The flat glucose response curve during oral glucose tolerance tests in Chinese pregnant women and its association with adverse outcomes

Abstract

Introduction

Flat glucose response curves observed during the oral glucose tolerance test (OGTT) in pregnant women are relatively prevalent. This study aimed to investigate the characteristics and perinatal outcomes of Chinese women who presented with flat OGTT curves during pregnancy.

Material and Methods

A total of 23 576 pregnant women without gestational diabetes mellitus (GDM) were recruited into this study. They were classified into two groups according to the shape of their glucose response curves obtained from OGTTs performed at 24–28 weeks of gestation. The curves were categorized as either flat or normal. Specifically, a flat curve was defined as a less than 16.5% increase in plasma glucose levels during the OGTT, while all other curves were regarded as normal. Logistic regression analysis was employed to examine the associations between these curve types and the risk of perinatal outcomes. Additionally, these relationships were evaluated across different maternal age groups and preconception body mass index (BMI) categories.

Results

Among the participants, 932 (3.95%) displayed a flat curve, while 22 644 (96.05%) showed a normal curve. Women with a flat curve were significantly younger (p < 0.001) and had a lower BMI (p < 0.001). Compared with those with a normal curve, women with a flat curve had lower incidences of gestational hypertension and preeclampsia. Additionally, neonates born to mothers with a flat curve had lower birth weights and lower occurrences of large for gestational age (LGA) and macrosomia. Logistic regression analyses, using the normal‐curve group as the reference, demonstrated that, regardless of confounder adjustments, the flat‐curve group was associated with a protective effect against gestational hypertension, preeclampsia, LGA, and macrosomia development (all p < 0.05). Moreover, these risks differed according to maternal age and preconception BMI. No significant differences were observed in other maternal or neonatal outcomes.

Conclusions

A flat OGTT curve is associated with lower birth weight and reduced risks of LGA, macrosomia, gestational hypertension, and preeclampsia. Identifying the flat curve as a protective factor for LGA, macrosomia, gestational hypertension, and preeclampsia, particularly among women with different maternal ages and preconception BMIs, may facilitate personalized risk assessment and management.

A flat OGTT curve during pregnancy is associated with lower birth weight, and decreased risks of large for gestational age infants, macrosomia, gestational hypertension, and preeclampsia. Moreover, these risks varied based on maternal age and preconception BMI.

Abbreviations

1 h‐PG

1‐hour plasma glucose

2 h‐PG

2‐hour plasma glucose

BMI

body mass index

FPG

fasting plasma glucose

GDM

gestational diabetes mellitus

GWG

gestational weight gain

IGF

insulin‐like growth factor

IUGR

intrauterine growth restriction

IVF

in vitro fertilization

LGA

large for gestational age

NICU

neonatal intensive care unit

OGTT

oral glucose tolerance test

PG

plasma glucose

RCV

reference change value

RI

resistance indices

RR

relative risk

SGA

small for gestational age

Key message.

A flat OGTT curve during pregnancy is associated with lower birth weight, and decreased risks of large for gestational age infants, macrosomia, gestational hypertension, and preeclampsia. Moreover, these risks varied based on maternal age and preconception BMI.

1. INTRODUCTION

At present, the quantity of prenatal tests capable of effectively identifying morbid conditions that are amenable to intervention is still restricted. Among these tests, the oral glucose tolerance test (OGTT) is remarkable for its proficiency in diagnosing gestational diabetes mellitus (GDM). Currently, the established gold standard for diagnosing GDM in China entails the administration of a 75‐gram OGTT during the 24th to 28th weeks of gestation. Plasma glucose (PG) levels are measured when the woman is in a fasting state and at 1‐h and 2‐h intervals post‐administration. ¹ Nevertheless, previous GDM screening research has mainly centered on glycemic results, overlooking the OGTT response curve. A typical response curve is characterized by a low fasting glucose level, which reaches its peak 30 to 60 min after glucose intake and then gradually declines. ² Multiple forms of PG curves, such as monophasic, biphasic, and others, have been documented in the literature. ² , ³ , ⁴ , ⁵ , ⁶ During the OGTTs carried out during pregnancy, atypical PG concentrations are occasionally observed. These situations involve pregnant women who seem to be unresponsive to the glucose challenge, failing to show the expected elevation in concentrations compared to their fasting plasma glucose (FPG) levels, thereby presenting a flat OGTT curve. ⁷ , ⁸ , ⁹ However, the clinical significance of this phenomenon during pregnancy has not been thoroughly explored in the existing literature. As a result, the majority of obstetricians do not modify the antepartum management for women with a flat OGTT curve.

