Risks of specific congenital anomalies in offspring of women with diabetes: A systematic review and meta-analysis of population-based studies including over 80 million births

Tie-Ning Zhang; Xin-Mei Huang; Xin-Yi Zhao; Wei Wang; Ri Wen; Shan-Yan Gao

doi:10.1371/journal.pmed.1003900

. 2022 Feb 1;19(2):e1003900. doi: 10.1371/journal.pmed.1003900

Risks of specific congenital anomalies in offspring of women with diabetes: A systematic review and meta-analysis of population-based studies including over 80 million births

Tie-Ning Zhang ^1,^2,³, Xin-Mei Huang ⁴, Xin-Yi Zhao ^1,^2,³, Wei Wang ³, Ri Wen ³, Shan-Yan Gao ^1,^2,^*

Editor: Jenny E Myers⁵

PMCID: PMC8806075 PMID: 35104296

Abstract

Background

Pre-gestational diabetes mellitus (PGDM) has been known to be a risk factor for congenital heart defects (CHDs) for decades. However, the associations between maternal PGDM and gestational diabetes mellitus (GDM) and the risk of specific types of CHDs and congenital anomalies (CAs) in other systems remain under debate. We aimed to investigate type-specific CAs in offspring of women with diabetes and to examine the extent to which types of maternal diabetes are associated with increased risk of CAs in offspring.

Methods and findings

We searched PubMed and Embase from database inception to 15 October 2021 for population-based studies reporting on type-specific CAs in offspring born to women with PGDM (combined type 1 and 2) or GDM, with no limitation on language. Reviewers extracted data for relevant outcomes and performed random effects meta-analyses, subgroup analyses, and multivariable meta-regression. Risk of bias appraisal was performed using the Cochrane Risk of Bias Tool. This study was registered in PROSPERO (CRD42021229217). Primary outcomes were overall CAs and CHDs. Secondary outcomes were type-specific CAs. Overall, 59 population-based studies published from 1990 to 2021 with 80,437,056 participants met the inclusion criteria. Of the participants, 2,407,862 (3.0%) women had PGDM and 2,353,205 (2.9%) women had GDM. The meta-analyses showed increased risks of overall CAs/CHDs in offspring born to women with PGDM (for overall CAs, relative risk [RR] = 1.99, 95% CI 1.82 to 2.17, P < 0.001; for CHDs, RR = 3.46, 95% CI 2.77 to 4.32, P < 0.001) or GDM (for overall CAs, RR = 1.18, 95% CI 1.13 to 1.23, P < 0.001; for CHDs, RR = 1.50, 95% CI 1.38 to 1.64, P < 0.001). The results of the meta-regression analyses showed significant differences in RRs of CAs/CHDs in PGDM versus GDM (all P < 0.001). Of the 23 CA categories, excluding CHD-related categories, in offspring, maternal PGDM was associated with a significantly increased risk of CAs in 21 categories; the corresponding RRs ranged from 1.57 (for hypospadias, 95% CI 1.22 to 2.02) to 18.18 (for holoprosencephaly, 95% CI 4.03 to 82.06). Maternal GDM was associated with a small but significant increase in the risk of CAs in 9 categories; the corresponding RRs ranged from 1.14 (for limb reduction, 95% CI 1.06 to 1.23) to 5.70 (for heterotaxia, 95% CI 1.09 to 29.92). The main limitation of our analysis is that some high significant heterogeneity still persisted in both subgroup and sensitivity analyses.

Conclusions

In this study, we observed an increased rate of CAs in offspring of women with diabetes and noted the differences for PGDM versus GDM. The RRs of overall CAs and CHDs in offspring of women with PGDM were higher than those in offspring of women with GDM. Screening for diabetes in pregnant women may enable better glycemic control, and may enable identification of offspring at risk for CAs.

In a systematic review and meta analysis, Tie-Ning Zhang and colleagues investigate the associations between maternal pre-gestational diabetes and gestational diabetes and congenital heart defects and other congenital anomalies in offspring.

Author summary

Why was this study done?

It is controversial whether maternal pre-gestational or gestational diabetes affects specific types of congenital heart defects (CHDs) and congenital anomalies (CAs) in other systems.
Comprehensive estimates of the risks of specific CAs for offspring of women with maternal diabetes are needed to counsel patients and for public health purposes.

What did the researchers do and find?

To the best of our knowledge, this is the first comprehensive systematic review and meta-analysis of population-based studies of over 80 million participants that demonstrates an increased risk of type-specific CAs, especially CHDs, in offspring born to women with pre-gestational or gestational diabetes.
Our study shows that maternal pre-gestational diabetes is associated with a significant increase in the risk of 38 out of 45 categories of CAs in offspring, while maternal gestational diabetes is associated with a small but significant increase in the risk of 16 out of the 45 categories.
The corresponding relative risks (RRs) of overall CAs/CHDs in offspring of women with pre-gestational diabetes are higher than those in offspring of women with gestational diabetes, with no overlap in the 95% CIs.

What do these findings mean?

In this study, we observed that there is an increased rate of CAs in offspring of women with maternal diabetes and noted the differences between pre-gestational and gestational diabetes.
Considering the substantial rise in the prevalence of maternal diabetes over recent decades, the expectation that this prevalence will continue to increase, the number of pregnancies worldwide, and the significant individual and global burdens associated with CAs, it is crucial that healthcare providers are aware of this association and can identify women and offspring who are at risk.

Introduction

Currently, the global prevalence of diabetes is increasing among women of reproductive age [1,2]. A diabetic intrauterine environment can cause placental dysfunction and hormone alterations, which could lead to various congenital anomalies (CAs) in offspring of women with diabetes [1]. Notably, pre-gestational diabetes mellitus (PGDM, which includes type 1 and 2 diabetes) has been known to be a risk factor for congenital heart defects (CHDs) for decades [3]. However, there is controversy among current research regarding the association between maternal PGDM and the risk of specific types of CHDs and other CAs of the nervous, digestive, genitourinary, and musculoskeletal systems [4–7]. Further studies are thus needed for clarification of this risk.

Gestational diabetes mellitus (GDM), which is defined as any degree of glucose intolerance with onset or first recognition during pregnancy, is one of the most common complications of pregnancy and affects up to 9%–26% of the obstetric population [8,9]. Similar to PGDM, GDM also has a considerable impact on the health outcomes of the mother and infant during pregnancy, delivery, and beyond. Recently, an increasing number of studies have concentrated on evaluating the risks of specific types of CAs in offspring born to women with GDM [4,5,10–12]. The early period of organogenesis, which occurs during the third to eighth week of gestation, is an important time for organ development. However, hyperglycemia associated with GDM occurs after this critical early window for organogenesis. Therefore, the question as to whether there is an association between GDM and the risk of specific types of CAs in offspring remains.

Previous meta-analyses have mainly focused on the associations between maternal diabetes and CHDs in offspring, and little is known about the influence of maternal diabetes on other specific types of CAs [13,14]. Additionally, new data from population-based studies of more than 36 million births have provided solid estimates of the risk of CHDs in offspring of women with diabetes [4,10–12]. This considerable amount of data could also be used to explore the association between maternal diabetes and other types of CAs. Currently, a quantitative summary of population-based studies on the associations between maternal diabetes (pre-gestational or gestational) and type-specific CAs in offspring is lacking. Comprehensive estimates of the risk of specific CAs associated with maternal diabetes are needed to counsel patients and for public health purposes. Moreover, it is essential that estimates are provided according to different types of maternal diabetes, given the diversity in etiology, treatment, and prognosis.

We performed a detailed systematic review and large-scale meta-analysis to summarize and quantify the existing population-based data on type-specific CAs in offspring of women with diabetes. Furthermore, we examined the extent to which specific types of maternal diabetes (i.e., pre-gestational or gestational) are associated with increased risk of CAs in offspring.

Methods

We performed a literature search in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [15] and Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines [16] (see S1 Text). Before study selection, the protocol for this review was registered in PROSPERO, registration number CRD42021229217 (S1 Protocol).

Search strategy and inclusion criteria

We searched PubMed and Embase from database inception to 15 October 2021. The search strategy combined Medical Subject Headings (MeSH) and Embase subject heading (Emtree) terms with other unindexed or free text terms, with no limitation on language. Details of the full search strategy are provided in S1 Text. Reference lists of retrieved articles and previous systematic and narrative reviews were searched manually to retrieve all relevant documents. Duplicate citations were removed.

Population-based cross-sectional, case–control, and cohort studies that reported original data were eligible for inclusion if they (1) reported any CAs in offspring born to women with diabetes (i.e., pre-gestational [combined type 1 and 2] or gestational diabetes), (2) had a comparison group that included mothers without diabetes, and (3) provided sufficient data from which a risk estimate could be calculated if a risk estimate was not reported. All conference abstracts, guidelines, case reports, case series, commentaries, letters, and animal studies were excluded.

Two independent authors (S-YG and T-NZ) reviewed the titles and abstracts to identify any relevant studies. The full texts of potentially eligible studies that appeared to meet the inclusion criteria were then obtained and independently evaluated by the 2 reviewers. Any disagreement was settled by consensus among all authors. If multiple studies were derived from the same dataset and reported the same associated outcome, the study with the most complete findings or the greatest number of participants was included for analysis. The literature review and study selection process referenced the PRISMA flowchart (Fig 1). When information needed for inclusion in the analyses was missing, the Library of Shengjing Hospital of China Medical University helped us get full access of the relevant data.

Data extraction

A standardized, pre-designed spreadsheet was used for extracting data from the included studies. Study quality and synthesis of evidence were assessed. The following data were recorded in the spreadsheet: first author, publication year, geographic location, study period, study design, data source, type of diabetes, sample size, types of birth, ascertainment of exposure, definition of outcome, outcome risk estimates and 95% confidence intervals (CIs), and adjusted confounders.

Primary outcomes were the rates of overall CAs and type-specific CHDs (heterotaxia, conotruncal defects, atrioventricular septal defect, anomalous pulmonary venous return, left ventricular outflow tract defect, right ventricular outflow tract defect, septal defects, and single ventricle). Secondary outcomes were the rates of other type-specific CAs (involving the nervous system; eye, ear, face, and neck; orofacial clefts; digestive system; abdominal wall; genitourinary system; and musculoskeletal system). S1 Table shows the definitions of these outcomes.

