How Can We Adopt the Glucose Tolerance Test to Facilitate Predicting Pregnancy Outcome in Gestational Diabetes Mellitus?
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Original Endocrinol Metab 2021;36:988-996
https://doi.org/10.3803/EnM.2021.1107
Article pISSN 2093-596X · eISSN 2093-5978
How Can We Adopt the Glucose Tolerance Test to Facilitate
Predicting Pregnancy Outcome in Gestational Diabetes
Mellitus?
Kyeong Jin Kim1,*, Nam Hoon Kim1,*, Jimi Choi1, Sin Gon Kim1, Kyung Ju Lee2
Division of Endocrinology and Metabolism, Department of Internal Medicine, 2Department of Obstetrics and Gynecology,
1
Korea University College of Medicine, Seoul, Korea
Background: We investigated how 100-g oral glucose tolerance test (OGTT) results can be used to predict adverse pregnancy out-
comes in gestational diabetes mellitus (GDM) patients.
Methods: We analyzed 1,059 pregnant women who completed the 100-g OGTT between 24 and 28 weeks of gestation. We com-
pared the risk of adverse pregnancy outcomes according to OGTT patterns by latent profile analysis (LPA), numbers to meet the
OGTT criteria, and area under the curve (AUC) of the OGTT graph. Adverse pregnancy outcomes were defined as a composite of
preterm birth, macrosomia, large for gestational age, low APGAR score at 1 minute, and pregnancy-induced hypertension.
Results: Overall, 257 participants were diagnosed with GDM, with a median age of 34 years. An LPA led to three different clusters of
OGTT patterns; however, there were no significant associations between the clusters and adverse pregnancy outcomes after adjusting
for confounders. Notwithstanding, the risk of adverse pregnancy outcome increased with an increase in number to meet the OGTT
criteria (P for trend=0.011); odds ratios in a full adjustment model were 1.27 (95% confidence interval [CI], 0.72 to 2.23), 2.16 (95%
CI, 1.21 to 3.85), and 2.32 (95% CI, 0.66 to 8.15) in those meeting the 2, 3, and 4 criteria, respectively. The AUCs of the OGTT curves
also distinguished the patients at risk of adverse pregnancy outcomes; the larger the AUC, the higher the risk (P for trend=0.007).
Conclusion: The total number of abnormal values and calculated AUCs for the 100-g OGTT may facilitate tailored management of
patients with GDM by predicting adverse pregnancy outcomes.
Keywords: Diabetes, gestational; Pregnancy outcome; Glucose tolerance test
INTRODUCTION of GDM has increased worldwide, and its clinical implications
have been highlighted in the context of the rapid increase in the
Gestational diabetes mellitus (GDM) is defined as glucose in- prevalence of early onset type 2 diabetes, especially for child-
tolerance first recognized during pregnancy, regardless of bearing women [1-4]. For decades, large clinical studies have
whether the condition started before pregnancy. The incidence focused on establishing diagnostic criteria that distinguish be-
Received: 14 May 2021, Revised: 23 July 2021, Accepted: 24 August 2021 Copyright © 2021 Korean Endocrine Society
This is an Open Access article distributed under the terms of the Creative Com
Corresponding author: Kyung Ju Lee
mons Attribution Non-Commercial License (https://creativecommons.org/
Department of Obstetrics and Gynecology, Korea University Anam Hospital,
licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribu
Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul
tion, and reproduction in any medium, provided the original work is properly
02841, Korea
cited.
Tel: +82-2-920-6844, Fax: +82-2-920-5357, E-mail: drlkj52551@korea.ac.kr
*These authors contributed equally to this work.
988 www.e-enm.orgPredictor of Gestational Diabetes Mellitus
tween GDM and healthy pregnancies [5-7]. The International clinics. Blood tests, including hemoglobin, fasting glucose, lipid
Association of Diabetes and Pregnancy Study Group [8] adopt- profile, C-peptide, and insulin, were conducted at 26 gestational
ed the results of the Hyperglycemia and Adverse Pregnancy weeks. Based on the Korean Diabetes Association guidelines
Outcome study [6] to diagnose GDM using a one-step 75-g oral [16], target glucose levels were as follows: fasting glucose 90 mm Hg after 20 gestational weeks.