In the nonpregnant state, a flat OGTT curve is generally not considered pathologic but rather a variant of normal carbohydrate tolerance. ¹⁰ , ¹¹ , ¹² There are studies demonstrating an association with endocrinological disorders. A hypoglycemic response to a sugar load may suggest a certain degree of hyperinsulinemia or abnormal glucose metabolism. ¹³ This, in turn, has the potential to impact pregnancy outcomes and disrupt fetal growth. Previous research has explored the correlation between OGTT glucose levels and pregnancy outcomes. ¹⁴ , ¹⁵ However, investigations into flat OGTT curves during pregnancy and their relationships with perinatal outcomes are scarce. The available data are scant and mainly originate from small‐scale cohorts. Only a few studies have specifically examined flat OGTT curves during pregnancy, and the findings have been inconsistent. Some studies have shown an increased risk of fetal growth restriction, ¹⁶ , ¹⁷ while others have revealed no significant disparities in perinatal outcomes. ⁸ , ¹⁸ , ¹⁹ Furthermore, the term “flat OGTT curve” has been in use for a long time, yet it lacks a unified definition. The literature has adopted diverse definitions and cut‐off values, and differences in testing protocols using 50‐g or 100‐g OGTT glucose loads may have affected the results. To date, there have been no reports on flat curves in the 75‐g OGTT test among pregnant Chinese women. Therefore, this study aimed to evaluate the flat OGTT during pregnancy, ascertain its prevalence, appraise maternal characteristics, and investigate its potential association with adverse perinatal outcomes.

2. MATERIAL AND METHODS

2.1. Study design and subjects

A retrospective cohort study was carried out, involving 40 629 women who received perinatal care and gave birth at the Women's Hospital, Zhejiang University School of Medicine from January 2018 to December 2019. Ethical approval was duly obtained from the hospital's ethics committee (approval number: IRB‐20240021‐R; approval date: January 22, 2024). Given that the study made use of anonymized participant records, the requirement for informed consent was waived. All pregnant women included in this study underwent a one‐step diagnostic test using a 75‐g glucose load to evaluate their glucose tolerance. Participants were excluded if they met any of the following criteria: (1) incomplete or duplicated medical records; (2) incomplete OGTTs; (3) under 18 years old; (4) multiple pregnancies; (5) gestational age at delivery ≤28 weeks; (6) abortion or stillbirth; (7) pre‐existing diabetes mellitus or chronic hypertension; (8) autoimmune diseases or malignancies; (9) fetal chromosomal abnormalities; or (10) GDM. The participant screening process is detailed in Figure 1.

FIGURE 1.

Flowchart illustrating the process of participant enrollment and group assignment. GDM, gestational diabetes mellitus; OGTT, oral glucose tolerance test.

2.2. Definitions

The reference change value (RCV) ²⁰ can act as an indicator for evaluating the clinical significance of observed differences. By assessing the magnitude of fluctuations, it takes into account both biological and analytical variability. RCVs are typically calculated using the method proposed by Harris and Yasaka ²¹ as well as Fraser, ²² , ²³ with the equation RCV = √2 × Z × √(S ² _A + S ² _I). In this formula, S _A represents the analytical coefficient of variation, S _I denotes the intra‐individual coefficient of variation, and Z corresponds to the standard normal distribution quantile for the selected level of significance. For example, when the two‐sided false positive error rate is 5%, Z = 1.96. In our laboratory measurements of clinical samples, the analytical coefficient of variation for PG was 2.0%. Given an average within‐subject biological variation in PG of 5.6%, we defined a curve as ‘flat’ when the increase in PG concentration between fasting and 1‐h post‐load was less than +16.5% [1.41 × 1.96 × √ (4 + 31.36)].

Body mass index (BMI) is calculated by dividing weight (kg) by the square of height (m²). The BMI categories are as follows: underweight (<18.5), normal (18.5–23.9), overweight (24–28), and obese (>28) kg/m². ²⁴

