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IDF Diabetes Atlas: Estimation of Global and Regional Gestational Diabetes Mellitus Prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group’s Criteria

Published:December 06, 2021DOI:https://doi.org/10.1016/j.diabres.2021.109050

      Abstract

      Aims

      The approaches used to screen and diagnose gestational diabetes mellitus (GDM) vary widely. We generated a comparable estimate of the global and regional prevalence of GDM by International Association of Diabetes in Pregnancy Study Group (IADPSG)'s criteria.

      Methods

      We searched PubMed and other databases and retrieved 57 studies to estimate the prevalence of GDM. Prevalence rate ratios of different diagnostic criteria, screening strategies and age groups, were used to standardize the prevalence of GDM in individual studies included in the analysis. Fixed effects meta-analysis was conducted to estimate standardized pooled prevalence of GDM by IDF regions and World Bank country income groups.

      Results

      The pooled global standardized prevalence of GDM was 14.0% (95% confidence interval: 13.97–14.04%). The regional standardized prevalence of GDM were 7.1% (7.0–7.2%) in North America and Caribbean (NAC), 7.8% (7.2–8.4%) in Europe (EUR), 10.4% (10.1–10.7%) in South America and Central America (SACA), 14.2% (14.0–14.4%) in Africa (AFR), 14.7% (14.7–14.8%) in Western Pacific (WP), 20.8% (20.2–21.4%) in South-East Asia (SEA) and 27.6% (26.9–28.4%) in Middle East and North Africa (MENA). The standardized prevalence of GDM in low-, middle- and high-income countries were 12.7% (11.0–14.6%), 9.2% (9.0–9.3%) and 14.2% (14.1–14.2%), respectively.

      Conclusions

      The highest standardized prevalence of GDM was in MENA and SEA, followed by WP and AFR. Among the three World Bank country income groups, high income countries had the highest standardized prevalence of GDM. The standardized estimates for the prevalence of GDM provide an insight for the global picture of GDM.

      Keywords

      1. Introduction

      Gestational diabetes mellitus (GDM) is defined as hyperglycaemia first recognised during pregnancy [

      Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy: a World Health Organization Guideline. Diabetes Res Clin Pract 2014;103:341–63. https://doi.org/10.1016/j.diabres.2013.10.012.

      ]. GDM is not only related to perinatal morbidity [
      • Metzger B.E.
      • Lowe L.P.
      • Dyer A.R.
      • Trimble E.R.
      • Chaovarindr U.
      • Coustan D.R.
      • et al.
      Hyperglycemia and adverse pregnancy outcomes.
      ] but also to an increased risk of diabetes and cardiovascular disease in the mother in later life [
      • Li N.
      • Yang Y.
      • Cui D.
      • Li C.
      • Ma R.C.W.
      • Li J.
      • et al.
      Effects of lifestyle intervention on long-term risk of diabetes in women with prior gestational diabetes: A systematic review and meta-analysis of randomized controlled trials.
      ,
      • Song C.
      • Lyu Y.
      • Li C.
      • Liu P.
      • Li J.
      • Ma R.C.
      • et al.
      Long-term risk of diabetes in women at varying durations after gestational diabetes: a systematic review and meta-analysis with more than 2 million women.
      ], and to childhood obesity in the offspring [
      • Tam W.H.
      • Ma R.C.W.
      • Ozaki R.
      • Li A.M.
      • Chan M.H.M.
      • Yuen L.Y.
      • et al.
      In utero exposure to maternal hyperglycemia increases childhood cardiometabolic risk in offspring.
      ].
      Although we have witnessed a rapid increase in the prevalence of GDM in many parts of the world such as in China [
      • Leng J.
      • Shao P.
      • Zhang C.
      • Tian H.
      • Zhang F.
      • Zhang S.
      • et al.
      Prevalence of gestational diabetes mellitus and its risk factors in Chinese pregnant women: a prospective population-based study in Tianjin, China.
      ], global estimation and comparison of the prevalence of GDM is difficult due to a large variation in the screening strategies and diagnostic criteria used to identify women with GDM. In the 9th edition of the International Diabetes Federation (IDF) Diabetes Atlas, we made an effort to estimate and project the prevalence of hyperglycaemia in pregnancy across seven regions in 2019 [

      International Diabetes Federation. IDF Diabetes Atlas. 9th edition, https://diabetesatlas.org/en/; 2019 [accessed 24 May 2021].

