Journal of Diabetes Research
Volume 2021 (2021), Article ID 6692695, 18 pages
https://doi.org/10.1155/2021/6692695

Research Article

Factors Associated with Gestational Diabetes Mellitus: A Meta-Analysis

Yu Zhang,¹ Cheng-Ming Xiao,² Yan Zhang,² Qiong Chen,¹ Xiao-Qin Zhang,¹ Xue-Feng Li,¹ Ru-Yue Shao,³ and Yi-Meng Gao²

¹Department of Obstetrics and Gynecology, People’s Hospital of Chongqing Banan District, Chongqing 401320, China
²Department of Obstetrics and Gynecology, Liaocheng People’s Hospital and LiaochengClinical School of Shandong First Medical University, Liaocheng, Shandong 252000, China
³School of Clinical Medicine, Chongqing Medical and Pharmaceutical College; Chongqing Engineering Research Center of Pharmaceutical Sciences, Shapingba District, Chongqing, 401331, China

Correspondence should be addressed to Yi-Meng Gao; gyimeng@126.com

Received 21 December 2020; Accepted 29 April 2021; Published 10 May 2021

Academic Editor: Akira Sugawara

Copyright © 2021 Yu Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Gestational diabetes mellitus (GDM) is a major public health issue, and the aim of the present study was to identify the factors associated with GDM. Databases were searched for observational studies until August 20, 2020. Pooled odds ratios (ORs) were calculated using fixed- or random-effects models. 103 studies involving 1,826,454 pregnant women were identified. Results indicated that years (OR: 2.466, 95% CI: (2.121, 2.866)), prepregnancy overweight or obese (OR: 2.637, 95% CI: (1.561, 4.453)), family history of diabetes (FHD) (OR: 2.326, 95% CI: (1.904, 2.843)), history of GDM (OR: 21.137, 95% CI: (8.785, 50.858)), macrosomia (OR: 2.539, 95% CI: (1.612, 4.000)), stillbirth (OR: 2.341, 95% CI: (1.435, 3.819)), premature delivery (OR: 3.013, 95% CI: (1.569, 5.787)), and pregestational smoking (OR: 2.322, 95% CI: (1.359, 3.967)) increased the risk of GDM with all , whereas history of congenital anomaly and abortion, and HIV status showed no correlation with GDM (). Being primigravida (OR: 0.752, 95% CI: (0.698, 0.810), ) reduced the risk of GDM. The factors influencing GDM included , prepregnancy overweight or obese, FHD, history of GDM, macrosomia, stillbirth, premature delivery, pregestational smoking, and primigravida.

1. Introduction

Gestational diabetes mellitus (GDM), defined as glucose intolerance of variable degree with onset or first recognition during pregnancy, is reported as one of the most common clinical complications of pregnancy [1, 2]. According to International Diabetes Federation (IDF) 2017, the prevalence of GDM is expected to be on the rise year by year [3]. Women with GDM may incur a potential risk of adverse outcomes [4, 5]. Mothers who have GDM are at risk of developing gestational hypertension and preeclampsia, at risk of suffering from caesarean section, and at risk of inducing subsequent type 2 diabetes mellitus (T2DM) and cardiovascular diseases [6–11]. Infants born from GDM women could be prone to abnormal fetal development such as being in macrosomia, having more congenital abnormalities, and having neonatal hypoglycemia [6, 12, 13]. Consequently, it is suggested that healthcare policy makers should be aware of the significance of GDM for early detection and further intervention.

To date, various relevant factors have been identified as predictors of GDM. Several studies have demonstrated that the frequently reported risk factors of GDM include older maternal ages, prepregnancy obesity, family history of diabetes (FHD) [14, 15], previous obstetric outcomes (e.g., macrosomia [16], stillbirth [17], abortion [18], premature delivery [19], congenital anomaly [16], being primigravida [20]), history of GDM [21], infection factors (e.g., Human Immunodeficiency Virus (HIV) [22]), pregestational smoking [23], and socioeconomic factors (educational level, occupation, and monthly household income) [24]. However, there are other evidences suggesting that maternal age, FHD, prepregnancy overweight or obesity, previous history of abortion, stillbirth, and macrosomia showed no significant association with GDM [25, 26]. Since most of the information regarding the main factors involved in GDM lack comprehensive analysis, it is necessary to conduct a meta-analysis to further explore the potential factors responsible for GDM.

2. Materials and Methods

Our study has been approved by the Open Science Framework (OSF) registries (https://osf.io/registries), and the registration number is 10.17605/OSF.IO/4HJGN. This meta-analysis was performed according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. Since this study was based on a meta-analysis of published studies, it did not require patient consent and ethical approval.

