Simplifying the screening of gestational diabetes by maternal age plus fasting plasma glucose at first prenatal visit: A prospective cohort study

Yi-Yun Tai; Chien-Nan Lee; Chun-Heng Kuo; Ming-Wei Lin; Kuan-Yu Chen; Shin-Yu Lin; Hung-Yuan Li

doi:10.1371/journal.pone.0237224

. 2020 Aug 20;15(8):e0237224. doi: 10.1371/journal.pone.0237224

Simplifying the screening of gestational diabetes by maternal age plus fasting plasma glucose at first prenatal visit: A prospective cohort study

Yi-Yun Tai ¹, Chien-Nan Lee ¹, Chun-Heng Kuo ², Ming-Wei Lin ¹, Kuan-Yu Chen ³, Shin-Yu Lin ^1,^*,^#, Hung-Yuan Li ^4,^*,^#

Editor: Clive J Petry⁵

PMCID: PMC7444589 PMID: 32817647

Abstract

Aim

The addition of maternal age to fasting plasma glucose (FPG) at 24–28 gestational weeks improves the performance of GDM screening as maternal age increases. However, this method delays the diagnosis of GDM. Since FPG at the first prenatal visit (FPV) is a screening option for pre-existing diabetes, we evaluated the performance of age plus FPG at the FPV to reduce the need for the OGTT.

Methods

Pregnant women were recruited consecutively in 2013–2018 (the training cohort) and 2019 (the validation cohort). We excluded women with twin pregnancies, unavailable FPG at the FPV or OGTT data, pre-pregnancy diabetes, or a history of GDM. All participants underwent FPG and haemoglobin A1c (HbA1c) at the FPV and received 75-g OGTT at 24–28 gestational weeks if FPG at the FPV was <92 mg/dL. GDM was diagnosed by the IADPSG criteria. Two algorithms were developed with the cutoffs determined when the percentage requiring OGTT (OGTT%) was the lowest and the sensitivity was ≥90%.

Results

The incidence of GDM increased with age. The “FPG at the FPV” algorithm reduced OGTT% to 68.8% with the FPG cutoff at 79 mg/dl. The "age plus FPG at the FPV" algorithm, with the cutoff of 114, further reduced OGTT% to 58.3%, with the sensitivity of 90.7% (9.3% GDM missed) and the specificity of 100%. These findings were replicated in the validation cohort.

Conclusions

Screening GDM by maternal age plus FPG at the FPV can reduce OGTT%, especially in populations with a significant proportion of pregnant women with advanced ages.

Introduction

For the diagnosis of gestational diabetes mellitus (GDM), the International Association of Diabetes and Pregnancy Study groups (IADPSG) and the World Health Organization (WHO) recommends the use of 75-g oral glucose tolerance test (OGTT) for all pregnant women at 24–28 weeks of gestation, which is also a diagnostic option suggested by the American Diabetes Association (ADA) in addition to the two-step method [1–3]. Since the OGTT is burdensome for the pregnant women and the healthcare system, some screening methods have been developed, such as risk factor-based models or the 1-hour 50g glucose challenge test used in the two-step method [4]. In addition, some studies have reported fasting plasma glucose (FPG)-based screening methods aimed at reducing the use of OGTTs [5–7]. However, these FPG-based screening methods have generally been targeted at 24–28 weeks of gestation instead of early pregnancy, and the use of an FPG-based method at 24–28 weeks essentially requires two steps to diagnose or exclude GDM [5–8].

On the other hand, the IADPSG recommends the measurement of FPG during early pregnancy to detect pre-existing diabetes and GDM [1, 9]; whereas the ADA suggests the screening of undiagnosed type 2 diabetes in the first prenatal visit (FPV) in women with risk factors by the standard criteria, such as FPG, hemoglobin A1c or even OGTT [3]. Therefore, pregnant women may have their FPG level tested at the FPV. Since a higher first-trimester FPG level is associated with an increased risk of GDM diagnosed at 24–28 weeks [10], the FPG at the FPV may be a good predictor to be used in the screening of GDM.

Although increasing maternal age is known to be a significant risk factor of diabetes mellitus (DM) [11], information regarding the relationship between age and GDM is reported relatively rarely. In most developed countries, women get pregnant at older ages [12–15]. Therefore, maternal age may be another important factor to be considered in the screening of GDM, especially in countries where women become pregnant at an older age.

Our previous study has shown that the rate of GDM with normal FPG increases as maternal age increases, and the addition of maternal age to FPG at 24–28 weeks of gestation can improve the performance of GDM screening [8]. However, if “age plus FPG at the FPV” can improve the performance of GDM screening remains unknown. Therefore, in this study, we aimed to evaluate the performance of “age plus FPG at the FPV” for the earlier screening of GDM as a means of reducing the need for the OGTT at 24–28 weeks.

Research design and methods

Data and sample collection

Two cohort studies were conducted for this project, including a prospective cohort study to develop the screening algorithms (the training cohort) and the other cohort study to validate the performance of the algorithms (the validation cohort). The training cohort study was conducted between January 2013 and June 2018 which recruited pregnant women who visited the obstetric clinic of National Taiwan University Hospital. The pregnant women in the validation cohort were recruited in 2019. The inclusion criterion for this study on screening for GDM was singleton pregnancy delivering a phenotypically normal neonate at or after 28 weeks' gestation. We excluded pregnancies with twin pregnancies, or FPG at the first prenatal visit could not be obtained, or pre-pregnancy diabetes mellitus (either FPG ≥126 or HbA1c ≥ 6.5%), received OGTT in other clinic or hospital. Since it is reasonable for pregnant women with a history of GDM to undergo OGTT, we excluded women with a history of GDM from the analyses in this study (women with history of GDM, N = 35 in the training cohort, and N = 7 in the validation cohort). Eligible participants received blood tests for FPG and haemoglobin A1c (HbA1c) at the FPV after fasting for 8–10 hours. Women with FPG at the FPV less than 92 mg/dL received 75-g OGTT at 24–28 gestational weeks. The diagnosis of GDM was based on the recommendations of the IADPSG if FPG at the FPV ≥92 mg/dL, or if one of the results from the OGTT exceeded the cutoffs, including FPG ≥92 mg/dL, plasma glucose at 1h ≥180 mg/dL, or plasma glucose at 2h ≥153 mg/dL [1]. The clinicians were not blinded to the results of FPG at FPV and OGTT. Their clinical characteristics were acquired by questionnaires, such as age, parity, pre-pregnancy BMI, history of GDM and polycystic ovary syndrome (PCOS), family history of diabetes (defined as first degree relative with diabetes mellitus), etc. All participants signed informed consent before enrollment, and the institutional review board of National Taiwan University Hospital reviewed and approved the study protocol.