Two authors (T-NZ and S-YG) independently performed data extraction according to the Cochrane Handbook guidelines [17]. Findings were reported according to PRISMA [15] and MOOSE guidance [16]. Any disagreement was settled by consensus among all authors. For studies that did not report any adjusted effect sizes, the crude risk estimate was used. If an included study reported several risk estimates, we extracted the fully adjusted effect sizes. For studies that reported the risk estimates of CAs stratified by isolated and multiple statuses, we used the effect sizes of the isolated CAs. Because odds ratios, prevalence rate ratios, and hazard ratios are excellent approximations of risk ratios in the case of rare outcomes [18], all risk estimates are referred to and reported as relative risks (RRs) for simplicity. If an included study lacked required data, we asked for help from the Library of Shengjing Hospital of China Medical University to get the missing information.

Risk of bias and study quality

The risk of bias assessment was conducted by T-NZ and S-YG using the Risk of Bias in Non-randomized Studies–of Interventions (ROBINS-I) tool [19]. This tool comprises 7 domains: bias due to confounding, bias in the selection of participants, bias in the classification of interventions, bias due to deviations from intended interventions, bias due to missing data, bias in measurement of outcomes, and bias in the selection of the reported result. We rated the possible risk of bias in each of the 7 domains as low risk, moderate risk, serious risk, critical risk, or no information for each available outcome of each included study.

Statistical analysis

For studies that reported effect sizes separately, the results were pooled using a fixed effects model to obtain an overall estimate and then included in the pooled effect size in the meta-analysis. The effective count method proposed by Hamling et al. [20] was used to recalculate the effect sizes. If a selected study did not include an effect size, the unadjusted risk estimate and 95% CI were calculated from the raw data for simplicity using EpiCalc 2000 (https://en.freedownloadmanager.org/Windows-PC/EpiCalc-2000-FREE.html). Estimates were pooled using the DerSimonian and Laird random effects model to calculate summarized RRs and 95% CIs [21], in which I² values were calculated as indicators of heterogeneity. I² values of ≤25%, 26%–50%, 51%–74%, and ≥75% were considered to indicate no, low, moderate, and high heterogeneity between the included studies, respectively [22]. For the primary outcomes of the study, subgroup analyses were undertaken to explore causes of heterogeneity: by region (Europe, North/South America, or Asia-Pacific), year of enrollment (categorized using the median as the cutoff value: before 1997 or in or after 1997), number of participants (categorized using the median as the cutoff value: <282,260 or ≥282,260), and adjustment for confounders (i.e., maternal age, race/ethnicity, body mass index, education, smoking/alcohol consumption, parity, and pregnancy complications). Heterogeneity between subgroups was evaluated by meta-regression analysis if data were reported in more than 10 studies, following to the Cochrane guidelines [23]. Meta-regression analyses were also used to examine the extent to which the types of maternal diabetes (i.e., pre-gestational or gestational) are associated with increased risk of overall CAs/CHDs in offspring. Publication bias was examined by inspecting funnel plots for the outcomes and was further evaluated with Begg’s test [24] and Egger’s test [25] if sufficient studies existed (n ≥ 10) [17]. A sensitivity analysis was undertaken to explore the association of each study with the overall pooled estimate. Statistical analyses were conducted using Stata version 13.0 (StataCorp, College Station, Texas). A 2-tailed P value less than 0.05 was considered statistically significant.

Results

Search results and study characteristics

We identified 24,989 potentially eligible articles in PubMed and Embase through the search strategy plus 1 additional article through hand searching. Of these, 2,331 records were duplicates (Fig 1). In total, 103 articles qualified for full-text review based on title and abstract screening. Of these, an additional 44 articles were excluded for the following reasons: 3 studies enrolled mothers with any type of diabetes and did not distinguish pre-gestational and gestational diabetes, 1 study did not have a comparison group that included mothers without diabetes, 4 studies were derived from the same dataset and reported the same associated outcome as another included study, 3 studies enrolled mothers with CAs, 6 studies included data that could not be extracted or calculated, and 27 studies were not population-based (S2 Table). Finally, 59 population-based studies (published from 1990 to 2021) that met all eligibility criteria contributed to the quantitative synthesis and included a total of 80,437,056 participants (range of participants per study: 155 to 29,211,974) for analysis. Of these, 2,407,862 (3.0%) women had PGDM and 2,353,205 (2.9%) women had GDM; 879,156 cases of overall CAs and 350,051 cases of CHDs were observed. S3 Table gives the method of ascertainment of maternal diabetes of the included studies. Of the 59 studies included, there were 27 studies from the European region (United Kingdom [26–30], Finland [31,32], Sweden [10,33–37], Denmark [10,28,35,38,39], Norway [6,28,40,41], Hungary [7,42–45], Germany [28,46], Netherlands [28], Belgium [28], Wales [28,30], Ireland [28,30], Switzerland [28], France [28,47], Italy [28,48,49], Spain [28], Portugal [28], and Malta [28]), 25 studies from North/South America (United States [4,5,11,12,14,50–57], Canada [50,58–68], and Brazil [69]), and 7 studies from the Asian-Pacific region (China [70–72], Russia [73], Australia [74], and Qatar [75,76]). Table 1 summarizes the characteristics of the included studies, and a more detailed breakdown can be found in S4 Table.

Table 1. Summary characteristics of included studies.

Characteristic	Number of studies (number of participants)
Eligible studies	59 (80,437,056)
Region
Europe	27 (19,297,559)
North/South America	25 (52,645,605)
Asia-Pacific	7 (8,493,892)
Year of enrollment
Before 1997	27 (14,896,852)
In or after 1997	32 (65,540,204)
Type of maternal diabetes
Pre-gestational diabetes	46^* (2,407,862)
Type 1 diabetes	11 (285,859)
Type 2 diabetes	7 (294,525)
Gestational diabetes	37 (2,353,205)
Primary outcomes
Overall CAs	24 (879,156)
CHDs	23 (350,051)

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The median (range) number of participants per study was 282,260 (155 to 29,211,974). Studies included 45 type-specific CAs. CA, congenital anomaly; CHD, congenital heart defect.

*Of 46 studies, 28 studies also reported results on gestational diabetes

7 studies also reported results on type 1 and type 2 diabetes.

Bias assessment

We assessed the risk of bias for 34 of 59 included studies using ROBINS-I. The assessments are summarized for primary outcomes in Figs A–D in S1 Fig. None of the included studies were rated with a low risk of bias in all domains. The main causes of serious or critical bias risk according to ROBINS-I were weaknesses in the confounding bias domain, selection of participant bias domain, and missing data bias domain.

Exposure to PGDM/GDM and overall CAs in offspring

We first explored whether there was an association between maternal diabetes and overall CAs (not including CHDs) in offspring. Nineteen studies investigated the relationship between maternal PGDM and overall CAs in offspring [4,11,12,27,29,38,44,46,49,56,58,61–63,65,67,70,74,76], and 15 studies investigated the relationship between maternal GDM and overall CAs in offspring [4,11,12,33,39,44,48,56,58,61,62,65,67,69,76]. Our results suggested that maternal PGDM was associated with overall CAs in offspring (RR = 1.99, 95% CI 1.82 to 2.17, I² = 90.0%, P < 0.001; Fig 2), with no evidence of publication bias (Begg’s P = 0.88, Egger’s P = 0.30; Fig E in S1 Fig). A similar association was observed for overall CAs in offspring of women with type 1 diabetes (RR = 2.03, 95% CI 1.66 to 2.48, I² = 82.5%, P < 0.001; Fig K1 in S1 Fig) and in offspring of women with GDM (RR = 1.18, 95% CI 1.13 to 1.23, I² = 76.0%, P < 0.001; Fig 3), with no evidence of publication bias (Begg’s P = 0.39, Egger’s P = 0.32; Fig F in S1 Fig). However, there was no statistically significant association of the risk of overall CAs in offspring of women with type 2 diabetes (RR = 1.31, 95% CI 0.80 to 2.15, I² = 98.2%, P < 0.001; Fig L1 in S1 Fig).

Fig 2 — Analytical weights are from random effects meta-analysis. Grey boxes represent study estimates; their size is proportional to the respective analytical weight. Lines through the boxes represent the 95% CIs around the study estimates. The diamond represents the mean estimate and its 95% CI. The vertical red dashed line indicates the mean estimate. CI, confidence interval; DL, DerSimonian and Laird random effects model; RR, relative risk.

Fig 3 — Analytical weights are from random effects meta-analysis. Grey boxes represent study estimates; their size is proportional to the respective analytical weight. Lines through the boxes represent the 95% CIs around the study estimates. The diamond represents the mean estimate and its 95% CI. The vertical red dashed line indicates the mean estimate. CI, confidence interval; DL, DerSimonian and Laird random effects model; RR, relative risk.

Exposure to PGDM and CHDs in offspring

A total of 18 studies reported on the association between maternal PGDM and CHDs in offspring [4,6,10–12,28–30,38,44,47,49,52,54,56,63,68,74]. Our results suggested that there is a statistically significant increase in risk of CHDs in offspring of women with PGDM (RR = 3.46, 95% CI 2.77 to 4.32, I² = 98.2%, P < 0.001; Fig 4), with no evidence of publication bias (Begg’s P = 0.60, Egger’s P = 0.85; Fig E in S1 Fig). Similarly, maternal type 1 and type 2 diabetes were associated with increased risk of CHDs in offspring (type 1: RR = 3.75, 95% CI 1.86 to 7.57, I² = 99.1%, P < 0.001; Fig K2 in S1 Fig; type 2: RR = 3.15, 95% CI 1.72 to 5.78, I² = 93.6%, P < 0.001; Fig J2 in S1 Fig). Notably, we found that maternal PGDM was associated with increased risk of all specific types of CHDs available for examination in the present study. The RRs of specific types of CHDs ranged from 2.23 (for hypoplastic left heart, 95% CI 1.07 to 4.64, I² = 64.0%, P = 0.040) to 12.16 (for truncus arteriosus, 95% CI 7.52 to 19.68, I² = 0%, P = 0.866) (Table 2; Figs G1–G18 in S1 Fig).

Fig 4 — Analytical weights are from random effects meta-analysis. Grey boxes represent study estimates; their size is proportional to the respective analytical weight. Lines through the boxes represent the 95% CIs around the study estimates. The diamond represents the mean estimate and its 95% CI. The vertical red dashed line indicates the mean estimate. CI, confidence interval; DL, DerSimonian and Laird random effects model; RR, relative risk.

Table 2. Pooled RR and 95% confidence intervals for associations between maternal diabetes and any type of congenital heart defects .