      ]. The report pointed out that the prevalence of hyperglycaemia varied greatly between different regions, with the highest prevalence being 27.0% in South East Asia (SEA) and the lowest being 7.5% in Middle East and North Africa (MENA) [

      International Diabetes Federation. IDF Diabetes Atlas. 9th edition, https://diabetesatlas.org/en/; 2019 [accessed 24 May 2021].

      ]. The lowest prevalence of hyperglycaemia in MENA contrasted with the highest prevalence of diabetes (i.e., 12.2%) in adults (20–79 years) in that region [

      International Diabetes Federation. IDF Diabetes Atlas. 9th edition, https://diabetesatlas.org/en/; 2019 [accessed 24 May 2021].

      ]. This inconsistency, probably, may have arisen from the variation in screening strategies and diagnostic criteria used in identifying GDM cases in different regions and countries. Previous studies have shown that the prevalence of GDM diagnosed by the International Association of Diabetes in Pregnancy Study Group (IADPSG)'s diagnostic criteria is 1.75 times higher than that of the old diagnostic criteria [
      • Saeedi M.
      • Cao Y.
      • Fadl H.
      • Gustafson H.
      • Simmons D.
      Increasing prevalence of gestational diabetes mellitus when implementing the IADPSG criteria: A systematic review and meta-analysis.
      ]. In addition, selective screening strategies would miss diagnosis of one-third of GDM cases [
      • Cosson E.
      • Benbara A.
      • Pharisien I.
      • Nguyen M.T.
      • Revaux A.
      • Lormeau B.
      • et al.
      Diagnostic and prognostic performances over 9 years of a selective screening strategy for gestational diabetes mellitus in a cohort of 18,775 subjects.
      ,
      • Cosson E.
      • Cussac-Pillegand C.
      • Benbara A.
      • Pharisien I.
      • Jaber Y.
      • Banu I.
      • et al.
      The diagnostic and prognostic performance of a selective screening strategy for gestational diabetes mellitus according to ethnicity in Europe.
      ].
      It is essential to obtain accurate global and regional estimates of the prevalence of GDM for allocation of health resources and shaping of prevention and management policies to cope with the rising prevalence of GDM [
      • Cho N.H.
      • Shaw J.E.
      • Karuranga S.
      • Huang Y.
      • da Rocha Fernandes J.D.
      • Ohlrogge A.W.
      • et al.
      IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045.
      ]. In the 10th edition of the IDF Diabetes Atlas, we have made an additional methodological attempt to standardize the global, regional and country income level specific prevalence of GDM by using the IADPSG's diagnostic criteria and universal oral glucose tolerance test (OGTT) strategy.

      2. Methods

      2.1 Study selection

      A literature search of PubMed, Web of science, EMBASE and related citations for studies on the prevalence of GDM conducted between January 1990 to December 2020 was conducted. The search terms included: “gestational diabetes mellitus”, “GDM”, “prevalence”, “incidence” and “screening” and <country name> or <region/continent>.
      Methodological information was extracted from the reported studies. These were classified according to the following criteria: sample size; study design (e.g. population-based, clinic-based, diabetes registration, medical record review); representativeness (e.g. national representativeness, regional representativeness); survey year; diagnostic criteria [e.g. IADPSG, World Health Organization (WHO), American Diabetes Association (ADA) and others]; and screening approach (e.g. universal OGTT strategy and others screening strategies).
      Studies were excluded using the following criteria: (i) scoring 3 or less by the scoring system [
      • Linnenkamp U.
      • Guariguata L.
      • Beagley J.
      • Whiting D.R.
      • Cho N.H.
      The IDF Diabetes Atlas methodology for estimating global prevalence of hyperglycaemia in pregnancy.
      ]; (ii) cohort studies in which the gestational age of the population screened was less than 24 weeks; (iii) insufficient methodological information, (iv) the prevalence results in age groups were less than 3 groups; (v) conducted in hospital or clinic-based settings. Authors from the studies were approached via e-mail to confirm the availability of information to increase the availability of data [
      • Linnenkamp U.
      • Guariguata L.
      • Beagley J.
      • Whiting D.R.
      • Cho N.H.
      The IDF Diabetes Atlas methodology for estimating global prevalence of hyperglycaemia in pregnancy.
      ]. The details of the scoring system have been reported elsewhere [
      • Linnenkamp U.
      • Guariguata L.
      • Beagley J.
      • Whiting D.R.
      • Cho N.H.
      The IDF Diabetes Atlas methodology for estimating global prevalence of hyperglycaemia in pregnancy.
      ].

