Abstract
Objective
To ascertain the association between small for gestational age (SGA) and growth hormone (GH)‐insulin‐like growth factor (IGF) system status.
Methods
This retrospective, observational single‐center study at a tertiary hospital classified 175 newborns into two groups based on their gestational age and birth weight: SGA (n = 87) or appropriate for gestational age (AGA; n = 88). Umbilical cord blood samples were collected during delivery, and GH, IGF‐1, and IGF‐binding protein‐3 (IGFBP‐3) levels were measured using electrochemiluminescence immunoassay, liquid chromatography–tandem mass spectrometry, and chemiluminescence immunoassay, respectively.
Results
Compared with the AGA group, the GH concentration was significantly higher in the SGA group and the IGF‐1 concentration was significantly lower; there was no significant difference in the IGFBP‐3 concentration between the two groups. Gestational age, cesarean section, pre‐pregnancy maternal body mass index (BMI), weight gain during pregnancy, gestational hypothyroidism, and IGF‐1 concentration had a significantly positive association with neonatal birth weight. After adjusting for maternal age, gestational age, fetal sex, mode of delivery, pre‐pregnancy BMI, gestational weight gain, gestational hypertension, gestational hypothyroidism, and gestational diabetes, elevated GH levels and decreased IGF‐1 levels were both significantly associated with an increased risk of SGA, but there was no association with IGFBP‐3.
Conclusions
Levels of IGF‐1 and GH in the GH‐IGF system are associated with SGA. Elevated GH levels and decreased IGF‐1 levels both increase the incidence of SGA. Imbalance in the GH‐IGF‐1 axis may restrict fetal growth and development, thereby increasing the risk of future metabolic diseases.
Keywords: growth hormone, insulin‐like growth factor‐1, insulin‐like growth factor‐binding protein‐3, small for gestational age, umbilical cord blood
Synopsis
Imbalance in the GH‐IGF‐1 axis may restrict fetal growth and development, thereby increasing the risk of future metabolic diseases.
1. INTRODUCTION
Small for gestational age (SGA) characterizes infants whose birth weight is below the 10th centile, or greater than two standard deviations (SD) below the average weight of neonates of the same gestational age and sex. 1 SGA increases the risk for various perinatal diseases and mortality. Moreover, infants born SGA can develop various long‐term metabolic and cardiovascular diseases. Notably, SGA elevates the risk for growth restriction and neurodevelopmental disorders, thereby negatively impacting the short‐ and long‐term quality of life of these children. 2 , 3 During fetal development, the intake and utilization of nutrients and growth and weight gain are intricately regulated by the placenta, fetal hormones, and growth factors, particularly insulin and insulin‐like growth factor (IGF) hormone. 4 The growth hormone (GH)‐IGF system, which comprises GH, IGF‐1, and IGF‐2, with their respective receptors and binding proteins, is crucial for fetal and neonatal metabolism, cell proliferation, cell differentiation, and growth. 5 , 6
The correlation between hormone concentrations of the GH‐IGF system in cord blood and SGA remains unclear. Japanese researchers observed significantly lower levels of IGF‐1 in the umbilical cord blood (UCB) of SGA neonates than in that of appropriate‐for‐gestational‐age (AGA) neonates. 7 However, another study failed to detect any notable differences in IGF‐1 and IGF‐2 levels between the two groups, although it did note a substantial decrease in IGF‐binding protein‐3 (IGFBP‐3) levels. 8 A deeper understanding of the intricate interplay between hormones, receptors, and binding proteins within the GH‐IGF system and SGA could offer new insights into fetal and neonatal growth and development. Accordingly, our study aimed to ascertain the association between SGA and GH‐IGF system status.
2. MATERIALS AND METHODS
2.1. Study design and study population
This was a retrospective observational study. Based on an SGA incidence rate of 6.5%, the overall sample size was calculated using simple random sampling with a P value of 0.05, an error margin of e = 0.05, and a 95% confidence interval of 1.96, resulting in a sample size of 118 cases per group. Ultimately, this study included 175 newborns born between January and December 2022, whose mothers were willing to participate in the trial design and provided written informed consent. The inclusion criteria were as follows: single pregnancy, live birth, full‐term birth (37–42 weeks), and the absence of severe structural abnormalities, chromosomal abnormalities, or serious genetic diseases in the newborn. Newborns meeting the SGA diagnostic criteria (birth weight below the 10th centile or more than two SD below the average weight of newborns of the same gestational age and sex) were classified into the SGA group; the remaining newborns were classified into the AGA group.
