Objective
We sought to estimate the risk of large for gestational age (LGA) across categories of glucose tolerance.
Study Design
In a cohort of 89,141 participants, women without gestational diabetes mellitus (GDM) were categorized by their screening and diagnostic test results; those with GDM were categorized as meeting the National Diabetes Data Group or only the American Diabetes Association (ADA) criteria. Multivariable logistic regression models estimated the risk of LGA; screening values 5.5-6.0 mmol/L comprised the referent.
Results
In women without GDM, the odds ratio for LGA was 1.89 (95% confidence interval [CI], 1.45–2.45) for fasting, 1.57 (95% CI, 1.31–1.89) for 1-hour, 1.60 (95% CI, 1.33–1.93) for 2-hour, and 1.62 (95% CI, 1.23–2.14) for 3-hour values meeting the ADA time point–specific thresholds.
Conclusion
For GDM identified in a 2-step procedure, our findings support the use of isolated abnormal fasting values according to the ADA threshold in identifying women who could benefit from treatment.
Gestational diabetes mellitus (GDM), defined as carbohydrate intolerance with onset or first recognition during pregnancy, is associated with an increased risk of adverse perinatal outcomes. In the United States, it is currently recommended that GDM be diagnosed in women with at least 2 plasma glucose values on a diagnostic 100-g, 3-hour oral glucose tolerance test (OGTT) that meet or exceed the thresholds recommended by the American Diabetes Association (ADA) in 2000 4 ; the 100-g, 3-hour OGTT is only performed in women with abnormal values (≥7.8 mmol/L) on a 50-g, 1-hour glucose challenge test screening.
The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, a large multicenter study designed to identify potential glucose thresholds on the 75-g, 2-hour OGTT associated with increased risk of adverse infant outcomes, found that the risk of delivering a large-for-gestational-age (LGA) infant or an infant with hyperinsulinemia increased continuously across increasing categories of maternal glycemia at all time points. Providers were blinded to glucose tolerance status except for among women with overt GDM, defined as fasting plasma glucose >5.8 mmol/L and/or 2-hour value >11.1 mmol/L, who were excluded from the primary analyses. Diagnostic criteria based on the presence of 1 abnormal value on the 75-g, 2-hour OGTT have been recently proposed from these data. The International Association of Diabetes and Pregnancy Study Groups Consensus Panel (IADPSG) used odds ratios (ORs) ≥1.75 relative to the cohort mean value for each time point in arriving at the following diagnostic criteria: fasting value ≥5.1 mmol/L, 1-hour value ≥10.0 mmol/L, or 2-hour value ≥8.5 mmol/L.
To provide further information on potential thresholds for the diagnosis of GDM, we estimated the risk of LGA across increasing categories of pregnancy glycemia assessed via 100-g, 3-hour OGTT in women who did not have GDM and did not receive treatment. We also estimated the risk of LGA among women with GDM by the ADA criteria only, who did not receive treatment during the study period, and women with GDM by the National Diabetes Data Group (NDDG) criteria, who were treated. Finally, we estimated the prevalence of GDM if only 1 abnormal glucose value according to the ADA thresholds, or lower glucose cut points previously found to be associated with LGA risk, were used for diagnosis.
Materials and Methods
The study setting is Kaiser Permanente Northern California (KPNC), a large group-practice prepaid health plan that provides comprehensive medical services to members residing in a 14-county region. Approximately 30% of the population that resides in the area served by the KPNC is enrolled in the health plan, which is representative of the underlying population.
To estimate the association between pregnancy glycemia and LGA, we identified 191,095 pregnancies to 158,073 women who delivered a live singleton infant from January 1995 through December 2004, were 15-45 years of age at delivery, had a maternal weight measurement available between 9-24 weeks’ gestation and no history of recognized diabetes. We then excluded 4862 pregnancies missing maternal glucose values on the 50-g, 1-hour glucose challenge test; 742 pregnancies missing ≥1 value on the 100-g, 3-hour OGTT; and 1862 pregnancies with screening glucose values ≥7.8 mmol/L but no 100-g, 3-hour OGTT on record. From among the remaining 183,629 pregnancies, we selected the first pregnancy to each woman during the study period, leaving 152,671 pregnancies. We then restricted the cohort to those with height available in the electronic records for the calculation of body mass index (BMI) (n = 90,966) and those who delivered between 35 and 42 weeks’ gestation, resulting in a final analytic cohort of 89,141 mother-infant pairs. To assess whether restriction of the cohort to those with height data biased our results, a sensitivity analysis was conducted among the larger cohort of women with weight data available (n = 149,699). Maternal BMI and maternal weight were highly correlated in those women with height data (R 2 = 0.86).