      2.2 Estimation of prevalence of gestational diabetes mellitus

      Analyses were conducted using the Stata SE 16.0 for Windows (StataCorp, College Station, Texas). As compared with random effects model, fixed effects model assigns more weights to the larger sample studies, which have more true information than the smaller sample studies for the same effect size [
      • Borenstein M.
      • Hedges L.V.
      • Higgins J.P.T.
      • Rothstein H.R.
      A basic introduction to fixed-effect and random-effects models for meta-analysis.
      ,

      Early Breast Cancer Trialists’ Collaborative Group. Treatment Of Early Breast Cancer: Worldwide Evidence 1985-1990. USA: Oxford University Press; 1990.

      ]. In this analysis, we defined that the prevalence of GDM was related to the sample size of the studies. Especially in the context of country, larger sample studies were more likely to obtain the true national prevalence of GDM than the smaller sample stuides. “Metaprop” was thus used to conduct fixed effects meta-analysis, and to estimate the non-standardized prevalence of GDM based on IDF regions and country income levels as classified by the World Bank Income groups [
      • Nyaga V.N.
      • Arbyn M.
      • Aerts M.
      Metaprop: a Stata command to perform meta-analysis of binomial data.
      ]. Poisson meta regression analysis is suitable for handling the incidence of rare events, which are positively skewed with non-normal error rates [
      • Stijnen T.
      • Hamza T.H.
      • Özdemir P.
      Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data.
      ]. Poisson meta regression analysis was therefore used to obtain prevalence rate ratios (PRRs), and corresponding 95% confidence intervals (CIs) of other diagnostic criteria versus the IADPSG's diagnostic criteria, other screening approaches versus universal OGTT strategy, and <25 and >30 years of age versus 25–30 years of age.
      We then used the IADPSG's diagnostic criteria and universal OGTT strategy, and age group of 25–30 years as standards to standardize the prevalence of GDM of the included individual studies with other practices or age groups. Finally, we repeated the fixed effects meta-analysis to estimate the practice (IADPSG's diagnostic criteria and universal OGTT strategy)-standardized, and the practice- & age-standardized pooled prevalence of GDM globally, by IDF regions and country income levels as classified by World Bank Income groups. P-values being less than 0.05 were regarded as statistically significant.

      3. Results

      3.1 Study selection

      The literature review identified 155 data sources, of which 57 met the inclusion criteria in the analysis, covering 45 countries. Seventeen (29.8%) of the studies were conducted in Europe (EUR) (two in Israel, two in Spain, two in United Kingdom, one in Belgium, One in Croatia, one in France, one in Hungary, one in Ireland, one in Italy, one in Netherlands, one in Norway, one in Poland, one in Sweden, one in Turkey), ten (17.5%) in North America and Caribbean (NAC) (five in the United States of America, two in Mexico, one in Barbados, one in Canada, one in Trinidad and in Tobago) and nine (15.8%) in Western Pacific (WP) (three in China, one in Australia, one in Vietnam, one in Japan, one in Malaysia, one in Singapore, one in Thailand). Africa (AFR) (one in Cameroon, one in Ethiopia, one in Kenya, one in Nigeria, one in Rwanda, one in South Africa, one in United Republic of Tanzania), MENA (one in Islamic Republic of Iran, one in Jordan, one in Pakistan, one in Qatar, one in United Arab Emirates), SEA (three in Bangladesh, one in India, one in Sri Lanka) and South and Central America (SACA) (one in Argentina, one in Brazil, one in Chile, one in Cuba) had seven (12.3%), five (8.8%), five (8.8%) and four (7.0%) studies included in the analysis, respectively. More than 50% of the studies used the IADPSG's diagnostic criteria. Notably 152 countries did not have data that met the inclusion criteria for analysis. The flow diagram (Fig. 1) depicted the selection process of studies. The main characteristics of the studies included were shown in Table S1.
      Figure thumbnail gr1
      Fig. 1Legends: Flow diagram of studies selected for inclusion in determining prevalence of gestational diabetes mellitus estimates.