2.2. Data collection
UCB samples were collected from 87 neonates who met the diagnostic criteria for SGA and 88 full‐term (37–42 weeks) neonates classified in the AGA (control) group.
2.3. GH, IGF1, and IGFBP‐3 measurement
Samples were analyzed by an external commercial diagnostic group. GH, IGF‐1, and IGFBP‐3 levels were assessed using an electrochemiluminescence immunoassay (Roche Cobas 8000 E602, Mannheim, Germany), liquid chromatography–tandem mass spectrometry (LC–MS/MS, Agilent K6460, Santa Clara, CA, USA), and a chemiluminescence immunoassay (Siemens Immulite 2000 xpi, Tarrytown, NY, USA), respectively. Standard and internal standard kits were sourced from Prospec (Rehovot Science Park, Israel), and additional reagents were obtained from Merck (Darmstadt, Germany).
2.4. Variables and definitions
Immediately after delivery, the neonatal birth weight was measured by the midwife using an electronic scale (DY‐1 Electronic Baby Scale, Shanghai Guangzheng Medical Equipment Co. Ltd.) with an accuracy of 10 g. Neonates with a birth weight below the 10th centile by age and sex based on newborn birth weight curves in China 9 were classified as SGA.
Those with a birth weight between the 10th and 90th centiles of the average weight for sex and gestational age were classified as AGA. The maternal pre‐pregnancy body mass index (BMI; calculated as weight in kilograms [from the hospital's early‐stage pregnancy health records] divided by the square of height in meters). Maternal pregnancy weight gain was subsequently calculated by subtracting the prenatal weight at delivery from the pre‐pregnancy weight.
Basic maternal characteristics (maternal age, delivery mode, pre‐pregnancy weight, height, weight gain during pregnancy, systolic blood pressure, diastolic blood pressure, gestational diabetes mellitus, gestational hypothyroidism, gestational hypertension) and newborn data (gestational age, gender, birth length) were obtained from the mother–child health records. GH, IGF‐1, and IGFBP‐3 levels were obtained through the collection and analysis of neonatal UCB samples.
2.5. Statistical analysis
All analyses were performed using Minitab version 25.0 (Minitab LLC, State College, PA, USA). For continuous variables, data are reported as the mean ± SD, whereas variables with skewed distributions are reported as the median and interquartile range. Categorical variables are presented as counts (percentages). Pearson correlation analysis was used to investigate the relationships between the variables. Statistical significance was set at a P value <0.05.
To investigate confounding factors, a multiple linear regression model was applied to evaluate the effects of GH, IGF‐1, and IGFBP‐3 concentrations in the UCB on birth weight, birth length; a logistic regression model was used to assess the impact of GH, IGF‐1, and IGFBP‐3 levels in the UCB on SGA. Confounding factors included the mode of delivery, gestational age, maternal age, pre‐pregnancy BMI, weight gain during pregnancy, neonatal sex, hypertensive disorders during pregnancy, gestational diabetes mellitus, and gestational hypothyroidism. Given the small sample size, assumptions of multiple linear regression (normality, multicollinearity) were tested (Figure 1 and Tables 2 and 3).
FIGURE 1.
Normal distribution probability plots of (a) growth hormone (GH); (b) insulin‐like growth factor‐1 (IGF‐1); and (c) insulin‐like growth factor‐binding protein‐3 (IGFBP‐3).
TABLE 2.
