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. 2022 Dec 12;17(12):e0278919. doi: 10.1371/journal.pone.0278919

Prevalence of prediabetes and type 2 diabetes mellitus in south and southeast Asian women with history of gestational diabetes mellitus: Systematic review and meta-analysis

Chockalingam Shivashri 1,2, Wesley Hannah 2, Mohan Deepa 2, Yonas Ghebremichael-Weldeselassie 1,3, Ranjit Mohan Anjana 2, Ram Uma 4, Viswanathan Mohan 2, Ponnusamy Saravanan 1,5,*
Editor: Diane Farrar6
PMCID: PMC9744276  PMID: 36508451

Abstract

Background

The burden of Gestational Diabetes Mellitus (GDM) is very high in south Asia (SA) and southeast Asia (SEA). Thus, there is a need to understand the prevalence and risk factors for developing prediabetes and type 2 diabetes mellitus (T2DM) postpartum, in this high-risk population.

Aim

To conduct a systematic review and meta-analysis to estimate the prevalence of prediabetes and T2DM among the women with history of GDM in SA and SEA.

Methods

A comprehensive literature search was performed in the following databases: Medline, EMBASE, Web of Knowledge and CINHAL till December 2021. Studies that had reported greater than six weeks of postpartum follow-up were included. The pooled prevalence of diabetes and prediabetes were estimated by random effects meta-analysis model and I2 statistic was used to assess heterogeneity.

Results

Meta-analysis of 13 studies revealed that the prevalence of prediabetes and T2DM in post-GDM women were 25.9% (95%CI 18.94 to 33.51) and 29.9% (95%CI 17.02 to 44.57) respectively. Women with history of GDM from SA and SEA seem to have higher risk of developing T2DM than women without GDM (RR 13.2, 95%CI 9.52 to 18.29, p<0.001). The subgroup analysis showed a rise in the prevalence of T2DM with increasing duration of follow-up.

Conclusion

The conversion to T2DM and prediabetes is very high among women with history of GDM in SA and SEA. This highlights the need for follow-up of GDM women for early identification of dysglycemia and to plan interventions to prevent/delay the progression to T2DM.

Introduction

There is a rapid increase in the prevalence of T2DM and the age of onset seems to be reducing globally [1]. It is estimated that 783 million adults will be affected by diabetes by 2045 [2]. South Asia (SA) and the southeast Asian (SEA) region are among the regions with the highest number of people having diabetes [3].

GDM is defined as glucose intolerance or hyperglycaemia that is first recognized or diagnosed in pregnancy [4]. It is estimated that GDM can affect more than 20 million live births every year [2, 4, 5]. Out of these, more than 90% are projected to occur in SA and SEA, which is one in every four live births [2, 5].

In addition to several short term maternal and offspring adverse outcomes, GDM contributes to adverse cardiometabolic outcomes for women in the long-term. These include T2DM, hypertension, and ischemic heart disease. The risk of developing T2DM among women with history of GDM can be up to 20-fold compared to healthy individuals [6, 7]. The prevalence of prediabetes post-GDM was observed to be between 3.9% and 50.9% based on the follow-up period after index delivery [8]. Similarly, the incidence of T2DM was reported to be between 2.6% and 70% from 6 weeks to 28 years postpartum [9], with the highest risk was observed around 3–6 years post-delivery [10]. Most of these studies were conducted in the western population and the studies in SA and SEA are limited, despite the higher prevalence of T2DM and GDM in these regions [3]. It has been suggested that ethnicity influences the rate of conversion to T2DM in women with GDM, but this claim has not been substantiated [11, 12]. Thus, this study aims to report on the prevalence and risk factors for prediabetes and T2DM in SA and SEA women with history of GDM by conducting a systematic review and meta-analysis.

Methods

This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (S2 Appendix) [13]. The protocol for this systematic review and meta-analysis was registered with PROSPERO, and is available at https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020189654.

Search strategy and selection criteria

A comprehensive literature search for observational studies that have followed-up women with history of GDM in SA and SEA, diagnosed by any criteria for GDM until end of December 2021 was performed in the following databases: Medline, EMBASE, Web of Knowledge and CINHAL. This was updated to include studies published until 30 June 2022. The search strategy included medical subject headings related to GDM (Gestational diabetes or Diabetes, Gestational, postpartum, Postpartum Period, postnatal or postnatal Care or post-natal) and T2DM (Diabetes Mellitus, Type 2/ or incidence of diabetes). A combination of these terms was modified for specific bibliographic databases in combination with database-specific filters. The keywords/filters specific to each database for the included countries (SA–Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka; SEA–Brunei, Cambodia, Indonesia, Thailand, Malaysia, Philippines, Singapore, Vietnam) were used in the searches. A secondary search was also performed to identify studies that have reported on prediabetes as an individual outcome. The search was restricted to studies conducted in humans and published in English. The detailed search strategy is available in the S1 Appendix. An alert system was set up in these databases to identify any additional studies that got published between January 2022 and submission of this manuscript (30 June 2022).

Two independent reviewers (SC and HW) screened the titles and abstracts to identify appropriate studies. Full texts of articles from the relevant studies were reviewed and included according to the inclusion criteria: observational studies (prospective, retrospective, cross-sectional, case-control) that have reported on the postpartum follow-up of women with the history of GDM in SA and SEA. Conference proceedings and letters to the editor relevant to the inclusion criteria were also included. Studies that reported on randomized controlled trials conducted to either mitigate the risk of GDM or management of GDM and studies that had followed-up women for less than six weeks of postpartum were excluded. The reference lists of relevant studies were hand searched for additional eligible studies. Any disagreement between the reviewers was resolved by discussion with a third reviewer (DM). The details of the study selection process are presented in the PRISMA flowchart (Fig 1).

Fig 1. PRISMA flow diagram showing the study selection process.