Gestational weight gain (GWG) is the difference between predelivery weight and preconception weight. According to the 2009 Institute of Medicine guidelines, ²⁵ the recommended GWG ranges are 12.5–18.0 kg for underweight individuals, 11.5–16.0 kg for normal‐weight individuals, 7.0–11.5 kg for overweight individuals, and 5.0–9.0 kg for obese individuals. Inadequate GWG is below these thresholds, while excess GWG is above them. Gestational hypertension is defined as systolic blood pressure ≥140 mm Hg or diastolic blood pressure >90 mm Hg at ≥20 weeks in previously normotensive women. ²⁶ Preeclampsia involves gestational hypertension plus proteinuria or new end‐organ dysfunction. ²⁶ Small for gestational age (SGA) refers to birth weight below the 10th percentile, whereas large for gestational age (LGA) refers to birth weight above the 90th percentile, based on gender and gestational age. ²⁷ Macrosomia is defined as a birth weight greater than 4000 g. ²⁸ Low birth weight is defined as a birth weight of less than 2500 g. ²⁹ Preterm birth occurs before 37 weeks of gestation. Neonatal hypoglycemia is defined as a glucose level <1.94 mmol/L within 2 h of birth and before the first non‐breastfeeding. ³⁰ Intrauterine growth restriction (IUGR) is defined as a condition in which the fetus does not reach its expected growth curve. The growth of the fetus (including weight, head circumference, abdominal circumference, etc.) is less than the 10th percentile of the normal fetal population at the same gestational age. ³¹ Neonatal asphyxia is defined as the failure of neonates to initiate and sustain breathing at birth. ³² Fetal distress refers to the fetus's acute and chronic hypoxic symptoms under the influence of various factors in the uterus. ³³

2.3. OGTT

All participants underwent a 75‐g OGTT with venous plasma glucose measurements during outpatient visits between 24 and 28 weeks of gestation. After an overnight fast, venous blood samples were first collected. Subsequently, the participants consumed a 75‐g glucose solution, and additional venous blood samples were obtained 1 and 2 h –post‐ingestion.

2.4. Clinical data and biochemical indicators

Maternal and neonatal data were retrospectively retrieved from the hospital information system. These data included maternal age, preconception weight, height, ethnicity, educational background, gestational age at delivery, history of cesarean section, mode of delivery, parity, gravidity, in vitro fertilization (IVF) status, GWG, gestational age, and comorbidities. Laboratory data, including FPG, 1‐hour plasma glucose (1 h‐PG), and 2‐hour plasma glucose (2 h‐PG) measurements, were obtained from the laboratory information system. Plasma glucose levels were analyzed using the hexokinase method on the Architect C16000 chemistry analyzer.

2.5. Statistical analyses

Statistical analyses were carried out using IBM SPSS Statistics 20.0 (Chicago, USA) for data processing and analysis, while GraphPad Prism 8.0 (California, USA) was utilized for figure generation. Categorical variables were presented as numbers and frequencies [n (%)], and quantitative variables were reported as means ± standard deviations. The percentile distribution of birth weight was presented as P50 (P5–P95). The chi‐square test was employed to evaluate differences between categorical variables, and Student's t‐test was used for continuous variables. Logistic regression analysis was performed to assess relative risks (RRs), both unadjusted and adjusted for potential confounders. These confounders included maternal age, preconception BMI, GWG, ethnicity, education, gestational age at delivery, previous cesarean delivery history, parity, gravidity, and IVF status. Throughout the study, a significance level of p < 0.05 was adopted.

3. RESULTS

3.1. Baseline characteristics according to the glucose response curve

In the initial phase of this study, a total of 40 629 pregnant women were recruited. After excluding 11 111 participants who did not meet the inclusion criteria and 5942 with abnormal OGTT values, the final sample consisted of 23 576 pregnant women. Among them, 932 (3.95%) exhibited a flat curve, while 22 644 (96.05%) had normal curves. Table 1 presented the demographic data along with the PG values from the OGTT for the enrolled women. Women in the flat‐curve group were significantly younger (p < 0.001) and less likely to be of advanced maternal age (p < 0.001). Moreover, they had a lower BMI and were more likely to be underweight (p < 0.001), yet less likely to be overweight or obese (p < 0.001). Statistically significant differences were also observed between the two groups in terms of educational background, gestational age at delivery, and history of cesarean section (p < 0.01). However, there were no significant differences between the groups regarding ethnicity, parity, gravidity, IVF, or GWG (all p > 0.05).

TABLE 1.

Demographic characteristics of the participants exhibiting a flat or normal oral glucose tolerance test curve.