Statistical analysis

Categorical variables were shown as numbers (percentages), and continuous variables with normal distribution were presented as means ± standard deviation (SD). Statistical significance between the GDM and non-GDM groups were analyzed by Student's t test and chi-squared test according to the nature of variables. Logistic regression models were used to estimate the odds ratios of GDM for various risk factors. Variables associated with GDM in univariate logistic regression analyses were included in the multiple logistic regression analysis, including age, FPG at FPV, HbA1c, family history of DM, and pre-pregnancy BMI. Then, two algorithms were constructed by the variables independently associated with GDM in the multivariate logistic regression model, including age and FPG at FPV. One algorithm used FPG at the first prenatal visits alone, and the other included both age and FPG at the first prenatal visits. To search for the optimal cutoffs to identify those who do not need further OGTT in the second trimester, we calculated the performance of each algorithm. The percentage of women requiring OGTT (OGTT%) was calculated as follows: (the number of women whose FPG at FPV<92 mg/dl and FPG at the FPV or age plus FPG at the FPV ≥ the cutoff value) / (the number of whole population). In both algorithms, pregnant women were diagnosed as GDM either by FPG at the FPV≥92 mg/dL or the results of OGTT. Therefore, the false positive rate (FPR) was 0% for both algorithms. By definition, specificity equals to 1 –FPR, which means the specificity in all algorithms should ideally be 100%, no matter which cutoffs were chosen. Therefore, the determination of the optimal cutoffs was a trade-off between sensitivity and OGTT%. The optimal cutoff was chosen when the sensitivity was greater than 90% and the OGTT% was the lowest. We then simulated the relationship between the percentage of pregnant women older than 35 years and OGTT% according to these two algorithms. For every cutoff to exclude GDM, the prevalence of GDM, OGTT% and sensitivity values in women younger than 35 years (prevalence_<35y/o, OGTT%_<35y/o, and sensitivity_<35y/o) and in women older than 35 years (prevalence_≥35y/o, OGTT%_≥35y/o, and sensitivity_≥35y/o) were calculated separately. The OGTT% and sensitivity in the whole population were calculated assuming the percentage of women older than 35 years (percentage_≥35y/o) were 0%, 10%, 20%, etc., to 100%. For each percentage_≥35y/o, OGTT% in the whole population was calculated as follows: (percentage_≥35y/o * OGTT%_≥35y/o + (1—percentage_≥35y/o) * OGTT%_<35y/o). For each percentage_≥35y/o, sensitivity in the whole population was calculated as follows: (percentage_≥35y/o * prevalence_≥35y/o * sensitivity_≥35y/o + (1—percentage_≥35y/o) * prevalence_<35y/o * sensitivity_<35y/o) / (percentage_≥35y/o * prevalence_≥35y/o + (1—percentage_≥35y/o) * prevalence_<35y/o). For each percentage of women older than 35 years, the optimal cutoff was determined when the sensitivity was above 90% and the OGTT% was the lowest according to the algorithm. A two-tailed p-value below 0.05 was considered significant. Stata/SE 14.0 for Windows (StataCorp LP, College Station, TX) was used for statistical analyses.

Result

The training cohort and the validation cohort

A total of 1065 women who had their FPV between January 2013 and June 2018 were enrolled consecutively in the training cohort and another 151 women were included in the validation cohort from January 2019 to December 2019. Of the 1065 women who agreed to join our research, 55 women were excluded because of twin pregnancies, history of GDM, or pre-existing diabetes. We also excluded 419 women who were recruited at the second trimester and their FPG at the FPV could not be obtained. Another 79 women were excluded because they received OGTT in other clinic or hospital and the data were not available. As a result, 512 women were included in our study. A flowchart of the study population is shown in Fig 1. Besides, another 151 women were included from January 2019 to December 2019 as the validation cohort. All the study subjects in the training and validation cohorts were Chinese Han. In S1 Table, clinical characteristics between the 512 women with FPG and HbA1c at FPV and the 419 women without FPG and HbA1c at FPV were compared. There was no significant difference in clinical characteristics between these two groups.

Table 1 displays the clinical and obstetric characteristics of the women with GDM and the women without GDM in the training cohort and validation cohort. In the training cohort, the incidence of GDM was 14.6%. The mean gestational age of women at FPV was 10 weeks. In the training cohort, the women were older and the numbers of women ≥35 years were significantly higher in the GDM group than that in the non-GDM group. The women with GDM in the training cohort and in the validation cohort had higher BW, BMI, and higher levels of FPV and HbA1c. Besides, we have compared clinical characteristics in women with or without GDM between the training cohort and validation cohort. Women without GDM in the validation cohort had a slightly higher FPG and slightly lower HbA1c than women without GDM in the training cohort. In the women with GDM, 1-hour plasma glucose during OGTT at 24–28 gestational weeks were lower in the validation cohort than that in the training cohort.

Table 1. Clinical characteristics and laboratory test results in pregnant women with and without Gestational Diabetes Mellitus (GDM) in the training and validation cohort.

	The training cohort			The validation cohort
Baseline characteristics	Non-GDM	GDM	P value	Non-GDM	GDM	P value
	N = 437 (85.4%)	N = 75 (14.6%)		N = 125 (80.2%)	N = 26 (17.2%)
Age (years)	33.5 (4.1)	35.1 (4.1)	0.002	33.3 (3.6)	33.8 (2.9)	0.55
Age≥35 (N, %)	190 (43.5%)	42 (56%)	0.04	46 (36.8%)	11 (42.3%)	0.32
Nulliparous (N, %)	142 (32.5%)	26 (34.7%)	0.68	46 (36.8%)	13 (50%)	0.48
Gestational age at the FPV (weeks)	10.1 (1.9)	10.3 (2.2)	0.81	10 (1.5)	9.9 (1.5)	0.58
Family history of DM (N, %)	96 (22%)	26 (34.7%)	0.02	21 (16.8%)	5 (19.2%)	0.81
History of PCOS	16 (3.7%)	3 (4%)	0.89	7 (5.6%)	3 (11.5%)	0.27
History of macrosomia	2 (0.5%)	2 (2.7%)	0.05	0	0	-
Pre-pregnancy BW (kg)	55.3 (8.4)	57.8 (14)	0.03	55.2 (8.1)	60.2 (8.5)	0.005
Pre-pregnancy BMI (kg/m2)	21.9 (3.3)	22.9 (5.3)	0.02	21.9 (3.2)	23.7 (3.5)	0.02
GWG at 24–28 gestational weeks (kg)	6.3 (3.7)	6.3 (2.7)	0.4	6.1 (3.2)	6.2 (2.3)	0.92
Laboratory test results at the first prenatal visit
FPG (mg/dL)	81.1 (5)	88 (7.4)	<0.001	82.8 (4.7)^*	90.9 (7.2)	<0.001
HbA1c (%)	5.2 (0.2)	5.4 (0.3)	<0.001	5.1 (0.3)^*	5.3 (0.3)	<0.001
Glucose level during OGTT at 24–28 gestational weeks
FPG during OGTT (mg/dL)	77.9 (5)	82.3 (6.9)	<0.001	78.8 (5)	85.2 (5.4)	<0.001
1hPG (mg/dL)	128.2 (24.3)	164.7 (29.5)	<0.001	127.6 (27.3)	149 (34.3)^†	<0.001
2hPG (mg/dL)	109.9 (19.9)	150.2 (29.4)	<0.001	108.6 (21.3)	135.6 (39.7)	<0.001

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Mean (standard deviations) or N (%) were shown.