Fig 1

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Risk of bias and study quality assessment

The Newcastle-Ottawa scale (NOS) for cohort studies, proposed by Wells et al was used to assess the quality of the included studies [14]. This scale which is designed to appraise the quality of non-randomized studies by three categories: selection, comparability, and outcome. Each category has a set of numbered items to evaluate the study. For example, a maximum of one star can be awarded from the selection and outcome categories and two stars for the category of comparability. Overall, a study can be awarded from zero (low quality) to nine (high quality) stars. The risk of bias in cross-sectional studies was done by the using scale developed by Hoy and colleagues [15]. This included domains of sample selection, non-response bias, data collection, and measurement of reliability and validity. The risk of bias was reported as low, medium, and high-risk for each category. Publication bias was evaluated using Egger’s test [16].

Data synthesis and statistical analysis

Data extraction was done by the two independent reviewers (SC and HW), by extracting the study characteristics that included information on study design, country of study, diagnosis of GDM and T2DM, characteristics of the women (age, BMI) at the baseline and at the follow-up period. When two studies reported outcomes from the same cohort, the study with more complete information related to this review was included for the analysis.

Sub-group analyses were carried out based on the diagnostic criteria of GDM, duration of follow-up and type of studies. As the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria [17] is increasingly used across the world, we assessed the prevalence of prediabetes and T2DM by IADPSG vs. other criteria. Duration of follow-up is split into less than two, between two to five and more than five years of duration for the prevalence of T2DM and less than two and more than two years for prediabetes. Type of studies were split into prospective vs. other type of studies.

The Metafor package was used for quantitative synthesis [18]. The inverse variance method was used to estimate the pooled prevalence expressed as the proportion of women with history of GDM who have developed T2DM/prediabetes. The study results were pooled by using random effects analysis models fitted in ‘R’ programming language [19]. The heterogeneity observed between the studies was estimated using the I2 statistic [16] which describes the percentage of variation not due to sampling errors between the studies. Funnel plots were developed to assess the publication bias among the studies. The asymmetry of the funnel plots was investigated using the Egger’s test. A sensitivity analysis was performed to investigate the effect of each study on the pooled prevalence. All analyses were done in ‘R’ software [19].

Results

Identification of studies

The electronic database search and the hand search together yielded 87 studies of which 26 studies were removed after evaluation for duplicate records. Sixty-one studies were then selected, of which 41 were excluded after initial title and abstract screening. Full text screening excluded seven studies because of the following reasons: different ethnicity, article on protocol, reported only about risk factors, an intervention study, conference proceedings with no baseline data, reported only on the uptake rates of postnatal OGTT screening and a conference proceeding which was a duplication of a subsequent full manuscript with more relevant data. Thus, thirteen studies were included for the final analyses (Fig 1).

Study characteristics

Among the 13 studies, seven were from India, two from Singapore and one each from Sri Lanka, Pakistan, Malaysia, and Thailand. The studies showed varied lengths of follow-up and diagnostic criteria for GDM. The diagnosis of T2DM was by WHO criteria. Prediabetes was defined as impaired fasting glucose (IFG) or impaired glucose tolerance (IGT) or combined IGT and IFG. The diagnosis of T2DM was predominantly by using oral glucose tolerance test except for one study which used HbA1c [38]. The follow-up period ranged between 0.25 to 15 years. Summary of study characteristics that reported the prevalence of prediabetes and/or diabetes are provided in Table 1. Summary of key maternal risk factors are shown in Table 2.

Table 1. Characteristics of studies that reported the prevalence of prediabetes and T2DM.

Study Study design Country of study Criteria of GDM diagnosis Total GDM nos. (n) GDM to T2DM
(n)		 GDM to Prediabetes
(n)		 Non-GDM
nos. (n)		 Non-GDM to T2DM (n) Prevalence
of T2DM (%)		 Prevalence of prediabetes
(%)		 Follow-up
(years)		 Response rate (%)
Dai et al 2022 [33] Retrospective Singapore IADPSG 942 33
124		 NR NR 3.5
13.2
0.25
45.1
Hewage et al 2021 [34] Prospective Singapore WHO 1999 117 13
38		 NR NR 11.2
32.8
5.0
NR
Aziz et al 2018 [35] Prospective Pakistan IADPSG 27 11 NR  NR NR 41.0 NR 2.0 35.8
Goyal et al 2018 [36] Cross sectional India IADPSG 267 28
126		 NR NR 10.5
47.2
1.67
31.4
Herath et al 2017 [37] Retrospective Sri Lanka WHO 1999 119 73 NR 456 18 61.0 NR 10.9 >70
Gupta et al 2017 [38] Prospective India CC and IADPSG 366 119
144
NR		 NR 33.0
39.3
1.16
37
Bhavadharani et al 2016 [26] Prospective India IADPSG 203 7
34		 NR NR 3.0
16.8
1.0
95.8
Jindal et al 2015 [39] Prospective India IADPSG 62 4 17 NR NR 6.0 27.4 0.25 82.7
Mahalakshmi et al 2014 [40] Retrospective India CC 174 101
19		 NR NR 58.0
10.9
4.5
NR
Youngwanichsetha and Phumdoung 2013 [41] Cross sectional Thailand ADA
2010		 210 NR 56 NR NR NR 27.6 0.12
NR
Chew et al 2012 [42] Cross sectional Malaysia WHO 1984 342 159 117 NR NR 46.5 34.2 0.25–15 NR
Krishnaveni et al 2007 [43] Prospective India CC 35 13
11		 489 8 37
31.4
5		 66.7
Kale et al 2004 [44] Prospective India WHO 1985 125 65
19		 240 14 52
15.2
4.5
69.2
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ADA—American Diabetes Association

CC-Carpenter & Coustan

IADPSG—International Association of Diabetes and Pregnancy Study Group

WHO—World Health Organisation

NR- Not reported

Table 2. Maternal characteristics reported by the studies included in the systematic review.