2.1. Literature Search Strategy

Four online databases (Web of Science, Embase, PubMed, and Cochrane Library) were systematically searched for articles published till August 20, 2020. We searched PubMed using the following terms: “diabetes mellitus” OR “diabetes” AND “pregnancy” OR “pregnancies” OR “gestation” OR “diabetes, gestational” OR “diabetes, pregnancy-induced” OR “diabetes, pregnancy induced” OR “pregnancy induced diabetes” OR “gestational diabetes” OR “diabetes mellitus gestational” OR “gestational diabetes mellitus” AND “risk factor” OR “risk factors.”

2.2. Inclusion and Exclusion Criteria

Inclusion criteria include the following: (1) women with GDM (the observation group) and with healthy pregnancies (the control group); (2) the reported relevant factors in our studies including years, prepregnancy overweight or obese, history of GDM, primigravida, history of congenital anomaly, FHD, history of macrosomia, HIV status, history of stillbirth, history of premature delivery, history of abortion, and pregestational smoking; and (3) observational studies.

Exclusion criteria include the following: (1) studies not published in English; (2) meta-analyses, reviews, conference summaries, case reports, letters, and guidelines; and (3) animal experiments.

2.3. Data Extraction and Quality Assessment

The data were extracted by two reviewers (Yu Zhang and Cheng-Ming Xiao) independently according to the inclusion and exclusion criteria. If a conflict existed, the third reviewer (Yi-Meng Gao) would join in extracting the data. The following study features were extracted from each article: the first author’s name, year of publication, country, study design, maternal age (years), sample size, the number of GDM cases, and quality assessment scores. The revised Joanna Briggs Institute (JBI) scale was used for cross-sectional studies to evaluate the quality of the literature, with 1-13 being low-risk of bias, and 14-20 being high-risk of bias. The modified Newcastle-Ottawa Scale (NOS) was used for case-control studies and cohort studies, and the studies with scores of 1-4 were considered low quality, while those with scores of 5-10 were considered high quality.

2.4. Statistical Analysis

Data were analyzed using Stata 15.1 software (Stata Corporation, College Station, TX, USA). The factors were assessed by odds ratios (ORs) and 95% confidence intervals (CIs). Heterogeneity tests were performed for each effect size, and random-effects models were adopted when ; otherwise, fixed effects models were performed. The publication bias was estimated using Egger’s test and adjusted by trim and fill method. A difference was considered statistically significant at .

3. Results

3.1. Literature Search

In this study, 3,586 articles were extracted from PubMed, 5,204 from Embase, 9,340 from Web of Science, 16 from the Cochrane Central, and 7 from other sources. After the removal of duplicate records (), 278 articles were excluded after screening of the titles and abstracts and another 103 through full-text screening for eligibility. Finally, a total of 103 studies (Supplementary Material 1) were included in our study for evaluating the relationship between these factors and GDM. The flow diagram is shown in Figure 1.

Figure 1: Flow diagram of search strategy.

3.2. Study Characteristics

A total of 1,826,454 pregnant women were enrolled in this meta-analysis, divided into the observation group (with GDM) composed of 120,696 subjects and the control group (without GDM) composed of 1,705,758 subjects. In terms of the quality of our included studies, scores from the assessment by the revised NOS and JBI scales were summarized in Table 1. The quality scores ranged from 4 to 16. Of the 103 included studies, 29 articles were low quality, while 74 were high quality (Table 1).

Table 1: Baseline characteristics of the included studies.

Author
Year
Country
Study design
Maternal age (years)
Sample sizes
GDM cases
Quality scores

