Abstract
Objective
To evaluate the potential value of 1-h post-load plasma glucose level in patients who are prone to suffer from metabolic associated fatty liver disease (MAFLD) in a group of non-diabetic obese children and adolescents.
Methods
Cardio-metabolic risk factors, oral glucose tolerance test outcomes, and Liver ultrasonic examination results were analyzed in 406 non-diabetic obese children and adolescents. Patients were divided into 4 groups: normal glucose tolerance with 1-h plasma glucose (NGT with 1-h PG) < 8.6 mmol/L, NGT with 1-h PG ≥ 8.6 mmol/L, impaired fasting glucose (IFG), and impaired glucose tolerance (IGT).
Results
In this study, 406 non-diabetic children and adolescents with obesity (249 males, 157 females, mean age: 11.71 ± 2.22 years) were included. Among them, 286 (70.4%) had NGT, 30 (7.4%) had IFG, and 90 (22.2%) had IGT. As compared with NGT with 1-h PG < 8.6 mmol/L, NGT with 1-h PG ≥ 8.6 mmol/L and IGT groups demonstrated significantly higher body fat percentage, obesity family history, triglycerides, gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT), aspartate aminotransferase (AST). After adjusting for age, sex, and BMI z-score, the 1-h PG level demonstrated significant positive correlations with ALT (Spearman’s ρ = 0.12, P = 0.018), AST (ρ = 0.15, P = 0.003), and GGT (ρ = 0.21, P < 0.001). In the multivariable-adjusted logistic regression model controlling for age, sex, BMI z-score, body fat percentage, homeostatic model assessment of insulin resistance and glycosylated hemoglobin, individuals with NGT and 1-h PG ≥ 8.6 mmol/L exhibited 1.882-fold higher odds of MAFLD (95% CI 1.045–3.388), while the IGT group showed significantly elevated odds (OR = 1.980, 95% CI 1.056–3.709). No statistically significant association was observed in the IFG group.
Conclusion
These data suggest that NGT with 1-h PG ≥ 8.6 mmol/L in non-diabetic obese pediatric patients can facilitate identifying individuals at higher risk of MAFLD.
Keywords: Metabolic associated fatty liver disease, Oral glucose tolerance test, 1-h post-load plasma glucose
Introduction
Metabolic associated fatty liver disease (MAFLD) was proposed by international hepatologists in 2020 to replace nonalcoholic fatty liver disease and emphasize its close relationship with systemic metabolic disorders [1]. MAFLD is not only manifested as hepatic steatosis, but also accompanied by insulin resistance, abnormal glucose and lipid metabolism and other multi-system dysfunction, which has become an important challenge to global public health [2]. In recent years, with the rapid increase of childhood and adolescents with obesity rate, the prevalence rate of MAFLD in pediatric population has increased significantly. Epidemiological data show that the incidence rate of MAFLD in obese children and adolescents is as high as 30-50%, and it shows a trend of younger age [3]. This disease is not only related to pathological progress such as Liver inflammation and fibrosis, but also closely related to long-term metabolic complications such as type 2 diabetes and cardiovascular disease, which seriously threatens children’s growth and development and long-term healthy outcome [4–6].
In the pathogenesis of MAFLD, abnormal plasma glucose metabolism is considered as one of the key driving factors [7]. Studies have shown that pre-diabetic states such as impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) are significantly related to the increased risk of MAFLD, and the updated diagnostic criteria of MAFLD in 2020 also take pre-diabetes as an important evaluation condition [2, 8–11]. It is worth noting that the plasma glucose (1-h PG) level during an oral glucose tolerance test (OGTT), as a sensitive index of the early reaction of glucose metabolism, can reflect the insulin secretion function and the glucose uptake efficiency of peripheral tissues [12]. Adult studies show that the increase of 1-h PG level (≥ 8.6mmol/L) is related to the increase of liver enzymes, the accumulation of liver fat and the increased risk of MAFLD, and it still has independent predictive value even in people with normal glucose tolerance (NGT) [13, 14]. However, the relationship between 1-h PG and MAFLD in non-diabetic obese children and adolescents is not clear, and its potential as an early screening marker needs to be explored urgently.
Based on this, this study focuses on non-diabetic obese children and adolescents. By analyzing the correlation between 1-h PG level and Liver enzymes, and evaluating the risk of MAFLD in different groups, it aims to reveal the potential value of 1-h PG in children’s MAFLD screening, and provide a new basis for early clinical identification of high-risk groups and formulation of intervention strategies.