Multivariate linear regression analysis of factors impacting birth weight, birth length, and small for gestational age a .
| b | 95% CI | P value | VIF b | |
|---|---|---|---|---|
| Birth weight | ||||
| Maternal age | −0.57 | −9.28 to 8.14 | 0.896 | 1.09 |
| Gestational age | 26.86 | 12.93–40.79 | <0.001*** | 1.18 |
| Sex (female = 1) | −86.40 | −143.6 to −29.20 | 0.003** | 1.04 |
| Delivery mode (CS = 1) | 449.00 | 135.00–764.00 | 0.006** | 1.11 |
| Gestational hypertension (yes = 1) | −21.20 | −140.00 to 97.60 | 0.724 | 1.05 |
| GDM (yes = 1) | 43.20 | −37.60 to 124.00 | 0.291 | 1.07 |
| Pre‐pregnancy BMI | 11.47 | 0.00–22.94 | 0.050* | 1.18 |
| Weight gain during pregnancy | 13.63 | 6.73–20.53 | <0.001** | 1.15 |
| GHT (yes = 1) | 82.40 | 2.40–162.50 | 0.044* | 1.15 |
| GH, ng/mL | −1.97 | −4.44 to 0.50 | 0.117 | 1.04 |
| IGF‐1, ng/mL | 2.39 | 0.24–4.54 | 0.030* | 1.96 |
| IGFBP‐3, μg/mL | −110.20 | −245.30 to 24.90 | 0.109 | 1.84 |
| Birth length | ||||
| Maternal age | 35.94 | 30.07–41.80 | 0.022* | 1.09 |
| Gestational age | 0.08 | 0.01–0.16 | <0.001*** | 1.18 |
| Sex (female = 1) | −0.55 | −1.02 to −0.08 | 0.024* | 1.04 |
| Delivery mode (CS = 1) | 2.83 | 0.24–5.43 | 0.033* | 1.11 |
| Gestational hypertension (yes = 1) | −0.08 | −1.06 to 0.90 | 0.877 | 1.05 |
| GDM (yes = 1) | 0.41 | −0.26 to 1.07 | 0.231 | 1.07 |
| Pre‐pregnancy BMI | 0.04 | −0.05 to 0.13 | 0.407 | 1.18 |
| Weight gain during pregnancy | 0.07 | 0.01–0.13 | 0.018* | 1.15 |
| GHT (yes = 1) | 0.29 | −0.37 to 0.95 | 0.384 | 1.15 |
| GH, ng/mL | −0.01 | −0.03 to 0.01 | 0.189 | 1.04 |
| IGF‐1, ng/mL | 0.01 | −0.01 to 0.03 | 0.329 | 1.96 |
| IGFBP‐3, μg/mL | −0.34 | −1.46 to 0.77 | 0.543 | 1.84 |
| OR | 95% CI | P value | VIF b | |
|---|---|---|---|---|
| SGA | ||||
| Maternal age | 0.98 | 0.86–1.12 | 0.776 | 1.09 |
| Gestational age | 1.13 | 0.91–1.40 | 0.273 | 1.18 |
| Sex (female = 1) | 1.54 | 0.63–3.77 | 0.347 | 1.1 |
| Delivery mode (CS = 1) | 0.00 | 0.00–0.00 | 0.971 | 1 |
| Gestational hypertension (yes = 1) | 1.13 | 0.21–6.12 | 0.884 | 1.05 |
| GDM (yes = 1) | 0.87 | 0.26–2.86 | 0.815 | 1.08 |
| Pre‐pregnancy BMI | 0.79 | 0.66–0.95 | 0.012* | 1.26 |
| Weight gain during pregnancy | 0.88 | 0.79–0.98 | 0.016* | 1.28 |
| GHT (yes = 1) | 0.17 | 0.04–0.68 | 0.012* | 1.25 |
| GH, ng/mL | 1.06 | 1.01–1.10 | 0.020* | 1.14 |
| IGF‐1, ng/mL | 0.96 | 0.93–0.99 | 0.018* | 2.05 |
| IGFBP‐3, μg/mL | 1.62 | 0.22–12.00 | 0.637 | 1.82 |
Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by the square of height in meters); CI, confidence interval; CS, cesarean section; GDM, gestational diabetes mellitus; GH, growth hormone; GHT, gestational hypothyroidism; IGF‐1, insulin‐like growth factor‐1; IGFBP‐3, insulin‐like growth factor‐binding protein‐3; OR, odds ratio; SGA, small for gestational age; SVD, spontaneous vaginal delivery; VIF, variance inflation factor.