Characteristics	Flat OGTT (n = 932, 3.95%)	Normal OGTT (n = 22 644, 96.05%)	p‐value
Maternal age (years)	29.7 ± 3.9	30.8 ± 4.1	<0.001
<35	815 (87.45)	18 229 (80.50)
≥35	117 (12.55)	4415 (19.50)
Preconception BMI (kg/m2)	20.1 ± 2.4	20.6 ± 2.6	<0.001
Underweight	244 (26.18)	4486 (19.81)
Normal weight	629 (67.49)	15 857 (70.03)
Overweight and obese	59 (6.33)	2301 (10.16)
Han ethnic group (n, %)	926 (99.36)	22 549 (99.58)	0.304
Education (n, %)			0.002
Primary or below	2 (0.21)	74 (0.32)
Middle school	156 (16.74)	2912 (12.87)
College or above	774 (83.05)	19 658 (86.81)
Gestational age at delivery (weeks)	39.3 ± 1.4	39.2 ± 1.5	0.004
Previous cesarean delivery (n, %)	139 (14.91)	4284 (18.92)	0.002
Parity (n, %)			0.060
0	422 (45.28)	9450 (41.73)
1	278 (29.83)	6885 (30.41)
≥2	232 (24.89)	6309 (27.86)
Gravidity (n, %)			0.075
0	610 (65.45)	14 043 (62.02)
1	305 (32.73)	8233 (36.36)
≥2	17 (1.82)	368 (1.62)
IVF (n, %)	31 (3.33)	958 (4.23)	0.177
Gestational weight gain (kg)	14.4 ± 4.4	14.3 ± 4.3	0.265
Adequate	411 (44.10)	10 378 (45.83)
Inadequate	233 (25.00)	5445 (24.05)
Excess	288 (30.90)	6821 (30.12)
OGTT (mmol/L)
FPG	4.41 ± 0.29	4.34 ± 0.30	<0.001
1 h‐PG	4.60 ± 0.55	7.64 ± 1.20	<0.001
2 h‐PG	5.67 ± 0.98	6.56 ± 0.99	<0.001

Note: Continuous variables were presented as means ± standard deviation. Categorical data were presented as frequencies (percentages).

Abbreviations: 1 h‐PG, 1‐hour plasma glucose; 2 h‐PG, 2‐hour plasma glucose; BMI, body mass index; FPG, fasting plasma glucose; IVF, in vitro fertilization; OGTT, oral glucose tolerance test.

3.2. Relationships between perinatal outcomes according to the OGTT curve

Maternal and neonatal outcomes stratified by the OGTT curve were summarized in Table 2. Compared with those in the normal OGTT group, women in the flat OGTT group had a significantly lower incidence of gestational hypertension (p < 0.001) and preeclampsia (p < 0.01) (Figure 2). No significant differences in maternal outcomes, such as the rates of primary cesarean delivery, placental abruption, premature rupture of membranes, postpartum hemorrhage, or shoulder dystocia, were detected between the groups (all p > 0.05).

TABLE 2.

Maternal and neonatal outcomes in participants with a flat or normal oral glucose tolerance test curve.

Outcomes	Flat OGTT (n = 932, 3.95%)	Normal OGTT (n = 22 644, 96.05%)	p‐value
Maternal outcomes (n, %)
Gestational hypertension	17 (1.82)	1037 (4.58)	<0.001
Preeclampsia	4 (0.43)	376 (1.66)	0.003
Primary cesarean delivery	182 (19.53)	4371 (19.30)	0.865
Placental abruption	15 (1.61)	382 (1.69)	0.857
Premature rupture of membranes	182 (19.53)	4927 (21.76)	0.105
Postpartum hemorrhage	36 (3.86)	1053 (4.65)	0.262
Shoulder dystocia	0 (0.00)	47 (0.21)	0.164
Neonatal outcomes
Birthweight (grams) a	3290 (2610‐3920)	3320 (2620‐4000)	0.023
LGA (n, %)	100 (10.73)	3390 (14.97)	<0.001
Macrosomia (n, %)	32 (3.43)	1219 (5.38)	0.009
SGA (n, %)	62 (6.65)	1249 (5.52)	0.138
Low birth weight (n, %)	26 (2.79)	698 (3.08)	0.612
Preterm birth (n, %)	36 (3.86)	1053 (4.65)	0.262
Male gender (n, %)	442 (47.42)	10 859 (47.96)	0.751
5‐min Apgar <8 (n, %)	1 (0.11)	53 (0.23)	0.428
NICU admission (n, %)	161 (17.27)	3990 (17.62)	0.786
Hypoglycemia (n, %)	8 (0.86)	168 (0.74)	0.686
IUGR (n, %)	5 (0.54)	148 (0.65)	0.663
Neonatal asphyxia (n, %)	6 (0.64)	162 (0.72)	0.799
Fetal distress (n, %)	182 (19.53)	3958 (17.48)	0.107

Note: Continuous variables were presented as means ± standard deviation. Categorical data were presented as frequencies (percentages).

Abbreviations: IUGR, intrauterine growth restriction; LGA, large for gestational age; NICU, neonatal intensive care unit; OGTT, oral glucose tolerance test; SGA, small for gestational age.

^a Presented with P50 (P5‐P95).

FIGURE 2.