Wagaarachchi
2001
Sri Lanka
Case-control
—
1004
41
5

Weijers
2002
Amsterdam
Case-control

561
71
5

Yang
2002
China
Case-control

9886
177
4

Dempsey
2004
USA
Case-control
—
541
155
6

Ozumba
2004
Nigeria
Case-control
—
400
200
5

Zhang
2004
China
Case-control
—
327
67
6

Hadaegh
2005
Iran
Case-control
—
700
62
6

Janghorbani
2006
UK
Case-control
—
3933
65
4

Wijeyaratne
2006
Sri Lanka
Case-control
—
442
274
5

Mamabolo
2007
South Africa
Case-control

262
23
4

Qiu
2007
USA
Case-control

201
105
5

Cypryk
2008
Poland
Case-control
—
1670
510
4

Hedderson
2008
USA
Case-control
—
1323
381
6

Hedderson
2008
USA
Case-control
—
455
251
6

Murgia
2008
Italy
Case-control

1103
247
5

Bhat
2010
India
Case-control

600
300
4

Harizopoulou
2010
Greece
Cross-sectional

160
40
5

Hedderson
2010
USA
Case-control
—
1134
341
5

Ogonowski
2010
Poland
Case-control

2425
1414
6

Kuti
2011
Nigeria
Case-control
—
765
106
4

Morisset
2011
Canada
Case-control

294
55
5

Qiu
2011
USA
Case-control

596
185
5

Anzaku
2013
Nigeria
Cross-sectional

253
21
5

Jao
2013
Cameroon
Cross-sectional
30.5 (27.5-34.5)
316
20
4

Khan
2013
Pakistan
Case-control

200
103
5

Fawole
2014
Ibadan
Cross-sectional
—
1086
35
12

Kirke
2014
Australia
Case-control

1636
73
4

Mwanri
2014
Tanzania
Cross-sectional
—
910
54
14

Padmanabhan
2014
Australia
Case-control
33.0 (29.0-36.0)
682
343
4

Rajput
2014
India
Case-control

913
127
6

Tabatabaei
2014
Canada
Case-control

96
48
4

Bibi
2015
Pakistan
Cross-sectional
—
190
50
11

Erem
2015
Turkey
Cross-sectional

815
39
15

Olagbuji
2015
Nigeria
Cohort
—
1059
91
5

Oppong
2015
Ghana
Cross-sectional
—
399
37
14

Robledo
2015
USA
Cohort
—
649952
11334
5

Singh
2015
India
Case-control

102
51
5

Bowers
2016
Danish
Case-control

699
350
4

Mohan
2016
India
Case-control
—
201
32
4

Nasiri-Amiri
2016
Iran
Case-control
—
200
100
6

Tomic
2016
Bosnia and Herzegovina
Cross-sectional
—
285
31
13

Abdelmola
2017
Saudi Arabia
Cross-sectional
—
36
36
14

Anand
2017
Canada
Case-control

1006
365
6

Collier
2017
UK
Case-control
—
47290
973
4

Farina
2017
Italy
Case-control
33.5 (24-40)
72
12
6

Liu
2017
China
Case-control

600
300
6

Mapira
2017
Rwanda
Cross-sectional
—
288
24
5

Oriji
2017
Nigeria
Case-control
—
235
35
5

Rawal
2017
USA
Case-control

321
107
5

Sedaghat
2017
Iran
Case-control

388
122
6

Sugiyama
2017
Palau
Case-control
—
1730
95
5

Bartakova
2018
Czech
Case-control
33 (29-36)
363
293
4

Egbe
2018
Cameroon
Cross-sectional
—
200
41
13

Feleke
2018
Ethiopia
Case-control
—
2257
567
5

Larrabure-Torrealva
2018
America
Cross-sectional

1300
205
15

Macaulay
2018
South Africa
Cohort
31 (27-36)
741
83
7

Macaulay
2018
South Africa
Cross-sectional
31 (27-36)
1900
174
15

Mak
2018
China
Cohort

1337
199
6

Nhidza
2018
Zimbabwe
Cross-sectional
—
150
10
5

Wu
2018
China
Case-control

4959
1080
6

Xiao
2018
China
Case-control
32 (29-34)
1585
599
5

Zaman
2018
Iran
Cross-sectional

520
260
16

Abualhamael
2019
Saudi Arabia
Case-control

196
103
7

Agah
2019
Iran
Cross-sectional
—
609
28
14

Asadi
2019
Iran
Case-control

278
130
6

Chakkalakal
2019
Tennessee
Case-control

89
40
4

Chen
2019
China
Case-control
—
9556
1464
4

Chen
2019
China
Case-control

249
123
5

Hrolfsdottir
2019
Iceland
Cohort

1651
264
6

Hu
2019
China
Cohort
—
1014
238
5

Huo
2019
China
Case-control

486
243
7

Ijas
2019
Finland
Cohort
—
24577
5680
5

Kouhkan
2019
Iran
Case-control

270
135
6

Li
2019
China
Case-control

496
248
4

Mak
2019
China
Cohort

1449
229
6

Muche
2019
Ethiopia
Cross-sectional
—
1027
131
12

Olmedo-Requena
2019
Spain
Cross-sectional

1466
291
16

Rajasekar
2019
Vellore
Cross-sectional

225
75
16

Rajput
2019
India
Case-control

100
50
7

Telejko
2019
Poland
Cohort
31 (27-35)
1508
397
7

Wan (China)
2019
China
Case-control

3419
398
5

Wan (Australia)
2019
Australia
Case-control

28594
1181
5

Wang
2019
China