      3.2 Prevalence rate ratios for GDM by different diagnostic criteria and screening strategies and by ages

      The PRR of the WHO's diagnostic criteria versus the IADPSG's diagnostic criteria was 0.536 (95% CI: 0.523–0.549). Similarly, the PRR of the ADA's diagnostic criteria versus the IADPSG's was 0.474 (95% CI: 0.470–0.478). The prevalence of GDM diagnosed by others' diagnostic criteria was also lower than the one diagnosed using the IADPSG's diagnostic criteria (PRR: 0.536, 95% CI: 0.523–0.549) (Table 1).
      Table 1The incidence rate ratio of different diagnostic criteria, screening strategies and ages for the prevalence of gestational diabetes mellitus.
      Variablesβ (95% CI)PRR (95% CI)P
      Diagnostic criteria
       IADPSG
       WHO−0.533 (−0.544 to −0.522)0.587 (0.580–0.593)<0.001
       ADA−0.746 (−0.755 to −0.737)0.474 (0.470–0.478)<0.001
       Others−0.624 (−0.648 to −0.599)0.536 (0.523–0.549)<0.001
      Screening strategies
       Universal OGTT
       Others−0.154 (−0.162 to −0.146)0.857 (0.851–0.864)<0.001
      Age groups
       <25 years0.133 (0.125–0.142)1.143 (1.133–1.153)<0.001
       25–30 years
       >30 years0.829 (0.819–0.839)2.291 (2.269–2.314)<0.001
      Abbreviations: CI, confidence interval; PRR, prevalence rate ratio; IADPSG, International Association of Diabetes in Pregnancy Study Group; WHO, World Health Organization; ADA, American Diabetes Association; OGTT, oral glucose tolerance test.
      As expected, other screening strategies had a lower prevalence of GDM than the universal OGTT strategy (PRR: 0.857, 95% CI: 0.851–0.864). If women aged 25–30 years were used as the reference group, the PRRs of women aged less than 25 and more than 30 years were, respectively, 1.143 (95% CI: 1.133–1.153) and 2.291 (95% CI: 2.269–2.314) (Table 1).