In this setting, during the entire study period, the diagnosis of GDM was based exclusively on the NDDG criteria (≥2 plasma glucose values on the 100-g, 3-hour OGTT at or higher than the NDDG thresholds: fasting value 5.8 mmol/L; 1-hour value 10.5 mmol/L; 2-hour value 9.1 mmol/L; 3-hour value 8.0 mmol/L). Among women who did not meet the NDDG criteria, GDM by the ADA criteria only was defined as the presence of 2 plasma glucose values meeting or exceeding the following thresholds: fasting value 5.3 mmol/L; 1-hour value 10.0 mmol/L; 2-hour value 8.6 mmol/L; and 3-hour value 7.8 mmol/L. Women who met the ADA criteria only for GDM did not receive treatment during the study period.
Infant birthweight and gestational age at birth were obtained from the KPNC electronic medical records. A previous study in this setting during the same period found 98.5% agreement between gestational age in the electronic medical records and estimates from ultrasonographic data collected <24 weeks. LGA was defined as birthweight >90th percentile for the study population’s race-ethnicity and gestational age–specific birthweight distribution. The following maternal racial-ethnic groups, obtained by linkage with the state of California’s birth certificate data (99% successful linkage) were used to define these distributions: Non-Hispanic white, African American, Asian, Hispanic, and other/unknown. Maternal age at delivery, height (meters), weight (kilograms), and gestational age (weeks) at the weight measurement were obtained from the KPNC electronic medical records. Maternal BMI was calculated as weight, in kilograms, at the second trimester divided by height, in meters, squared. Plasma glucose values on the screening 50-g, 1-hour glucose challenge test (hereafter referred to as the screening test) and the diagnostic 100-g, 3-hour OGTT (hereafter referred to as the diagnostic test) were obtained from the KPNC clinical laboratory database.
Logistic regression models were used to obtain OR that estimated the relative risk of LGA associated with increasing levels of maternal glycemia. A separate model was constructed for each time point of the 100-g, 3-hour OGTT. Women with normal screening values were categorized as follows: <5.5 mmol/L, 5.5-6.0 mmol/L, 6.1-6.6 mmol/L, 6.7-7.1 mmol/L, and 7.2-7.7 mmol/L. Women with abnormal screening values (plasma glucose ≥7.8 mmol/L) who did not have ≥2 values on the 100-g, 3-hour OGTT meeting or exceeding the ADA glucose thresholds for the diagnosis of GDM were further categorized by their plasma glucose values on the diagnostic test. There were 6 groups for each time point of the diagnostic test; fasting, 1-hour, 2-hour, and 3-hour plasma glucose value classifications are displayed in Figures 1 and 2 . For each time point, the sixth group included women with only 1 abnormal value on the 100-g, 3-hour OGTT according to the ADA thresholds. Each model included the screening categories listed above, the plasma glucose categories specific to that time point on the diagnostic test ( Figures 1 and 2 ), and 2 indicator terms for GDM (by the ADA criteria only and by the NDDG criteria ). Among women with normal screening results (plasma glucose values <7.8 mmol/L), the mean 1-hour value was 5.83 mmol/L. Similar to the referent selection method of the IADPSG, the screening category that contained this mean value, 5.5-6.0 mmol/L, served as the reference for all models; the prevalence of LGA in this group was 9.2%.
The models were adjusted for maternal age (15-24 years as the reference, 25-29 years, 30-34 years, and 35-45 years), race-ethnicity (non-Hispanic white as the reference, African American, Asian, Hispanic, and other/unknown), parity (0 as the reference, 1, 2, ≥3, and unknown), education (high school graduate, some college, completed college as the reference, beyond college, and unknown), maternal BMI in the second trimester (<25.0 as the reference, 25.0-29.9, and >30), and gestational age at the second-trimester weight measurement (continuous).
To assess the impact of lower glucose thresholds on the prevalence of GDM, we identified 33,921 women who delivered a live or stillborn infant at KPNC in 2007. We then estimated the prevalence of GDM if 1 vs 2 abnormal glucose values according to the ADA thresholds for the 100-g, 3-hour OGTT were used for diagnosis. A hierarchical, stepwise approach was used. To estimate the prevalence of GDM if only 1 abnormal value was used for diagnosis, we first identified women with fasting glucose ≥5.3 mmol/L. From among the remaining women, we identified those with 1-hour glucose ≥10.0 mmol/L, followed by those with 2-hour glucose ≥8.6 mmol/L and lastly, those with 3-hour glucose ≥7.8 mmol/L. To estimate the prevalence of GDM by the current ADA criteria (2 abnormal values), women with 2 abnormal values who had a fasting glucose ≥5.3 mmol/L were first identified. Next, we identified women with 2 abnormal values who had a 1-hour glucose ≥10.0 mmol/L and had not been captured in the previous step. Women with 2 abnormal values who had a 2-hour glucose ≥8.6 mmol/L and were not identified in the first 2 steps were captured last.
Software (SAS 9.1; SAS Institute Inc, Cary, NC) was used for all analyses. This study was approved by the human subjects committees of the Kaiser Foundation Research Institute and the state of California.
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