Case-control

1552
776
7

Yan
2019
China
Cohort

78572
13846
7

Yen
2019
China
Cohort
—
527
74
5

Zahra
2019
Pakistan
Case-control
—
200
103
5

Zhang
2019
China
Cohort
29.0 (27-32)
2093
241
5

Zhu
2019
China
Case-control

3110
399
5

Zhu
2019
China
Case-control

3289
429
5

Aburezq
2020
Kuwait
Cross-sectional

653
92
15

Alsaedi
2020
Saudi Arabia
Case-control

347
279
5

Bar-Zeev
2020
Ohio
Case-control
—
222408
12897
5

Basu
2020
India
Case-control

715
127
6

Dos Santos
2020
Brazil
Cross-sectional
—
2284
126
14

Francis
2020
USA
Case-control

321
107
7

Ganapathy
2020
India
Case-control

140
70
6

Giles
2020
Australia
Cross-sectional
—
671227
54805
12

Kong
2020
China
Cohort

1441
114
6

Lan
2020
China
Cohort

1910
620
6

Li
2020
China
Case-control

610
305
5

Mishra
2020
India
Case-control
—
373
100
5

Rayis
2020
Saudi Arabia
Case-control
30 (25-34)
259
48
4

Siddiqui
2020
Saudi Arabia
Cross-sectional

218
53
16

Yong
2020
The Netherlands
Cohort

452
48
5

GDM: gestational diabetes mellitus.

The numbers of the included studies according to different factors are as follows: , ; prepregnancy overweight or obese, ; history of GDM, ; primigravida, ; history of congenital anomaly, ; FHD, ; history of macrosomia, ; HIV status, ; history of stillbirth, ; history of abortion, ; history of premature delivery, ; and pregestational smoking, .

3.3. Factors Associated with GDM

The results demonstrated that (OR: 2.466, 95% CI: (2.121, 2.866), ), prepregnancy overweight or obese (OR: 2.637, 95% CI: (1.561, 4.453), ), history of GDM (OR: 21.137, 95% CI: (8.785, 50.858), ), FHD (OR: 2.326, 95% CI: (1.904, 2.843), ), history of macrosomia (OR: 2.539, 95% CI: (1.612, 4.000), ), history of stillbirth (OR: 2.341, 95% CI: (1.435, 3.819), ), history of premature delivery (OR: 3.013, 95% CI: (1.569, 5.787), ), and pregestational smoking (OR: 2.322, 95% CI: (1.359, 3.967), ) were associated with a higher risk of GDM. Nonetheless, there were no significant differences in terms of the history of congenital anomaly (OR: 1.837, 95% CI: (0.418, 8.067), ), HIV status (OR: 1.168, 95% CI: (0.902, 1.512), ), and history of abortion (OR: 1.546, 95% CI: (0.906, 2.639), ). In addition, being primigravida (OR: 0.752, 95% CI: (0.698, 0.810), ) was associated with the reduced risk of GDM (Table 2, Figures 2(a)–2(f) and 3(a)–3(f)).

Table 2: Summary of the meta-analysis of associated factors for GDM.

No.
Factors
No. studies included
OR
95% CI

heterogeneity

Bias heterogeneity

1

36
2.466
2.121, 2.866
96.2
<0.001
0.19
0.243

2
Prepregnancy overweight or obese
48
2.637
1.561, 4.453
99.8
<0.001
4.85
0.001

3
FHD
74
2.326
1.904, 2.843
94.7
<0.001
1.83
0.081

4
Primigravida
56
0.752
0.698, 0.810
94.7
<0.001
1.53
0.132

5
History of congenital anomaly
3
1.837
0.418, 8.067
0.0
0.421
—
—

6
History of GDM
24
21.137
8.785, 50.858
96.9
<0.001
1.35
0.181

7
History of macrosomia
26
2.539
1.612, 4.000
86.6
<0.001
2.24
0.035

8
HIV status
4
1.168
0.902, 1.512
0.0
0.238
—
—

9
History of stillbirth
11
2.341
1.435, 3.819
52.0
0.001
0.18
0.862

10
History of abortion
19
1.546
0.906, 2.639
94.3
0.110
0.26
0.800

11
History of premature delivery
3
3.013
1.569, 5.787
0.0
0.001
—
—

12
Pregestational smoking
9
2.322
1.359, 3.967
66.7
0.002
—
—

CI: confidence interval; FHD: family history of diabetes mellitus; GDM: gestational diabetes mellitus; HIV: human immunodeficiency virus; OR: odds ratio.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 2: Forest plot for factors associated with GDM: (a) ; (b) prepregnancy overweight or obese; (c) FHD; (d) history of GDM; (e) HIV status; (f) pregestational smoking.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 3: Forest plot for previous history of obstetric factors associated with GDM: (a) macrosomia; (b) stillbirth; (c) premature delivery; (d) abortion; (e) congenital anomaly; (f) primigravida.