METHODS
Statistical analyses
Study design and subjects Continuous data are presented as mean±standard deviation for
This retrospective cohort study included 2,789 pregnant women normally distributed variables and as medians and interquartile
who delivered at Gangnam CHA Medical Center (Seoul, Korea) ranges (IQRs) for non-normally distributed variables. Categori-
between July 1, 2007, and December 31, 2009. Those with twin cal data are presented as frequencies and percentages. Student’s
pregnancy, fetal anomaly, hypertensive disorder before preg- t test, Mann-Whitney U test, chi-square test, and Fisher’s exact
nancy, diabetes, and missing pre-pregnancy or delivery weights test were used to compare baseline characteristics between the
were excluded. Among these participants, we analyzed 1,058 normal and GDM groups. Latent profile analysis (LPA) was
pregnant women who completed the 100-g OGTT after a 50-g performed to identify glucose patterns in patients with GDM
glucose challenge test between 24 and 28 weeks of gestation. based on four measurements during the OGTT. This method as-
Routine prenatal examinations, including maternal body weight, sumes that unobserved latent profiles generate patterns of re-
blood pressure, and fetal crown-rump length, were performed at sponses in a series of continuous variables. The optimal number
11, 16, 26, and 35 gestational weeks at obstetrics outpatient of clusters was determined by considering the Bayesian infor-
Copyright © 2021 Korean Endocrine Society www.e-enm.org 989Kim KJ, et al. mation criterion (BIC) value, distribution of cluster membership prevalent in the GDM group than in the normal group. The pre- probabilities, cluster sizes, and interpretability of the identified gestational BMI was 21.6 kg/m2 (IQR, 19.7 to 24.0) in the patterns [19,20]. A three-cluster model was selected because it GDM group and 20.3 kg/m2 (IQR, 18.9 to 22.3) in the normal had a lower BIC value than the other models, and all cluster siz- group. Glycosylated hemoglobin at 26 gestational weeks was es were >10% of the number of patients with GDM (Supple- 34 mmol/mol (5.3%) (IQR, 32 to 37 [5.1% to 5.5%]) and 33 mental Table S1). To classify individuals exclusively into three mmol/mol (5.2%) (IQR, 31 to 37 mmol/mol [5.0% to 5.5%]) in glucose patterns, we assigned patients to the cluster with the the GDM and normal groups, respectively (P=0.002). The me- highest cluster membership probability. The individual area un- dian levels of glucose during the 100-g OGTT in the GDM der the curve (AUC) for the OGTT was adopted to evaluate the group were 84 mg/dL (IQR, 78 to 91), 185 mg/dL (IQR, 168 to severity of maternal hyperglycemia by summing the area of 198), 173 mg/dL (IQR, 161 to 188), and 150 mg/dL (IQR, 141 three trapezoids as follows: (0-hour+1-hour glucose)/2, (1- to 164) at baseline, 1, 2, and 3 hours, respectively. Systolic hour+2-hour glucose)/2, and (2-hour+3-hour glucose)/2. Binary (116.3 mm Hg vs. 112.8 mm Hg) and diastolic blood pressure logistic regression analysis was performed to compare the prev- (69.3 mm Hg vs. 66.8 mm Hg) at 26 weeks of gestation were alence of outcomes between the normal and three latent glucose significantly higher in the GDM group than in the normal pattern groups, four groups by classifying quartiles of individual group. Homeostatic model assessment of insulin resistance was AUCs, or three groups according to the number of criteria in significantly higher in the GDM group than in the normal group GDM patients. Two multiple logistic regression models were (1.31 vs. 0.94, P
Predictor of Gestational Diabetes Mellitus Table 1. Demographic characteristics of GDM and normal participants Characteristic Total (n=1,058) Normal (n=801) GDM (n=257) P value Maternal age, yr 33 (30–35) 32 (30–35) 34 (31–36)
Kim KJ, et al.