Methods
Study population
This is an observational cross-sectional study. The study population covers children and adolescents who were admitted to the General Hospital of Tianjin Medical University from January 2019 to April 2023 due to obesity. The inclusion criteria were as follows: (a) aged 4–18 years; (b) meet the obesity criteria [15]; (c) children and adolescents were treated for the first time (without treatment). The exclusion criteria included: children and adolescents diagnosed with diabetes, chronic gastrointestinal and/or cardiovascular disease, endocrine pathology or suspected obesity syndrome.
This study protocol was approved by the Ethics Committee of Tianjin Medical University General Hospital.
Measurements
Physical examination was carried out by experienced pediatricians on the first day of treatment. The specific examination items and operating specifications were as follows: ① Height and weight measurements: participants should wear Light clothes and no shoes. Height measurement was accurate to 0.1 cm, and weight measurement was accurate to 0.1 kg. According to the measured height and weight data, calculate body mass index (BMI) (kg/m) = weight (kg)/square of height (m2). And BMI z-score was recorded according to Chinese criteria [16]. Body fat percentage (%): Male=(1.20 × BMI) + (0.23 × age) − 16.2; Female=(1.20 × BMI) + (0.23 × age) − 5.4 [17].② Blood pressure measurement: Blood pressure was measured by medical electronic sphygmomanometer (Omron Automation Co., Ltd., China). ③ Waist and hip measurements: For waist measurement, encircle the abdomen horizontally at the mid - point of the Line connecting the lower edge of the 12th rib and the anterior superior iliac spine at the end of the examinee’s quiet breathing, with the result accurate to 1 cm. For hip circumference measurement, measure at the highest point of the hip, ensuring appropriate tightness, and record the result accurate to 1 cm. During the measurement, the measurement result of tightness was accurate to 1 cm. Waist-to-body ratio was calculated by dividing waist circumference (cm) by height (cm).
After fasting for 12 hours, venous blood samples were drawn for laboratory testing, and the evaluated parameters include alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), albumin (ALB), alkaline phosphatase (APL), uric acid (URIC), total bilirubin (TBIL), direct bilirubin (DBIL), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), HbA1c and 25(OH)D. And then, all participants underwent standard OGTT, as previously described [18]. In summary, every participant took an oral glucose solution containing 1.75 g/kg body weight (up to 75 g glucose), and plasma samples were taken at 30, 60, 120, and 180 minutes to detect changes in plasma glucose and insulin levels. Insulin levels were measured by chemiluminescence (BS-2000 M, Mindray, China). The homeostatic model assessment of insulin resistance (HOMA-IR) was calculated by Fasting plasma glucose (FPG) × 0-h Insulin/22.5.
All participants were scanned by two uniformly trained ultrasound doctors, using the same ultrasound diagnosis system (Philips HD11, China). For lacking of semiquantitative ultrasound assessment of the steatosis degree, we classified children and adolescents into present or absent of hepatic steatosis.
Definitions
Normal glucose tolerance (NGT): FPG < 5.6 mmol/L, 1-h PG < 8.6 mmol/L, 2-h PG < 7.8 mmol/L [19]. According to their 1 h PG level, this group were divided into two groups: NGT with 1-h PG < 8.6 mmol/L and NGT with 1-h PG ≥ 8.6 mmol/L [20].
IFG: FPG 5.6-7.0 mmol/L, 2-h PG < 7.8 mmol/L [19].
IGT: FPG < 5.6 mmol/L, 2-h PG 7.8–11.1 mmol/L [19].
IFG + IGT: FPG 5.6-7.0 mmol/L, 2-h PG 7.8–11.1 mmol/L [19]. The number of patients in this group was 8, so it was not analyzed as a separate group in this article.
Obesity: BMI > 95th percentile for age and sex [15].
MAFLD: All patients met the obesity criteria and abdominal B - ultrasound showed fatty liver [21].
Statistical analysis
Continuous variables were were described using mean ± standard deviation, whereas categorical variables were described by frequency or constituent ratio (%). To evaluate differences in anthropometric and metabolic measures among various glucose tolerance groups, the Kruskal-Wallis H test was utilized for continuous variables, and the chi-square test was employed for categorical variables. By drawing scatter correlation diagrams with ggplot2, ggpubr and ggpmisc packages in R language, the correlation between 1-h PG level and ALT, AST and GGT is explored. Subsequently, MAFLD was the dependent variable, logistic regression analysis was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the study groups and MAFLD (Model 1: adjusted for age and sex; Model 2: adjusted for Model 1 and BMI z-score; Model 3: adjusted for Model 2 and body fat percentage; Model 4: adjusted for Model 3 and obesity family history; Model 5: adjusted for Model 4, HOMA-IR and HbA1c). All statistical analyses were executed using SPSS (26.0).