Study Study design Country Maternal age
at follow-up (yr)		 BMI or weight at index pregnancy BMI or weight at follow-up
Dai et al 2022 [33] Retrospective Singapore 32.7±4.7 <18.5 (n = 15)
18.5–24.9 (n = 245)
25–29.9 (n = 235)
≥30 (n = 206)
 NR
Hewage et al 2021 [34] Prospective Singapore Normoglycemia 32.8±4.5##
Dysglycemia$ 33.9±5.2		 Normoglycemia 22.7 ±3.8
Dysglycemia$ 25.0±4.3		 Normoglycemia <23–27, 23 to <27.5–20,
≥ 27.5–5
Dysglycemia <23–13, 23 to <27.5–14,
≥ 27.5–15
Aziz et al 2018 [35] Prospective Pakistan 28.94±2.84  GDM 69.5± 8.22 kg
Non-GDM 56.54±5.42 kg		  GDM 73.26±6.86 kg
Non-GDM 67.23±4.65 kg
Goyal et al 2018 [36] Cross-sectional India Normoglycemia 31.3±4.4
Dysglycemia^ 33.3±4.5		 NR Normoglycemia <25–44, 25–29.9–50, ≥30–19
Dysglycemia^ <25–47, 25–29.9–54, ≥30–53
Herath et al 2017 [37] Retrospective Sri Lanka GDM
42.7±5.37
Non-GDM
38.7±5.36		 GDM
< 18.5–1
18.5–24.9–39
>25–28–28		 NR
Gupta et al 2017 [38] Prospective India 30.2±4.9 23.6±4.7* 27.6±5.2
Bhavadharani et al 2016 [26] Prospective India Dysglycemia^ 29.6±4.2
Normoglycemia 28.6±4.3		 Dysglycemia^
28.0±5.0
Normoglycemia
25.8±4.7		 NR
Jindal et al 2015 [39] Prospective India Normoglycemia
32.24±3.60,
Dysglycemia^
31±3.50		 NR NR
Youngwanichsetha and Phumdoung 2013 [41] Cross sectional Thailand 34.54 NR 25.0–29.9–34
30.0–39.9–22
Mahalakshmi et al 2014 [40] Retrospective India 29±4 28.6±4.1 NR
Chew et al 2012 [42] Cross sectional Malaysia Normoglycemia 37.6±5.3, IGT 37.7±5.0, IFG 38.9±5.6, Combined IFG/IGT 39.7±6.8, T2DM 39.4±4.5 NR Normoglycemia 25.69±4.85, IGT 26.59±4.84, IFG 26.22±4.33, Combined IFG/IGT 28.53±5.07, T2DM 30.26±4.62
Krishnaveni et al 2007 [43] Prospective India GDM: Normoglycemia-32.2, IFG/IGT-34, T2DM-33.5,
Non-GDM: Normoglycemia -28.1, IFG/IGT-29.3, T2DM- 28.6		 NR GDM: Normoglycemia -23.6, IFG/IGT -26.1, T2DM—26.7,
Non-GDM: Normoglycemia-23.2, IFG/IGT-24.8, T2DM—28.9
Kale et al 2004 [44] Prospective India GDM: Normoglycemia- 33, IGT– 33, T2DM—34 NR GDM: Normoglycemia -25.8, IGT—25.4, T2DM- 26.2
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IFG- Impaired fasting glucose

IGT–Impaired glucose tolerance

^—includes isolated IFG + isolated IGT + combined IFG/IGT + diabetes

$ —includes diabetes and prediabetes

*—prepregnancy BMI, ##- age at delivery

NR- Not reported

Assessment of study quality

The quality appraisal revealed that out of the 10 cohort studies, three had a low risk of bias, four showed unclear risk and the remaining three showed a high risk of bias. All three cross-sectional studies showed a low risk of bias (S1 Fig). The detailed quality assessment of studies is provided in S1 and S2 Tables.

Prevalence of prediabetes

Eleven studies have reported the rates of prediabetes (IFG or IGT and combined IGT and IFG) among the participants with previous GDM. This included a total of 2843 participants. The pooled prevalence of prediabetes was 25.9% (95% CI 18.94, 33.51) (Fig 2A). Significant heterogeneity among studies was observed (I2 = 94.3%, p<0.001). Of these 11 studies, seven were from India (Table 1).

Fig 2.

Fig 2

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Prevalence of (a) prediabetes and (b)T2DM in women with history of GDM in SA and SEA.

Prevalence of T2DM

Twelve studies reported the prevalence of T2DM. This included a total of 2779 participants. The pooled prevalence of T2DM was 29.9% (95% CI, 17.02, 44.57) (Fig 2B). Significant heterogeneity was observed (I2 = 98.3%, p<0.001).

Sensitivity analysis

Because of the high heterogeneity, we carried out a sensitivity analysis to estimate the pooled prevalence of prediabetes and T2DM, by excluding one study at a time. For prediabetes, this ranged between 25.0% and 27.7%. For T2DM, this was 27.2% to 33.4%. These estimates were close to the overall prevalence estimates of 25.9% and 29.9% for prediabetes and T2DM, respectively, when all the studies were included. This indicates that no single study had significantly influenced the overall estimates (S2 Fig).

Subgroup analyses

Subgroup analyses were performed to assess whether diagnostic criteria of GDM or the duration of follow-up have any differential effect on the prevalence of T2DM/prediabetes

Based on follow-up period

For prediabetes, 12 studies have reported the follow-up period. These were grouped as less (seven studies, n = 2220) or more (five studies, n = 623) than two years of follow-up. No significant difference was seen in the pooled prevalence of prediabetes (less than 2 years: 27.2%; 95%CI: 18.97, 36.27; more than 2 years: 23.9%, 95%CI: 14.14, 35.26; p = 0.65) (Fig 3A).

Fig 3.

Fig 3

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Prevalence of (a) prediabetes and (b) T2DM based on the follow-up period.