BMI, body mass index; BW, body weight; DM, diabetes mellitus; FPG, fasting plasma glucose; FPV, first prenatal visit; GDM, gestational diabetes mellitus; GWG, gestational weight gain; HbA1c, hemoglobin A1c; OGTT, oral glucose tolerance tests; FPG during OGTT, fasting plasma glucose during oral glucose tolerance tests; PCOS, Polycystic ovary syndrome; 1hPG, 1-hour plasma glucose during oral glucose tolerance tests; 2hPG, 2-hour plasma glucose during oral glucose tolerance tests.

* p<0.05 vs. women without GDM in the training cohort.

† p<0.05 vs. women with GDM in the training cohort.

The incidence of GDM by age

As shown in Fig 2, the incidence of GDM increased with age. The incidence of GDM was 8.9% for the age group below 30 years, 15.4% for the age group 30–34 years, 20.2% for the age group 35–39 years, and 34.8% for the age group above 40 years. In addition, the level of FPG at the FPV also increased slightly with age (S1 Fig). The FPG at the FPV was also significantly higher in women with old age.

The development of the screening algorithms for GDM

Age and FPG at the FPV were the independent predictors of GDM in multivariate logistic model (S2 Table). Since the odds ratios and regression coefficients for age and FPG were similar, "age plus the FPG" was used to develop a screening algorithm for GDM. Two screening algorithms were constructed in women with FPG < 92 mg/dl, which used “FPG at the FPV” and “age plus FPG at the FPV” to exclude GDM. As for the optimal cutoffs, the performances of different cutoffs for “FPG at the FPV” and "age plus FPG at the FPV" in excluding GDM were calculated. In Table 2, as the cutoff increased, both the use of OGTT (OGTT%) and sensitivity decreased. Therefore, we chose cutoffs with a sensitivity ≥90% as the optimal cutoffs. The optimal cutoff value for FPG at the FPV in the "FPG at the FPV" algorithm was 79 mg/dl. Besides, the optimal cutoff value for age plus FPG at the FPV in the “age plus FPG at the FPV” algorithm was 114. By the cutoff of 114, if a 30-year-old woman whose FPG at the FPV was greater than 84 mg/dl, she should receive OGTT by the "age plus FPG at the FPV" algorithm, since her age plus FPG at FPV exceeded 114. Similarly, if a 35-year-old woman whose FPG at the FPV was greater than 79 mg/dl, then she should receive OGTT.

Table 2. Performance for different cutoffs to screen Gestational Diabetes Mellitus (GDM) by the “FPG at the FPV” algorithm and "age plus FPG at the FPV" algorithm.

FPG at the FPV				Age plus FPG at the FPV
Cutoffs to exclude GDM	OGTT (%)	Sensitivity (%)	Specificity (%)	Cutoffs to exclude GDM	OGTT (%)	Sensitivity (%)	Specificity (%)
77	78.1%	96%	100%	112	66.2%	93.3%	100%
78	72.9%	96%	100%	113	62.1%	90.7%	100%
79	68.8%	92%	100%	114	58.3%	90.7%	100%
80	63.8%	89.3%	100%	115	53.5%	89.3%	100%

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FPG, fasting plasma glucose; FPV, first prenatal visit; GDM, gestational diabetes mellitus; OGTT, oral glucose tolerance test.

The flowchart of the algorithms to screen GDM were shown in Fig 3. In both algorithms, 29 pregnant women (5.7%) were diagnosed with GDM at FPV because their FPG was greater than or equal to 92 mg/dL. In algorithm of “FPG at the FPV” shown in Fig 3(A), if FPG at the FPV was below 79 mg/dL, GDM was excluded, and no OGTT was needed. If FPG was between 79 and 91 mg/dl, OGTT was recommended to confirm the diagnosis of GDM. The other "age plus FPG at the FPV" algorithm was shown in Fig 3(B). Among women with FPG <92 mg/dl, if “age plus FPG at the FPV” was below 114, GDM was excluded; if “age plus FPG at the FPV” was greater than or equals to 114, OGTT was suggested. In the training cohort, we found that the percentage of women who required OGTT was reduced to 68.8% by the “FPG at the FPV” algorithm. Compared to the “FPG at the FPV” algorithm, the “age plus FPG at the FPV” algorithm further reduced OGTT% to 58.5%. To better illustrate how the performance of the algorithms were calculated, we have added the numbers of women (N) in both algorithms (S2 Fig). The calculation of the sensitivity and the specificity for "the FPG at the FPV algorithm" with the cutoff at 79 mg/dl were illustrated in the S3 Table. The numbers were derived from S2(A) Fig. In the "FPG at the FPV" algorithms, GDM was diagnosed by FPG at the FPV ≥ 92 mg/dl (N = 29) or the results of OGTT when FPG at FPV ≥79 mg/dl (N = 40). FPG at the FPV ≥92 mg/dl and the results of OGTT are both diagnostic criteria of GDM by the IADPSG. No matter what the cutoff was chosen, the results of OGTT could correctly diagnose GDM. Therefore, there was no false positive by the algorithm, and the specificity of the algorithm was 100%. On the other hand, the "FPG at the FPV" algorithm excluded GDM if FPG <79 mg/dl (N = 131) or by the results of OGTT when FPG at FPV ≥79 mg/dl (N = 312). There were 6 women with GDM by the IADPSG criteria who were classified as "GDM excluded" (false negatives). Therefore, the sensitivity was 69/(6+69), which was equal to 92%. Since the results of OGTT could always correctly diagnose GDM by the IADPSG criteria, the false negatives came from women with FPG <79 mg/dl. If the FPG at the FPV cutoff was higher, there would be more false negatives (lower sensitivity) and less OGTT performed. The performance of the "age plus FPG at the FPV" algorithm was calculated similarly. Similarly, the OGTT% was decreased to 73.6% by the “FPG at the FPV” algorithm and 61.5% by the “age plus FPG at the FPV” algorithm in the validation cohort (Table 3).

Table 3. Performance of “FPG at the FPV” algorithm and “age plus FPG at the FPV” algorithm to screen Gestational Diabetes Mellitus (GDM) in the training cohort and in the validation cohort.

Algorithm	Threshold to exclude GDM	Training cohort			Validation cohort
Algorithm	Threshold to exclude GDM	OGTT (%)	Sensitivity (%)	Specificity (%)	OGTT (%)	Sensitivity (%)	Specificity (%)
FPG at the FPV	< 79	68.8	92 (83.4–97)	100	72.8	89 (62.1–96.8)	100
“Age plus FPG at the FPV”	< 114	58.5	90.7 (81.7–96.2)	100	59.6	92.3 (74.9–99.1)	100

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Estimates (95% confidence interval) were shown.

FPG, fasting plasma glucose; FPV, first prenatal visit; GDM, gestational diabetes mellitus; OGTT, oral glucose tolerance tests; FPG is in conventional units (mg/dL).

The impact of age

To evaluate the impact of maternal age on OGTT%, we created several simulated populations with different percentages of pregnant women older than 35 years. As shown in Fig 4, when the percentage of women ≥35 years was between 30% and 60%, the screening algorithm with an “age plus FPG at the FPV” cutoff could reduce OGTT% while maintaining a sensitivity ≥90% and a specificity of 100%, compared with the screening algorithm using FPG at FPV alone.