Result
The study included 406 non-diabetic obese children and adolescents, comprising 249 males and 157 females with an average age of 11.71 ± 2.22 years. Of these children and adolescents, 212 patients (52.22%) had NGT with 1-h PG < 8.6 mmol/L, 74 patients (18.23%) had NGT with 1-h PG ≥ 8.6 mmol/L, 30 (7.4%) had IFG, and 90 (22.2%) had IGT. Table 1 presents the clinical and biochemical characteristics of the four study groups. There were no significant differences in age and BMI z-score among all groups.
Table 1.
Baseline characteristics of study subjects stratified according to glucose tolerance
| Variables | Whole study group | NGT with 1 h PG < 8.6 mmol/L (0) | NGT with 1 h PG ≥ 8.6 mmol/L (1) | IFG (2) | IGT (3) | P Value (1) | P Value (2) | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 vs. 1 | 0 vs. 2 | 0 vs. 3 | 1 vs. 2 | 1 vs. 3 | 2 vs. 3 | |||||||
| N (male/female) | 406(249/157) | 212(140/72) | 74(42/32) | 30(23/7) | 90(44/46) | 0.249 | 0.165 | 0.219 | 0.005 | 0.046 | 0.318 | 0.005 |
| Age (year) | 11.71 ± 2.22 | 11.54 ± 2.42 | 11.74 ± 2.07 | 11.8 ± 1.8 | 12.07 ± 1.92 | 0.063 | 0.514 | 0.573 | 0.063 | 0.902 | 0.293 | 0.49 |
| BMI (kg/m 2) | 29.26 ± 4.75 | 28.9 ± 4.66 | 29.28 ± 5.14 | 30.07 ± 5.14 | 29.83 ± 4.48 | < 0.001 | 0.559 | 0.205 | 0.108 | 0.478 | 0.46 | 0.81 |
| BMI z-score | 2.19 ± 0.43 | 2.22 ± 0.42 | 2.18 ± 0.56 | 2.26 ± 0.26 | 2.13 ± 0.38 | 0.317 | 0.542 | 0.655 | 0.092 | 0.434 | 0.407 | 0.156 |
| Fat mass (%) | 25.79 ± 7.5 | 24.8 ± 7.61 | 26.31 ± 7.12 | 25.12 ± 7.82 | 27.9 ± 7.07 | 0.010 | 0.135 | 0.832 | 0.001 | 0.452 | 0.156 | 0.072 |
| Obesity family history | 201(49.5%) | 93(43.8%) | 38(51.3%) | 16(53.3%) | 54(60.0%) | 0.031 | 0.268 | 0.331 | 0.007 | 0.754 | 0.254 | 0.511 |
| SBP (mm Hg) | 124.89 ± 15.7 | 124.67 ± 16.11 | 127.33 ± 17.02 | 126 ± 11.45 | 123.6 ± 14.74 | 0.065 | 0.351 | 0.72 | 0.651 | 0.593 | 0.351 | 0.84 |
| DBP (mm Hg) | 73.98 ± 11.88 | 73.77 ± 12.19 | 72.92 ± 11.86 | 74.6 ± 10.22 | 75.19 ± 11.54 | 0.562 | 0.691 | 0.769 | 0.43 | 0.856 | 0.123 | 0.33 |
| Waist (cm) | 86.16 ± 11.90 | 85.53 ± 11.18 | 86.42 ± 13.35 | 86.56 ± 14.15 | 87.30 ± 11.62 | 0.688 | 0.583 | 0.657 | 0.24 | 0.954 | 0.638 | 0.771 |
| Hip (cm) | 97.68 ± 14.06 | 97.35 ± 13.19 | 98.13 ± 16.07 | 96.80 ± 15.82 | 98.39 ± 13.86 | 0.910 | 0.680 | 0.842 | 0.558 | 0.662 | 0.909 | 0.593 |
| WHtR | 0.55 ± 0.06 | 0.54 ± 0.06 | 0.55 ± 0.07 | 0.56 ± 0.07 | 0.56 ± 0.07 | 0.479 | 0.940 | 0.500 | 0.142 | 0.575 | 0.223 | 0.800 |
| Tanner scale | 0.213 | 0.234 | 0.423 | 0.540 | 0.124 | 0.132 | 0.432 | |||||
| I | 30 | 22 | 5 | 1 | 2 | |||||||
| II | 139 | 73 | 24 | 13 | 29 | |||||||
| III | 57 | 25 | 13 | 7 | 12 | |||||||
| IV | 149 | 73 | 28 | 7 | 41 | |||||||
| HbA1c(%) | 5.63 ± 0.49 | 5.53 ± 0.48 | 5.66 ± 0.3 | 5.62 ± 0.26 | 5.82 ± 0.64 | 0.615 | 0.040 | 0.335 | < 0.001 | 0.555 | 0.05 | 0.101 |
| HOMA-IR | 6.04 ± 5.53 | 4.31 ± 1.79 | 6.01 ± 2.99 | 8.43 ± 4.47 | 7.73 ± 3.89 | < 0.001 | < 0.001 | < 0.001 | < 0.001 | < 0.001 | < 0.001 | 0.249 |
| ALT (U/L) | 38.8 ± 35.52 | 33.15 ± 28.06 | 48.97 ± 44.22 | 33.10 ± 15.73 | 45.67 ± 44.36 | 0.001 | 0.001 | 0.994 | 0.005 | 0.037 | 0.548 | 0.089 |
| AST (U/L) | 29.91 ± 20.45 | 25.45 ± 13.32 | 35.81 ± 27.45 | 30.50 ± 21.13 | 35.38 ± 24.76 | < 0.001 | < 0.001 | 0.196 | < 0.001 | 0.22 | 0.089 | 0.247 |