For T2DM, these were categorized into less than two years (five studies, n = 1840), 2–5 years, (six studies, n = 648) and greater than five years (two studies, n = 291) of follow-up period. Compared to the less than 2 years follow-up period (10.5%; 95%CI: 2.08, 23.98), the prevalence of T2DM was significantly higher in the 2–5 years group (38.7%; 95%CI: 22.98, 55.68; p<0.0001) and more than 5 years group (40.9%; 95%CI: 22.98, 55.68; p = 0.003) (Fig 3B).

Based on diagnostic criteria of GDM

The studies were categorized based on IADPSG vs other criteria. The prevalence of prediabetes by IADPSG criteria (five studies, n = 1542) was 27.1% (95% CI, 15.25, 40.85) compared to 26.5% (95% CI, 18.45, 35.45) by other criteria (seven studies, n = 1301) (p = 0.28) (S3 Fig). The prevalence of T2DM by IADPSG criteria (six studies, n = 1569) was 14.6% (95% CI, 3.31, 31.51) compared to 47.9% (95% CI, 40.47, 55.45) by other criteria (seven studies, n = 1210) (p = 0.004) (S3 Fig).

Based on study design

Among the studies included in the review, seven studies had a prospective study design. The prevalence of prediabetes and T2DM for prospective studies were 24.0% (95% CI: 17.09, 31.96) and 22.0% (95%CI: 0.00, 65.44) respectively. The prevalences of other study design were 23.81% (95% CI: 12.67, 37.13) and 30.27% (95% CI: 8.91, 57.55) for prediabetes and T2DM, respectively. There was no significant difference observed based on the study design for both prediabetes (p = 0.985) or T2DM (p = 0.590) (S4 Fig).

Relative risk of T2DM

Overall, only three studies (n = 1465) have reported the prevalence of T2DM in GDM and non-GDM mothers (Table 1) Relative risk of developing T2DM were calculated based on the reported prevalence (S3 Table). Women with history of GDM were at 13 times higher risk of developing T2DM than women without the history of GDM (RR13.2, 95%CI 9.52 to 18.29, p<0.001). There was no heterogeneity observed among these studies (I2 = 0.0%, p = 0.2969).

Risk factors for the development of prediabetes and diabetes

Unfortunately, not many studies reported the risk factors (such as age and BMI and family history) for the development of prediabetes and diabetes. While most reported age and reported BMI at the time of follow-up, none reported their impact on the onset of prediabetes/diabetes post GDM (Table 2).

Assessment of publication bias

There was no indication of publication bias, using Egger’s tests (p = 0.38). The funnel plot of 13 studies included in the meta-analysis is provided in S5 Fig.

Discussion

This systematic review and meta-analysis involving women with the previous history of GDM living in SA and SEA revealed a pooled prevalence of 29.9% for T2DM (n = 2779) and 25.9% for prediabetes (n = 2843) at postpartum follow-up. SA and SEA women with history of GDM had 13-times higher risk of developing T2DM compared to those without the history of GDM.

To the best of our knowledge, this is the first study to quantify the risk of prediabetes and T2DM among women with GDM in SA and SEA. Earlier systematic reviews published in 2009 and 2020 have reported 7-times and 10-times higher risk of conversion to T2DM among women with history of GDM involving all ethnic groups [6, 7]. These studies reported no difference across ethnicities as they were underpowered to observe any ethnic differences. Our findings reveal that SA and SEA women with GDM are at higher risk of conversion to T2DM compared to global estimates. Our findings reveal that SA and SEA women with GDM are at higher risk of conversion to T2DM compared to global estimates. However, this is based on limited number of studies. Among 13 studies included in this systematic review, only three had high risk of bias. These findings need to be confirmed through well-designed longitudinal studies that also controls for other risk factors (such as age, BMI, and family history) to assess the independent role of GDM in these ethnic groups.

The SA and SEA ethnicities have higher predisposition to T2DM compared to other ethnic groups [20]. Anjana et al [21] has reported that the age of onset of T2DM among South Asians is earlier compared to other ethnic groups. In addition to the pooled prevalence rate of 29.9% for T2DM, which is higher beyond the first 2 years of postpartum, rate of prediabetes is also high (25.9%) in women with history of GDM. These rates are much higher than previously reported and in other populations [22, 23]. Thus, GDM might be a key contributing factor for the decreasing age of onset of T2DM in these population, at least for women.

The combined prevalence of more than 50% of prediabetes and T2DM highlights the importance of improving the postpartum screening for all forms of dysglycemia in women with recent history of GDM. However, the uptake rate of postpartum glucose monitoring is sub-optimal even in well developed countries [24]. In addition, there seem to be limited evidence to compare the prevalence of T2DM post-GDM in different ethnicities including PIMA Indians. A recent study by Napoli et al [25] in Italy, reported that only 34.4% of women from ‘STRONG’ observational study underwent postpartum glucose monitoring. We also observed that only three studies had a follow-up rate of more than 70%, although we have showed earlier that it is feasible at least in research settings, with a follow up of 95.8% [26]. Whether this can be replicated in real-world settings in SA and SEA will require additional studies. A targeted approach for postpartum screening may be a better approach similar to a study by Nishanthi et al. [27] A simple machine learning approach using the routinely available antenatal factors could may identify who is unlikely to attend postpartum screening and enable better, targetted follow-up. The simple risk calculator proposed by Nishanthi et al [27] is easy to use for healthcare providers. However, the validity of this in SA and SEA population is not proven. A similar strategy for predicting the onset of prediabetes and T2DM can offer prevention strategies to be implemented in post-GDM women. In addition, with high birth rates in these countries [28], such interventions in between pregnancies can also reduce the risk of recurrence of GDM. Such ‘inter-pregnancy’ preventive interventions could be vital in reducing adverse metabolic programming in the offspring [5]. With the effective strategies (both lifestyle and metformin) available in these populations [29], such interpregnancy interventions are urgently warranted.