Normal (n=801) Normal (n=801)
Cluster 1 (n=65) 2 abnormal values (n=138)
Cluster 2 (n=150) 3 abnormal values (n=97)
240 Cluster 3 (n=42) 240 4 abnormal values (n=22)
230 230
220 220
210 210
200 200
190 190
Glucose (mg/dL)
Glucose (mg/dL)
180 180
170 170
160 160
150 150
140 140
130 130
120 120
110 110
100 100
90 90
80 80
70 70
60 60
0 1 2 3 0 1 2 3
(hr) A (hr) B
Fig. 1. Pattern of plasma glucose levels according to the latent glucose class (A) and the total number of abnormal values (B) during a 100-g
oral glucose tolerance test.
Unadjusted OR Adjusted OR by Model A
Adjusted OR (95% CI) for AUC (ref.=370.6)
Crude OR (95% CI) for AUC (ref.=370.6)
3 3
2 2
1 1
350 400 450 500 550 350 400 450 500 550
OGTT AUC (mg . h/dL) A OGTT AUC (mg . h/dL) B
Adjusted OR by Model B
Adjusted OR (95% CI) for AUC (ref.=370.6)
Fig. 2. Restricted cubic spine curves of unadjusted odds ratios (ORs)
(A) and adjusted ORs (B), and composite adverse pregnancy out-
3 comes (C) according to the area under the curve (AUC) for the oral
glucose tolerance test (OGTT). The reference AUC value for ORs
was 370.6, which is the mean AUC of the healthy group. The verti-
2
cal dashed lines represent the first, second, and third quartiles of the
AUC, respectively. Model A was adjusted for age, preexisting hy-
pertension, family history of diabetes mellitus, family history of hy-
1
pertension, pre-pregnancy body mass index, parity (yes or no), and
gestational age (before delivery). Model B was adjusted for Model A
plus systolic blood pressure at 35 weeks of gestation, diastolic blood
350 400 450 500 550 pressure at 35 weeks of gestation, fasting blood glucose at 35 weeks
OGTT AUC (mg . h/dL) C of gestation, and insulin treatment. CI, confidence interval.
the risk of adverse outcomes. The log unadjusted OR continu- shows that the highest quartile group had a significantly higher
ously increased as AUC increased. After adjusting for con- OR (OR, 2.31; 95% CI, 1.09 to 4.91) in the full adjustment
founders (model A and model B), linear associations between model for adverse outcomes than the normal group did.
AUC and log OR still existed, albeit with a wider CI. Table 4
992 www.e-enm.org Copyright © 2021 Korean Endocrine SocietyPredictor of Gestational Diabetes Mellitus
Table 2. Odds Ratio for Each Latent Glucose Pattern Class for Composite Adverse Pregnancy Outcomes Relative to the Normal Group
No. of events, Unadjusted
P value Model 1a P value Model 2b P value
no./total (%) model
Normal 124/801 (15.5) 1 (Reference) 1 (Reference) 1 (Reference)
GDM
Cluster 1 16/65 (24.6) 1.78 (0.98–3.24) 0.057 1.58 (0.80–3.10) 0.189 1.57 (0.72–3.45) 0.260
Cluster 2 38/150 (25.3) 1.85 (1.22–2.81) 0.004 1.54 (0.97–2.45) 0.071 1.59 (0.93–2.74) 0.091
Cluster 3 19/42 (45.2) 4.51 (2.39–8.53)Kim KJ, et al.
DISCUSSION substance because the number of abnormal values and AUCs
during the 100-g OGTTs were independent risk factors for ad-
This study has demonstrated that a higher number of patients verse pregnancy outcomes. Moreover, the ORs for three and
meeting the diagnostic criteria of the 100-g OGTT or a higher four abnormal values in Table 3 slightly decreased after addi-
AUC of the OGTT curve is significantly associated with in- tional adjustment for treatment-related factors in Model 2, un-
creased adverse pregnancy outcomes in GDM than those in derpinning the importance of hyperglycemic control and related
normal glucose tolerant subjects, suggesting that a more thor- risk factor management. The OR for the four abnormal values
ough interpretation of OGTT results should be made at the time in Table 3 was not significant after full adjustment for the con-
of GDM diagnosis. founding factors. This loss of statistical significance, albeit the
Over the past decades, the prevalence of obesity and diabetes highest adverse event rate, might be deduced by the absolute
mellitus has increased robustly, and both have become serious small number of patients and event numbers in this group. In
health problems worldwide [3]. According to the International the regression analysis, to present ORs for the adverse outcomes
Diabetes Federation, one in six live births, approximately 20 in each classification, most of the major confounding variables
million, is affected by hyperglycemia during pregnancy, with addressed at baseline were adjusted in Model 2 to reduce selec-
84% of mothers having gestational diabetes [22]. Compared to tion bias.