Discussion

In the present study, we found that the incidence of GDM increased with age. The "FPG at the FPV" algorithm could reduce OGTT% to 68.8% with the FPG cutoff at 79 mg/dl. The "age plus FPG at the FPV" algorithm, with the cutoff of 114, further reduced OGTT% to 58.3%, with the sensitivity of 90.7% (9.3% GDM missed) and the specificity of 100%. These findings were replicated in the validation cohort. Besides, we also shown that when the percentage of women ≥35 years was between 30% and 60%, the screening algorithm with an “age plus FPG at the FPV” cutoff could reduce OGTT%, compared with the screening algorithm using FPG at FPV alone.

Age is an important determinant for insulin resistance and blood glucose levels. In non-pregnant adults, plasma glucose levels increase with age [16]. Several studies have shown that glucose tolerance declines progressively with age [17–24]. Aging-related glucose intolerance is more prominent in the third decade and continues throughout adulthood [25, 26]. However, the association has not been investigated in pregnant women in the literature. In the present study, we observed that FPG at the FPV increased by maternal age, and age was strongly associated with the prevalence of GDM. Our findings suggest that aging may have a similar effect on insulin resistance in pregnant women. Since women tend to get pregnant at older ages in most developed countries, advanced age in pregnancy may have greater impact on the risk of GDM in these countries.

OGTT is the “gold standard” to diagnose GDM. However, it is time-consuming for both patients and laboratories [27]. Therefore, many studies have proposed different screening methods to reduce the need of OGTT, and FPG is the most frequent studied screening tool since its measurement is easy, inexpensive, and reliable. Poomalar et al. have reported that FPG at 22–28 weeks is an effective screening tool for GDM [7]. With the cutoff value of 85 mg/dL, the sensitivity was 88% and the specificity was 95%. In our study, a lower FPG at the FPV cutoff value (79 mg/dL) had the best performance, with a sensitivity of 90% and a specificity of 100%. In our previous report, FPG at 24–28 gestational weeks has been shown to be a useful screening method, and the optimal cutoff value was 73 mg/dL [8]. Since FPG decreases gradually during the first half of pregnancy [28], the difference in the timing to test FPG may explain the different optimal cutoffs found in these two studies. Besides, using FPG at 24–28 weeks as the screening method may delay the diagnosis of GDM; whereas this can be avoided by using FPG at FPV. Furthermore, FPG at FPV can also be used to identify women with undiagnosed pre-pregnancy DM, as recommended by the IADPSG [1].

Our study suggests a potential way to predict GDM at the time of the first prenatal visit. We found that age and FPG at the FPV were the independent predictors for the development of GDM, which was supported by a report in the literature [29]. However, there is another study which evaluated the performance of FPG at FPV to predict GDM, and performance was not as good as our results [28]. There are some differences between that study and the present study. First, the diagnostic criteria of GDM were different. In their study, GDM was diagnosed when any one of the following value was met or exceeded during the 75g OGTT at 24–28 weeks, including fasting plasma glucose ≥92 mg/dL, 1-hour plasma glucose ≥180 mg/dL or 2-hour plasma glucose, ≥153 mg/dL. FPG at FPV ≥92 mg/dL was not a criterion to diagnose GDM. In our study, GDM was diagnosed according to the IADPSG criteria, which means that women with FPG at FPV ≥92 mg/dL, or data from OGTT exceeds the above-mentioned cutoffs were diagnosed GDM. As a result, the false positive rate was 0% in our algorithm, no matter which cutoffs were chosen. By definition, specificity equals to 1 –false positive rate. Therefore, the specificity in the algorithm is always 100%. In addition, although the mean age of the cohort in that paper was not described, we believe that it would not be high, since women often get pregnant at relatively young ages in China. In contrast, the mean age in our cohort was 33.7 years. All these differences may lead to the different results in these two studies. In addition, the NICE guidelines do not recommend the use of FPG to assess risk of developing GDM [30]. However, the NICE guidelines suggest a different criteria to diagnose GDM, ie. fasting plasma glucose greater than 100 mg/dl or 2-hour plasma glucose greater than 140 mg/dl. Therefore, the difference in the diagnostic criteria may contribute to the discrepancies between the recommendation from the NICE guidelines and the present study.

The strength of this study is that the algorithms used to screen GDM proposed in the study are simple, practical, and can be used clinically. The study was able to not only compare the predictive ability of age plus FPG at the FPV for GDM diagnosis but also to describe the potential impact for implementation of the algorithm in clinical practice. Third, a simpler model with fewer variables would facilitate its implementation in daily practice, especially in under-resourced and overcrowded settings. “Age plus FPG at the FPV” showed good test characteristics and could thus serve as a screening option to decrease resource and time waste due to the excessive use of OGTTs. Furthermore, the mean gestational age of women at FPV in our study is 10 weeks. Identifying first-trimester biomarkers could serve both to diminish the need for provocative testing in all pregnant women and allow for early intervention to improve outcomes or prevent GDM. However, the study has several limitations. Firstly, all the study subjects were Han Chinese. Studies on other ethnic groups should be done to see if the findings could be generalized. Secondly, pregnant women with FPG at FPV ≥ 92 mg/dl and <126 mg/dl were diagnosed as GDM according to the IADPSG guidelines, in addition to the criteria by the results of OGTT at 24-28th gestational weeks. Since the diagnostic criteria for GDM varied in different countries [31], we have to emphasize that the conclusions of the present study are only applicable when GDM is diagnosed by the IADPSG criteria, which requires that all women have a fasting glucose test early in pregnancy. This may be difficult logistically and unpleasant for women. Besides, there were some small differences, such as FPG at the FPV, HbA1c, and 1-hour plasma glucose during OGTT, between women with or without GDM in the training cohort and in the validation cohort. Although the differences between these groups were small, those differences still may lead to the different performance. It should be noted when interpreting the result. Thirdly, the sample size is relatively small. Further studies with larger sample size are needed to verify the findings in this study.

In conclusion, FPG at the FPV and the prevalence of GDM increase by age. By using maternal age plus FPG at the FPV, we suggest a way to diagnose GDM with reduced number of people taking OGTT. This may be especially useful in populations with a significant proportion of women getting pregnant until older ages. Our results provide an alternative screening strategy to previously published algorithms using the combination of various risk factors of GDM, such as history of GDM, family history of diabetes, ethnicity, parity, and BMI [32, 33].

Supporting information

S1 Table. Clinical characteristics and laboratory test results at the first prenatal visit and OGTT result at 24–28 gestational weeks in pregnancy women with and without FPG and HbA1c at the first prenatal visit.

(DOCX)

Click here for additional data file.^{(18.4KB, docx)}

S2 Table. The relationship between clinical characteristics at the first prenatal visit and of Gestational Diabetes Mellitus (GDM) at early pregnancy.

(DOCX)

Click here for additional data file.^{(16.8KB, docx)}

S3 Table. The 2x2 table illustrating the calculation of the sensitivity and the specificity for "the FPG at the FPV algorithm" with the cutoff at 79 mg/dl.