Incidence of adverse outcomes in participants with flat versus normal oral glucose tolerance test curves. LGA, large for gestational age; OGTT, oral glucose tolerance test. **p < 0.01; ***p < 0.001.

In terms of neonatal outcomes, the median birth weight of newborns in the flat‐curve group was 3290 g at a mean gestational age of 39.3 weeks, while it was 3320 g at 39.2 weeks in the normal‐curve group (p < 0.05). The incidence of LGA infants (p < 0.001) and macrosomia (p < 0.01) also showed significant differences between the two groups (Figure 2). However, there were no significant differences in the incidence of SGA infants or low birth weights (all p > 0.05). Additionally, no other significant differences were observed between the groups in neonatal outcomes, including preterm birth, sex distribution, 5‐min Apgar scores, neonatal intensive care unit (NICU) admission, hypoglycemia, IUGR, neonatal asphyxia, or fetal distress (all p > 0.05).

3.3. Relative risks of adverse outcomes according to the OGTT curves

The risk of adverse outcomes was evaluated among individuals with two distinct OGTT curves, using those with a normal curve as the reference group. Logistic regression analysis was employed to assess these risks (Table 3, Figure 3). In the unadjusted model, compared to the normal‐curve group, individuals with a flat OGTT curve had significantly lower risks of gestational hypertension [RR: 0.39 (0.24–0.63), p < 0.001], preeclampsia [RR: 0.26 (0.10–0.69), p < 0.01], LGA infants [RR: 0.68 (0.55–0.84), p < 0.001], and macrosomia [RR: 0.63 (0.44–0.89), p < 0.05]. After adjusting for multiple factors including maternal age, preconception BMI, GWG, ethnicity, education, gestational age at delivery, history of previous cesarean delivery, parity, gravidity, and IVF status, the adjusted model still revealed a reduced risk for those with a flat curve. The adjusted relative risks were as follows: for gestational hypertension [RR: 0.46 (0.28–0.76), p < 0.01], for preeclampsia [RR: 0.31 (0.11–0.83), p < 0.05], for LGA infants [RR: 0.75 (0.60–0.93), p < 0.01], and for macrosomia [RR: 0.63 (0.44–0.91), p < 0.05].

TABLE 3.

Relative risks of adverse outcomes in participants with flat or normal oral glucose tolerance test curves.

Adverse outcomes	N (%)	Relative risks (95% CI)
		Crude a	p‐value	Adjusted b	p‐value
Gestational hypertension
Normal OGTT	1037 (4.58)	Reference		Reference
Flat OGTT	17 (1.82)	0.39 (0.24–0.63)	<0.001	0.46 (0.28–0.76)	0.002
Preeclampsia
Normal OGTT	376 (1.66)	Reference		Reference
Flat OGTT	4 (0.43)	0.26 (0.10–0.69)	0.007	0.31 (0.11–0.83)	0.020
LGA
Normal OGTT	3390 (14.97)	Reference		Reference
Flat OGTT	100 (10.73)	0.68 (0.55–0.84)	<0.001	0.75 (0.60–0.93)	0.009
Macrosomia
Normal OGTT	1219 (5.38)	Reference		Reference
Flat OGTT	32 (3.43)	0.63 (0.44–0.89)	0.010	0.63 (0.44–0.91)	0.013

Abbreviations: CI, confidence interval; LGA, large for gestational age; OGTT, oral glucose tolerance test.

^a Unadjusted.

^b Adjusted for maternal age, preconception body mass index, gestational weight gain, ethnicity, education, gestational age at delivery, previous cesarean delivery history, parity, gravidity, and in vitro fertilization.

FIGURE 3.

Forest plot depicting the relative risks of adverse outcomes among participants with flat or normal oral glucose tolerance test curves. Crude, unadjusted; Adjusted, adjusted for maternal age, preconception body mass index, gestational weight gain, ethnicity, education, gestational age at delivery, previous cesarean delivery history, parity, gravidity, and in vitro fertilization. LGA, large for gestational age; OGTT, oral glucose tolerance test.

3.4. Relationships between two OGTT curves and adverse outcomes in women with varying demographic parameters

Factors such as maternal age and BMI are frequently associated with LGA infants, macrosomia, gestational hypertension, and preeclampsia, as evidenced by the data (Table 1). We categorized these factors into distinct groups to examine potential disparities between OGTT curve patterns. Specifically, maternal age was divided into two categories: ≥35 years and <35 years. BMI was classified as underweight (BMI <18.5 kg/m²), normal weight (BMI 18.5–24.9 kg/m²), or overweight/obese (BMI ≥25 kg/m²). Additionally, each of these categories was further subdivided into flat‐curve and normal‐curve subgroups for in‐depth analysis. Compared with the normal‐curve group, all flat‐curve subgroups in the <35 years category had a lower risk of gestational hypertension, preeclampsia, LGA, and macrosomia (Figure 4). Moreover, within the normal‐weight group, the flat‐curve subgroup showed a reduced risk of gestational hypertension, LGA, and macrosomia (Figure 4). However, no significant differences were observed in the advanced‐age, underweight, or overweight/obese subgroups (all p > 0.05).

