMA-IR

Homeostatic Model Assessment for Insulin Resistance

IGF-1

Insulin-like Growth Factor 1

INS

Insulin

LEP

Leptin

NCUG-SGA

Non-Catch-Up Growth Small for Gestational Age

SDS

Standard Deviation Scores

SGA

Small for Gestational Age

TC

Total Cholesterol

TG

Triglycerides

Author contributions

Zhiya Dong, Ning Li, Huayan Hu, Wei Cao, Junqi Wang contributed to the study conception and design. Material preparation and data analysis were performed by Xiwen Zhang, Yanan Liu, Xianglan Wen. Data collection was performed by Yanan Liu, Xianglan Wen, Yanghua Dan Yanchun Shan, Ruifang Wang, Wendi Zhou, Meiling Yan, Qiong Tang, Hongmei Dai, Li Zhou, Kan Ye, Meizhu Xue. The first draft of the manuscript was written by Xiwen Zhang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The authors have no relevant financial or non-financial interests to disclose.

Data availability

The datasets generated and analysed during the current study are not publicly available due to protect patient privacy and sensitive information but are available from the corresponding author on reasonable request.

Declarations

Ethical approval

The studies involving human participants were reviewed and approved by Ethics Committees involving Human Research, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. Written informed consent was obtained from the individual(s), and minor(s)’ legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.

Competing interests

The authors declare no competing interests.