| ALP (U/L) | 231.69 ± 94.78 | 234.07 ± 88.11 | 227.97 ± 90.19 | 239.73 ± 95.69 | 226.31 ± 113.11 | 0.787 | 0.615 | 0.745 | 0.566 | 0.557 | 0.919 | 0.561 |
| GGT(U/L) | 31.43 ± 32.87 | 25.23 ± 18.56 | 50.84 ± 61.14 | 28.33 ± 20.56 | 31.09 ± 22.05 | < 0.001 | < 0.001 | 0.615 | 0.141 | 0.001 | < 0.001 | 0.561 |
| URIC (umol/L) | 423.05 ± 113.71 | 410.54 ± 117.79 | 439.03 ± 114.77 | 409.27 ± 88.22 | 443.78 ± 107.49 | < 0.001 | 0.073 | 0.955 | 0.023 | 0.205 | 0.786 | 0.115 |
| TC (mg/ | 4.14 ± 0.89 | 4.1 ± 0.9 | 4.08 ± 0.85 | 4.04 ± 1.09 | 4.32 ± 0.85 | 0.146 | 0.901 | 0.749 | 0.046 | 0.833 | 0.08 | 0.15 |
| TG (mg/dL) | 1.39 ± 0.68 | 1.33 ± 0.7 | 1.31 ± 0.61 | 1.66 ± 0.74 | 1.5 ± 0.64 | 0.021 | 0.851 | 0.019 | 0.052 | 0.017 | 0.065 | 0.257 |
| HDL-C (mg/dL) | 1.17 ± 0.28 | 1.14 ± 0.28 | 1.31 ± 0.37 | 1.12 ± 0.21 | 1.13 ± 0.19 | 0.285 | 0.001 | 0.698 | 0.704 | 0.002 | < 0.001 | 0.836 |
| LDL-C (mg/dL) | 2.67 ± 1.22 | 2.6 ± 0.74 | 2.51 ± 0.57 | 2.72 ± 0.74 | 2.96 ± 2.21 | 0.031 | 0.379 | 0.41 | 0.138 | 0.138 | 0.104 | 0.572 |
| 25(OH)D （nmol/L） | 38.41 ± 17.4 | 40.66 ± 17.64 | 39.1 ± 15.81 | 34.63 ± 11.21 | 32.63 ± 18.34 | 0.655 | 0.503 | 0.035 | 0.001 | 0.229 | 0.022 | 0.538 |
| MAFLD (n) | 224(55.1%) | 100(47.1%) | 47(63.5%) | 17(56.7%) | 60(66.6%) | 0.001 | 0.015 | 0.332 | 0.002 | 0.52 | 0.675 | 0.327 |
NGT Normal glucose tolerance, IFG Impaired fasting glucose, IGT Impaired glucose tolerance, SBP Systolic pressure, DBP Diastolic pressure, BMI Body mass index, ALT Alanine aminotransferase, AST Aspartate aminotransferase, GGT Gamma-glutamyl transferase, ALP Alkaline phosphatase, HbA1c Glycosylated hemoglobin, TC Total cholesterol, TG Triglyceride, HDL-C High-density lipoprotein cholesterol, LDL-C Low-density lipoprotein cholesterol, URIC Uric acid, 25(OH)D 25-Hydroxyvitamin D. The part of P＜0.05 in the table were bold, suggesting that there were statistical significance between the two groups
P Value (1): P value of comparison between groups; P Value (2): P value of intra-group comparison
Compared to NGT with 1-h PG < 8.6 mmol/L individuals, those in the NGT with 1-h PG ≥ 8.6 mmol/L group exhibited significantly elevated ALT, AST, GGT, HbA1c, the IFG group showed higher TG. In contrast, the IGT group had higher significantly higher body fat percentage, obesity family history, TC, ALT, AST, GGT, URIC, and lower 25 (OH)D levels. Compared to NGT with 1-h PG ≥ 8.6 mmol/L, the IGT group had lower HDL-C. Based on the dynamic monitoring results of OGTT, the abnormal characteristics of glucose metabolism in each group showed significant heterogeneity: in NGT with 1-h PG ≥ 8.6 mmol/L group, the plasma glucose and insulin levels reached the peak at 1 h after load, suggesting that the compensatory enhancement of early insulin secretion was accompanied by the decrease of peripheral insulin sensitivity; IGT group: the peak of plasma glucose and insulin was delayed to 2 h, reflecting the delay of insulin secretion and the impairment of glucose clearance ability in the later stage; In IFG group, fasting glucose glucose and insulin levels increased, suggesting that basal insulin secretion was deficient (Fig. 1).
Fig. 1.
Changes of plasma glucose and insulin in different groups of children and adolescents during OGTT