Multiple linear regression analysis was conducted after adjusting for the following factors: maternal age, gestational age, delivery method, infant sex, pre‐pregnancy BMI, pregnancy weight gain, gestational hypertension, gestational diabetes, hypothyroidism during pregnancy, and GH, IGF‐1, and IGFBP‐3 levels in the umbilical cord blood. The significance levels were denoted as follows: *P < 0.05, **P < 0.01, and ***P < 0.001 for extreme significance.
Multicollinearity VIF test: all VIF = 1, there is no multicollinearity; 1 < VIF ≤ 5, the predictor variables are correlated; VIF > 5, the estimated regression coefficient for that term is not ideal.
TABLE 3.
Multivariate linear regression analysis of the factors influencing levels GH, IGF‐1, and IGFBP‐3 in the cord blood a .
| b | 95% CI | P value | VIF b | |
|---|---|---|---|---|
| GH | ||||
| Maternal age | 0.38 | −0.29 to 1.06 | 0.260 | 1.09 |
| Gestational age | 0.21 | −0.78 to 1.20 | 0.672 | 1.13 |
| Sex (female = 1) | −1.15 | −5.34 to 3.03 | 0.586 | 1.03 |
| Delivery mode (CS = 1) | 1.80 | −21.60 to 25.20 | 0.880 | 1.1 |
| Pre‐pregnancy BMI | 0.18 | −1.35 to 0.41 | 0.296 | 1,21 |
| Weight gain during pregnancy | −0.01 | −0.56 to 0.54 | 0.963 | 1.18 |
| Gestational hypertension (yes = 1) | 0.18 | −8.16 to 8.52 | 0.966 | 1.05 |
| GHT (yes = 1) | 2.68 | −3.00 to 8.37 | 0.352 | 1.09 |
| GDM (yes = 1) | −3.97 | −9.8 to 1.85 | 0.179 | 1.05 |
| SGA | 3.76 | −0.59 to 8.10 | 0.090 | 1.14 |
| IGF‐1 | ||||
| Maternal age | 0.05 | −0.94 to 1.03 | 0.929 | 1.09 |
| Gestational age | −1.56 | −3.07 to −0.06 | 0.042* | 1.13 |
| Sex (female = 1) | 3.41 | −3.11 to 9.93 | 0.302 | 1.03 |
| Delivery mode (CS = 1) | −24.50 | −60.60 to 11.50 | 0.180 | 1.1 |
| Pre‐pregnancy BMI | −1.42 | −2.70 to −0.14 | 0.03* | 1.21 |
| Weight gain during pregnancy | 0.38 | −0.44 to 1.19 | 0.364 | 1.18 |
| Gestational hypertension (yes = 1) | −2.60 | −16.35 to 11.15 | 0.709 | 1.05 |
| GHT (yes = 1) | −11.39 | −20.46 to −2.32 | 0.014* | 1.09 |
| GDM (yes = 1) | −1.21 | −10.05 to 7.64 | 0.788 | 1.05 |
| SGA | −12.52 | −19.19 to −5.86 | <0.001** | 1.14 |
| IGFBP‐3 | ||||
| Maternal age | 0.00 | −0.01 to 0.02 | 0.647 | 1.09 |
| Gestational age | −0.01 | −0.03 to 0.01 | 0.367 | 1.13 |
| Sex (female = 1) | 0.08 | −0.03 to 0.18 | 0.146 | 1.03 |
| Delivery mode (CS = 1) | −0.23 | −0.80 to 0.34 | 0.427 | 1.1 |
| Pre‐pregnancy BMI | −0.01 | −0.03 to 0.01 | 0.472 | 1,21 |
| Weight gain during pregnancy | 0.00 | −0.01 to 0.02 | 0.455 | 1.18 |
| Gestational hypertension (yes = 1) | −0.01 | −0.23 to 0.20 | 0.898 | 1.05 |
| GHT (yes = 1) | −0.13 | −0.29 to 0.03 | 0.119 | 1.09 |
| GDM (yes = 1) | 0.08 | −0.07 to 0.23 | 0.282 | 1.05 |
| SGA | −0.08 | −0.19 to 0.02 | 0.114 | 1.14 |
Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by the square of height in meters); CI, confidence interval; CS, cesarean section; GDM, gestational diabetes mellitus; GH, growth hormone; GHT, gestational hypothyroidism; IGF‐1, insulin‐like growth factor‐1; IGFBP‐3, insulin‐like growth factor‐binding protein‐3; OR, odds ratio; SGA, small for gestational age; SVD, spontaneous vaginal delivery; VIF, variance inflation factor.