3.4. Sensitivity Analysis and Publication Bias

Sensitivity analysis of each factor was conducted, and the results were found to have stability without any difference in homogeneity and the synthesized results, despite the change of the factors that affected the results (Supplementary Material 2). Results of Egger’s test indicated that there was no significant publication bias in (, ), history of GDM (, ), primigravida (, ), FHD (, ), history of stillbirth (, ), and history of abortion (, ). Prepregnancy overweight or obese (, ) and history of macrosomia (, ) showed a publication bias, and after adjustments by the trim and fill method, there was no obvious asymmetry in the funnel plots, meaning no publication bias was detected (Table 2, Figures 4(a)–4(b)).

(a)

(b)

Figure 4: Egger’s funnel plot of the publication bias improved by the trim and fill method for factors of GDM: (a) prepregnancy overweight or obese and (b) history of macrosomia.

4. Discussion

In this meta-analysis of 1,826,454 pregnant women from diverse international cohorts, our findings suggested that factors such as , prepregnancy overweight or obese, pregestational smoking, FHD, previous history of GDM, macrosomia, stillbirth, and premature delivery significantly increased the risk of GDM. Besides, being primigravida was associated with a lower risk of GDM, whereas history of congenital anomaly, HIV status, and history of abortion showed no impact on the risk of GDM; controlling these relevant factors for GDM could reduce the serious increase of the occurrence of GDM.

Maternal age was reported to be closely associated with GDM. Older maternal age increased the risk of developing GDM, and the threshold for lower risks was recommended as 25 years old by the American Diabetic Association [27], similar to the result of our meta-analysis. However, other studies differed with the result mentioned above, i.e., they recommended that maternal age greater than 35 years was more prone to GDM [20, 28]. Although it is shown that there is a certain difference in the cutoff value of maternal age, there is an inevitable risk of developing GDM with the annual increase of age in modern society [29]. The reason for increasing older ages at pregnancy may be related to the implementation of the universal two-child policy, especially in China, as well as a longer period of education and better access to birth control technologies.

Prepregnancy overweight or obese was another major risk factor identified in the current study. A study conducted by Mohan and Chandrakumar also demonstrated that prepregnancy weight management could reduce a woman’s risk of GDM [30]. There were other studies with similar results to ours [31, 32], despite their varieties of dietary habits and with most people consuming large amounts of alcoholic beverages. Counselling for pregnant women should emphasize the need for women to avoid sedentary lifestyles before pregnancy and to be aware of the risks of GDM to both themselves and the unborn child.

Our study also suggested that FHD (particularly in a first-degree relative) was strongly related to an increased risk of GDM, which had been observed in a previous study [33]. This was partly because of an increased susceptibility to GDM due to a genetic deficiency in insulin secretion from their first-degree relatives [34]. Therefore, it is important to emphasize that healthcare education providers must obtain accurate personal or family history from their recipients in order to identify at-risk mothers for preventing GDM.

Another significant medical factor associated with a higher risk for developing GDM was history of GDM. Interestingly, a retrospective study [35] and two case-control studies [21, 36] also had similar results showing that history of GDM was thought to be a common risk factor in repeated pregnancies [34].

Among the obstetric factors of GDM, Anzaku and Musa pointed out that women with previous history of macrosomia were the only independent risk factor for GDM in the next pregnancy [16], which was similar to our results. A case-control study indicated that women having a history of abortion increased the risk of developing GDM at the central hospitals of the Amhara region, Ethiopia [34]. In contrast to this finding, our study showed no significant association between GDM and previous history of abortion, while another study showed a similar result to ours [19]. Limited literatures reported an association between a history of fetal congenital anomaly or premature delivery and GDM. Our results, supported by a previous study, revealed that they had no link [37]. However, women who had a history of premature delivery would be prone to the development of GDM, and it can be attributed to the intrauterine damage of the mother and the fetus [38]; however, more research is required to affirm this result. The current study also indicated that pregnant women with a history of stillbirth would have a higher risk of developing GDM during future pregnancies. This finding was in line with a review conducted in Africa by Muche et al. [23]. A study conducted in Pakistan demonstrated that the incidence of GDM in primigravida Pakistani women was <1% [39]. A previous meta-analysis of 5 included studies implied that being primigravida would reduce the risk of GDM [37]. Our study of a larger trial containing 56 relevant studies has reached the same conclusion.