FIGURE 4.

Relative risks of adverse outcomes among participants with flat or normal oral glucose tolerance test curves stratified by baseline characteristics. Relative risks were adjusted for maternal age, preconception body mass index, gestational weight gain, ethnicity, education, gestational age at delivery, previous cesarean delivery history, parity, gravidity, and in vitro fertilization. LGA, large for gestational age. *p < 0.05; **p < 0.01; ***p < 0.001.

4. DISCUSSION

To the best of our knowledge, this study is the first to conduct a comprehensive examination of the flat glucose response curve during pregnancy in Chinese women and its association with adverse perinatal outcomes. In our Chinese cohort, we found that the prevalence of a flat OGTT curve was 3.95%. Women with a flat OGTT curve were generally younger and had lower BMIs. Moreover, these women had lower median birth weights and a reduced risk of having LGA infants, macrosomia, gestational hypertension, and preeclampsia. Significantly, this decreased risk remained even after adjusting for multiple confounding factors. Additionally, a flat OGTT curve was not significantly associated with other adverse maternal or neonatal outcomes. Intriguingly, compared with the normal‐curve group, the flat‐curve group presented distinct adjusted risks across different demographic factors, including maternal age and preconception BMI.

Glucose is a crucial nutrient for both the mother and fetus during pregnancy. Thus, maintaining glucose homeostasis is essential for optimal maternal and fetal health. ³⁴ The OGTT is an indispensable tool for assessing glucose homeostasis. Previous studies have established correlations between different OGTT subtypes ³⁵ or OGTT curves and parameters such as insulin sensitivity and pancreatic β‐cell function. ³⁶ , ³⁷ , ³⁸ , ³⁹ In women undergoing OGTT, the typical response to glucose ingestion follows a “monophasic” pattern, with glucose levels peaking 30–60 min after ingestion and then gradually declining. ² , ⁵ , ⁴⁰ However, a subset of pregnant women shows an atypical response, where the 1‐h PG levels do not increase as expected compared to fasting PG levels, resulting in a flat curve. The exact pathophysiology of this phenomenon remains unclear. Proposed explanations include delayed gastric emptying, which leads to later glucose spikes ⁴¹ and insulin hypersensitivity ¹⁰ as evidenced by Szoke et al., ⁴² who reported that pregnant women with flat OGTT curves had significantly lower insulin levels than those with normal curves. Despite these findings, research on the potential impact of OGTT curve patterns on maternal and fetal health remains limited. Robust evidence has shown a positive linear correlation between maternal glucose concentrations and neonatal birth weight. ⁴³ Glucose is transported to the fetus via facilitated diffusion, connecting maternal glucose metabolism to fetal growth. Maternal hyperglycemia causes fetal β‐cell hyperplasia and increased endogenous production of insulin and insulin‐like growth factor (IGF) 1. ⁴⁴ The resulting fetal hyperinsulinemia decreases fetal glucose levels, thereby increasing the glucose concentration gradient across the placenta and promoting glucose flux to the fetus. This further induces fetal hyperglycemia and hyperinsulinemia, which stimulate mitogenic and anabolic pathways in developing muscles, connective tissues, and adipose tissue. ⁴⁵ Fetal hyperinsulinemia leads to overgrowth, while fetal insulin deficiency is associated with IUGR. ⁴⁶ In contrast, there is limited consensus on the impact of maternal hypoglycemia on neonatal birth weight. Only a few studies have evaluated this effect using OGTTs during pregnancy, often with small sample sizes and inconsistent results. For instance, some studies found lower birth weights ⁴⁷ or an increased incidence of SGA neonates ⁴⁸ in the hypoglycemia group, while others showed no significant association between hypoglycemia during OGTT and low birth weights. ⁴⁹ Bayraktar et al. ⁵⁰ reported lower birth weights when comparing the hypoglycemia group with those who had normal OGTTs. However, direct comparisons were challenging due to the authors' exclusion criteria, which included women aged <19 or ≥35 years, with a BMI over 30 kg/m², or with any comorbidities. In this study, our findings suggested that a flat glucose response may affect fetal growth. Specifically, women with flat OGTT curves delivered newborns with lower median birth weights, which was consistent with previous studies, ⁷ , ⁹ , ⁵¹ despite having a greater gestational age. Moreover, our results indicated a significantly lower risk of macrosomia, in line with the findings of Lopian et al. ⁹ and Naeh et al. ⁸ However, we did not observe a higher prevalence of SGA neonates in the flat OGTT group than in the normal OGTT group, although there was a trend toward increased SGA risk. These observations suggested that women with flat OGTT curves may be more likely to deliver neonates that do not reach their full growth potential.