Multiple linear regression analysis was conducted after adjusting for the following factors: maternal age, gestational age, sex, delivery mode, pre‐pregnancy BMI, weight gain during pregnancy, gestational hypertension, abnormal thyroid function during pregnancy, gestational diabetes, and SGA. The significance levels are denoted as follows: *P < 0.05, significance; **P < 0.01, high significance; ***P < 0.001, extreme significance.
Multicollinearity VIF test: all VIF = 1, there is no multicollinearity; 1 < VIF ≤5, the predictor variables are correlated; VIF >5, the estimated regression coefficient for that term is not ideal.
2.6. Statement of ethics
All participants signed written informed consent forms, and patient privacy was maintained and data were anonymized in accordance with the law. This study was approved by the Ethics Committee of Chongqing Health Center for Women and Children (approval no.: [2022] 018).
3. RESULTS
3.1. Baseline characteristics of participants
Of the 175 neonates, 87 and 88 were classified in the SGA and AGA groups, respectively. Baseline characteristics of the mothers and neonates are summarized in Table 1. The following between‐group differences were identified: pre‐pregnancy weight (P < 0.01), maternal height (P < 0.05), pre‐pregnancy BMI (P < 0.05), birth weight (P < 0.001), and birth length (P < 0.001).
TABLE 1.
Baseline characteristics of mothers and birth outcomes of newborns a .
| Characteristics | Total (n = 175) | SGA (n = 87) | AGA (n = 88) | P value b |
|---|---|---|---|---|
| Maternal information | ||||
| Maternal age, years | 29.6 ± 3.2 | 29.4 ± 3.18 | 29.7 ± 3.23 | 0.597 |
| Delivery mode (SVD/CS) | ||||
| SVD | 174 (99.4%) | 87 (100%) | 87 (98.9%) | 1.000 |
| CS | 1 (0.57%) | 0 (0.00%) | 1 (1.14%) | |
| Pre‐pregnancy weight, kg | 50.7 ± 7.8 | 49.1 ± 7.09 | 52.3 ± 8.16 | 0.006** |
| Height, cm | 158 ± 4.67 | 157 ± 4.21 | 159 ± 4.98 | 0.023* |
| Prepregnancy BMI | 20.3 ± 2.72 | 19.9 ± 2.43 | 20.8 ± 2.91 | 0.028* |
| Weight gain during pregnancy, kg | 11.87 ± 4.06 | 11.44 ± 4.3 | 12.29 ± 3.78 | 0.168 |
| Systolic pressure, mm Hg | 119 ± 12.3 | 118 ± 12.6 | 120 ± 11.9 | 0.313 |
| Diastolic pressure, mm Hg | 73.5 ± 9.24 | 73.8 ± 10.3 | 73.3 ± 8.17 | 0.717 |
| GDM | ||||
| Yes | 26 (14.9%) | 11 (12.6%) | 15 (17.0%) | 0.544 |
| No | 149 (85.1%) | 76 (87.4%) | 73 (83.0%) | |
| Gestational hypothyroidism | ||||
| Yes | 29 (16.6%) | 10 (11.5%) | 19 (21.6%) | 0.111 |
| No | 146 (83.4%) | 77 (88.5%) | 69 (78.4%) | |
| Gestational hypertension | ||||
| Yes | 11 (6.29%) | 7 (8.05%) | 4 (4.55%) | 0.521 |
| No | 64 (93.7%) | 80 (92.0%) | 84 (95.5%) | |
| Neonatal information | ||||
| Gestational age, weeks | 41 ± 2.31 | 41.3 ± 2.09 | 40.8 ± 2.48 | 0.108 |
| Gender | ||||
| Male | 74 (42.3%) | 40 (46.0%) | 34 (38.6%) | 0.407 |
| Female | 101 (57.7%) | 47 (54.0%) | 54 (61.4%) | |
| Birth weight, g | 2733 ± 184 | 2646 ± 183 | 2820 ± 139 | <0.001*** |
| Birth length, cm | 47.9 ± 1.37 | 47.5 ± 1.26 | 48.3 ± 1.37 | <0.001*** |
| Ponderal index, kg/m3 | 2.49 ± 0.16 | 2.47 ± 0.16 | 2.51 ± 0.16 | 0.164 |
| Cord blood | ||||