We noted that after adjusted for age, sex and BMI z-score, the 1-h PG level was positively correlated with ALT, AST and GGT, spearman correlation coefficients were 0.12, 0.15 and 0.21, with P value were 0.018, 0.003 and < 0.001, respectively (Fig. 2). The IGT group had the highest Likelihood of MAFLD, followed by the NGT with 1 h PG > 8.6 mmol/L group. After adjusting for potential confounding factors, this association still remained (Table 2). Using the NGT group with 1-h PG < 8.6 mmol/L as the reference and adjusting for age, sex, BMI z-score, body fat percentage, HOMA-IR and HbA1c, the OR for MAFLD was 1.980 (95% CI 1.056–3.709, P = 0.033) in the IGT group and 1.882 (95% CI 1.045–3.388, P = 0.035) in the NGT subgroup with 1-h PG ≥ 8.6 mmol/L. No significant association was observed in the IFG group (OR = 0.999, 95% CI 0.425–2.347, P = 0.998).
Fig. 2.
Correlation between 1 h PG level and ALT, AST, GGT
Table 2.
Odds ratios (95% CI) of MAFLD by glucose tolerance groups: results from multiple logistic regression models adjusted for Stepwise covariates
| Group | NAFLD | ||
|---|---|---|---|
| P | OR | 95% CI | |
| Model 1 | |||
| NGT with 1 h glucose < 8.6 mmol/L | 1 | — | — |
| NGT with 1 h glucose ≥ 8.6 mmol/L | 0.013 | 2.004 | 1.155–3.479 |
| IFG | 0.405 | 1.391 | 0.64–3.02 |
| IGT | 0.002 | 2.311 | 1.366–3.91 |
| Model 2 | |||
| NGT with 1 h glucose < 8.6 mmol/L | 1 | — | — |
| NGT with 1 h glucose ≥ 8.6 mmol/L | 0.011 | 2.062 | 1.180–3.604 |
| IFG | 0.45 | 1.352 | 0.619–2.955 |
| IGT | 0.001 | 2.417 | 1.419–4.118 |
| Model 3 | |||
| NGT with 1 h glucose < 8.6 mmol/L | 1 | — | — |
| NGT with 1 h glucose ≥ 8.6 mmol/L | 0.015 | 2.017 | 1.149–3.541 |
| IFG | 0.511 | 1.304 | 0.591–2.879 |
| IGT | 0.003 | 2.243 | 1.308–3.846 |
| Model 4 | |||
| NGT with 1 h glucose < 8.6 mmol/L | 1 | — | — |
| NGT with 1 h glucose ≥ 8.6 mmol/L | 0.017 | 1.98 | 1.127–3.48 |
| IFG | 0.538 | 1.283 | 0.581–2.834 |
| IGT | 0.004 | 2.274 | 1.306–3.96 |
| Model 5 | |||
| NGT with 1 h glucose < 8.6 mmol/L | 1 | — | — |
| NGT with 1 h glucose ≥ 8.6 mmol/L | 0.035 | 1.882 | 1.045–3.388 |
| IFG | 0.998 | 0.999 | 0.425–2.347 |
| IGT | 0.033 | 1.980 | 1.056–3.709 |
Note: Model 1: adjusted for age and sex;
Model 2: adjusted for Model 1 and BMI z-score;
Model 3: adjusted for Model 2 and body fat percentage;
Model 4: adjusted for Model 3 and obesity family history;
Model 5: adjusted for Model 4, HbA1c and HOMA-IR
NGT Normal glucose tolerance, IFG Impaired fasting glucose, IGT Impaired glucose tolerance, HbA1c Glycated hemoglobin, HOMA-IR Homeostatic Model Assessment of Insulin Resistance
Discussion
At present, the early identification of children’s MAFLD faces multiple challenges. Although liver biopsy is the “gold standard” for diagnosis, it is difficult to popularize because of its invasion and potential complications. Imaging examination (such as abdominal ultrasound and CT) is widely used, but it has some limitations such as high operation dependence, radiation exposure or high cost [22, 23]. In addition, traditional plasma glucose indicators (such as FPG and 2-h PG) are not sensitive enough to early metabolic disorders, which may delay the identification of high-risk groups. The level of 1-h PG reflects the body’s early plasma glucose response ability after glucose load, which is closely related to insulin secretion, glucose uptake and utilization by liver and peripheral tissues [12]. In this study, it was confirmed for the first time that 1-h PG was positively correlated with liver