With respect to the reduced risks of gestational hypertension and preeclampsia in women with a flat OGTT curve, this finding has significant implications. However, few studies have reported a significant correlation between flat OGTT curves and gestational hypertension diseases. Only Valensise et al. ¹⁶ have suggested a potential association. Their study explored the predictive effects of the mid‐pregnancy uterine artery flow velocity waveform and OGTT curves on IUGR. They reported that the combination of elevated uterine artery resistance indices (RIs) and a flat OGTT curve could improve the positive predictive value for IUGR. Since an elevated RI is often seen in women with gestational hypertension and is associated with a flat OGTT response curve, ¹⁶ we speculate that there may be a relationship between a flat OGTT curve and gestational hypertension. However, further research is needed to confirm this hypothesis. In our study, women with a flat OGTT curve had lower risks of gestational hypertension and preeclampsia, even after adjusting for various confounding factors. A flat OGTT represents a distinct clinical entity characterized by both low absolute glucose levels and the absence of a blood glucose increase after an OGTT glucose load. ⁸ This condition implies glucose homeostasis and reduced insulin resistance. Insulin resistance is a critical factor in pregnancy‐related metabolic dysfunction. It promotes endothelial dysfunction, oxidative stress, and systemic inflammation, all of which are key pathways in the pathogenesis of gestational hypertension diseases. ⁵² Postprandial hyperglycemia triggers the generation of reactive oxygen species and the release of inflammatory cytokines. A flat glucose response minimizes glycemic variability, reducing oxidative stress and chronic low‐grade inflammation. ⁵³ Additionally, hyperglycemia and insulin resistance impair the bioavailability of endothelial nitric oxide and upregulate vasoconstrictors. A flat OGTT may protect endothelial‐dependent vasodilation by maintaining glucose stability. ⁵⁴ Overall, a flat OGTT likely mitigates the risk of gestational hypertension and preeclampsia by reducing insulin sensitivity, suppressing oxidative stress and inflammation, and preserving endothelial function.

To date, numerous risk factors, such as maternal age and BMI, have been associated with LGA infants, macrosomia, gestational hypertension, and preeclampsia. ⁵⁵ , ⁵⁶ Our research data showed a statistically significant difference between the flat‐curve and normal‐curve groups. However, owing to the paucity of studies examining the relationship between OGTT curves and adverse outcomes in conjunction with these critical risk factors, further investigation is warranted. Therefore, we conducted a stratified analysis on the basis of maternal age and preconception BMI. Compared with the normal‐curve group, all the flat‐curve subgroups had a lower risk of gestational hypertension, preeclampsia, LGA, and macrosomia in the <35 years age group. Additionally, in the normal‐weight category, the flat‐curve subgroups had a lower risk of gestational hypertension, LGA, and macrosomia. However, no significant differences were observed in the advanced‐age, underweight, or overweight/obese groups. The potential reasons for this are as follows: Underweight pregnant women often experience multiple micronutrient deficiencies. Maintaining appropriate concentrations of these elements during pregnancy can reduce the risk of complications such as hypertension, preeclampsia, and low birth weight. ⁵⁷ , ⁵⁸ , ⁵⁹ Additionally, underweight status may be associated with abnormal hormonal profiles, including disruptions in the IGF axis. Reduced levels of IGF‐1 can limit fetal growth potential and increase the risk of adverse outcomes. ⁶⁰ Conversely, being overweight or obese before pregnancy may lead to elevated concentrations of glucose, amino acids, and free fatty acids, as well as increased insulin resistance, which clinically manifests as glucose intolerance and fetal overgrowth. ⁶¹ , ⁶² Moreover, the accumulation of fat can lead to higher estrogen levels, which mediate aldosterone secretion and sodium retention through the renin‐angiotensin system or by directly increasing renal tubule reabsorption, resulting in gestational hypertension. ⁶³ Additionally, advanced maternal age is a significant risk factor for gestational hypertension, ⁶⁴ as well as for LGA and macrosomia, ⁶⁵ compared to younger women. These confounding factors may have obscured the relationship between the flat OGTT curve and the pregnancy risks under study. In addition, the limited sample size in our study examining the frequency of adverse pregnancy outcomes among advanced‐age, underweight, and overweight/obese groups may have restricted the statistical power to detect significant differences between the flat OGTT curve group and other groups. This limitation could potentially obscure true associations due to insufficient data points. Despite this, it is reassuring that a flat OGTT curve was not associated with an increased risk of adverse maternal or neonatal outcomes, suggesting that this specific glucose response pattern may not be indicative of poor pregnancy outcomes in the same manner as abnormal hyperglycemia patterns.