As for infection factors, Egbe et al. found that there were 13 out of 200 (6.5%) HIV-positive respondents through analysis, but no association between HIV and GDM was observed [17]. This finding was consistent with our meta-analysis, which was also supported by the research of Jao et al. [22] and a previous meta-analysis study conducted by Natamba et al. [37]. Because few studies have reported a link between HIV and GDM, their association still needs to be further explored through more researches.

With the exception of the most common risk factors, such as maternal age, prepregnancy overweight or obese, FHD, obstetric factors, and infection factors, this study demonstrated that there was a significant correlation between pregestational smoking and GDM. Previous studies have also noted that pregestational smoking was considered to be a risk factor, although its association has been rarely investigated at present [40]. This condition might be explained by the fact that there were several limitations in the way data collection related to smoking was conducted in our study. A recent systematic review examined the relationship between pregestational smoking and the risk of GDM, but no correlation was found [36]. The aspect of smoking in the development of GDM deserves further investigations. Possible uncontrolled confounding factors should be considered, such as the differences in socioeconomic status between groups, selection bias, or even passive smoking.

Strengths and limitations should be taken into account in further interpreting our findings. In terms of strengths, due to the high prevalence of GDM, our meta-analysis included studies conducted in different countries such as China, USA, Australia, and India, covering a number of nationwide representative populations, which to a certain extent had reduced the possible selection bias and reaching some relatively generalized conclusions. Nevertheless, the present study also had some limitations. Firstly, the role of confounding factors cannot be completely eliminated in our observational studies. Although majority of the articles included in the analysis evaluated multiple factors, limited studies have shown the association between other variables such as living quarters, substance abuse, dietary diversity, and physical activity issues with GDM. Prospective review studies need to clarify the correlation between GDM and the other factors mentioned above. Secondly, there is a high heterogeneity in our results which might also be attributed to the different demographic characteristics among populations in more than 37 countries covered by this meta-analysis. Additionally, qualitative studies about the reasons for GDM pathologically should be added in this review.

5. Conclusions

In our study, years, prepregnancy overweight or obese, FHD, previous history of GDM, macrosomia, stillbirth and premature delivery, pregestational smoking, and being primigravida were considered as all independent risk factors of GDM. It is strongly recommended that all pregnant women in the future be screened early for GDM, especially those identified at higher risks of GDM, thereby leading to early diagnosis of GDM and early intervention.

Data Availability

The data used to support the findings of this study are included within the article.

Ethical Approval

Since this study was based on a meta-analysis of published studies, it did not require patient consent and ethical approval.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Acknowledgments

We thank the participants included in our study for their contributions. This work was supported by the Key Clinical Specialty Development Project of Chongqing (grant number 2012143).

Supplementary Materials

References of included studies. Sensitivity analysis: (A) ; (B) prepregnancy overweight or obese; (C) FHD; (D) history of GDM; (E) HIV status; (F) pregestational smoking; (G) history of macrosomia; (H) history of stillbirth; (I) history of premature delivery; (J) history of abortion; (K) history of congenital anomaly; (L) primigravida. (Supplementary Materials)

References

T. A. Buchanan, A. H. Xiang, and K. A. Page, “Gestational diabetes mellitus: risks and management during and after pregnancy,” Nature Reviews. Endocrinology, vol. 8, no. 11, pp. 639–649, 2012.

D. R. Coustan, “Gestational diabetes mellitus,” Clinical Chemistry, vol. 59, no. 9, pp. 1310–1321, 2013.

N. H. Cho, J. E. Shaw, S. Karuranga et al., “IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045,” Diabetes Research and Clinical Practice, vol. 138, pp. 271–281, 2018.

U. Kampmann, L. R. Madsen, G. O. Skajaa, D. S. Iversen, N. Moeller, and P. Ovesen, “Gestational diabetes: a clinical update,” World Journal of Diabetes, vol. 6, no. 8, pp. 1065–1072, 2015.

G. M. Brawerman and V. W. Dolinsky, “Therapies for gestational diabetes and their implications for maternal and offspring health: evidence from human and animal studies,” Pharmacological Research, vol. 130, pp. 52–73, 2018.

B. E. Metzger, D. R. Coustan, and E. R. Trimble, “Hyperglycemia and adverse pregnancy outcomes,” Clinical Chemistry, vol. 65, no. 7, pp. 937-938, 2019.

Z. Li, Y. Cheng, D. Wang et al., “Incidence rate of type 2 diabetes mellitus after gestational diabetes mellitus: a systematic review and meta-analysis of 170,139 women,” Journal Diabetes Research, vol. 2020, article 3076463, pp. 1–12, 2020.