Outcome	Number of events	Pre-gestational diabetes				Gestational diabetes
Outcome	Number of events	Number of studies	Pooled RR (95% CI)	I² (%)	P value	Number of studies	Pooled RR (95% CI)	I² (%)	P value
Heterotaxia	1,098	4	8.78 (6.66 to 11.56)	0.0	0.423	2	5.70 (1.09 to 29.92)	85.7	0.008
Conotruncal defects	5,495	4	3.76 (2.58 to 5.48)	68.3	0.024	—
Truncus arteriosus	435	3	12.16 (7.52 to 19.68)	0.0	0.866	2	1.77 (0.80 to 3.92)	40.2	0.196
Transposition of great vessels	6,700	9	3.25 (2.54 to 4.15)	15.9	0.301	2	1.29 (0.99 to 1.67)	61.2	0.109
Tetralogy of Fallot	5,360	6	3.46 (2.27 to 5.28)	64.4	0.015	2	1.41 (1.20 to 1.66)	0.0	0.600
APVR	1,239	4	3.47 (2.13 to 5.64)	0.0	0.684	2	1.42 (0.79 to 2.56)	53.3	0.117
LVOT defects	6,672	7	3.46 (2.59 to 4.62)	37.8	0.140	4	1.67 (1.15 to 2.41)	50.0	0.112
Coarctation of aorta	6,606	5	3.35 (2.25 to 4.99)	61.4	0.035	2	1.50 (1.23 to 1.83)	35.4	0.213
Hypoplastic left heart	2,319	4	2.23 (1.07 to 4.64)	64.0	0.040	2	1.23 (0.54 to 2.82)	81.7	0.019
RVOT defects	6,163	7	3.41 (2.65 to 4.38)	20.9	0.270	3	1.25 (1.03 to 1.53)	0.0	0.739
Pulmonary artery anomalies	17,215	3	2.81 (2.48 to 3.18)	0.0	0.865	2	1.02 (0.36 to 2.87)	71.6	0.060
Pulmonary valve stenosis	7,273	5	2.51 (1.51 to 4.17)	76.2	0.002	2	1.30 (0.96 to 1.76)	64.5	0.093
Septal defects	12,368	2	3.23 (2.20 to 4.74)	86.2	0.007	—
AVSD	5,126	6	3.94 (2.95 to 5.26)	40.0	0.139	3	1.02 (0.83 to 1.24)	0.0	0.751
VSD	64,844	10	3.10 (2.32 to 4.16)	90.2	<0.001	2	1.31 (1.24 to 1.38)	0.0	0.960
ASD	91,683	7	3.12 (2.42 to 4.02)	81.9	<0.001	2	1.45 (1.40 to 1.50)	0.0	0.426
VSD + ASD	1,089	2	6.36 (4.38 to 9.24)	0.0	0.527	—
Single ventricle	1,228	4	5.91 (2.43 to 14.38)	80.2	0.002	2	1.14 (0.77 to 1.69)	0.0	0.851

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APVR, anomalous pulmonary venous return; ASD, atrial septal defect; AVSD, atrioventricular septal defect; CHD, congenital heart defect; CI, confidence interval; LVOT, left ventricular outflow tract; RR, relative risk; RVOT, right ventricular outflow tract; VSD, ventricular septal defect.

Exposure to GDM and CHDs in offspring

Eleven studies explored the relationship between GDM and CHDs in offspring [4,6,11,12,38,44,47,51,52,56,72]. Our results suggested that maternal GDM is associated with CHDs (RR = 1.50, 95% CI 1.38 to 1.64, I² = 81.2%, P < 0.001; Fig 5), with no evidence of publication bias (Begg’s P = 0.837, Egger’s P = 0.885; Fig F in S1 Fig). Regarding specific types of CHDs, we found that offspring of women with GDM had an increased risk of heterotaxia (RR = 5.70, 95% CI 1.09 to 29.92, I² = 85.7%, P = 0.008), tetralogy of Fallot (RR = 1.41, 95% CI 1.20 to 1.66, I² = 0%, P = 0.600), left ventricular outflow tract defect (RR = 1.67, 95% CI 1.15 to 2.41, I² = 50.0%, P = 0.112), coarctation of aorta (RR = 1.50, 95% CI 1.23 to 1.83, I² = 35.4%, P = 0.213), right ventricular outflow tract defect (RR = 1.25, 95% CI 1.03 to 1.53, I² = 0%, P = 0.739), ventricular septal defect (RR = 1.31, 95% CI 1.24 to 1.38, I² = 0%, P = 0.960), and atrial septal defect (RR = 1.45, 95% CI 1.40 to 1.50, I² = 0%, P = 0.426) (Table 2; Figs I1–I15 in S1 Fig).

Fig 5 — Analytical weights are from random effects meta-analysis. Grey boxes represent study estimates; their size is proportional to the respective analytical weight. Lines through the boxes represent the 95% CIs around the study estimates. The diamond represents the mean estimate and its 95% CI. The vertical red dashed line indicates the mean estimate. CI, confidence interval; DL, DerSimonian and Laird random effects model; RR, relative risk.

Exposure to PGDM and other type-specific CAs in offspring

We examined the associations between maternal PGDM and other type-specific CAs in offspring. Our results suggested that offspring of women with PGDM had an increased risk of CAs of the nervous system (RR = 2.54, 95% CI 1.73 to 3.73, I² = 94.8%, P < 0.001); eye, ear, face, and neck (RR = 3.14, 95% CI 2.90 to 3.39, I² = 0%, P = 0.444); digestive system (RR = 2.02, 95% CI 1.24 to 3.28, I² = 92.3%, P < 0.001); genitourinary system (RR = 1.73, 95% CI 1.35 to 2.21, I² = 89.2%, P < 0.001); and musculoskeletal system (RR = 1.98, 95% CI 1.45 to 2.72, I² = 94.4%, P < 0.001), as well as an increased risk of multiple CAs (RR = 3.06, 95% CI 2.36 to 3.96, I² = 39.6%, P = 0.158). The associations were statistically significant in 14 of 16 type-specific CA categories. The corresponding RRs ranged from 1.57 (for hypospadias, 95% CI 1.22 to 2.02, I² = 74.1%, P < 0.001) to 18.18 (for holoprosencephaly, 95% CI 4.03 to 82.06, I² = 66.3%, P = 0.085) (Table 3; Figs H1–H25 in S1 Fig).

Table 3. Pooled RRs and 95% confidence intervals for associations between maternal diabetes and other type-specific congenital anomalies.

Outcome	Number of events	Pre-gestational diabetes				Gestational diabetes
Outcome	Number of events	Number of studies	Pooled RR (95% CI)	I² (%)	P value	Number of studies	Pooled RR (95% CI)	I² (%)	P value
Nervous system defects	42,339	9	2.54 (1.73 to 3.73)	94.8	<0.001	2	1.64 (0.74 to 3.61)	78.6	0.031
Neural tube defects	8,791	6	2.74 (1.46 to 5.14)	75.5	0.001	2	1.06 (0.55 to 2.06)	0.0	0.669
Anencephaly	3,859	3	2.72 (2.16 to 3.44)	0.0	0.416	3	0.80 (0.62 to 1.04)	25.4	0.262
Encephalocele	1,108	3	5.53 (3.24 to 9.45)	52.8	0.120	2	1.03 (0.67 to 1.59)	3.5	0.309
Spina bifida	9,948	8	1.89 (1.15 to 3.09)	71.1	0.001	5	1.10 (0.99 to 1.22)	0.0	0.459
Hydrocephaly	10,733	5	3.46 (1.62 to 7.42)	85.0	<0.001	4	1.34 (1.16 to 1.54)	0.0	0.960
Holoprosencephaly	301	2	18.18 (4.03 to 82.06)	66.3	0.085	3	1.87 (1.09 to 3.22)	0.0	0.558
Eye, ear, face, and neck defects	39,570	6	3.14 (2.90 to 3.39)	0.0	0.444	2	1.15 (1.09 to 1.22)	0.0	0.355
Orofacial clefts	6,602	5	1.27 (0.54 to 2.98)	90.4	<0.001	—
Cleft palate	11,259	6	1.75 (1.04 to 2.94)	74.6	0.001	5	1.21 (0.95 to 1.56)	54.9	0.064
Cleft lip with or without cleft palate	32,641	7	1.89 (1.22 to 2.92)	81.1	<0.001	5	1.26 (1.19 to 1.34)	0.0	0.547
Digestive system defects	14,286	7	2.02 (1.24 to 3.28)	92.3	<0.001	—
Diaphragmatic hernia	5,882	3	1.66 (1.32 to 2.10)	0.0	0.520	4	1.21 (1.08 to 1.37)	0.0	0.779
Abdominal wall defects	1,691	2	1.31 (0.80 to 2.15)	0.0	0.729	—
Omphalocele	4,163	3	1.90 (1.48 to 2.44)	0.0	0.447	2	1.21 (1.05 to 1.40)	0.0	0.743
Gastroschisis	9,268	3	0.92 (0.68 to 1.24)	0.0	0.399	4	0.71 (0.58 to 0.85)	0.0	0.424
Genitourinary system defects	128,657	10	1.73 (1.35 to 2.21)	89.2	<0.001	2	1.82 (0.90 to 3.66)	93.4	<0.001
Renal agenesis/dysgenesis	5,239	6	5.63 (2.48 to 12.76)	86.1	<0.001	2	0.90 (0.25 to 3.25)	78.8	0.030
Hypospadias	44,963	9	1.57 (1.22 to 2.02)	74.1	<0.001	6	1.29 (1.16 to 1.44)	45.9	0.100
CAKUT	4,143	3	1.80 (1.41 to 2.30)	0.0	0.865	3	1.28 (0.99 to 1.66)	31.1	0.234
Musculoskeletal system defects	123,365	11	1.98 (1.45 to 2.72)	94.4	<0.001	3	1.18 (1.15 to 1.22)	0.0	0.424
Limb reduction	23,963	9	2.73 (1.98 to 3.76)	81.7	<0.001	5	1.14 (1.06 to 1.23)	0.0	0.866
Polydactyly/syndactyly	20,328	4	0.95 (0.57 to 1.57)	71.8	0.003	2	0.84 (0.42 to 1.66)	62.5	0.102
Multiple congenital anomalies	2,448	5	3.06 (2.36 to 3.96)	39.6	0.158	2	1.15 (0.59 to 2.24)	63.0	0.100
Major congenital anomalies	52,171	6	2.14 (1.65 to 2.77)	81.8	<0.001	3	1.23 (1.03 to 1.47)	18.5	0.293

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CAKUT, congenital anomalies of the kidney and urinary tract; CI, confidence interval; RR, relative risk.