| GH, ng/mL | 20 ± 13.1 | 22.8 ± 15.6 | 17.3 ± 9.25 | 0.005** |
| IGF‐1, ng/mL | 50.5 ± 18.4 | 46.1 ± 17.5 | 54.9 ± 18.3 | 0.001** |
| IGFBP‐3, μg/mL | 1.31 ± 0.27 | 1.29 ± 0.3 | 1.34 ± 0.25 | 0.165 |
Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by the square of height in meters); CS, cesarean section; GDM, gestational diabetes mellitus; GH, growth hormone; IGF‐1, insulin‐like growth factor‐1; IGFBP‐3, insulin‐like growth factor‐binding protein‐3; SVD, spontaneous vaginal delivery.
Data are presented as mean ± standard deviation for continuous variables or as number (percentage) for categorical variables.
P values were calculated from one‐way analysis of variance to compare means of continuous variables or χ 2 tests to compare proportions of categorical variables between groups. Significance levels are denoted as *P < 0.05, **P < 0.01, highly significant, and ***P < 0.001, extremely significant.
3.2. Between‐group comparison of the GH‐IGF axis
Between‐group differences in the GH‐IGF axis hormone levels are reported in Table 1 and Figure 2. Significant between‐group differences were identified for GH and IGF‐1 concentrations (P < 0.01) but not for IGFBP‐3. The GH concentration was significantly higher and the IGF‐1 concentration was significantly lower in the SGA group than in the AGA group.
FIGURE 2.
Boxplots comparing (a) growth hormone (GH); (b) insulin‐like growth factor‐1 (IGF‐1); and (c) insulin‐like growth factor‐binding protein‐3 (IGFBP‐3) between the appropriate‐for‐gestational‐age group (0) and the small‐for‐gestational‐age group (1).
3.3. Pearson correlation analysis between SGA and the GH‐IGF axis
Neonatal birth weight was positively correlated with maternal height (r = 0.23, P < 0.01) and pre‐pregnancy weight (r = 0.15, P < 0.05). Cord GH concentration was positively correlated with SGA (r = −0.19, P < 0.05), and IGF‐1 concentration was negatively correlated with SGA (r = −0.24, P < 0.01). There was no association between IGFBP‐3 concentration and SGA.
3.4. Determinants of neonatal birth weight, length, and SGA
The correlation between measured factors and birth weight, length, and SGA is reported in Table 2 and Figure 3. Birth weight had a significantly positive association with gestational age (b = 26.86, P < 0.001), cesarean section (b = 449.00, P < 0.01), pre‐pregnancy BMI (b = 11.47, P < 0.05), weight gain during pregnancy (b = 13.63, P < 0.001), gestational hypothyroidism (b = 82.40, P < 0.05), and IGF‐1 concentration (b = 2.39, P < 0.05). Birth weight also had a significantly negative association with female sex in newborns (b = 86.40, P < 0.001).
FIGURE 3.
Scatterplot of birth weight versus (a) growth hormone (GH); (b) insulin‐like growth factor‐1 (IGF‐1); and (c) insulin‐like growth factor‐binding protein‐3 (IGFBP‐3).
Birth length had a significantly positive association with maternal age (b = 35.94, P < 0.05), gestational age (b = 0.08, P < 0.001), cesarean section (b = 2.83, P < 0.05), and weight gain during pregnancy (b = 0.07, P < 0.05). Birth length had a significantly negative association with female sex (b = −0.55, P < 0.05).