In summary, our research findings are largely consistent with those of previous studies, yet there are notable inconsistencies and novel discoveries. The discrepancies among studies can be attributed to several factors. First, the term “flat OGTT curve” has been in use for a long time but lacks a uniform definition. Most studies use an absolute cut‐off for all evaluations; however, they typically use a 100‐gram glucose solution, which differs from the 75‐gram glucose load in our study, making their results less comparable. ⁸ , ⁹ Additionally, variations in the degree of difference between FBG and 2 h‐PG levels, ⁵¹ as well as differences in calculated index values ¹⁷ and the percentage increase relative to fasting glucose, ¹⁶ contribute to these inconsistencies. From a laboratory medicine perspective, we found that the method proposed by Szoke et al. ⁴² was more reasonable. In laboratory medicine, two consecutive quantitative measurements of the same analyte in an individual often exhibit some degree of variation, which may be due to pathophysiological mechanisms or simple instrumental error. This variation is called the RCV. ²⁰ The RCV integrates both biological variability and laboratory measurement variability, making it a suitable method for evaluating blood glucose fluctuations; hence, this study adopted this approach. Second, differences in testing protocols, such as the use of 50‐ gram or 100‐gram OGTT glucose loads, may have influenced the results, whereas our study utilized a 75‐gram OGTT glucose load. Finally, disparities in population size, characteristics, and geographic regions across studies also contributed to the observed differences.

To our knowledge, this study is the largest investigation to date examining the impact of a flat OGTT response curve on pregnancy outcomes. We evaluated maternal and neonatal outcomes in more than 23 000 pregnant women using what we consider to be the most appropriate definition of a flat OGTT curve. All participants underwent the same standardized OGTT protocol, and the results were analyzed in a single laboratory. Our study covered a wide range of maternal and neonatal outcomes. However, several limitations should be acknowledged. First, the study was conducted among pregnant Chinese women, which may limit the generalizability of the findings. Second, owing to the retrospective design, data on lifestyle interventions and treatments received by the participants were not available, precluding an evaluation of therapeutic efficacy. Third, the relatively low incidence of adverse pregnancy outcomes in women with a flat OGTT curve limits the ability to perform a detailed analysis of specific outcomes. Finally, certain residual confounders, such as dietary habits, alcohol consumption, smoking status, exercise patterns, and other potential variables, potentially influence the outcomes.

5. CONCLUSION

This study has uncovered that a flat OGTT curve is associated with a younger and leaner obstetric population. Notably, the implementation of the OGTT as a reliable prenatal diagnostic tool not only aids in identifying women with GDM but also highlights that pregnant women exhibiting a flat OGTT curve have a reduced risk of gestational hypertension, preeclampsia, LGA infants, and macrosomia. Importantly, when the relationship between OGTT curve patterns and adverse pregnancy outcomes is assessed, maternal age and preconception BMI must be considered critical factors. Future research endeavors should focus on further clarifying the physiological mechanisms underlying the flat OGTT curve and its implications for fetal growth and pregnancy‐related complications. In conclusion, our study provides novel insights into the significance of a flat glucose response curve during pregnancy among Chinese women, which may contribute to enhanced pregnancy management and a deeper understanding of the intricate relationship between glucose metabolism and pregnancy outcomes.

AUTHOR CONTRIBUTIONS

YX, QW and YB contributed to the study concept and design. QW and YB performed the data collection and data analysis. YX, QW and JZ were involved in the interpretation of the data and the drafting of the manuscript. BY and YB were involved in the critical revision of the manuscript and study supervision. All the authors read and approved the final manuscript.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

ETHICS STATEMENT

The study protocol was approved by the ethics committee of Women's Hospital, Zhejiang University School of Medicine (approval number: IRB‐20240021‐R) on January 22, 2024, and an informed consent exemption was granted due to the use of anonymous participants' records.

Xi Y, Wu Q, Yin B, Zhang J, Bai Y. The flat glucose response curve during oral glucose tolerance tests in Chinese pregnant women and its association with adverse outcomes. Acta Obstet Gynecol Scand. 2025;104:1918‐1928. doi: 10.1111/aogs.70016

DATA AVAILABILITY STATEMENT

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.