enzyme levels (ALT, AST, GGT) in non-diabetic obese children and adolescents, and the risk of NGT with 1-h PG ≥ 8.6 mmol/L was nearly 2 times higher than that of 1-h PG normal group. This finding is consistent with the adult study: in the adult cohort, individuals with elevated 1-h PG (≥ 8.6 mmol/L) not only significantly increased their liver enzyme levels, but also significantly increased their risk of intermediate/advanced liver fibrosis assessed by FIB-4 score and the prevalence of fatty liver diagnosed by abdominal ultrasound [13, 24]. Further multivariate analysis showed that 1-h PG lever was an independent predictor of hepatic fat accumulation (β = 0.274, P = 0.008), and was independently related to the degree of hepatic steatosis evaluated by transient elastography, suggesting that the early abnormality of glucose metabolism may play a key role in the pathophysiological process of MAFLD [25].
The increase of liver enzymes (ALT, AST, GGT) reflects the persistence of hepatocyte injury and lipid toxicity microenvironment [26]. Hyperglycemia itself may lead to Liver injury, which is also due to the activation of chronic state of low inflammation. It is worth noting that obese children with elevated 1-h PG show higher liver insulin resistance, that is the core driving factor [27]. The increase of 1-h PG may reflect the hidden insulin secretion defect or insulin resistance in peripheral tissues. These abnormalities accelerate the progress of MAFLD by promoting liver fat synthesis and inhibiting lipolysis [12, 28]. In the state of insulin resistance, the sensitivity of liver to insulin inhibition of gluconeogenesis decreases, which leads to the continuous increase of postprandial plasma glucose; At the same time, abnormal insulin signaling pathway may form a vicious circle by inhibiting fatty acid oxidation and promoting lipid accumulation [29]. For children and adolescents, a complete 2-hour OGTT takes more time and resources than a simple fasting sample, while a shorter 1-hour OGTT is in between. 1-h- glucose provides an efficient tool for early screening of high-risk groups [30].
There are some Limitations in this study. First of all, this study is a single-center study, and the sample size is relatively Limited, so there may be some selection bias. In the future, multi-center and large sample research is needed to further verify the results of this study. Secondly, this study only made a preliminary analysis of the level of 1-h PG and the risk of MAFLD, and did not discuss its specific molecular mechanism in depth. In addition, the patients were not followed up for a long time in this study, and the relationship between the increase of 1-h PG level and the long-term prognosis of MAFLD could not be clarified. Long-term follow-up study is needed in the future to evaluate the role of 1-h PG level in the natural course of MAFLD.
Conclusion
This study shows that the level of 1-h PG is positively correlated with Liver enzymes in non-diabetic obese children and adolescents, and 1-h PG was associated with an increased risk of MAFLD, independent of age, sex, BMI z-score, body fat percentage, HOMA-IR and HbA1c. This result provides a new basis for early screening and prevention of MAFLD in non-diabetic obese children and adolescents.
Acknowledgements
Thanks to all staff participated in this study.