Exposure to GDM and other type-specific CAs in offspring

Maternal GDM was associated with an increased risk of CAs of the eye, ear, face, and neck (RR = 1.15, 95% CI 1.09 to 1.22, I² = 0%, P = 0.355) and musculoskeletal system (RR = 1.18, 95% CI 1.15 to 1.22, I² = 0%, P = 0.424) in offspring. In addition, maternal GDM also contributed to an increased risk of specific types of CAs in offspring, including hydrocephaly (RR = 1.34, 95% CI 1.16 to 1.54, I² = 0%, P = 0.960), holoprosencephaly (RR = 1.87, 95% CI 1.09 to 3.22, I² = 0%, P = 0.558), cleft lip with or without cleft palate (RR = 1.26, 95% CI 1.19 to 1.34, I² = 0%, P = 0.547), diaphragmatic hernia (RR = 1.21, 95% CI 1.08 to 1.37, I² = 0%, P = 0.779), omphalocele (RR = 1.21, 95% CI 1.05 to 1.40, I² = 0%, P = 0.743), and hypospadias (RR = 1.29, 95% CI 1.16 to 1.44, I² = 45.9%, P = 0.100) (Table 3; Figs J1–J22 in S1 Fig).

Subgroup, meta-regression, and sensitivity analyses

The sensitivity analysis evaluated the effect of omitting 1 study at a time from each analysis. In the sensitivity analysis, we observed that the high I² value of 81.2% shown in the results for CHDs in offspring of mothers with GDM reduced to a moderate I² value of 55.0% when excluding the study by Billionnet et al. [47]. Although the increased risk association remained robust across scenarios, some moderate to significant heterogeneity still persisted and could not be reduced in sensitivity analyses. To explore the source of heterogeneity, we performed subgroup and meta-regression analyses in the predefined subgroups of study location, year of enrollment, study sample size, and adjustment for confounders (Tables 4 and 5). The findings of increased overall CA/CHD risk associated with maternal diabetes were consistently observed in most of the subgroup analyses. The results of the subgroup analyses suggested that differences in study sample size, population region, year of enrollment, and adjustment for confounders were major sources of heterogeneity. We observed that the high I² value of 81.2% observed in the results for CHDs in offspring of mothers with GDM was reduced to no (I² = 0%) or low (I² = 47.7%) heterogeneity after adjustment for race/ethnicity, body mass index, education, smoking/alcohol consumption, parity, and pregnancy complications (Table 5). In addition, the results of meta-regression analyses showed statistically significant differences in the RRs of CAs/CHDs in PGDM versus GDM (all P_{meta-regression} < 0.001) (Fig 6).

Table 4. Subgroup analysis of the association between maternal diabetes and risk of overall congenital anomalies in offspring: Results of meta-analyses.

Subgroup	Pre-gestational diabetes					Gestational diabetes
Subgroup	Number of studies	Pooled RR (95% CI)	I² (%)	P value^*	P value^**	Number of studies	Pooled RR (95% CI)	I² (%)	P value^*	P value^**
Region					0.39					0.32
Europe	6	2.10 (1.44 to 3.04)	94.5	<0.001		4	1.09 (0.97 to 1.23)	73.7	<0.001
North/South America	10	2.03 (1.85 to 2.22)	88.5	<0.001		10	1.19 (1.13 to 1.25)	79.5	<0.001
Asia-Pacific	3	1.67 (1.31 to 2.11)	42.1	0.178		1	1.18 (0.79 to 1.77)	—	—
Year of enrollment ^†					0.87					0.18
Before 1997	8	1.94 (1.51 to 2.49)	93.2	<0.001		5	1.10 (0.97 to 1.25)	81.1	<0.001
In or after 1997	11	2.04 (1.86 to 2.23)	90.0	<0.001		10	1.21 (1.16 to 1.26)	67.4	0.001
Number of participants ^†					0.44					0.78
<282,260	4	1.56 (3.32 to 9.74)	57.9	0.068		3	1.40 (0.71 to 2.77)	47.9	0.147
≥282,260	15	1.96 (1.79 to 2.15)	91.7	<0.001		12	1.17 (1.13 to 1.22)	79.5	<0.001
Adjustment for confounders
Maternal age					0.19					0.71
Yes	16	1.92 (1.77 to 2.10)	89.0	<0.001		13	1.17 (1.13 to 1.22)	77.8	<0.001
No	3	2.31 (1.17 to 4.57)	91.8	<0.001		2	2.59 (0.36 to 18.9)	73.3	0.053
Race/ethnicity					0.14					0.24
Yes	7	2.20 (1.98 to 2.45)	88.6	<0.001		5	1.22 (1.17 to 1.27)	74.2	0.004
No	12	1.82 (1.54 to 2.16)	91.2	<0.001		10	1.13 (1.04 to 1.23)	73.3	<0.001
Body mass index					0.70					0.70
Yes	4	2.08 (1.55 to 2.80)	83.4	<0.001		4	1.19 (1.08 to 1.32)	74.3	0.009
No	15	1.96 (1.78 to 2.17)	89.6	<0.001		11	1.16 (1.11 to 1.22)	68.8	<0.001
Education					0.40					0.76
Yes	5	1.85 (1.59 to 2.15)	93.7	<0.001		6	1.17 (1.10 to 1.24)	87.7	<0.001
No	14	2.05 (1.80 to 2.33)	88.0	<0.001		9	1.18 (1.12 to 1.26)	33.9	0.147
Smoking/alcohol consumption					0.55					0.49
Yes	6	2.11 (1.67 to 2.66)	79.1	<0.001		6	1.21 (1.11 to 1.32)	59.7	0.030
No	13	1.94 (1.75 to 2.15)	90.5	<0.001		9	1.16 (1.10 to 1.21)	73.4	<0.001
Parity					0.89					0.92
Yes	8	1.99 (1.80 to 2.20)	90.1	<0.001		8	1.17 (1.11 to 1.23)	84.4	<0.001
No	11	1.98 (1.59 to 2.47)	90.8	<0.001		7	1.18 (1.08 to 1.29)	43.0	0.104
Pregnancy complications					0.05					0.44
Yes	6	1.69 (1.29 to 2.20)	93.6	<0.001		6	1.13 (1.02 to 1.25)	87.5	<0.001
No	13	2.20 (2.00 to 2.42)	90.0	<0.001		9	1.20 (1.16 to 1.24)	28.4	0.192

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CI, confidence interval; RR, relative risk.

*P for heterogeneity within each subgroup.

**P for heterogeneity between subgroups with meta-regression analysis.

^†Categorized using the median as the cutoff value.

Table 5. Subgroup analysis of the association between maternal diabetes and risk of congenital heart defects in offspring: Results of meta-analyses.

Subgroup	Pre-gestational diabetes					Gestational diabetes
Subgroup	Number of studies	Pooled RR (95% CI)	I² (%)	P value^*	P value^**	Number of studies	Pooled RR (95% CI)	I² (%)	P value^*	P value^**
Region					0.04					0.98
Europe	9	2.63 (1.81 to 3.80)	97.6	<0.001		4	1.57 (1.09 to 2.27)	90.8	<0.001
North/South America	8	4.90 (3.92 to 6.13)	96.2	<0.001		6	1.46 (1.37 to 1.56)	64.0	0.016
Asia-Pacific	1	2.84 (1.89 to 4.26)	—	—		1	1.80 (1.31 to 1.55)	—	—
Year of enrollment ^†					0.46					0.07
Before 1997	10	3.17 (2.49 to 4.03)	88.4	<0.001		5	1.29 (1.17 to 1.44)	23.9	0.255
In or after 1997	8	3.80 (2.67 to 5.40)	99.2	<0.001		6	1.68 (1.50 to 1.88)	86.7	<0.001
Number of participants ^†					0.20					0.62
<282,260	3	4.93 (3.61 to 6.74)	0.0	0.714		3	1.37 (1.09 to 1.72)	61.1	0.08
≥282,260	15	3.27 (2.57 to 4.15)	98.5	<0.001		8	1.55 (1.40 to 1.70)	82.3	<0.001
Adjustment for confounders
Maternal age					0.97					0.40
Yes	16	3.46 (2.72 to 4.39)	98.4	<0.001		10	1.55 (1.42 to 1.69)	77.9	<0.001
No	2	3.50 (2.84 to 4.32)	0.0	0.789		1	1.19 (1.05 to 1.35)	—	—
Race/ethnicity					0.21					0.94
Yes	6	4.14 (3.42 to 5.01)	89.3	<0.001		5	1.52 (1.49 to 1.55)	0.0	0.964
No	12	3.15 (2.11 to 4.69)	98.7	<0.001		6	1.53 (1.19 to 1.98)	89.1	<0.001
Body mass index					0.17					0.83
Yes	4	4.62 (4.30 to 4.96)	0.0	0.989		4	1.51 (1.44 to 1.58)	0.0	0.654
No	14	3.20 (2.45 to 4.20)	98.5	<0.001		7	1.50 (1.26 to 1.77)	87.7	<0.001
Education					0.38					0.90
Yes	3	4.18 (3.32 to 5.27)	95.2	<0.001		4	1.52 (1.49 to 1.55)	0.0	0.671
No	15	3.33 (2.34 to 4.75)	98.3	<0.001		7	1.51 (1.20 to 1.89)	87.0	<0.001
Smoking/alcohol consumption					0.02					0.91
Yes	4	4.68 (4.38 to 5.01)	0.0	0.491		4	1.50 (1.43 to 1.58)	0.0	0.706
No	14	2.98 (2.24 to 3.96)	98.6	<0.001		7	1.50 (1.29 to 1.76)	87.8	<0.001
Parity					0.17					0.86
Yes	6	4.23 (3.38 to 5.30)	97.5	<0.001		4	1.52 (1.49 to 1.55)	0.0	0.676
No	12	3.13 (2.20 to 4.47)	96.6	<0.001		7	1.50 (1.17 to 1.93)	86.9	<0.001
Pregnancy complications					0.88					0.61
Yes	4	3.58 (2.11 to 6.09)	91.6	<0.001		4	1.45 (1.23 to 1.71)	47.7	0.13
No	14	3.44 (2.62 to 4.50)	98.5	<0.001		7	1.56 (1.33 to 1.82)	86.7	<0.001

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CI, confidence interval; RR, relative risk.