The subgroup analyses revealed that different diagnostic criteria (lower rates of T2DM but higher prediabetes rates if IADPSG criteria was used to diagnose GDM) influence the rates of dysglycemia postpartum. It is conceivable that IADPSG detects a ‘milder’ form of dysglycemia in pregnancy than other criteria, albeit important for the short-term adverse outcomes in pregnancy. The stratification based on study design showed no significant difference in the prevalence of prediabetes and T2DM. However, only three cohort studies have prospectively followed up women with history of GDM and the non-GDM group.

Strengths and limitations

This is the first study to report on the population- specific prevalence of T2DM and prediabetes in women with GDM in SA and SEA, where the metabolic burden in young adults is very high. Our study has important limitations. First, while there were adequate number of women for estimating the prevalence rates, the relative risk estimation is based on only three studies and there were not enough studies to assess the prevalence for each country within these regions. Second, as risk factors contributing to the prevalence of T2DM are not reported in most studies, we are not able to assess the contribution of individual risk factors to the development of T2DM, post-GDM. Third, none of the studies reported the influence of rapid urbanization, which is a major contributing factor to differential rates of T2DM in these countries. Finally, we did not find studies that reported other co-existing cardiometabolic disorders such as hypertension, dyslipidemia and cardiovascular disorders.

Implications for research and clinical practice

Our study highlights the gaps in the existing evidence in SA and SEA countries where the prevalence of both GDM and T2DM are high. The high rates of prediabetes and diabetes in these populations soon after an index pregnancy with GDM, suggest the importance of postpartum testing and early detection of T2DM. Despite the existence of guidelines [30–32] over a long period, only limited women with history of GDM adhere to postpartum glucose screening, due to several barriers [24]. Our findings raise the following key questions for future research: 1) What are the barriers/enablers for glucose testing post-GDM in different SA and SEA countries? 2) What are the risk factors (including modifiable risk factors such as postpartum weight retention) in women with history of GDM that contribute to the high prevalence of prediabetes and T2DM? 3) Do other cardiometabolic disorders co-exist in these women, similar to studies reported in western populations? 4) Can we develop an individualised prediction of incident T2DM post-GDM? and 5) Can we develop personalised strategies for interpregnancy interventions for prevention of GDM and subsequent T2DM?

Conclusion

Women living in SA and SEA countries with GDM have high rates of prediabetes and T2DM on postpartum follow-up. Despite the lack of adequate data, which requires carefully designed longitudinal studies, these findings highlight the importance of prioritising women with history of GDM for T2DM prevention strategies. Development of precision medicine would be key for individualised strategies, which is likely to have better adherence rates for both screening and prevention.

Patient and public involvement

Patients and/or the public were not involved in the design or conduct or reporting or dissemination plans of this research.

Supporting information

S1 Fig. Study quality appraisal of the included studies.

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S2 Fig

Sensitivity analyses for the prevalence of (a) prediabetes and (b) T2DM.

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S3 Fig

Prevalence of (a) prediabetes and (b) T2DM based on the diagnostic criteria of GDM.

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S4 Fig

Prevalence of (a) design prediabetes and (b) T2DM based on the study.

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S5 Fig. Funnel plot for publication bias.

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S1 Table. Quality assessment of cohort studies using Newcastle Ottawa scale (n = 10).

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S2 Table. Quality assessment of cross-sectional studies (n = 3).

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S3 Table. Relative risk of T2DM in women with history of GDM compared with healthy controls in SA and SEA.

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S1 Appendix. Detailed search strategy.

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S2 Appendix. PRISMA checklist.

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Data Availability

The dataset pertaining to our systematic review is available in the Open Science Framework repository: osf.io/tfkh.

Funding Statement

PS and YW are part funded by Medical Research Council, UK Grant number: MR/R020981/1 Funder: MRC- UK URL: https://mrc.ukri.org/funding/. SC’s PhD scholarship is funded by the international doctoral training program (iDTP) by Novo Nordisk Plc, Copenhagen, Denmark based at The University of Warwick, Coventry, UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Diane Farrar

2 Oct 2022

PONE-D-22-18537Prevalence of prediabetes and type 2 diabetes mellitus in south and southeast Asian women with history of gestational diabetes mellitus: systematic review and meta-analysis

PLOS ONE

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: The authors report the prevalence of pre-diabetes and diabetes among women with prior GDM. The main challenge in understanding these data is the high level of heterogeneity. The heterogeneity is not surprising given the variation in populations, in follow-up time and in diagnostic criteria of both GDM and of the outcomes of T2DM and pre-diabetes. This heterogeneity precludes simple summary statistics such as overall prevalence. It makes no sense to average the prevalence of diabetes in cohorts that are so different (eg 27 women followed for two years in Pakistan with 342 women followed for up to 15 years in Malaysia), since the average can’t be applied to anyone. A narrative description with some attempt to group by important risk factors is the approach needed here. This also relates to the study aims, which indicate an intention to estimate pooled prevalence, when such an outcome was never likely to be possible. Even if the attempt was to estimate a prevalence for the region, it can only have meaning when stratified by follow-up time, and depends on getting studies from each country in the region. Furthermore, weighting the prevalence estimates by sample size (as per meta-analysis) does not help in estimating the prevalence in the region, as a large study from a small country will skew results towards the small country.

I couldn’t find the supplementary tables.

I was concerned about the quality and representativeness of some of the studies. Three studies followed fewer than 100 women with GDM, and it seems rather unlikely that such studies have an adequate epidemiological design to provide T2DM incidence in a manner that can be generalized. For example, reference 35 provides data on only 27 women. Looking at the paper itself, it becomes apparent that the recruitment was from a tertiary hospital, and that of the 78 women identified with GDM, only 27 were re-screened for T2DM. The 78 is likely a biased sample of the general GDM population, and the 27 are likely a biased sample of the 78. Such a study doesn’t contribute usefully to the authors’ aims. How carefully have other studies been reviewed?