Y. Yogev, E. M. Xenakis, and O. Langer, “The association between preeclampsia and the severity of gestational diabetes: the impact of glycemic control,” American Journal of Obstetrics and Gynecology, vol. 191, no. 5, pp. 1655–1660, 2004.

D. Marchetti, D. Carrozzino, F. Fraticelli, M. Fulcheri, and E. Vitacolonna, “Quality of life in women with gestational diabetes mellitus: a systematic review,” Journal Diabetes Research, vol. 2017, article 7058082, pp. 1–12, 2017.

E. Vounzoulaki, K. Khunti, S. C. Abner, B. K. Tan, M. J. Davies, and C. L. Gillies, “Progression to type 2 diabetes in women with a known history of gestational diabetes: systematic review and meta-analysis,” BMJ, vol. 369, 2020.

R. Retnakaran, Y. Qi, P. W. Connelly, M. Sermer, B. Zinman, and A. J. Hanley, “Glucose intolerance in pregnancy and postpartum risk of metabolic syndrome in young women,” The Journal of Clinical Endocrinology and Metabolism, vol. 95, no. 2, pp. 670–677, 2010.

R. Retnakaran and B. R. Shah, “Glucose screening in pregnancy and future risk of cardiovascular disease in women: a retrospective, population-based cohort study,” The Lancet Diabetes and Endocrinology, vol. 7, no. 5, pp. 378–384, 2019.

T. D. Clausen, E. R. Mathiesen, T. Hansen et al., “High prevalence of type 2 diabetes and pre-diabetes in adult offspring of women with gestational diabetes mellitus or type 1 diabetes: the role of intrauterine hyperglycemia,” Diabetes Care, vol. 31, no. 2, pp. 340–346, 2008.

A. W. Mwanri, J. Kinabo, K. Ramaiya, and E. J. Feskens, “Gestational diabetes mellitus in sub-Saharan Africa: systematic review and metaregression on prevalence and risk factors,” Tropical Medicine & International Health, vol. 20, no. 8, pp. 983–1002, 2015.

K. Cypryk, W. Szymczak, L. Czupryniak, M. Sobczak, and A. Lewiński, “Gestational diabetes mellitus—an analysis of risk factors,” Endokrynologia Polska, vol. 59, no. 5, pp. 393–397, 2008.

A. S. Anzaku and J. Musa, “Prevalence and associated risk factors for gestational diabetes in Jos, north-central, Nigeria,” Archives of Gynecology and Obstetrics, vol. 287, no. 5, pp. 859–863, 2013.

T. O. Egbe, E. S. Tsaku, R. Tchounzou, and M. N. Ngowe, “Prevalence and risk factors of gestational diabetes mellitus in a population of pregnant women attending three health facilities in Limbe, Cameroon: a cross-sectional study,” The Pan African Medical Journal, vol. 31, 2018.

B. E. Feleke, “Determinants of gestational diabetes mellitus: a case-control study,” The Journal of Maternal-Fetal & Neonatal Medicine, vol. 31, no. 19, pp. 2584–2589, 2018.

J. Agah, F. Roodsarabi, A. Manzuri, M. Amirpour, and A. Hosseinzadeh, “Prevalence and associated risk factors of gestational diabetes mellitus in a tertiary hospital in Iran,” Clinical and Experimental Obstetrics & Gynecology, vol. 46, pp. 85–89, 2019.

P. A. dos Santos, J. M. Madi, E. R. da Silva, D. de Oliveira Pereira Vergani, B. F. de Araújo, and R. M. R. Garcia, “Gestational diabetes in the population served by Brazilian public health care. Prevalence and risk factors,” Revista Brasileira de Ginecologia e Obstetrícia / RBGO Gynecology and Obstetrics, vol. 42, 2020.

A. Ganapathy, R. Holla, B. B. Darshan et al., “Determinants of gestational diabetes mellitus: a hospital-based case–control study in coastal South India,” International Journal of Diabetes in Developing Countries, vol. 41, 2021.

J. Jao, M. Wong, R. B. Van Dyke et al., “Gestational diabetes mellitus in HIV-infected and -uninfected pregnant women in Cameroon,” Diabetes Care, vol. 36, no. 9, pp. e141–e142, 2013.

A. A. Muche, O. O. Olayemi, and Y. K. Gete, “Prevalence and determinants of gestational diabetes mellitus in Africa based on the updated international diagnostic criteria: a systematic review and meta-analysis,” Archives of Public Health, vol. 77, no. 1, p. 36, 2019.