Authors’ contributions
Rongxiu Zheng, the corresponding authors, instructed the project and coordinated contributions from authors. Xiaoxiao Liu, and Zesheng Peng served as the lead authors, conducted the primary experiments and article writing. Shifeng Ma, Fei Liu, Mingyue Liu and Xiaowen Li helped in theoretical frameworks. All the authors contributed to the article and approved the submitted version.
Funding
This research was supported by the General Hospital Clinical Research Project (22ZYYLCCG03), Tianjin Key Medical Discipline (Specialty) Construction Project (TJWJ2022XK008, TJYXZDXK-068 C), and Tianjin Science and Technology Plan Project (22KPHDRC00120).
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
The data were obtained from medical records in our department, without disclosing personal identity information. This work was approved by the Ethics Committee of the Academy of Tianjin Medical University General Hospital (ZYY-IRB-SOP-016(F)-002-04).
Consent for publication
Informed consent was obtained from all subjects involved in the study.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Xiaoxiao Liu and Zesheng Peng contributed equally to this work.
References
- 1.Eslam M, Sanyal AJ, George J. MAFLD: A Consensus-Driven proposed nomenclature for metabolic associated fatty liver disease. Gastroenterology. 2020;158(7):1999–2014. e1991. [DOI] [PubMed] [Google Scholar]
- 2.Eslam M, Newsome PN, Sarin SK, Anstee QM, Targher G, Romero-Gomez M, et al. A new definition for metabolic dysfunction-associated fatty liver disease: an international expert consensus statement. J Hepatol. 2020;73(1):202–9. [DOI] [PubMed] [Google Scholar]
- 3.Xing Y, Fan J, Wang HJ, Wang H. Comparison of MAFLD and NAFLD Characteristics in Children. Child (Basel Switzerland). 2023;10(3):560. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Kim D, Konyn P, Sandhu KK, Dennis BB, Cheung AC, Ahmed A. Metabolic dysfunction-associated fatty liver disease is associated with increased all-cause mortality in the united States. J Hepatol. 2021;75(6):1284–91. [DOI] [PubMed] [Google Scholar]
- 5.Kaya E, Yilmaz Y. Metabolic-associated fatty liver disease (MAFLD): A Multi-systemic disease beyond the liver. J Clin Translational Hepatol. 2022;10(2):329–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Pouwels S, Sakran N, Graham Y, Leal A, Pintar T, Yang W, et al. Non-alcoholic fatty liver disease (NAFLD): a review of pathophysiology, clinical management and effects of weight loss. BMC Endocr Disorders. 2022;22(1):63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Leite NC, Villela-Nogueira CA, Cardoso CR, Salles GF. Non-alcoholic fatty liver disease and diabetes: from physiopathological interplay to diagnosis and treatment. World J Gastroenterol. 2014;20(26):8377–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hoyles L, Fernández-Real JM, Federici M, Serino M, Abbott J, Charpentier J, et al. Molecular phenomics and metagenomics of hepatic steatosis in non-diabetic obese women. Nat Med. 2018;24(7):1070–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Li CH, Chou YT, Shen WC, Lu FH, Yang YC, Wu JS, et al. Increased risks of different grades of non-alcoholic fatty liver disease in prediabetic subjects with impaired fasting glucose and glucose tolerance, including the isolated glycosylated hemoglobin levels of 5.7–6.4% in a Chinese population. J Diabetes Invest. 2020;11(5):1336–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Rajput R, Ahlawat P. Prevalence and predictors of non-alcoholic fatty liver disease in prediabetes. Diabetes Metabolic Syndrome. 2019;13(5):2957–60. [DOI] [PubMed] [Google Scholar]
- 11.Yamada T, Fukatsu M, Suzuki S, Wada T, Yoshida T, Joh T. Fatty liver predicts impaired fasting glucose and type 2 diabetes mellitus in Japanese undergoing a health checkup. J Gastroenterol Hepatol. 2010;25(2):352–6. [DOI] [PubMed] [Google Scholar]
- 12.Nie Q, Jin X, Mu Y, Huang Y, Dong A. Insulin resistance and β-cell dysfunction in individuals with normal glucose tolerance but elevated 1-h post-load plasma glucose. Front Endocrinol. 2025;16:1507107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Sesti G, Hribal ML, Fiorentino TV, Sciacqua A, Perticone F. Elevated 1 h postload plasma glucose levels identify adults with normal glucose tolerance but increased risk of non-alcoholic fatty liver disease. BMJ Open Diabetes Res Care. 2014;2(1):e000016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Succurro E, Arturi F, Grembiale A, Iorio F, Fiorentino TV, Andreozzi F, et al. One-hour post-load plasma glucose levels are associated with elevated liver enzymes. Nutr Metab Cardiovasc Dis: NMCD. 2011;21(9):713–8. [DOI] [PubMed] [Google Scholar]