*P for heterogeneity within each subgroup.

**P for heterogeneity between subgroups with meta-regression analysis.

^†Categorized using the median as the cutoff value.

Fig 6 — Relative risks (RRs) and 95% confidence intervals (CIs) are presented to show the risk of overall congenital anomalies and congenital heart defects in offspring born to women with different types of maternal diabetes compared with the risk among offspring born to women without diabetes. P_{meta-regression} values were <0.001 for the comparison within congenital anomalies and congenital heart defects between gestational diabetes and pre-gestational diabetes.

Discussion

To the best of our knowledge, the present study is the first comprehensive systematic review and meta-analysis of population-based studies of over 80 million participants that shows an increased risk of type-specific CAs, especially CHDs, in offspring of women with pre-gestational or gestational diabetes. The study findings suggested that maternal PGDM was associated with a significant increase in the risk of CAs in offspring in 38 of 45 categories, while maternal GDM was associated with a small but significant increase in the risk of CAs in 16 of 45 categories. The corresponding RRs of overall CAs/CHDs in offspring of women with PGDM were higher than those in offspring of women with GDM, with no overlap in the 95% CIs.

Although the exact pathophysiology of the relationship between maternal diabetes and CAs in offspring remains unclear, metabolic changes in women with diabetes could play a critical role in the development of CAs in their offspring. Sustained hyperglycemia is the main characteristic of diabetes; this activates multiple metabolic pathways that play a role in the formation of CAs [77]. Notably, a common mechanism behind diabetic complications is mitochondrial superoxide radical production [77]. The production of reactive oxygen species (ROS) is induced by hyperglycemia, which is the crucial process in diabetes mellitus pathogenesis, and oxidative stress (OS) is known to affect embryonic development [77–79]. Although OS does not cause a direct genotoxic effect, a previous study showed that OS affects the expression of some genes involved in the various stages of embryonic development, and each gene affected might have a specific sensitivity to hyperglycemic conditions and changes in the cellular redox state, thus mediating the formation of CAs in offspring [77]. Studies have suggested that the formation of CAs involves AMP-activated protein kinase (AMPK), an enzyme with kinase activity that is activated in response to an increase in adenosine monophosphate nucleoside levels [78,80]. AMPK could play a key role in the formation of CAs; it participates in the regulation of energy metabolism and, once activated, moves into the cell nucleus and phosphorylates multiple proteins, including hypoxia-inducible factor 1α, which could mediate the development of CAs [77]. However, further study is needed to discern the exact mechanisms involved in the activation of AMPK and the induction of CAs by ROS.

An important finding from our meta-regression analyses is the statistically significant difference in the risk of overall CAs in offspring of women with PGDM versus offspring of women with GDM. That is, the risk of CAs in offspring was higher in women with PGDM than in those with GDM. Pregnancy begins at fertilization, and organogenesis begins during the third to eighth week post-conception and continues until birth. Therefore, the first trimester of pregnancy is the most critical period for organogenesis. In women with PGDM, there can be a lengthy period of sustained hyperglycemia before and during pregnancy, which can significantly impact organogenesis and contribute to CAs in offspring. This differs from GDM, which is usually diagnosed during the 24th to 28th week of gestation [8]. Therefore, in a woman with GDM, blood glucose levels could be normal or just slightly elevated during the first trimester, leading to minimal influence on organogenesis. This could partly explain why offspring of women with PGDM were at greater risk for CAs compared with offspring of women with GDM. However, women who develop GDM during pregnancy usually have evidence of metabolic dysfunction before pregnancy, such as pancreatic β-cell defects and increased insulin resistance [81,82], which may contribute to the development of hyperglycemia and thus increase the rate of malformations in infants, although further studies are needed to elucidate the potential mechanisms involved. Another key finding from our meta-regression analysis was the similar result observed regarding CHDs in offspring of women with PGDM and offspring of women with GDM. The heart is the first functional organ to develop and starts to beat and pump blood at around 22 days after fertilization [83]. The septum, including the interatrial septum, begins to form at 4 to 7 weeks of gestation.

Hyperglycemia could have a more critical influence on heart development in the early stage of pregnancy than in the late stage of pregnancy. Therefore, screening for diabetes in pregnant women will enable better glycemic control, which might reduce the rate of malformations, especially during organogenesis. However, the exact mechanisms underlying the influence of diabetes on organogenesis in different stages remain unclear and require further study.

In the present study, the results regarding specific types of CHDs in offspring with maternal PGDM were consistent with 2 previous meta-analyses [13,14]. A recent systematic review and meta-analysis conducted by Chen and colleagues involved a pooled analysis of 24 population-based studies and 18 hospital-based studies; the findings suggested that maternal GDM was significantly associated with the risk of most phenotypes of CHDs [13]. New data from population-based studies of more than 36 million births provided solid estimates of the associations between maternal GDM and specific types of CHDs in offspring [4,10–12]. However, these studies mainly focused on the association between different types of maternal diabetes and CHDs. Little is known about the association between maternal diabetes and other specific types of CAs in offspring or the extent to which types of maternal diabetes are associated with the increased risk of CAs.

One recent meta-analysis by Parimi and colleagues explored the association between maternal diabetes and the risk in offspring of CAKUT, which refers to a range of structural and functional anomalies of the kidney, collecting system, bladder, and urethra [84]. Our findings were in line with the results from Parimi et al. [84] that showed that offspring of mothers with PGDM had an almost 2-fold increased risk of CAKUT; however, results regarding the association between maternal GDM and the risk of CAKUT were inconsistent. Our findings demonstrated associations between maternal diabetes and 23 CA categories (excluding CHD-related categories) in offspring and suggested that offspring of women with PGDM had an increased risk of 21 specific types of CAs, while increased risks of 9 specific types of CAs were observed in offspring of women with GDM.

Strengths and limitations

Our study has several strengths. The first strength is the large sample size of over 80 million births from population-based data, which provides robust evidence regarding the risk of CAs in offspring of women with diabetes and are widely generalizable. Second, our study examined the associations between maternal diabetes and various types of CAs across multiple categories of maternal diabetes. Unlike previous studies [13,14,84] that only assessed the risk of CHDs or CAKUT in maternal diabetes, the present study systematically and quantitatively summarized the associations between maternal diabetes and 45 type-specific CAs in offspring. Third, consistent results of the pooled RRs supported the robustness of the findings of our study. Finally, the current study examined the extent to which types of maternal diabetes (i.e., pre-gestational and gestational) are associated with increased risk of CAs in offspring. The relative consistency of associations observed appears to support the hypothesis that maternal diabetes, especially PGDM, increases the likelihood of type-specific CAs in offspring.

However, several limitations should be noted. Although the increased risk association remained robust across various scenarios, some high levels of statistical heterogeneity generally persisted and could not be reduced in subgroup and sensitivity analyses. There were some causes of heterogeneity in the included studies. First, there is lack of consensus and uniformity in the screening standards and diagnostic criteria for GDM. Also, pre-pregnancy diabetes is sometimes unrecognized and discovered only during pregnancy as GDM, which could lead to overestimation of RRs associated with GDM. Second, the ascertainment of some CAs may vary substantially between studies. Some CAs are easy to ascertain (e.g., anencephaly), while some may not be recognized immediately after birth and may be discovered only later in infancy (e.g., milder atrial septal defects). This also contributes to the heterogeneity of the results. Third, most studies included live births only; the lack of information on stillbirths and terminations of pregnancy for fetal anomaly could introduce selection bias and lead to underestimation of the strength of the associations between maternal diabetes and risk of CAs in offspring. Fourth, there may be other unmeasured confounding factors in addition to those adjusted for in each study. In this regard, further study could be performed to reduce the aforementioned causes of heterogeneity in a more in-depth analysis. An additional limitation was that although we summarized and quantified the existing population-based data on overall CAs/CHD observed under maternal type 1 or type 2 diabetes, data on other type-specific CAs in offspring associated with maternal type 1 or type 2 diabetes are limited. Additional studies are needed to address this issue. Furthermore, information on treatment (e.g., insulin use) or how well-controlled blood glucose levels were in the study participants was not available in most of the studies included in the current study. Further work should strive to address this lack of information. Finally, we observed a negative association between maternal GDM and risk of gastroschisis. The reasons for why maternal GDM was inversely associated with the risk of gastroschisis are currently unknown; this finding warrants confirmation and further investigation in future studies. Residual confounding may contribute to the inverse association, but further confirmation is still needed.

Conclusion

In the present study, we observed an increased rate of CAs in the offspring of women with maternal diabetes and noted the differences between PGDM and GDM. Considering the substantial rise in the prevalence of maternal diabetes over recent decades, the expectation that this prevalence will continue to increase, the number of pregnancies worldwide, and the significant individual and global burdens associated with CAs in offspring, screening for diabetes in pregnant women may enable better glycemic control, and may enable identification of offspring at risk for CAs.

Supporting information

S1 Fig. Risk of bias, funnel plots, and forest plots regarding the associations between maternal diabetes and congenital anomalies in offspring.