      3.3 Standardized global and regional prevalence of GDM

      Table 2 showed that the global prevalence of GDM standardized to a universal OGTT strategy and the IADPSG's diagnostic criteria were 14.2% (95% CI: 14.2–14.3%) and 14.0% (95% CI: 13.97–14.04%) after being further standardized to 25–30 years of age.
      Table 2The pooled prevalence of gestational diabetes mellitus.
      RegionNon-standardized prevalence, %Standardized prevalence1
      Standardized prevalence1: The prevalence of GDM standardized to IADPSG’s criteria and universal OGTT strategy.
      , %
      Standardized prevalence2
      Standardized prevalence2: The prevalence of GDM standardized to IADPSG’s criteria, universal OGTT strategy and 25–30 years of age.
      , %
      Global6.6 (6.6–6.7)14.2 (14.2–14.3)14.0 (13.97–14.04)
      IDF regions
       MENA30.2 (29.4–30.9)30.2 (29.5–30.9)27.6 (26.9–28.4)
       SEA12.7 (12.3–13.2)23.7 (23.2–24.3)20.8 (20.2–21.4)
       WP12.4 (12.3–12.5)14.7 (14.7–14.8)14.7 (14.7–14.8)
       AFR7.8 (7.2–8.5)14.3 (14.2–14.4)14.2 (14.0–14.4)
       SACA7.6 (7.5–7.8)14.2 (14.0–14.5)10.4 (10.1–10.7)
       EUR7.0 (7.0–7.1)12.3 (11.5–13.1)7.8 (7.2–8.4)
       NAC6.0 (6.0–6.0)11.7 (11.7–11.8)7.1 (7.0–7.2)
      World Bank Income countries
       Low income countries11.7 (10.0–13.5)14.7 (12.9–16.7)12.7 (11.0–14.6)
       Middle income countries7.7 (7.6–7.9)9.9 (9.7–10.1)9.2 (9.0–9.3)
       High income countries6.6 (6.6–6.7)14.4 (14.3–14.4)14.2 (14.1–14.2)
      Abbreviations: IDF, International Diabetes Federation; MENA, Middle East and North Africa; SEA, South-East Asia; WP, Western Pacific; AFR, Africa; SACA, South and Central America, EUR, Europe; NAC, North America and Caribbean.
      * Standardized prevalence1: The prevalence of GDM standardized to IADPSG’s criteria and universal OGTT strategy.
      ** Standardized prevalence2: The prevalence of GDM standardized to IADPSG’s criteria, universal OGTT strategy and 25–30 years of age.
      The prevalence of GDM varied by IDF regions. The highest standardized prevalences of GDM were found in the MENA and SEA regions, where 30.2% (95% CI: 29.5–30.9%) and 23.7% (95% CI: 23.2–24.3%) respectively, of the pregnant women were been diagnosed with GDM if the IADPSG's diagnostic criteria and universal OGTT strategy would be used to identify GDM cases. The other five regions with a greater than 10% prevalence of GDM were WP (14.7%, 95% CI: 14.7–14.8%), AFR (14.3%, 95% CI: 14.2–14.4%), SACA (14.2%, 95% CI: 14.0–14.5%), EUR (12.3%, 95% CI: 11.5–13.1%) and NAC (11.7%, 95% CI: 11.7–11.8%), if the IADPSG's diagnostic criteria and universal OGTT strategy would be used (Table 2).
      Additionally, age adjustment decreased the practice-standardized prevalence in these seven regions by 0.0–4.6%. The practice- & age-standardized prevalence of GDM in pregnant women ranged from 7.1% (95% CI: 7.0–7.2%) in NAC to 27.6% (95% CI: 26.9–28.4%) in MENA. The second, third and fourth highest practice- & age- stardardized prevalence of GDM were SEA (20.8%, 20.2–21.4%), WP (14.7%, 14.7–14.8%) and AFR (14.2%, 14.0–14.4%) (Table 2). SACA and EUR had a lower practice- & age- standardized prevalence of GDM, at 10.4% (10.1–10.7%) and 7.8% (7.2–8.4%), respectively. The heterogeneity among the included studies was low (P > 0.05).

      3.4 Standardized prevalence of GDM by country income levels

      After being standardized to the IADPSG's diagnostic criteria and universal OGTT strategy, the prevalence of GDM was 14.7% (95% CI: 12.9–16.7%), 9.9% (95% CI: 9.7–10.1%) and 14.4% (95% CI: 14.3–14.4%) in low income countries (LIC), middle income countries (MIC) and high income countries (HIC), respectively. After being further standardized to 25–30 years of age, the prevalence of GDM was 12.7% (95% CI: 11.0–14.6%) in LIC, 9.2% (95% CI: 9.0–9.3%) in MIC and 14.2% (95% CI: 14.1–14.2%) in HIC (Table 2), with little heterogeneity among the included studies (P > 0.05).

      4. Discussion

      In this meta-analysis with standardization of the prevalence of GDM to a universal OGTT strategy and IADPSG's diagnostic criteria, we have provided a global standardized prevalence of 14.2%, with a small decrease after adjusting for age. Notably, we have generated a comparable prevalence of GDM by IDF regions. MENA, SEA, WP, AFR, SACA, EUR and NAC had a prevalence of GDM at 27.6%, 20.8%, 14.7%, 14.2%, 10.4%, 7.8% and 7.1% in that order if the universal OGTT strategy and IADPSG's diagnostic criteria had been uniformly used and pregnant women were at 25–30 years of age. We have also generated a standardized prevalence of GDM by World Bank country income groups, ranging from 12.7% in LIC and 14.2% in HIC to a lower standardized prevalence in MIC (9.2%).
      To our knowledge, this is the first attempt to standardize the prevalence to universal OGTT, use of the IADPSG's diagnostic criteria and 25–30 years of age by IDF regions and country income levels. In our study, MENA and SEA, to a lesser extent, WP and AFR, were most affected regions by GDM. These findings are consistent with the high prevalence of diabetes in MENA, SEA and WP [

      International Diabetes Federation. IDF Diabetes Atlas. 9th edition, https://diabetesatlas.org/en/; 2019 [accessed 24 May 2021].