- 15.Li H, Zong XN, Ji CY, Mi J. Body mass index cut-offs for overweight and obesity in Chinese children and adolescents aged 2–18 years. Zhonghua Liu Xing Bing Xue Za zhi = Zhonghua Liuxingbingxue Zazhi. 2010;31(6):616–20. [PubMed] [Google Scholar]
- 16.Li H, Ji CY, Zong XN, Zhang YQ. [Body mass index growth curves for Chinese children and adolescents aged 0 to 18 years]. Zhonghua Er Ke Za zhi = chinese. J Pediatr. 2009;47(7):493–8. [PubMed] [Google Scholar]
- 17.Deurenberg P, Weststrate JA, Seidell JC. Body mass index as a measure of body fatness: age- and sex-specific prediction formulas. Br J Nutr. 1991;65(2):105–14. [DOI] [PubMed] [Google Scholar]
- 18.Maruszczak K, Radzikowski K, Schütz S, Mangge H, Bergsten P, Forslund A, et al. Determinants of hyperglucagonemia in pediatric non-alcoholic fatty liver disease. Front Endocrinol. 2022;13:1004128. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Andes LJ, Cheng YJ, Rolka DB, Gregg EW, Imperatore G. Prevalence of prediabetes among adolescents and young adults in the united states, 2005–2016. JAMA Pediatr. 2020;174(2):e194498. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Buysschaert M, Bergman M, Valensi P. 1-h post-load plasma glucose for detecting early stages of prediabetes. Diabetes Metab. 2022;48(6):101395. [DOI] [PubMed] [Google Scholar]
- 21.Rinella ME, Lazarus JV, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology (Baltimore MD). 2023;78(6):1966–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Zhu L, Baker SS, Gill C, Liu W, Alkhouri R, Baker RD, et al. Characterization of gut microbiomes in nonalcoholic steatohepatitis (NASH) patients: a connection between endogenous alcohol and NASH. Hepatology (Baltimore MD). 2013;57(2):601–9. [DOI] [PubMed] [Google Scholar]
- 23.Schwimmer JB, Johnson JS, Angeles JE, Behling C, Belt PH, Borecki I, et al. Microbiome signatures associated with steatohepatitis and moderate to severe fibrosis in children with nonalcoholic fatty liver disease. Gastroenterology. 2019;157(4):1109–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Jagannathan R, Fiorentino TV, Marini MA, Sesti G, Bergman M. One-hour post-load glucose is associated with severity of hepatic fibrosis risk. Diabetes Res Clin Pract. 2022;189:109977. [DOI] [PubMed] [Google Scholar]
- 25.Andreozzi F, Mancuso E, Mazza E, Mannino GC, Fiorentino TV, Arturi F, et al. One-hour post-load glucose levels are associated with hepatic steatosis assessed by transient elastography. Diabetes Obes Metab. 2024;26(2):682–9. [DOI] [PubMed] [Google Scholar]
- 26.van Beek JH, de Moor MH, de Geus EJ, Lubke GH, Vink JM, Willemsen G, et al. The genetic architecture of liver enzyme levels: GGT, ALT and AST. Behav Genet. 2013;43(4):329–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Stefan N, Kantartzis K, Häring HU. Causes and metabolic consequences of fatty liver. Endocr Rev. 2008;29(7):939–60. [DOI] [PubMed] [Google Scholar]
- 28.Manco M, Panunzi S, Macfarlane DP, Golay A, Melander O, Konrad T, et al. One-hour plasma glucose identifies insulin resistance and beta-cell dysfunction in individuals with normal glucose tolerance: cross-sectional data from the relationship between insulin sensitivity and cardiovascular risk (RISC) study. Diabetes Care. 2010;33(9):2090–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Peng H, Xiang J, Pan L, Zhao M, Chen B, Huang S, et al. METS-IR/HOMA-IR and MAFLD in U.S. Adults: dose-response correlation and the effect mediated by physical activity. BMC Endocr Disorders. 2024;24(1):132. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Brar PC, Mehta S, Brar A, Pierce KA, Albano A, Bergman M. Value of 1-Hour plasma glucose during an oral glucose tolerance test in a multiethnic cohort of obese children and adolescents. Clin Med Insights Endocrinol Diabetes. 2023;16:11795514231177206. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
No datasets were generated or analysed during the current study.