Abstract results. ‘The relative risk of T2DM among women with history of GDM from SA and SEA was 13 times higher than’. A relative risk can’t be higher in one group than in another. Only the risk can be higher.

Line 68-69. ‘The rate of prediabetes among these women was observed to be between 3.9% and 50.9%’. The word ‘rate’ implies a time component, but these look like prevalence data. Please be more precise with language.

Line 71. ‘during 6 weeks postpartum to 28 years postpartum’. Replace ‘during’ with ‘from’.

Line 229. Relative risk of diabetes appears to have been calculated from the published data, and presumably therefore was unable to adjust for confounders such as age. If this is the case, then the RR is quite possibly very misleading, and should not be presented.

Line 249. ‘indicating that it lies closer to the pooled overall prevalence’. Closer than what?

English language use needs to be improved throughout by a native English speaker.

Reviewer #2: The growing prevalence of type 2 diabetes in SA and SEA regions is a major concern. Gestational Diabetes is well recognised as a major risk factor for future diabetes. However, the lack of data in SA /SEA populations until recently, has been a major limitation to understand the true impact of this problem and to develop effective interventions. Improved understanding of the risks and opportunities to mitigate this can have he benefits for the prevention of T2DM and maternal health. This systematic review and meta- analysis addresses this important issue and provides a fairly good insights on the risk of T2DM in patients with GDM. The work presented here is of good quality but there are a few points that need clarification.

1. Abstract : This is good overall. It would be good to provide some data/figures in relation to the last sentence of the results section.

2. Introduction: It is well written and references and places the research in context.

3. Methods: A lot of detail is provided and the essential ,principles of systematic review have been followed. PRISMA guidelines have been adhered to. I would suggest it would be better to describe the combinations of SEARCH terms used to identify the relevant papers.

It is surprising to see that Chinese data was not considered in this study. Why? What group would Chinese be categorised under by the authors?

Although SA and SEA share many common characteristics, many would regard them as distinct ethnic groups. What is the rationale in combining them?

As evident from the data and also the meta analysis, there is considerable heterogeneity between the studies. Moreover , including retrospective, cross sectional and prospective studies together actually weakens the study. Wonder if it would be more prudent to focus on prospective studies that have a nonGDM comparison group.

It is common experience that most GDM patients do not attend follow up. It is therefore difficult to ascertain if all those with GDM were assessed for the duration of the studies and if they had a test to confirm the glycemic status. Lot of attrition can be expected in these situations and can therefore lead to under or over estimation of the true risk. No data is provided regarding this. Also, were there any other tests eg HbA1c used to diagnose pre-diabetes and diabetes?

Given the high prevalence of T2DM in these populations, it is possible that some of the subjects classed as GDM may in fact be T2DM or PRE DIABETES prior to pregnancy. How was this addressed in the studies?

For the 3 studies that were used for computing Relative Risk, was that based on incident Pre-diabetes or Diabetes?

It would be good to see some data on additional risk factors if available eg parity, family history?

A risk score /calculator I mentioned in the manuscript but I could not find it ( apologies if I missed it).

Discussion

The discussion is well written and balanced. It can be improved by some reference to data in other populations and how these findings compare by providing some figures and data. More so with reference to other ethnic groups with similarly high pre disposition to T2DM such as PIMA Indians etc.

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Reviewer #2: Yes: Srikanth Bellary

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PLoS One. 2022 Dec 12;17(12):e0278919. doi: 10.1371/journal.pone.0278919.r002

Author response to Decision Letter 0

16 Nov 2022

Response to Reviewers

Reviewer #1:

Comments: The authors report the prevalence of pre-diabetes and diabetes among women with prior GDM. The main challenge in understanding these data is the high level of heterogeneity. The heterogeneity is not surprising given the variation in populations, in follow-up time and in diagnostic criteria of both GDM and of the outcomes of T2DM and pre-diabetes. This heterogeneity precludes simple summary statistics such as overall prevalence. It makes no sense to average the prevalence of diabetes in cohorts that are so different (eg 27 women followed for two years in Pakistan with 342 women followed for up to 15 years in Malaysia), since the average can’t be applied to anyone. A narrative description with some attempt to group by important risk factors is the approach needed here. This also relates to the study aims, which indicate an intention to estimate pooled prevalence, when such an outcome was never likely to be possible. Even if the attempt was to estimate a prevalence for the region, it can only have meaning, when stratified by follow-up time, and depends on getting studies from each country in the region. Furthermore, weighting the prevalence estimates by sample size (as per meta-analysis) does not help in estimating the prevalence in the region, as a large study from a small country will skew results towards the small country.

Response: We thank the reviewer for this comment. Yes, we completely agree that the studies included in this systematic review have high level of heterogeneity. To address the heterogeneity, we tried to include sensitivity analysis by removing one study at a time (Supplementary Figure 5), stratifying the studies based on diagnostic criteria (Supplementary Figure 2) and follow-up period (Supplementary Figure 3). However, it is unfortunate that these supplementary Tables/Figures were not accessible to the reviewer. We have uploaded all files and our system shows all supplementary materials. Apologies for the inconvenience caused and we have now highlighted this issue to the Editor.

Taking the reviewer’s comment on board, the sub-group analysis on follow-up period is now moved to the current Figure 3, in the revised version of the manuscript. Further, the results of the sensitivity analysis, shows that the observed prevalences (25% to 27.7%;27.2% to 33.4%) are close to the pooled prevalences of prediabetes (25.9%) and T2DM (29.9%), respectively. We hope that this partly addresses the issue of heterogeneity. We have made this explicit and reflected this in the results section in our revised manuscript (line 199-203).