(DOCX)

Click here for additional data file.^{(17.7KB, docx)}

S1 Fig. Fasting Plasma Glucose (FPG) at the first prenatal visit by age group.

(DOCX)

Click here for additional data file.^{(37.7KB, docx)}

S2 Fig

Algorithms to screen gestational diabetes mellitus (GDM) by (A) fasting plasma glucose (FPG) at the first prenatal visit (FPV) and (B) age plus FPG at the FPV.

(DOCX)

Click here for additional data file.^{(204.7KB, docx)}

Acknowledgments

The authors would like to thank Mr. Chin-Mao Huang of National Taiwan University Hospital, and the staff of the eighth Core Lab, Department of Medical Research, National Taiwan University Hospital, Taipei, Taiwan, for technical and computing assistance.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This work is supported in part by a grant (MOST 103-2314-B-002-157-MY2) from the Ministry of Science and Technology, Taiwan, and a grant (NTUH.105-S3192) from National Taiwan University Hospital, Taiwan.

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PLoS One. doi: 10.1371/journal.pone.0237224.r001

Decision Letter 0

Clive J Petry

4 Feb 2020

PONE-D-20-00081

Simplifying the gestational diabetes screen by maternal age plus fasting plasma glucose at first prenatal visit: a prospective cohort study

PLOS ONE

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Reviewer #1: The authors present a prospective cohort study examining the relationship between age and fasting plasma glucose

for the diagnosis of gestational diabetes. The paper looks interesting, but needs major revisions in all chapters. In particular, the presentation must be changed and made clearer. I have the following comments and suggestions for revision:

Introduction

Page 5 Line 54

“The international diabetes study groups, including the International Association of

Diabetes and Pregnancy Study groups (IADPSG) and the American Diabetes Association

(ADA) have adopted the 75-g oral glucose tolerance test (OGTT) at 24–28 weeks of gestation

as a screening test for gestational diabetes mellitus (GDM).”

If you mention ADA and IADPSG, the correct reference must be reported both in the text and in the bibliography. In the references IADPSG has been reported with number 12.

Page 5 line 60

“However, these FPG-based screening methods have

generally been targeted at 24–28 weeks of gestation instead of early pregnancy, and the use of

an FPG-based method at 24-28 weeks essentially requires two steps to diagnose or exclude

DM.”

Please report the refereneces for this sentence.

Page 5 line 64

“On the other hand, FPG is nowadays routinely measured during early pregnancy to detect

pre-existing diabetes according to the IADPSG and ADA (4) diagnostic protocols.”

Please check the references and position them at the end of the sentence.

Page 5 line 69

“As such, the FPG level

at the FPV, which is easy to obtain, may be a good predictor to be used in screening GDM.”

Please report the references for this sentence.

Page 6 line 70

“Aging is thought to be one of the most important factors affecting the pathogenic

mechanisms associated with diabetes (6). However, despite the widely reported fact that

increasing age increases the prevalence of diabetes, information regarding the relationship

between age and GDM is reported relatively rarely. Furthermore, advanced maternal age is

also a growing trend. In most developed countries, women get pregnant at older ages (7-10).

Therefore, maternal age may be another important factor to be considered in GDM screening,

especially when women become pregnant at an older age.”

This paragraph does not seem to be written in correct English, please review the English grammar and the connection between the various sentences.

Research design and methods

Page 7

It is unclear how women with a blood glucose value ≥ 92 mg/dl are managed in the first trimester. OGTT ha salso been performed in these women?

According to the IADPSG guidelines, if the value is ≥ 92 and <126 mg/dl a diagnosis of gestational diabetes must be made and treated for this complication. What treatment were they subjected to? Did they perform OGTT even if in therapy?

If OGTT has been performed on women already on therapy this could be an important bias in your study.

Page 7 line 100

“Maternal blood samples were collected after 8–10 hours of overnight fasting

during and were used to determine HbA1c and FPG.”

In which trimester was this maternal blood sampesl performed? This sentence is unclear ans seems to be redundant. Please clarify.

Results

Page 8 – Page 14

Personally I find impossible to interpret the results as interesting as they seem. Text, tables and captions have been put together. Please rephrase the results completely and put them in an understandable way and following the submission guidelines.

Page 8 line 119

“A total of 547 patients were enrolled in the present study. A flowchart of the study

120 population is shown in S1 Figure.”

It is not clear from the description how the cohort was selected from the general population that affer to your Institute. I assume that more than 1065 women have been visited between January 2013 and June 2018. Was this a convenience sample? Were women recruited on certain days? This needs to be clarified as this a source of unrecognized bias that would need to be highlighted in the limitations.

Page 9 line 124

Table 1

Usually the tables must be presented in an attachment to the paper and not within the text. Please check the submission methods for this journal. Also personally I find the "All" column useless

Page 11 line 137

“As shown in Figure 1, the prevalence of GDM increased with age. 138

Fig 1. Prevalence of gestational diabetes mellitus (GDM) by age group. * p for trend

<0.001.”

This sentence at this point in the text has no meaning.

Page 9

In my opinion, some important factors are missing in interpreting the results: ethnicity, weight gain on the first visit and weight gain at the time of the OGTT.

If the weight gained during pregnancy is not available, please specify it.

Is it not possible that the women with increased fasting plasma glucose in 1st trimester had a superior weight gain compared to the other groups? Also, adding GWG to the multivariate model has clinical plausibility.

Also specify whether the women were of different or equal ethnic groups. This factor influenced the possibility of developing gestational diabetes.

Table S3

It is not personally clear whether a univariate or multivariate analysis was performed. These are very important data for your work and I would try to express them more clearly.

Discussion

Page 15 line 182

“Such studies have shown that plasma

glucose levels progressively increase with age.”

Please report references.

Page 17 line 221

“Our study demonstrates that GDM can be accurately predicted in early pregnancy based

on simple maternal clinical parameters available at the time of the first visit blood screening.”

The hypothesis of your study is that a first trimester screening for gestational diabetes based on risk factors could be performed.

This statement on a study carried out on 547 women, of whom only 98 diagnosed with gestational diabetes, can accurately predict gestational diabetes, seems very strong to me.

Pag 17 line 249

However, the limitations of this study is

that all the study subjects were Han Chinese, studies on other ethnic groups should be done to

see if the findings could be generalized.

Are there no weaknesses to the study? Please add.

Page 18 line

“In conclusion, FPG at the FPV and the prevalence of GDM increase by age. The

screening algorithm using maternal age plus FPG at the FPV can greatly simplify the

IADPSG diagnostic algorithm and reduce the use of the OGTT, especially in populations with

a significant proportion of women who become pregnant at older ages. The optimal cutoff

value for age plus FPG at the FPV is 115.”

Sea above, this conclusion is too strong for such a reduced study cohort. Furthermore in literature other methods are described to implement diabetes screening already in the first trimester as algorithms, maternal characteristics, ultrasound evaluation of maternal adipose tissue, etc… personally I would quote some.