Fig A: Risk of bias summary: Effect on congenital anomalies in offspring of women with pre-gestational diabetes. Fig B: Risk of bias summary: Effect on congenital heart defects in offspring of women with pre-gestational diabetes. Fig C: Risk of bias summary: The effect on congenital anomalies in offspring of women with gestational diabetes. Fig D: Risk of bias summary: The effect on congenital heart defects in offspring of women with gestational diabetes. Fig E: Funnel plots of the relative risks in population-based studies for pre-gestational diabetes mellitus and the risk of congenital anomalies. Fig F: Funnel plots of the relative risks in population-based studies for gestational diabetes mellitus and the risk of congenital anomalies. Fig G: Forest plot of the relative risks in population-based studies for maternal pre-gestational diabetes and the risk of any type of congenital heart defect—G1: heterotaxia; G2: conotruncal defects; G3: truncus arteriosus; G4: transposition of great vessels; G5: tetralogy of Fallot; G6: atrioventricular septal defects; G7: anomalous pulmonary venous return; G8: left ventricular outflow tract defect; G9: coarctation of aorta; G10: hypoplastic left heart; G11: right ventricular outflow tract defect; G12: pulmonary artery anomalies; G13: pulmonary valve stenosis; G14: septal defects; G15: ventricular septal defects; G16: atrial septal defects; G17: ventricular septal defect and atrial septal defect; G18: single ventricle. Fig H: Forest plot of the relative risks in population-based studies for maternal pre-gestational diabetes and the risk of other type-specific congenital anomalies—H1: nervous system defects; H2: neural tube defects; H3: anencephaly; H4: encephalocele; H5: spina bifida; H6: hydrocephaly; H7: holoprosencephaly; H8: eye, ear, face, and neck defects; H9: orofacial clefts; H10: cleft palate. H11: cleft lip with or without cleft palate; H12: digestive system defects; H13: diaphragmatic hernia; H14: abdominal wall defects; H15: omphalocele; H16: gastroschisis; H17: genitourinary system defects; H18: renal agenesis/dysgenesis; H19: hypospadias; H20: congenital anomalies of the kidney and urinary tract; H21: musculoskeletal system defects; H22: limb reduction; H23: polydactyly/syndactyly; H24: multiple congenital anomalies; H25: major congenital anomalies. Fig I: Forest plot of the relative risks in population-based studies for maternal gestational diabetes and the risk of any type of congenital heart defect—I1: heterotaxia; I2: truncus arteriosus; I3: transposition of great vessels; I4: tetralogy of Fallot; I5: atrioventricular septal defects; I6: anomalous pulmonary venous return; I7: left ventricular outflow tract defect; I8: coarctation of aorta; I9: hypoplastic left heart; I10: right ventricular outflow tract defect; I11: pulmonary artery anomalies; I12: pulmonary valve stenosis; I13: ventricular septal defects; I14: atrial septal defects; I15: single ventricle. Fig J: Forest plot of the relative risks in population-based studies for maternal gestational diabetes and the risk of other type-specific congenital anomalies—J1: nervous system; J2: neural tube defects; J3: anencephaly; J4: encephalocele; J5: spina bifida; J6: hydrocephaly; J7: holoprosencephaly; J8: eye, ear, face, and neck defects; J9: cleft palate; J10: cleft lip with or without cleft palate; J11: diaphragmatic hernia; J12: omphalocele; J13: gastroschisis; J14: genitourinary system defects; J15: renal agenesis/dysgenesis; J16: hypospadias; J17: congenital anomalies of the kidney and urinary tract; J18: musculoskeletal system defects; J19: limb reduction; J20: polydactyly/syndactyly; J21: multiple congenital anomalies; J22: major congenital anomalies. Fig K1: Forest plot of the relative risks in population-based studies for maternal type 1 diabetes and the risk of overall congenital anomalies. Fig K2: Forest plot of the relative risks in population-based studies for maternal type 1 diabetes and the risk of congenital heart defects. Fig L1: Forest plot of the relative risks in population-based studies for maternal type 2 diabetes and the risk of overall congenital anomalies. Fig L2: Forest plot of the relative risks in population-based studies for maternal type 2 diabetes and the risk of congenital heart defects.

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S1 Protocol. The registered protocol for this review in PROSPERO.

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S1 Table. EUROCAT, ICD-10, and ICD-9 codes used to identify and define congenital anomalies.

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S2 Table. References of studies excluded in the systematic review and meta-analysis of population-based studies.

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S3 Table. Ascertainment of maternal diabetes of the included studies in the systematic review and meta-analysis of population-based studies.

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S4 Table. Characteristics of population-based studies of maternal diabetes and congenital anomalies.

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S1 Text. Search strategy.

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S2 Text. MOOSE checklist.

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S3 Text. PRISMA 2020 checklist.

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Acknowledgments

We thank Professor Qi-Jun Wu, who kindly provided suggestions for this meta-analysis.

Abbreviations

AMPK: AMP-activated protein kinase
CHD: congenital heart defect
CA: congenital anomaly
CAKUT: congenital abnormalities of the kidney and the urinary tract
CI: confidence interval
GDM: gestational diabetes mellitus
MOOSE: Meta-analysis Of Observational Studies in Epidemiology
OS: oxidative stress
PGDM: pre-gestational diabetes mellitus
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
RR: relative risk
ROBINS-I: Risk of Bias in Non-randomized Studies–of Interventions
ROS: reactive oxygen species

Data Availability

The metadata underlying the reported analyses have been deposited in Zenodo (DOI: 10.5281/zenodo.5783967). https://doi.org/10.5281/zenodo.5783967.

Funding Statement

The authors received no specific funding for this work.

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PLoS Med. doi: 10.1371/journal.pmed.1003900.r001

Decision Letter 0

Caitlin Moyer

5 Apr 2021

Dear Dr Gao,

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PLoS Med. doi: 10.1371/journal.pmed.1003900.r002

Decision Letter 1

Caitlin Moyer

13 Oct 2021

Dear Dr. Gao,

Thank you very much for submitting your manuscript "Risks of specific congenital anomalies in offspring of diabetic mothers: a systematic review and meta-analysis of population-based studies of more than 76 million births" (PMEDICINE-D-21-01554R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to four independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

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We look forward to receiving your revised manuscript.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

1. Data availability statement: PLOS Medicine requires that the de-identified data underlying the specific results in a published article be made available, without restrictions on access, in a public repository or as Supporting Information at the time of article publication, provided it is legal and ethical to do so. Please see the policy at

http://journals.plos.org/plosmedicine/s/data-availability

and FAQs at

http://journals.plos.org/plosmedicine/s/data-availability#loc-faqs-for-data-policy

It seems as if the data underlying the analyses are provided in the metadata files provided, however, there do not seem to be any descriptions of these files.

2. Author Summary: Please move the Author Summary to follow the Abstract.

3. Author Summary (and throughout): Please temper statements of primacy with “To the best of our knowledge, this is the first comprehensive systematic review…” or similar.

4. Author Summary (and throughout): Please define all abbreviations at first use in the text (RR).

5. Abstract: Please note the eligibility criteria, including language restrictions, of included studies.

6. Abstract: Methods and Findings: Please quantify the main results (with 95% CIs and p values).

7. Abstract: Methods and Findings: In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

8. Abstract: Conclusion: Please address the study implications without overreaching what can be concluded from the data; the phrase "In this study, we observed ..." may be useful.

9. Introduction: Lines 121-124: Here and throughout, please revise to avoid causal language and please refer to associations instead: “...examined the extent to which types of maternal diabetes (i.e. pregestational or gestational) are associated with increased risk of CAs in offspring.” or similar.

10. Methods: Please update your search to the present time (currently search has an end date of December 2020).

11. Methods: Line 154: Please reference the PRISMA flowchart.

12. Methods: Line 208-209: Please provide more detail on how adequacy of controlling for confounders was determined.

13. Results: For the main results presented in the tables and in the text, please report both 95% CIs and p values.

14. Results: Line 285-288: Please avoid language implying causality, and please refer to associations here: “...statistically significant effect of maternal type 1 and type 2 diabetes on CHD…” and “Notably, we found that maternal PGDM increased the risk of all available specific types of CHD…”

15. Discussion: Please present and organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion.

16. Discussion: Line 450-452: Here and throughout the discussion, please avoid the use of language implying causality: “Finally, the current study examined the extent to which types of maternal diabetes (i.e., pregestational or gestational) increase risk of CAs in their offspring.”

17. S1 Text: Thank you for including the PRISMA and MOOSE checklists. When completing the checklists, please use section and paragraph numbers rather than page numbers to refer to locations within the text.

Comments from the reviewers:

Reviewer #1: This systematic review and meta-analysis examines the association between pre-pregnancy diabetes mellitus, gestational diabetes and congenital anomalies. This is a thorough review, well written and comprehensively presented. I have only relatively minor comments.

Comments:

1. Table 1: I suppose type 1 and type 2 diabetes mellitus would always be 'pre-gestational diabetes'. In the category 'pre-gestational diabetes', did the results of all 42 studies relate to a combined any type 1 or type 2 diabetes? Perhaps, instead of 'pre-gestational diabetes', you can use, for instance, 'pre-gestational diabetes (combined type 1 or 2)'. Just a suggestion.

2. Similar issue in Table 2: The indentation of subcategories in the table suggests these sub-categories are included in the total above. Can you, please, clarify, for instance, 'LVOT' category includes all LVOT anomalies, perhaps instead of 'LVOT' you can use 'LVOT combined' or 'All LVOT). The same applies to 'Conotruncal defect' - you can use 'Conotruncal defects (any)', etc. Similar issue is in Table 3, most rows look like subcategories. Just a suggestion.

3. Page 11, Line 196: Do you mean effect size (instead of effective size)?

4. Page 11, line 209: Can you specify adequate and not adequate control for confounding? What set of confounders would you consider adequate adjustment in the studies that adjusted for confounders? They are listed in Supplemental tables (Table D) but what was considered an 'adequate control' is not stated.

5. Fig 1 suggests that after identification of 23,748 records in PubMed and Embase and 1 record extra, the majority was excluded due to duplicates ('records after duplicates removed n=2,096'). You could perhaps place this box with the right side arrow and use the text 'Duplicates removed (n=2,096)'

6. In sensitivity analyses, strata categories are: before year 2000, between 2000 and 2010, and after 2010. It is not wrong, I am just wondering if there was any rationale for these categories.

7. Discussion: In study limitations, it would be nice to address the lack of information on how well the diabetes was controlled (e.g., mothers with well controlled diabetes would be expected to have lower rates of CA). Similarly, information on treatment (e.g., insulin use) was not available in most studies included in the review.

8. In Table 3, a relative risk of gastroschisis is below 1 (i.e., a 'protective effect' of maternal gestational diabetes). Is this consistent across the studies? Why would there be a negative association? It would be nice to mention in the discussion.

9. You mentioned ascertainment of maternal diabetes in the limitations. Besides varying criteria for GDM, pre-pregnancy diabetes is sometimes unrecognized and discovered only during pregnancy as GDM, which could lead to overestimation of relative risks associated with GDM.

10. In addition to the limitations, the ascertainment of some congenital anomalies may vary substantially between studies. Some CAs are easy to ascertain (e.g., anencephaly), while some may not be recognized immediately after birth and be discovered only later in infancy (e.g., milder atrial septal defects). This also contributes to the heterogeneity of the results.

11. The manuscript needs some editing.

Minor comments:

Page 16 Line 287: "Notably, we found that maternal PGDM increased the risk of all available specific types of CHD in the present study". I suggest "Notably, we found that maternal PGDM increased the risk of all specific types of CHD available to examine in the present study".