Comments: I couldn’t find the supplementary tables. I was concerned about the quality and representativeness of some of the studies. Three studies followed fewer than 100 women with GDM, and it seems rather unlikely that such studies have an adequate epidemiological design to provide T2DM incidence in a manner that can be generalized. For example, reference 35 provides data on only 27 women. Looking at the paper itself, it becomes apparent that the recruitment was from a tertiary hospital, and that of the 78 women identified with GDM, only 27 were-screened for T2DM. The 78 is likely a biased sample of the general GDM population, and the 27 are likely a biased sample of the 78. Such a study doesn’t contribute usefully to the authors’ aims. How carefully have other studies been reviewed?

Response: Apologies to know that the supplementary material was not accessible to the reviewer. The quality assessment of the included studies was carefully performed using the Newcastle Ottawa Scale for cohort studies and the scale proposed by Hoy and colleagues for cross sectional studies. The detailed quality assessment was presented in the supplementary tables 1 & 2 (S1 and S2 tables). We also present the summary of the quality appraisal of the included studies in the supplementary figure 1 (S1_Fig). Of the three studies that have less than 100 women with GDM, one had low risk of bias and the other two had high risk of bias. Our discussion is modified to highlight this more explicitly (line no.264-265) in the revised manuscript.

Comments: Abstract results. ‘The relative risk of T2DM among women with history of GDM from SA and SEA was 13 times higher than’. A relative risk can’t be higher in one group than in another. Only the risk can be higher.

Response: Thank you for pointing out this. We have now rephrased the sentence.

Comments: Line 68-69. ‘The rate of prediabetes among these women was observed to be between3.9% and 50.9%’. The word ‘rate’ implies a time component, but these look like prevalence data. Please be more precise with language.

Response: Thank you for pointing out this error. This has been changed (line no.66)

Comments: Line 71. ‘during 6 weeks postpartum to 28 years postpartum’. Replace ‘during’ with ‘from’.

Response: Thank you. This is changed

Comments: Line 229. Relative risk of diabetes appears to have been calculated from the published data, and presumably therefore was unable to adjust for confounders such as age. If this is the case, then the RR is quite possibly very misleading, and should not be presented.

Response: Yes, this was calculated based on the published data. This is similar to other studies (including the recent publication on the global estimates of T2DM incidence from Vounzoulaki et al, 2020) that have reported the relative risk. We have done so to give a comparative estimate in SA and SEA as opposed to the global estimates presented by earlier studies. However, in view of the reviewer’s concern, we have now moved this Figure to the Supplementary table 3 (S3 Table) and modified the manuscript to highlight the limitations of this observation in the revised version of the manuscript (line 237-239).

Comments: Line 249. ‘indicating that it lies closer to the pooled overall prevalence’. Closer than what? English language use needs to be improved throughout by a native English speaker.

Response: We would like to clarify to the Reviewer that in the Supplementary Figure 2, we performed the sensitivity analysis by excluding one study at a time, which gave the prevalence of prediabetes between 25.0% to 27.7%. This was close to the overall estimate of 25.9% when all the studies were included. This sensitivity analysis gave the reassurance that no specific study had significantly influenced the overall pooled prevalence estimate. We have rephrased the sentence for better clarity in the revised version of the manuscript (line 199-203). The manuscript is modified, and the forest plot is shown in the Supplementary Fig 2 (S2_Fig). The manuscript has been read and improved through by a native English speaker.

Reviewer #2:

The growing prevalence of type 2 diabetes in SA and SEA regions is a major concern. Gestational Diabetes is well recognised as a major risk factor for future diabetes. However, the lack of data in SA /SEA populations until recently, has been a major limitation to understand the true impact of this problem and to develop effective interventions. Improved understanding of the risks and opportunities to mitigate this can have he benefits for the prevention of T2DM and maternal health.

This systematic review and meta- analysis addresses, this important issue and provides a fairly good insights on the risk of T2DM in patients with GDM. The work presented here is of good quality but there are a few points that need clarification.

Comments. Abstract: This is good overall. It would be good to provide some data/figures in relation to the last sentence of the results section.

Response: We thank you for the kind words. In response to the reviewer’s comment, we have now moved the analysis based on follow-up period from Supplementary Figure 3 in the older version to main Figure 3 in the revised version of manuscript. The subgroup analysis based on the follow-up period is included in the results section in the revised manuscript (line 207).

Comment: Introduction: It is well written and references and places the research in context.

Response: Thank you very much.

Comment: Methods: A lot of detail is provided and the essential, principles of systematic review have been followed. PRISMA guidelines have been adhered to. I would suggest it would be better to describe the combinations of SEARCH terms used to identify the relevant papers.

Response: We would like to clarify that this information was already provided in the supplementary material 1 (S1 Appendix). It is unfortunate that the reviewers could not access the Supplementary materials. We have raised this issue to the Editor and hope it is accessible in the revised version.

Comments: It is surprising to see that Chinese data was not considered in this study. Why? What group would Chinese be categorised under by the authors?

Response: We have categorised the countries based on the United Nations Statistics

Division, based on which SA & SEA include the following countries, SA – Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka; SEA – Brunei, Cambodia, Indonesia, Thailand, Malaysia, Philippines, Singapore, Vietnam. China was not included as it does not come under this category.

Comments: Although SA and SEA share many common characteristics, many would regard them as distinct ethnic groups. What is the rationale in combining them?

Response: We agree that these regions may be regarded as distinct ethnic groups. However, as per the International Diabetes Federation, these two regions have the highest risk of GDM (28%). This was the rationale for us reporting the regional prevalence in SA & SEA.

Comments: As evident from the data and the meta-analysis, there is considerable heterogeneity between the studies. Moreover, including retrospective, cross sectional and prospective studies together actually weakens the study. Wonder if it would be more prudent to focus on prospective studies that have a non-GDM comparison group.