Reviewer #2: The authors present a cohort study aimed at devising a strategy to decrease the need for OGTT at 24-28 weeks gestation based on the combination of maternal age and fasting plasma glucose (FPG) at the first prenatal visit (FPV). This is a subject that has practical clinical applicability and could be useful in clinical settings if proven safe and effective. Many studies have attempted to create predictive models in early pregnancy to predict mid trimester GDM. Most are complex and/or have poor sensitivity, specificity or both. My comments regarding the current study can be found below. The paper will benefit from additional grammatical revision

1. I don’t understand the subtitle: “Early screen of gestational diabetes: role of maternal age” appears to be redundant or inserted by mistake.

Abstract:

2. As below – FPG at FPV is not universally performed or recommended

3. 547 were not consecutively recruited as > 1000 were eligible and approached

Introduction

4. line 54-57: Inaccurate - ADA has allowed the OPTION of one step 75 gram OGTT but also allow the wo step 50 g GCT/100 gram GTT

5. FPG at first visit is NOT universally recommended by ADA – only in those with risk factors. Even in those the recommendation is to test using “standard criteria”

6. Line 64 – 66 again, FPG not universally performed at FPV or first trimester (likely the same)

7. Line 67-69 – sentence contradicts itself if “poorly predictive” of GDM in early pregnancy why would FPG at the FPV (which is also early pregnancy) be a “good predictor” ?

8. Line 70: Really? Age is more independently important than obesity, family history previous GDM etc?

Methods

9. If I understand correctly, women were recruited at FPV and were eligible if they were singleton, viable and had no known preexisting DM. Then they had FPG and A1C. If they had PDM after these tests they were excluded. All that remained had a OGTT at 24-28 weeks. This should be clarified and shown in a flow diagram with numbers as Fig 1 (not as supplementary material)

10. From figure S1 – I note that nearly half of the base population did not have FPG and A1C. In line 92 you say that “ALL pregnant women were done with FPG and hemoglobin A1c (HbA1c)…” Please clarify this. Was in fact FPG and A1C a standardized protocol at the time of the study? If so why did > 400 women not have this done. Were they perhaps different clinically than those that did? Were all 1065 approached and consented if singleton and no PDM? If so – did the 419 without FPG and A1C not consent? This is all confusing and needs to be clarified.

11. Was there a gestational age criteria for the FPV? If someone had a FPV at 20 weeks was she included? Please clarify

12. When defining BMI was prepregnancy BMI used? Early pregnancy?

13. Were the FPG and A1C results available to the clinicians? What was done if at FPV the fPG was > 5.1 or A1C as 5.9-6.4?

Results

14. Was gestational weight gain collected and if not – why?

15. Why is FPG of >91 at FPV (10 weeks mean) diagnostic of GDM (Fig 2)?? This is not standard. The IADPSG values are extrapolated from HAPO which used GTT values at 24-28 weeks. There are no outcome based validated thresholds for early GTT. Were these women treated (see comment 11)

16. Line 142-143: In addition to age, previous GDM was also independently associated with GDM OR 8.14. This is not mentioned. Also what about the effect of BMI? It was significant on the univariate analysis and it is mentioned in the legend (and in line 232 of the discussion) but not in the body of the table. What happened to this variable?

Discussion

17. Discussion overall VERY repetitive.

18. Line 227 – provide references for evidence that early identification of “GDM” leads to better outcomes

19. These results are not consistent with the study by Zhu et al DIABETES CARE, VOLUME 36, MARCH 2013. In this study even if FPG at FPV as < 4.1 8.1% had a subsequent diagnosis of GDM at 24-28 weeks. In addition they were able to show that overall – there was poor correlation between the FPG at FPV and the FPG at the OGTT. 100% specificity was achieved only > 5.7 mmol (100.8) and with corresponding low sensitivity. Please explain the difference between this (large) study’s results and yours. In addition discuss why FPG performs favorably as a screening tool in this study in contrast with what is known regarding the poor performance of early FPG as a screening tool for future GDM (See summary of evidence in NICE guidelines).

20. Missing references for other predictive models including recent: Zheng, T., Ye, W., Wang, X. et al. A simple model to predict risk of gestational diabetes mellitus from 8 to 20 weeks of gestation in Chinese women. BMC Pregnancy Childbirth 19, 252 (2019). https://doi.org/10.1186/s12884-019-2374-8.

21. Missing many of the other limitations:

a. The “missing” 419 women that did not have FPG and A1C at FPV. If you can show that their characteristics are not different from those that had testing then that will provide some support for the validity of the results

b. The lack of clinician blinding

c. Unlike the authors previous study – there is no validation cohort

d. No reporting of gestational weight gain

e. No reporting of perinatal outcomes. What are the outcomes for those missed by this strategy (90% sensitivity compared with 100% sensitivity with universal OGTT)

22. The conclusion is way too conclusive. The most you can say is that these data suggest an alternative method for early screening for GDM but needs to be prospectively validated in multiple ethnic cohorts and with appropriate reporting of perinatal outcomes. The authors should consider publishing only after external validation of their findings

Reviewer #3: The paper handles the intriguing matter of how to rationalise screening for GDM- increasing incidence of GDM due to demographic changes and diagnostic thresholds have led to increased burden on health care systems. This paper investigates a method of using risk-factor based screening, tailored by early pregnancy fasting glucose, to increase the diagnostic performance of the mid trimester OGTT, without too great an impact on missed GDM diagnoses.

Abstract

The abstract needs to state whether all women underwent fasting glucose screening (universal or risk factor based). The abstract also needs to describe what the OGTT eligibility criteria were (not including maternal age and fasting glucose)- this could be a brief statement just mentioning risk factor based or universal. It appears that 62% of pregnant women were OGTT screened at baseline: was this because certain thresholds of BMI or family history were used to determine eligibility for OGTT? Or did a large group of women decline screening by OGTT? The abstract needs to state which threshold was used for fasting glucose, and how this was determined, and which age threshold was applied- generally adding a little more numerical data on the paper’s findings. Also, please add the number of diagnoses of GDM missed when the number of OGTTs is reduced by the use of maternal age, and fasting glucose.

Introduction

Needs to specify what the current recommendations are of guidelines (e.g. ADA, WHO) regarding universal or risk-factor based screening or prognostic model guided screening. To my knowledge, the guidelines to not specify a preference-this would be good to point out in the introduction.

Using prognostic models is not a new approach.

Methods

Could benefit from specifying which variables were eligible for the prognostic model; did BMI or family history or obstetric history feature in the multivariable logistic model?

Also, needs to state that pregnant women who qualified for GDM diagnosis after just a fasting glucose, were not eligible to continue in the study.

Was PCOS diagnosis available in this study, or was history of macrosomia available? These are additional risk factors that are sometimes used to specify eligibility for OGTT.

The comment ‘the specificity for all models was 100%, regardless of the threshold used’ is likely due to the way the model was established. Specificity is the probability that, given that someone has no GDM, the test would indicate that she had no GDM: given that the authors were looking at the effect of reducing the number of OGTTs performed, they are not going to find false positives (so lower specificity). This ‘self-fulfilling prophecy’ could be more clearly explained in the last paragraph of the methods’.

It is unclear why the authors have focussed on only age and fasting glucose in their attempt to reduce the number of OGTTs, when more data is available. This needs to be elaborated.