      ]. Presumably, the same group of lifestyle risk factors underlying the high prevalence of diabetes in these regions such as high prevalence of obesity and metabolic syndrome also contributed to an increased risk of GDM [
      • Hills A.P.
      • Arena R.
      • Khunti K.
      • Yajnik C.S.
      • Jayawardena R.
      • Henry C.J.
      • et al.
      Epidemiology and determinants of type 2 diabetes in south Asia.
      ,
      • Khashayar P.
      • Heshmat R.
      • Qorbani M.
      • Motlagh M.E.
      • Aminaee T.
      • Ardalan G.
      • et al.
      Metabolic Syndrome and Cardiovascular Risk Factors in a National Sample of Adolescent Population in the Middle East and North Africa: The CASPIAN III Study.
      ]. AFR had a lower crude prevalence of GDM at 7.8% but practice- & age- standardized prevalence of GDM turned out to be 14.3%, much higher than expected. The most comparable group to our study using IADPSG's showed that the prevalence of GDM in AFR was 17.2% [
      • Saeedi M.
      • Cao Y.
      • Fadl H.
      • Gustafson H.
      • Simmons D.
      Increasing prevalence of gestational diabetes mellitus when implementing the IADPSG criteria: A systematic review and meta-analysis.
      ]. It seems that AFR is not exempted from GDM and more attention needs to be paid to prevention of GDM and its adverse outcomes in AFR.
      We also observed that, once the IADPSG's diagnostic criteria and universal OGTT strategy had been uniformly used, the global prevalence of GDM increased sharply from 6.6% to 14.2%, which translated into a prevalence rate difference (PRD) of 7.6%. Similarly, as comparared with the non-standardized method, the increased PRDs of GDM ranged from 1.0% in the MENA to 2.0% in the EUR if the IADPSG's diagnostic criteria and universal OGTT strategy were used to identify GDM cases. It is noticed that women identified to have GDM by the IADPSG's diagnostic criteria had an increased risk for adverse perinatal outcomes as compared to women identified by others' diagnostic criteria, e.g., the WHO’s criteria [
      • Metzger B.E.
      • Gabbe S.G.
      • Persson B.
      • Buchanan T.A.
      • Catalano P.A.
      • Damm P.
      • et al.
      International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy.
      ]. A switch from others' diagnostic criteria to the IADPSG's was expected to result in an increase in the prevalence of GDM, which has been confirmed in a number of studies [
      • Saeedi M.
      • Cao Y.
      • Fadl H.
      • Gustafson H.
      • Simmons D.
      Increasing prevalence of gestational diabetes mellitus when implementing the IADPSG criteria: A systematic review and meta-analysis.
      ,
      • Gerome J.M.
      • Bucher L.K.M.
      • Dogbey G.
      Effects of Implementing International Association of Diabetes and Pregnancy Study Groups Gestational Diabetes Screening on Pregnancy Outcomes at a Small Community Teaching Hospital.
      ,
      • Feldman R.K.
      • Tieu R.S.
      • Yasumura L.
      Gestational Diabetes Screening: The International Association of the Diabetes and Pregnancy Study Groups Compared With Carpenter-Coustan Screening.
      ,
      • Benhalima K.
      • Hanssens M.
      • Devlieger R.
      • Verhaeghe J.
      • Mathieu C.
      Analysis of Pregnancy Outcomes Using the New IADPSG Recommendation Compared with the Carpenter and Coustan Criteria in an Area with a Low Prevalence of Gestational Diabetes.
      ,
      • Duran A.
      • Sáenz S.
      • Torrejón M.J.
      • Bordiú E.
      • del Valle L.
      • Galindo M.
      • et al.
      Introduction of IADPSG criteria for the screening and diagnosis of gestational diabetes mellitus results in improved pregnancy outcomes at a lower cost in a large cohort of pregnant women: the St. Carlos Gestational Diabetes Study.
      ]. McMahon et al. analyzed three Irish national databases and found that overall national prevalence of GDM increased from 3.1% in 2008 to 14.8% in 2017, a five-fold increase mainly due to use of the IADPSG's diagnostic criteria [
      • McMahon L.E.
      • O'Malley E.G.
      • Reynolds C.M.E.
      • Turner M.J.