Increased pre‐pregnancy BMI (odds ratio [OR] 0.79, P < 0.05), increased weight gain during pregnancy (OR 0.88, P < 0.05), gestational hypothyroidism (OR 0.17, P < 0.05), and elevated IGF‐1 concentration (OR 0.96, P < 0.05) were significantly associated with a reduced incidence of SGA. Elevated GH concentration was significantly associated with an increased incidence of SGA (OR 1.06, P < 0.05).
3.5. Analysis of factors influencing the GH‐IGF axis
The association between measured factors and the GH‐IGF axis hormone concentrations is shown in Table 3. After adjusting for maternal age, gestational age, sex, mode of delivery, pre‐pregnancy BMI, weight gain during pregnancy, gestational hypertension, gestational hypothyroidism, gestational diabetes mellitus, and SGA factors, a multivariate linear regression analysis showed that neither GH (b = 3.76, P > 0.05) nor IGFBP‐3 concentration (b = −0.08, P > 0.05) was significantly associated with SGA. IGF‐1 concentration had a significantly negative association with gestational age (b = −1.56, P < 0.05), pre‐pregnancy BMI (b = −1.42, P < 0.05), gestational hypothyroidism (b = −11.39, P < 0.05), and SGA (b = −12.52, P < 0.001).
4. DISCUSSION
Our findings identified a relationship between concentrations of the GH‐IGF axis hormones, measured from UCB, and SGA. Both decreased IGF‐1 and increased GH levels were associated with an increased incidence of SGA. Compared with the AGA group, the GH level was significantly higher and the IGF‐1 level was significantly lower in the SGA group. The IGFBP‐3 level was not associated with SGA.
The GH‐IGF axis is essential for longitudinal growth and development. 10 GH activity relies on the growth hormone receptor, which influences IGF‐1 gene expression and subsequently triggers IGF‐1 secretion. 11 IGF‐1 exerts its effects by binding to the IGF‐1 receptor, which plays a critical role in fetal and neonatal growth, cardiovascular health, central nervous system function, and lung development. 12 , 13 Previous studies have indicated that IGF‐1 levels are lower in neonates born SGA compared with those born AGA, with gestational age and birth weight identified as independent predictors of UCB IGF‐1 levels. 7 Our study corroborated these findings, revealing that IGF‐1 concentrations in the UCB in neonates born SGA were significantly lower than those observed in AGA neonates. After confounding factor adjustment, IGF‐1 concentration had a significantly negative association with birth weight and SGA. Additionally, IGF‐1 concentration had a significantly negative association with gestational age, pre‐pregnancy BMI, and gestational hypothyroidism, indicating that IGF‐1 likely plays a key role in determining fetal weight. 7 , 14 , 15
Some previous studies have reported no significant difference in IGF‐1 levels between AGA and SGA neonates. 8 , 16 These discrepancies may be related to differences in study cohorts. In our study, all newborns were healthy and born full term, excluding preterm births and those with severe congenital diseases. We further adjusted for common diseases during pregnancy to exclude the influence of maternal diseases on IGF‐1 levels.
The differentiation and proliferation of growth‐hormone‐producing cells allow the anterior pituitary gland to synthesize and release growth hormones. This process depends on a complex developmental pathway that requires the activation of various transcription factors at specific times and locations. By gestational week 10, the GH detectable in the fetal bloodstream is entirely derived from the fetal pituitary gland and therefore operates independently of maternal growth hormone‐releasing hormone, pituitary GH, and placental GH. During the later stages of pregnancy, increasing serum endogenous IGF‐1 levels may lead to feedback inhibition, contributing to a gradual decline in fetal GH secretion at birth. Our research indicates that the UCB GH concentration was markedly higher in the SGA group than in the AGA group.