general diabetes, GDM has more significant clinical implica- This study had some limitations that require comments. The
tions in that it can influence both neonates and mothers. Al- first is the fundamental limitation of the single-center retrospec-
though numerous studies have been conducted on diagnostic tive study design. Nevertheless, this single-center design in-
criteria, treatment targets, and prognostic factors, many of these volved a uniform prenatal screening protocol and patient man-
findings remain controversial. agement, as well as standardized data collection for adverse
Several previous reports have investigated the association be- pregnancy outcomes. Second, primary CS was not included
tween the AUC of the OGTT and pregnancy outcomes. Kim et among adverse pregnancy outcomes since it is not the result of
al. [23] reported that the AUC for the 100-g OGTT was associ- an adverse pregnancy outcome but rather a preference in Korea,
ated with an increased risk of LGA in GDM. Another study where the CS rate is high. Third, the lack of information on in-
from China demonstrated that a higher AUC for the 75-g OGTT sulin levels constrained the investigation of the association be-
was related to adverse pregnancy outcomes, such as hyperten- tween insulin response and OGTT patterns. Finally, the lack of
sive disease and macrosomia [24]. Our study findings are con- information on short-term follow-up OGTT results of GDM pa-
sistent with these results, demonstrating that hyperglycemia it- tients and long-term adverse events, such as future maternal dia-
self is an important pitfall for adverse perinatal outcomes betes mellitus or early childhood obesity, hindered us from
though a more meticulous analysis of the OGTT results. It is completely examining the natural course of overall adverse
also necessary to explain why primary cesarean section (CS) pregnancy outcomes. Therefore, larger and longer-term clinical
was not included as an adverse pregnancy outcome in our study. studies are warranted to arrive at definite conclusions.
Despite the efforts of the Korean government to reduce the CS In summary, this study elucidated that risk stratification for
rate, our study shows that most of the GDM patients (85.2% adverse pregnancy outcomes in GDM patients is conceivable at
[219/257]) had undergone primary CS. GDM patients preferred the time of GDM diagnosis, suggesting that aggressive risk
CS since health service accessibility in Korea was high and management and tailored treatment are warranted in GDM pa-
GDM patients wanted to avoid complications during the vaginal tients with higher numbers to meet the diagnostic criteria of the
delivery [25-27]. 100-g OGTT or higher AUC values for OGTT curves. Our re-
We clustered all subjects based on the LPA, presenting specif- sults also suggest that the AUC value is an independent predic-
ic patterns such as impaired fasting glucose-like pattern, im- tor of adverse pregnancy outcomes, requiring further long-term,
paired glucose tolerance-like pattern, and combined patterns large-sample studies.
(Fig. 1A), since we initially expected the OGTT patterns reflect-
ing the individual insulin response to play an important role in CONFLICTS OF INTEREST
adverse pregnancy outcomes. However, we did not find signifi-
cant associations between OGTT patterns and pregnancy out- No potential conflict of interest relevant to this article was re-
comes. We postulate that hyperglycemia itself is a matter of ported.
994 www.e-enm.org Copyright © 2021 Korean Endocrine SocietyPredictor of Gestational Diabetes Mellitus
ACKNOWLEDGMENTS Groups Consensus Panel, Metzger BE, Gabbe SG, Persson
B, Buchanan TA, Catalano PA, et al. International associa-
The authors thank the participants in the study cohort and the tion of diabetes and pregnancy study groups recommenda-
staffs at Gangnam CHA Hospital, Seoul, Korea, for critical tions on the diagnosis and classification of hyperglycemia in
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9. Vandorsten JP, Dodson WC, Espeland MA, Grobman WA,
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Drafting the work or revising: K.J.K., N.H.K. Final approval of 2017;130:E17-31.
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