Reviewer #2: This is a well-conducted systematic review and meta-analysis on the risks of specific congenital anomalies in offspring of diabetic mothers using population-based studies. The study design, datasets, statistical methods and analyses, and presentation (tables and figures) and interpretation of the results are mostly adequate and of a good standard. Only a few minor issues needing attention.

1) In table 1, For Type of maternal diabetes, the number of studies doesn't add to the 56 total by whatever combination of Pregestational diabetes, Type 1 diabetes, Type 2 diabetes, and Gestational diabetes. There must be overalps. Can authors make it clearer? especially what is the exact overlap and in which subtype?

2) Meta-regression analyses were nicely done to show significant differences in RRs of CAs/CHD in PGDM versus GDM. However, as the selected studies are over about 50 years' period, could the meta-analysis be done over time to show whether any difference in RRs of CAs/CHD over time?

3) On page 15, High I-squared of 90% was shown on the results for overall CAs in offspring of mothers with PGDM. Similar high I-squared were also found in the results with T1D (82.5%) and GDM (76%). This happened even after using the random effect model. Can authors explain this high heterogeneity and potential impact on the results?

Reviewer #3: Overview

This is a very extensive systematic review of the risks of specific congenital anomalies and GDM and PGDM. The authors have done an excellent job in collating all this information

Major Comments

1. Table 3 - what is the bottom line based on 4 and 2 studies ? How does it relate to data in Figure E

2. Lines 327 - I do not understand what is being done here in the sensitivity analyses- the authors say that they reduced the I2 by excluding individual studies, with one range going from 55% to 82.9%. Does this not imply that one single study was responsible for a large amount of the variation and in which case more information should be given about that individual study and why it was so discrepant from the other studies.

3. Lines 389 - The authors state "Therefore, blood glucose during the first trimester in a mother with GDM could be normal or just slightly elevated, leading to minimal influence on organogenesis." And yet they present results showing that there is an increased risk of anomalies in mothers with GDM. This needs more clarification as to why you would expect an increased risk in GDM

4. The paper concludes that "timely screening for diabetes in women who are pregnant or planning to be pregnant provides a window of opportunity to prevent CAs in offspring.". However it is unclear from this analysis how such prevention would occur. Was there any data in the studies about how well controlled the blood glucose levels were in the mothers - comparing studies with well controlled vs less well controlled would be extremely useful to determine the possibility of prevention . As the authors make statements about the possibility of prevention they need to investigate this in their data. If it is not possible it should be flagged up as an area that needs further work.

5. I think it is not correct to include all categories of exposed women in the same analysis eg in Fig E. GDM,PGDM,T1 Diabetes and T2 Diabetes are all included and an overall estimate is produced. There is in expectation differences between PGDM and GDM so it is not relevant to combine them. 4 separate plots will make it easier to see how each individual study varies from the combined estimate for all the studies with the same exposure. I think analysing the studies separately may also slightly alter the estimates as the distribution of variances will be altered. The x-axis scale should also be more informative than just 0.1,0 and 10. Consider how to display study by "Oliveria-Brancali" as it is clearly a small study and is causing all other studies to appear very compact.

6. Forest plots are an important part of systematic reviews and I therefore suggest that revised Figure E is included in the main manuscript.

7. I would prefer to see the forest plots for the different anomalies rather than the funnel plots in figure F

Minor Comments

Table 1 : Years should be categorised Before 2000, 2000-2009 and In or after 2010

Table 2 and Table 3: would it be possible to give the numbers of each specific defect in order to have a feeling for how large the samples are

Reviewer #4: Briefly, my comments are as follows: This is a meta-analysis on a very large population of pregnant women with PGDM and GDM, assessing the rate of major congenital malformations (congenital anomalies) and cardiac malformations among the offspring of these mothers. The findings are important, but not new. However, since the studied population is very large and the study is well designed, I think that it merits publication in the journal. I have several comments related to the description of embryologic and teratologic issues. First, the authors use "time post conception" yet they call that "time of pregnancy". The usual definition of pregnancy is from the last menstrual period while fertilization generally occurs about two weeks later. Hence, they should state that their definition of pregnancy is from the time of fertilization. In addition, when they describe the heart development (i.e. lines 394-399) they should state the weeks when the cardiac septum develops (weeks 4-7 post fertilization) not just "first 2 months" that is simply incorrect. It is expected to be more careful with embryological and teratological descriptions

The authors make no attempt to explain the increased rate of anomalies in GDM and the differences between PGDM and GDM. This is perhaps not mandatory, but mentioning several times the need for screening for diabetes in pregnant women as a means to prevent the increased rate of malformations need explanations. They should explain that this will enable better glycemic control which might reduce the rate of malformations, especially during organogenesis, and discuss several studies in this direction. The tables and figures are OK. Thanks. Asher Ornoy, MD .

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2022 Feb 1;19(2):e1003900. doi: 10.1371/journal.pmed.1003900.r003

Author response to Decision Letter 1

29 Oct 2021

Attachment

Submitted filename: Comments Plos Medicine response_2021_10_28.docx

Click here for additional data file.^{(174.2KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1003900.r004

Decision Letter 2

Caitlin Moyer

14 Dec 2021

Dear Dr. Gao,

Thank you very much for re-submitting your manuscript "Risks of specific congenital anomalies in offspring of women with diabetes: a systematic review and meta-analysis of population-based studies of over 80 million births" (PMEDICINE-D-21-01554R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by two reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Dec 21 2021 11:59PM.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

Comments from Reviewers:

1. Title: Please revise the title slightly, and please capitalize the first word of the subtitle, after the colon. We suggest: “Risks of specific congenital anomalies in offspring of women with diabetes: A systematic review and meta-analysis of population-based studies including over 80 million birth”

2. Financial disclosure: Please state whether any sponsors or funders (other than the named authors) played any role in: Study design, Data collection and analysis, Decision to publish, Preparation of the manuscript. If sponsors or funders had no role in the research, please include this sentence: “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

3. Data availability statement: Please revise the data availability statement entered into the manuscript submission system, as this is the statement that will be used in the event of publication. This currently reads “All relevant data will be made available upon request to the corresponding author via e-mail.” Please change this to:“The metadata underlying the reported analyses have been deposited in Zenodo (DOI: 10.5281/zenodo.5595068).” or similar, and please check that the DOI is correct.

4. Metadata: In the online repository of excel files, it would be helpful to include a “readme” type of file in the repository, explaining the meaning of the column headers in each spreadsheet.

5. Abstract: Lines 53-57: “Of the 23 type-specific CAs of other systems in offspring, maternal PGDM was associated with a significantly increased risk of CAs in 21 categories; the corresponding RRs ranged from 1.57 (for hypospadias, 95% CI: 1.22 to 2.02; P < 0.001) to 18.18 (for holoprosencephaly, 95% CI: 4.03 to 82.06; P = 0.085).” It should be noted that it does not seem to be the case that the RR for holoprosencephaly reached statistical significance, though the sentence is worded as if it does.

The same wording inconsistency is present at Lines 57-60. “Maternal GDM was associated with a small but significant increase in the risk of CAs in nine categories; the corresponding RRs ranged from 1.14 (for limb reduction, 95% CI: 1.06 to 1.23; P = 0.866) to 5.70 (for heterotaxia, 95% CI: 1.09 to 29.92; P = 0.008).” We suggest revising to make it clear that you are reporting 1) the number of significant associations between either PGDM or GDM and type specific CAs out of the total type-specific CAs, and then 2) reporting the highest and lowest corresponding RRs out of the total of 23 type-specific CAs.

6. Abstract: Lines 53-57: Please revise the description of numbers of type specific CAs and CHD examined, as the Author Summary and Discussion mention 42 categories assessed.

7. Abstract: Line 67; We suggest removing “with no overlap in the 95% CIs”

8. Abstract: Line 67-69: Your findings do not directly inform on the causality of the relationship. We suggest revising to temper this conclusion. It may be helpful to focus the sentence more on how screening may inform clinical care and identifying risk.

9. Author summary: Please avoid directly copying text from the abstract for the author summary (for example, at Lines 72-75). Please revise to make this section distinct. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

10. Author summary: Lines 83-85: The text here implies there are 42 categories of CAs, while the abstract describes 23 type-specific CAs. Please revise to resolve the discrepancy.

11. Author summary: Lines 84-85: Please revise to: “while maternal gestational diabetes is associated with a small but significant increase in the risk of CAs…”

12. Author summary: Lines 97-98: We suggest removing this point, as the study does not examine insulin treatment for blood glucose control during pregnancy.

13. Introduction: Line 116: As mentioned by a reviewer, please clarify if the meaning is “third to eighth week of gestation” in this sentence.

14. Methods: Line 142: Please include a copy of the registered protocol as supporting information, and please refer to it here (e.g. S1_Protocol).

15. Methods: Line 164: Please reference the PRISMA flowchart (e.g. Fig 1, PRISMA flowchart).

16. Methods: Line 175: Please revise to clarify what specifically is meant by “overall CAs and CHD” and type-specific CAs for primary and secondary outcomes (e.g. rates).

17. Results: Line 274: Please clarify the p values reported for results, for example, whether or not the risk of overall CAs in offspring of women with type 2 diabetes was statistically significant or not. Please check if the reference to Fig J1 here should be Fig L1.

18. Results: Please clarify the p values reported for tests of significance of relative risks throughout this section, as in some cases there seems to be discrepancy between the 95% CIs and p values given.

19. Results: Please check all references to the figures and tables located in S1 Fig, to ensure accuracy.

20. Discussion: Line 354: Please revise to “...while maternal GDM was associated with a small but significant increase in risk of…”.

21. Discussion: Line 395-397: As mentioned by a reviewer, please use consistent language when referring to stages of pregnancy. In this paragraph, it is not clear if “22 days” is in reference to fertilization or LMP, for example. At line 397, please clarify if this should be “4-7 weeks gestation” or similar.

22. Discussion: Line 417: Please fully define “CAKUT” in this sentence.

23. Discussion: Line 432: Please check here and throughout that the number “45” is consistently the number of type-specific CAs examined (elsewhere, the number 42 is mentioned).

24. Discussion: Line 473: We suggest tempering this statement slightly, such as: “screening for diabetes in pregnant women may enable better glycemic control, and may enable identification of women and offspring at risk for CAs.” or similar.

25. Table 1: Please indicate in the legend that “No” refers to “Number” within the table.

26. Line 542: Please remove the Author Contributions, Competing Interests, Data Availability Statement, and Funding from the main text and please ensure all information is completely and accurately entered in the relevant fields of the manuscript submission system.