After adjusting for confounding variables, elevated GH levels were associated with an increased incidence of SGA, a finding consistent with previous studies. 17 , 18 The elevated GH levels with SGA might result from decreased IGF‐1 concentrations in the fetal circulation, commonly due to malnutrition and low insulin levels, prompting a feedback increase in GH secretion. Elevated GH level has been linked to increased lipolysis, insulin resistance, and hyperglycemia, along with higher circulating levels of free fatty acids, ketones, and glucose, all of which are related to anabolic metabolism. 19 Studies have reported no significant association between UCB GH concentration and SGA or AGA, which may be related to elevated placental GH levels. 4 , 20 After adjusting for confounding variables, GH concentration was not significantly associated with birth weight or SGA. This may be related to the adjustment for confounding factors or the limited sample size of the study. Further research is necessary to investigate the association between GH concentration and birth weight or SGA.
Specific binding proteins control the bioavailability of IGF and GH at the cellular level. Among the six identified IGFBPs, IGFBP‐3 has the highest affinity for IGF‐1, surpassing the combined affinities of all other binding proteins. 21 IGFBP‐3 regulates the effects of IGF‐1 by controlling the levels of free IGF‐1, thus preventing its movement from the bloodstream into the tissues and modulating the interaction between IGF‐1 and its receptors. Additionally, IGFBP‐3 possesses numerous IGF‐independent functions, such as the inhibition of cell growth and promotion of apoptosis. 21 , 22 , 23 Notably, several studies did not record differences in IGFBP‐3 levels between SGA and AGA groups, 24 consistent with our findings. In our study group, IGFBP‐3 was not associated with gestational age, birth weight, or SGA, consistent with previous findings. 8 , 25 The relationship between IGFBP‐3 and SGA remains a controversial issue, with recent findings of lower IGFBP‐3 protein levels in SGA than AGA neonates born in southern India. 8 Similarly, a Brazilian study reported lower levels of IGFBP‐3 among neonates classified as SGA, with significant associations between IGFBP‐3 levels and birth weight, length, head circumference, and BMI. 26 In contrast, in the present study, a correlation between UCB IGFBP‐3 levels and birth weight or SGA status was not identified. Several factors may explain these discrepancies. First, our study included only full‐term neonates, excluding preterm neonates. Second, UCB may not reflect IGFBP‐3 levels throughout pregnancy and may differ from neonatal IGFBP‐3 levels. Third, our sample size was relatively small. Given the inconsistent findings in the literature, large‐scale studies are necessary to clarify the impact of IGFBP‐3 on birth weight and SGA.
The primary strength of our study was its multifaceted nature. We not only compared the levels of GH, IGF‐1, and IGFBP‐3 in UCB but also categorized the infants based on their gestational age and birth weight, while accounting for confounding factors. However, it should be noted that the study participants were all from a single hospital in China, and these women may have a general understanding of health issues, which may have led to fewer SGA phenotypes, such as growth restriction (short stature) and emaciation (thin physique). As our sample size was limited to 175 cases, a type II error may have been introduced. Therefore, future multicenter studies should include various SGA phenotypes.
In conclusion, an investigation into the potential association between SGA and GH‐IGF‐1 axis status revealed that elevated UCB GH concentrations and decreased IGF‐1 concentrations significantly increased the probability of SGA. Prospective studies are required to further examine the association between the GH‐IGF‐1 axis status during fetal development and SGA in diverse populations. Understanding their underlying mechanisms could enhance prenatal care and interventions and reduce the incidence and mortality of SGA, thereby lowering the risk of future chronic diseases associated with SGA.
AUTHOR CONTRIBUTIONS
Jing Wen contributed to the investigation, performed the formal analysis, and wrote the original draft; Yan Zhao performed the conceptualization and methodology, and contributed to the review and editing; Ruoling Xian curated the data and wrote the original draft; and Xin Fan performed the project administration, and contributed to the investigation and to the review and editing.
CONFLICT OF INTEREST STATEMENT
The authors have no conflicts of interest.
Supporting information
Appendix S1
ACKNOWLEDGMENTS
This study was supported by the Chongqing Medical Scientific Research Project (Maternal and child health care research and cultivation project) (grant number 2022FY104). The funder had no role in the study design, collection, analysis or interpretation of data, writing of the report, or decision to submit the article for publication.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Appendix S1
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.