Results

Given that almost half of women were excluded, it would be good to present a table with baseline characteristics of those with and without OGTT data available (so not twins or those with suspected pre-conception DM2), to establish generalisability of this study’s findings.

Figure 1 states it reports the prevalence, when I think the authors mean incidence. Please amend.

S2 Figure states that the ‘p for trend’ is starred, but does not specify for which comparison the star is presented: is this the trend across all ages, or the difference between highest and lowest age category.

Figure 2 presents the number of OGTTs in populations of varying age distributions. The models used to create this figure are not described in the methods, and must be added. Presumably, there would be some uncertainty (SD or 95% confidence interval) which could be better demonstrated in the figure.

In line 158, at the end of the results, the actual age threshold used need to be added to the text. Did the authors toggle the age threshold, as they did for fasting glucose?

The last sentence of the results (line 159-160) now reads: the addition of FPG could reduce OGTT from 62 to 52%, this is strictly speaking not correct. The reduction from the current policy describe in the methods, is 100% OGTT to 52% OGTT screening when maternal age>=35 years and FPG> =80 are applied as eligibility criteria.

The methods need to show whether women with a history of GDM were also excluded from OGTT screening if they were neither past 35 or had fasting glucose <80. I am assuming this is the case, but due to the rather large impact on chance of GDM, this needs to be explicitly stated.

Minor comments

Generally, the English is fine, although it could be improved by English language editing. Examples include (Introduction) ‘…higher first-trimester FPG levels in early pregnancy is actually poorly….’ Which should be ‘…are actually poorly…’;

and (line 92 methods) ‘….All pregnant women were done with FPG…’ Which should be ‘…all pregnant women underwent FPG …’

and (line 102 methods) ‘… Maternal blood samples without fasting were excluded…’ which should be ‘… we excluded maternal blood samples of participants who had not fasted before the blood sample was drawn…’

Supporting files flow chart ‘preformed’ which should be ‘performed’

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Rebecca C. Painter

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PLoS One. 2020 Aug 20;15(8):e0237224. doi: 10.1371/journal.pone.0237224.r002

Author response to Decision Letter 0

19 Apr 2020

Please referred to the file "Response to Reviewers".

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PLoS One. doi: 10.1371/journal.pone.0237224.r003

Decision Letter 1

Clive J Petry

6 May 2020

PONE-D-20-00081R1

Simplifying the screening of gestational diabetes by maternal age plus fasting plasma glucose at first prenatal visit: a prospective cohort study

PLOS ONE

Dear Dr. Lin,

==============================

I agree with the reviewers that the various revisions made have improved the manuscript a lot. However there is a disagreement between the reviewers, both of whom also reviewed the original manuscript, over the merits of the revised version. Reviewer #1 is happy with the manuscript, which is a valid point. I feel that the comments of reviewer #2 also have merit, and therefore that a further round of revision needs to be performed. It would be wrong to accept the manuscript for publication when these issues have been raised. I think that points 2 and 5 of reviewer #2's comments need to be dealt with particularly robustly in a second revision, the other points should be easier to address. I have no additional comments and look forward to reading the second revision of the manuscript.

==============================

We would appreciate receiving your revised manuscript by Jun 20 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

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Clive J Petry, PhD

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: No

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Reviewer #1: Thank you for having clearly answered all the questions and for the changes made. No further corrections are required.

Reviewer #2: I would like to thank the authors for the thoughtful revisions. I still have significant reservations regarding the data presented with regards to specificity and sensitivity of the models (see comment 5) General: Some additional grammatic editorial work is needed, especially in the parts that were revised.

1. Introduction line 75 : should be …FPG level is associated

2. From the new manuscript I now see that only those that had FPG at FPV of < 92 had a 24-28 week 75 gram OGTT. I thus assume that those with FPG > 91 were treated as GDM. Please clarify. If these women were in fact treated as GDM, this makes it very difficult to calculate the performance of age+FPG at FPV in diagnosing later GDM. In order to do this, FPG at FPV would be drawn in a blinded fashion (unless overt diabetes was found) and all would have an unblinded 75 gram OGTT at 24-28 weeks. Thus any conclusions from this study are only applicable to women with FPG < 92 at booking. This needs to be stated in the limitations as many countries do not view a FPG of >91 at FPV as synonymous with a diagnosis of GDM.

3. Methods Line 108: What does it mean the clinicians were “blinded to the results of the statistical analysis” The analysis was done retrospectively after the cohorts were formed so this would not be relevant. What is relevant, is to indicate that clinicians were not blinded to the FPG at FPV

4. Methods line 125 “One algorithm made use FPG…” revise the sentence

5. Methods lines 131 – 134, Discussion 304-306, Table 2 and Figure 3. I need to disagree with the statement that specificity was 100%. If one is doing a FPG and FPV (diagnosing GDM at FPV on all above arbitrary threshold) and then a GTT (diagnostic test) in all the remaining, then one is NOT doing screening but rather universal diagnostic testing with, by design, 100% sensitivity and 100% specificity in the entire cohort. Thus, do to the study design, the specificity was 100% in the cohort but this would not be true for the models using different thresholds for FPG at FPV. In your models I believe you are actually attempting to answer the question: how does FPV FPG below the diagnostic threshold perform as a screening test for 24-28 week GDM compared with FPG+Age at FPV? Based on Figure 3 the specificity is definitely not 100%. In both algorithms 59.9% and 49.4% that go on to have the OGTT based on the FPG or FPG+age cutoffs respectively, are found NOT to have GDM and thus are FALSE POSITIVES for the FPG screening test and the outcome of GDM. From the figure – there is no information on the false negatives i.e those below the threshold (FPG or FPG+Age) that were diagnosed with GDM. We know that number is not 0 as in the results (Table 2) the sensitivity is < 100%. One can design the algorithms to perform at a prespecified sensitivity or specificity (even a theoretical 100%) but always (unless you have found the perfect screening test when compared to the gold standard), high specificity will be at the expense of sensitivity and vice versa. Neither the false negatives or the false positives are correctly indicated in the figure or in the text and thus the interpretation of this study appears problematic. I think that in the algorithms the authors need to reflect on their assumptions regarding specificity and sensitivity or at least recheck the numbers. Once this is sorted out – reporting a ROC curve and the PPV and NPV would be useful.

6. Methods – line 140-143: language is unclear

7. Results Line 151-155 – you are repeating methodology – should not be in the results

8. Table 1: Ideally there would be a comparison of the characteristics of the two cohorts to demonstrate that over time there was no change in population characteristics

9. Results will be easier to follow if arranged with subheadings

10. Table 4. The outcome analysis. The comparison should not be between outcomes in women with and without GDM (as obviously the rate will be higher in GDM) but rather between rate of outcomes in each cohort compared with gold standard (IADPSG). Due to the way this study was designed the results are not representative of true perinatal risk as NO GDMs were undiagnosed and untreated, which is what would happen if using a first trimester screening test with < 100% sensitivity. Based on this this table can likely be omitted and replaced with a statement that “ The impact of adopting either algorithm needs to be evaluated in future prospective blinded trials”

11. Discussion: Line 328-330. Actually I do not believe that not having GWG at the FPV is significant. There is minimal weight gain in the first 10 weeks of pregnancy thus the impact would be minimal. Adjusting for GWG up until the 24-28 week GTT is relevant

**********

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Reviewer #1: No

Reviewer #2: No

PLoS One. 2020 Aug 20;15(8):e0237224. doi: 10.1371/journal.pone.0237224.r004

Author response to Decision Letter 1

15 Jun 2020

Please refer to the attached files.