27. Figure 6: Please clarify “All P meta-regression ˂ 0.001 for pre-gestational diabetes versus gestational diabetes.” to indicate that this is true for both the comparison within congenital anomalies and congenital heart defects between gestational diabetes and pre-gestational diabetes. Please indicate in the legend if there are any other comparisons to report.

28. Table E and F within S1 Table file: Please move these Tables to the main text of the manuscript.

29. Supporting Information files: The supporting information name and number are required in a caption, and we highly recommend including a one-line title as well. You may also include a legend in your caption, but it is not required. Format your supporting information captions as follows: S1 Text. Title is strongly recommended. Legend is optional.

30. S1 Text: We suggest the search strategy and each checklist be provided as its own supporting information file.

31. PRISMA Checklist: Please revise items 1 and 2 of the PRISMA checklist, referring to Title and Abstract rather than page numbers. We note that there is no assessment of certainty of evidence. We suggest reporting on certainty of each outcome described in the results, in light of the bias assessment reported at line 255.

Reviewer #2: Many thanks authors for their great effort to improve the manuscript. I am satisfied with the response and revision. No further issues needing attention.

Reviewer #3: The authors have addressed all my earlier comments

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2022 Feb 1;19(2):e1003900. doi: 10.1371/journal.pmed.1003900.r005

Author response to Decision Letter 2

16 Dec 2021

Attachment

Submitted filename: Response Letter_2021_12_15.docx

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PLoS Med. doi: 10.1371/journal.pmed.1003900.r006

Decision Letter 3

Caitlin Moyer

21 Dec 2021

Dear Dr. Gao,

Thank you very much for re-submitting your manuscript "Risks of specific congenital anomalies in offspring of women with diabetes: A systematic review and meta-analysis of population-based studies including over 80 million birth" (PMEDICINE-D-21-01554R3) for review by PLOS Medicine.

Provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Please take these into account before resubmitting your manuscript:

[LINK]

In revising the manuscript for further consideration here, please ensure you address the specific points made by the editors. In your rebuttal letter you should indicate your response to the editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

We expect to receive your revised manuscript within 2 weeks. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Jan 04 2022 11:59PM.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

1. Data Availability Statement: Please include the complete link to the study data in the Data Availability Statement: https://doi.org/10.5281/zenodo.5783967

2. Title: Please change the title to: “Risks of specific congenital anomalies in offspring of women with diabetes: A systematic review and meta-analysis of population-based studies including over 80 million births”

3. Abstract and Results: Thank you for clarifying the reporting of the p values from the tests for heterogeneity. Please report the relative risks together with both 95% CIs and p values for results described in the text. Reporting of the results for the tests of heterogeneity in the text provides additional useful information.

4. Throughout: CA risk categories. In the abstract at line 52, the total number of type specific CA categories is indicated as 23. However, in the author summary and the discussion, 45 categories are mentioned. Please clarify in the Abstract that the 23 includes the CA categories that exclude the CHD-related categories.

5. Abstract: Lines 47-50: Please include the p values associated with the increase in risk for overall CA/CHD in women with PGDM or GDM.

6. Results: Lines 265-274: Please include the p values for RR for CAs associations with PGDM, type 1 diabetes, type 2 diabetes (p value associated with heterogeneity test may be reported separately).

7. Results: Lines 278-288: Please include the p values for RR for CHD associations with PGDM, type 1 and type 2 diabetes (p value associated with heterogeneity test may be reported separately).

8. Results: Lines 290-300: Please include p values for tests for RR for GDM associations with CHD (p value associated with heterogeneity test may be reported separately).

9. Results: Lines 303-314: Please include the p values for the RR reported for GDM and PGDM associated with type-specific CAs (p value associated with heterogeneity test may be reported separately).

10. Lines 553-555: Please remove the Financial Disclosure section from the main text, and please make sure all information is accurately entered in the Funding section of the manuscript submission system. You note here that the funders had no role in the study. However, the Funding section of the manuscript notes that “The authors received no specific funding for this work.” Please clarify.

11. Figures 2, 3, 4, and 5: Please report the overall RR with 95% CIs and p values. Please define “DL” in the legends.

12. Figure 6: Please report the p value in addition to the RR and 95% CIs.

13. Tables 2, 3, 4, and 5: Please report the p values in addition to the 95% CIs for the pooled RRs.

14. Figures G - L in S1 Fig: Please note the overall RR with 95% CIs and p values. Please define the abbreviation “DL” in the legends.

15. Line 58-60, Line 330-332, Line 435-437 and Line 442-444: There seems to be some overlap in text with https://doi.org/10.1136/bmj.m3222. Please revise to avoid this.

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2022 Feb 1;19(2):e1003900. doi: 10.1371/journal.pmed.1003900.r007

Author response to Decision Letter 3

21 Dec 2021

Attachment

Submitted filename: Response Letter 2021_12_22.docx

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PLoS Med. doi: 10.1371/journal.pmed.1003900.r008

Decision Letter 4

Caitlin Moyer

22 Dec 2021

Dear Dr Gao,

On behalf of my colleagues and the Academic Editor, Jenny Myers, I am pleased to inform you that we have agreed to publish your manuscript "Risks of specific congenital anomalies in offspring of women with diabetes: A systematic review and meta-analysis of population-based studies including over 80 million births" (PMEDICINE-D-21-01554R4) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

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Associated Data

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

Supplementary Materials

S1 Fig. Risk of bias, funnel plots, and forest plots regarding the associations between maternal diabetes and congenital anomalies in offspring.

(PDF)

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S1 Protocol. The registered protocol for this review in PROSPERO.

(PDF)

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S1 Table. EUROCAT, ICD-10, and ICD-9 codes used to identify and define congenital anomalies.

(DOCX)

Click here for additional data file.^{(36.1KB, docx)}

S2 Table. References of studies excluded in the systematic review and meta-analysis of population-based studies.

(DOCX)

Click here for additional data file.^{(28.9KB, docx)}

S3 Table. Ascertainment of maternal diabetes of the included studies in the systematic review and meta-analysis of population-based studies.

(DOCX)

Click here for additional data file.^{(44.2KB, docx)}

S4 Table. Characteristics of population-based studies of maternal diabetes and congenital anomalies.

(DOCX)

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S1 Text. Search strategy.

(DOCX)

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S2 Text. MOOSE checklist.

(DOCX)

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S3 Text. PRISMA 2020 checklist.

(DOCX)

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Attachment

Submitted filename: Comments Plos Medicine response_2021_10_28.docx

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Attachment

Submitted filename: Response Letter_2021_12_15.docx

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Attachment

Submitted filename: Response Letter 2021_12_22.docx

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Data Availability Statement

The metadata underlying the reported analyses have been deposited in Zenodo (DOI: 10.5281/zenodo.5783967). https://doi.org/10.5281/zenodo.5783967.

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PERMALINK

Risks of specific congenital anomalies in offspring of women with diabetes: A systematic review and meta-analysis of population-based studies including over 80 million births

Tie-Ning Zhang

Xin-Mei Huang

Xin-Yi Zhao

Wei Wang

Ri Wen

Shan-Yan Gao

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Search strategy and inclusion criteria

Fig 1. Flowchart of selection of studies included in the meta-analysis.

Data extraction

Risk of bias and study quality

Statistical analysis

Results

Search results and study characteristics

Table 1. Summary characteristics of included studies.

Bias assessment

Exposure to PGDM/GDM and overall CAs in offspring

Fig 2. Forest plot of the RRs in population-based studies for maternal pre-gestational diabetes and the risk of overall congenital anomalies (RR = 1.99, 95% CI 1.82 to 2.17, I2 = 90.0%, P < 0.001).

Fig 3. Forest plot of the RRs in population-based studies for maternal gestational diabetes and the risk of overall congenital anomalies (RR = 1.18, 95% CI 1.13 to 1.23, I2 = 76.0%, P < 0.001).

Exposure to PGDM and CHDs in offspring

Fig 4. Forest plot of the RRs in population-based studies for maternal pre-gestational diabetes and the risk of congenital heart defects (RR = 3.46, 95% CI 2.77 to 4.32, I2 = 98.2%, P < 0.001).

Table 2. Pooled RR and 95% confidence intervals for associations between maternal diabetes and any type of congenital heart defects .

Exposure to GDM and CHDs in offspring

Fig 5. Forest plot of the RRs in population-based studies for maternal gestational diabetes and the risk of congenital heart defects (RR = 1.50, 95% CI 1.38 to 1.64, I2 = 81.2%, P < 0.001).

Exposure to PGDM and other type-specific CAs in offspring

Table 3. Pooled RRs and 95% confidence intervals for associations between maternal diabetes and other type-specific congenital anomalies.

Exposure to GDM and other type-specific CAs in offspring

Subgroup, meta-regression, and sensitivity analyses

Table 4. Subgroup analysis of the association between maternal diabetes and risk of overall congenital anomalies in offspring: Results of meta-analyses.

Table 5. Subgroup analysis of the association between maternal diabetes and risk of congenital heart defects in offspring: Results of meta-analyses.

Fig 6. Risks of overall congenital anomalies and congenital heart defects in offspring according to different types of maternal diabetes.

Discussion

Strengths and limitations

Conclusion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Caitlin Moyer

Roles

Decision Letter 1

Caitlin Moyer

Roles

Author response to Decision Letter 1

Decision Letter 2

Caitlin Moyer

Roles

Author response to Decision Letter 2

Decision Letter 3

Caitlin Moyer

Roles

Author response to Decision Letter 3

Decision Letter 4

Caitlin Moyer

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

Fig 2. Forest plot of the RRs in population-based studies for maternal pre-gestational diabetes and the risk of overall congenital anomalies (RR = 1.99, 95% CI 1.82 to 2.17, I² = 90.0%, P < 0.001).

Fig 3. Forest plot of the RRs in population-based studies for maternal gestational diabetes and the risk of overall congenital anomalies (RR = 1.18, 95% CI 1.13 to 1.23, I² = 76.0%, P < 0.001).

Fig 4. Forest plot of the RRs in population-based studies for maternal pre-gestational diabetes and the risk of congenital heart defects (RR = 3.46, 95% CI 2.77 to 4.32, I² = 98.2%, P < 0.001).

Fig 5. Forest plot of the RRs in population-based studies for maternal gestational diabetes and the risk of congenital heart defects (RR = 1.50, 95% CI 1.38 to 1.64, I² = 81.2%, P < 0.001).