A. B. Kirke, S. F. Evans, and B. N. Walters, “Gestational diabetes in a rural, regional centre in south Western Australia: predictors of risk,” Rural and Remote Health, vol. 14, 2014.

F. Zaman, S. Nouhjah, H. Shahbazian, N. Shahbazian, S. M. Latifi, and A. Jahanshahi, “Risk factors of gestational diabetes mellitus using results of a prospective population-based study in Iranian pregnant women,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 12, no. 5, pp. 721–725, 2018.

S. Abualhamael, H. Mosli, M. Baig, A. M. Noor, and F. M. Alshehri, “Prevalence and associated risk factors of gestational diabetes mellitus at a university hospital in Saudi Arabia,” Pak J Med Sci, vol. 35, no. 2, pp. 325–329, 2019.

T. T. Lao, L. F. Ho, B. C. Chan, and W. C. Leung, “Maternal age and prevalence of gestational diabetes mellitus,” Diabetes Care, vol. 29, no. 4, pp. 948-949, 2006.

S. Bibi, U. Saleem, and N. Mahsood, “The frequency of gestational diabetes mellitus and associated risk factors at khyber teaching hospital Peshawar,” Journal of Postgraduate Medical Institute, vol. 29, pp. 43–46, 2015.

Y. Li, X. Ren, L. He, J. Li, S. Zhang, and W. Chen, “Maternal age and the risk of gestational diabetes mellitus: a systematic review and meta-analysis of over 120 million participants,” Diabetes Research and Clinical Practice, vol. 162, p. 108044, 2020.

M. A. Mohan and A. Chandrakumar, “Evaluation of prevalence and risk factors of gestational diabetes in a tertiary care hospital in Kerala,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 10, no. 2, pp. 68–71, 2016.

I. W. Yen, C. N. Lee, M. W. Lin et al., “Overweight and obesity are associated with clustering of metabolic risk factors in early pregnancy and the risk of GDM,” PLoS One, vol. 14, no. 12, article e0225978, 2019.

L. Wu, L. Han, Y. Zhan, L. Cui, W. Chen, and L. Ma, “Prevalence of gestational diabetes mellitus and associated risk factors in pregnant Chinese women: a cross-sectional study in Huangdao, Qingdao, China,” Asia-Pacific Journal of Clinical Oncology, vol. 27, pp. 383–388, 2018.

M. Moosazadeh, Z. Asemi, K. B. Lankarani et al., “Family history of diabetes and the risk of gestational diabetes mellitus in Iran: a systematic review and meta-analysis,” Diabetes and Metabolic Syndrome: Clinical Research and Reviews, vol. 11, Suppl 1, pp. S99–s104, 2017.

Y. Shen, Y. Jia, Y. Li et al., “Genetic determinants of gestational diabetes mellitus: a case-control study in two independent populations,” Acta Diabetologica, vol. 57, no. 7, pp. 843–852, 2020.

R. A. Barnes, T. Wong, G. P. Ross et al., “Excessive weight gain before and during gestational diabetes mellitus management: what is the impact?” Diabetes Care, vol. 43, no. 1, pp. 74–81, 2020.

Y. Y. Wang, Y. Liu, C. Li et al., “Frequency and risk factors for recurrent gestational diabetes mellitus in primiparous women: a case control study,” BMC Endocrine Disorders, vol. 19, no. 1, p. 22, 2019.

B. K. Natamba, A. A. Namara, and M. J. Nyirenda, “Burden, risk factors and maternal and offspring outcomes of gestational diabetes mellitus (GDM) in sub-Saharan Africa (SSA): a systematic review and meta-analysis,” BMC Pregnancy and Childbirth, vol. 19, no. 1, p. 450, 2019.

K. W. Lee, S. M. Ching, V. Ramachandran et al., “Prevalence and risk factors of gestational diabetes mellitus in Asia: a systematic review and meta-analysis,” BMC Pregnancy and Childbirth, vol. 18, no. 1, p. 494, 2018.

A. Jawa, F. Raza, K. Qamar, A. Jawad, and J. Akram, “Gestational diabetes mellitus is rare in primigravida Pakistani women,” Indian Journal of Endocrinology and Metabolism, vol. 15, no. 3, pp. 191–193, 2011.

N. Tabatabaei, Y. Giguere, J.-C. Forest, C. J. Rodd, R. Kremer, and H. A. Weiler, “Osteocalcin is higher across pregnancy in Caucasian women with gestational diabetes mellitus,” Canadian Journal of Diabetes, vol. 38, no. 5, pp. 307–313, 2014.