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PLoS One. doi: 10.1371/journal.pone.0237224.r005

Decision Letter 2

Clive J Petry

23 Jun 2020

PONE-D-20-00081R2

Simplifying the screening of gestational diabetes by maternal age plus fasting plasma glucose at first prenatal visit: a prospective cohort study

PLOS ONE

Dear Dr. Lin,

==============================

The manuscript has improved by the various revisions made. Sorry to have to ask for further revision but the reviewer still has a few very minor points that need addressing (mainly language-related, but an important one regarding the specificity, which is the most important point to deal with).

Having been highlighted by the reviewer I think that there should be one final round of revision. I look forward to seeing a final version that, in particular, addresses the point about specificity.

==============================

Please submit your revised manuscript by Aug 07 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Clive J Petry, PhD

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: No

**********

6. Review Comments to the Author

Reviewer #2: The authors have provided responses to the previous comments and improved the paper. I have a few remaining issues:

After all the revisions, the paper needs additional English language editing (e.g “The pregnant women in the validation cohort was recruited..” “Their clinical characteristics were acquired by questionnaire and recorded such as age, parity...”)

Page 10 line 163 – repeats page 10 line 156-157. Repeat description of validation cohort

Page 16 line 238: TYPO “FPG at the FPG ≥92 mg/dl and the results of OGTT are both…”

Table 1: Title Suggest instead of ” Clinical characteristics and laboratory test results at the first prenatal visit and OGTT result at 24-28 gestational weeks in pregnancy women with and without gestational diabetes mellitus (GDM) in the training cohort and validation cohort” - should be simplified to “Clinical characteristics and laboratory test results in pregnant women with and without gestational diabetes mellitus (GDM) in the training and validation cohort” Also add n=XX under the column headings

From table 1 – you appropriately highlight the small differences in blood sugar results between the validation and derivation cohort. This should be mentioned as a possible limitation to the validation portion of the analysis.

The prevalence of GDM is quite high – overall 14.6%. Does this seem reasonable for a universally tested Taiwanese population? If not – please address.

The authors state in the discussion that “the algorithms used to screen GDM proposed in the study are simple, practical, and can be used clinically”. This is true but it needs to be mentioned that it requires that all women have a fasting glucose test early in pregnancy. This is often difficult logistically and unpleasant for women that are likely experiencing nausea and vomiting. This can be mentioned as a drawback to this strategy.

The discussion would usually start with a summary statement of the major findings. This is not the case here.

Finally, I need to come back to the statement that there are no false positives and thus the specificity is 100%. See 2X2 table for the screening strategy of FPG at FPV using a threshold of 79 in all women with FPG <92 at FPV and using the values from S2 Fig:

SEE UPLOADED DOCUMENT

**********

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Reviewer #2: No

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

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PLoS One. 2020 Aug 20;15(8):e0237224. doi: 10.1371/journal.pone.0237224.r006

Author response to Decision Letter 2

20 Jul 2020

We have revised according to reviewers’ suggestions, point by point. All the questions raised by the reviewers are carefully addressed in the response to reviewers.

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PLoS One. doi: 10.1371/journal.pone.0237224.r007

Decision Letter 3

Clive J Petry

23 Jul 2020

Simplifying the screening of gestational diabetes by maternal age plus fasting plasma glucose at first prenatal visit: a prospective cohort study

PONE-D-20-00081R3

Dear Dr. Lin,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Clive J Petry, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0237224.r008

Acceptance letter

Clive J Petry

6 Aug 2020

PONE-D-20-00081R3

Simplifying the screening of gestational diabetes by maternal age plus fasting plasma glucose at first prenatal visit: a prospective cohort study

Dear Dr. Lin:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

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on behalf of

Dr. Clive J Petry

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PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

(DOCX)

Click here for additional data file.^{(18.4KB, docx)}

S2 Table. The relationship between clinical characteristics at the first prenatal visit and of Gestational Diabetes Mellitus (GDM) at early pregnancy.

(DOCX)

Click here for additional data file.^{(16.8KB, docx)}

S3 Table. The 2x2 table illustrating the calculation of the sensitivity and the specificity for "the FPG at the FPV algorithm" with the cutoff at 79 mg/dl.

(DOCX)

Click here for additional data file.^{(17.7KB, docx)}

S1 Fig. Fasting Plasma Glucose (FPG) at the first prenatal visit by age group.

(DOCX)

Click here for additional data file.^{(37.7KB, docx)}

S2 Fig

Algorithms to screen gestational diabetes mellitus (GDM) by (A) fasting plasma glucose (FPG) at the first prenatal visit (FPV) and (B) age plus FPG at the FPV.

(DOCX)

Click here for additional data file.^{(204.7KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

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Attachment

Submitted filename: Response to Reviewers.docx

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Attachment

Submitted filename: Simplifying the gestational diabetes screen BERGER revision 2.docx

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Attachment

Submitted filename: Response to Reviewers.docx

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Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

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PERMALINK

Simplifying the screening of gestational diabetes by maternal age plus fasting plasma glucose at first prenatal visit: A prospective cohort study

Yi-Yun Tai

Chien-Nan Lee

Chun-Heng Kuo

Ming-Wei Lin

Kuan-Yu Chen

Shin-Yu Lin

Hung-Yuan Li

Roles

Abstract

Aim

Methods

Results

Conclusions

Introduction

Research design and methods

Data and sample collection

Statistical analysis

Result

The training cohort and the validation cohort

Fig 1. The flow chart for the inclusion and exclusion of the study population.

Table 1. Clinical characteristics and laboratory test results in pregnant women with and without Gestational Diabetes Mellitus (GDM) in the training and validation cohort.

The incidence of GDM by age

Fig 2. The incidence of Gestational Diabetes Mellitus (GDM) by age group.

The development of the screening algorithms for GDM

Table 2. Performance for different cutoffs to screen Gestational Diabetes Mellitus (GDM) by the “FPG at the FPV” algorithm and "age plus FPG at the FPV" algorithm.

Fig 3.

Table 3. Performance of “FPG at the FPV” algorithm and “age plus FPG at the FPV” algorithm to screen Gestational Diabetes Mellitus (GDM) in the training cohort and in the validation cohort.

The impact of age

Fig 4. The impact of maternal age on the percentage of OGTT needed (OGTT%) in simulated populations with different percentages of pregnant women older than 35 years.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

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Clive J Petry

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Author response to Decision Letter 0

Decision Letter 1

Clive J Petry

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Author response to Decision Letter 1

Decision Letter 2

Clive J Petry

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Author response to Decision Letter 2

Decision Letter 3

Clive J Petry

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Acceptance letter

Clive J Petry

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Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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