Response: Thank you for these comments. Taking the reviewer’s comment on board, we have carried out additional subgroup analysis based on study design and is included in the revised manuscript (line no. 228) and the forest plot is included as supplementary figure 3. We agree that there is significant heterogeneity and there are only three prospective studies which included non-GDM comparison group. We have reflected this (line 301-303) in the discussion section of revised manuscript.

Comments: It is common experience that most GDM patients do not attend follow up. It is therefore difficult to ascertain if all those with GDM were assessed for the duration of the studies and if they had a test to confirm the glycemic status. Lot of attrition can be expected in these situations and can therefore lead to under or over estimation of the true risk. No data is provided regarding this.

Response: Thank you for this comment, we agree that providing the attrition rates could help the readers understand about the quality of the studies. Hence, we have included the rate of follow-up in table 1, pg no. 8 in the revised manuscript and added a sentence to reflect this in the discussion (281). The follow-up rates are between 31.4% and 95.8%.

Comments: Also, were there any other tests eg HbA1c used to diagnose pre-diabetes and diabetes?

Response: Thank you. There was one study (Gupta et al (Ref 38)) that has used HbA1c along with OGTT for the diagnosis of T2DM. We have added this information in line 159 of the revised version of manuscript.

Comments: Given the high prevalence of T2DM in these populations, it is possible that some of the subjects classed as GDM may in fact be T2DM or PRE DIABETES prior to pregnancy. How was this addressed in the studies?

Response: Undiagnosed diabetes or pre-diabetes is increasingly common in younger women of child-bearing age. We accept the Reviewer’s concern, and this is a challenge. The only way to identify the proportion of this group identified as GDM is by early pregnancy screening. Although this is recommended in many guidelines, this is not carried out routinely in many countries including UK. Most of studies included in this systematic review have only mentioned that the follow-up was conducted in women who underwent screening for GDM during the index pregnancy and only two studies explicitly stated that women without history of T2DM was included in the baseline.

Comments: For the 3 studies that were used for computing Relative Risk, was that based on incident Pre-diabetes or Diabetes? It would be good to see some data on additional risk factors if available eg parity, family history?

Response: The relative risk was calculated based on the incident T2DM reported in the studies. There is limited evidence on the additional risk factors, as most of the studies have not reported on the incident rates after adjustment of potential risk factors. This point is highlighted as in the discussion section of the revised manuscript (line 310-312).

Comments: A risk score /calculator I mentioned in the manuscript, but I could not find it (apologies if I missed it).

Response: We apologize for not making it clear. The risk score calculator is freely available as a supplementary material in the open access article by Nishanthi et al. We have made this explicit and highlighted that this is not yet validated in SA and SEA populations, in the discussion section of the revised version of the manuscript (line 288-289).

Comments: The discussion is well written and balanced. It can be improved by some reference to data in other populations and how these findings compare by providing some figures and data. More so with reference to other ethnic groups with similarly high predisposition to T2DM such as PIMA Indians etc.

Response: Thank you for the kind words. Yes, we have attempted to compare the with other cohorts including the STRONG study. Evidence of the risk of T2DM in woman with history of GDM from the PIMA Indians is lacking. We have reflected this as “However, the uptake rate of postpartum glucose monitoring is sub-optimal even in well developed countries. [24] And also there seem to be limited evidence to compare the prevalence of T2DM post-GDM in different ethnicities including PIMA Indians. A recent study by Napoli et al [25] in Italy, reported that only 34.4% of women from ‘STRONG’ observational study underwent postpartum glucose monitoring. We also observed only three studies had a follow-up rate of more than 70%, although we have showed earlier that it is feasible with a follow up of 95.8%, at least in research setting. [26]”, in the revised manuscript (line 278-281).

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Decision Letter 1

Diane Farrar

24 Nov 2022

Prevalence of prediabetes and type 2 diabetes mellitus in south and southeast Asian women with history of gestational diabetes mellitus: systematic review and meta-analysis

PONE-D-22-18537R1

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Additional Editor Comments:

I have enjoyed reading this informative, very well written and conducted review, I commend you and your colleagues for your hard work

Acceptance letter

Diane Farrar

4 Dec 2022

PONE-D-22-18537R1

Prevalence of prediabetes and type 2 diabetes mellitus in south and southeast Asian women with history of gestational diabetes mellitus: systematic review and meta-analysis

Dear Dr. Saravanan:

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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. Study quality appraisal of the included studies.

    (TIF)

    Click here for additional data file. (516.1KB, tif)
    S2 Fig

    Sensitivity analyses for the prevalence of (a) prediabetes and (b) T2DM.

    (TIF)

    Click here for additional data file. (971.5KB, tif)
    S3 Fig

    Prevalence of (a) prediabetes and (b) T2DM based on the diagnostic criteria of GDM.

    (TIF)

    Click here for additional data file. (1.1MB, tif)
    S4 Fig

    Prevalence of (a) design prediabetes and (b) T2DM based on the study.

    (TIF)

    Click here for additional data file. (304.9KB, tif)
    S5 Fig. Funnel plot for publication bias.

    (TIF)

    Click here for additional data file. (897.8KB, tif)
    S1 Table. Quality assessment of cohort studies using Newcastle Ottawa scale (n = 10).

    (DOCX)

    Click here for additional data file. (29.6KB, docx)
    S2 Table. Quality assessment of cross-sectional studies (n = 3).

    (DOCX)

    Click here for additional data file. (17.9KB, docx)
    S3 Table. Relative risk of T2DM in women with history of GDM compared with healthy controls in SA and SEA.

    (DOCX)

    Click here for additional data file. (14.2KB, docx)
    S1 Appendix. Detailed search strategy.

    (DOCX)

    Click here for additional data file. (13.1KB, docx)
    S2 Appendix. PRISMA checklist.

    (DOCX)

    Click here for additional data file. (31.7KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (32.5KB, docx)

    Data Availability Statement

    The dataset pertaining to our systematic review is available in the Open Science Framework repository: osf.io/tfkh.

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