      The impact of revised diagnostic criteria on hospital trends in gestational diabetes mellitus rates in a high income country.
      ]. An early meta-analysis reported a sharp difference in the prevalence of GDM by different diagnostic criteria, with 1.5–15.5% by the ADA's criteria, 20.8% by the Australian Diabetes in Pregnancy Society's criteria, 13.6% by the Diabetes in Pregnancy Study Group India's criteria, 1.6% by the European Association for the Study of Diabetes' criteria, 0.56% by the National Diabetes Data Group's criteria and 0.4–24.3% by the WHO 1999's criteria, in contrast to 8.9–20.4% by the IADPSG's criteria [
      • Kanguru L.
      • Bezawada N.
      • Hussein J.
      • Bell J.
      The burden of diabetes mellitus during pregnancy in low- and middle-income countries: a systematic review.
      ].
      Pregnancy is a window period and occurrence of GDM during pregnancy represents an opportunity to reduce short and long term risk of adverse health outcomes in the mothers and their children [
      • Landon M.B.
      • Spong C.Y.
      • Thom E.
      • Carpenter M.W.
      • Ramin S.M.
      • Casey B.
      • et al.
      A multicenter, randomized trial of treatment for mild gestational diabetes.
      ,
      • Crowther C.A.
      • Hiller J.E.
      • Moss J.R.
      • McPhee A.J.
      • Jeffries W.S.
      • Robinson J.S.
      Effect of treatment of gestational diabetes mellitus on pregnancy outcomes.
      ]. Of note, intensive management of GDM was able to decrease the risk of adverse pregnancy outcomes but not the risk of maternal diabetes in later life or childhood obesity in the offspring [
      • Gillman M.W.
      • Oakey H.
      • Baghurst P.A.
      • Volkmer R.E.
      • Robinson J.S.
      • Crowther C.A.
      Effect of treatment of gestational diabetes mellitus on obesity in the next generation.
      ,
      • Landon M.B.
      • Rice M.M.
      • Varner M.W.
      • Casey B.M.
      • Reddy U.M.
      • Wapner R.J.
      • et al.
      Mild gestational diabetes mellitus and long-term child health.
      ]. In addition, lifestyle intervention in early pregnancy for prevention of GDM only achieved a 20% risk reduction in the risk of GDM [
      • Song C.
      • Li J.
      • Leng J.
      • Ma R.C.
      • Yang X.
      Lifestyle intervention can reduce the risk of gestational diabetes: a meta-analysis of randomized controlled trials.
      ]. In this regard, a crude prevalence of GDM of 6.6% may under-estimate the true global prevalence of GDM, as our study showed that the use of a universal OGTT strategy with IADPSG's diagnostic criteria sharply raised the prevalence to 14.0% of the pregnant women in the world, with a particular high standardized prevalence in MENA, SEA, WP and AFR. More medical resources should be shifted to prevention of GDM and its long-term health oucomes in these high risk regions.
      This analysis has strengths and limitations. The major strength is that the pooled prevalence of GDM obtained by the standardization approaches of the practice-, and practice- & ages- (25–30 years of age) method allowed generation of a comparable prevalence of GDM by IDF regions and country income levels. However, our meta-analysis had several limitations. First, even with the same diagnostic criteria, screening approach and age group, the prevalence of GDM varies with population characteristics, such as ethnicity, body mass index (BMI), lifestyle (physical activity and diet), and the prevalence of type 2 diabetes in the background population [
      • Behboudi-Gandevani S.
      • Amiri M.
      • Bidhendi Yarandi R.
      • Ramezani T.F.
      The impact of diagnostic criteria for gestational diabetes on its prevalence: a systematic review and meta-analysis.
      ,
      • McIntyre H.D.
      • Catalano P.
      • Zhang C.
      • Desoye G.
      • Mathiesen E.R.
      • Damm P.
      Gestational diabetes mellitus.
      ,
      • Bottalico J.N.
      Recurrent gestational diabetes: risk factors, diagnosis, management, and implications.
      ]. As these key risk factors were not reported and not available to the analysis, we were unable to address whether their differences contributed to the regional- and country income-level differences in the prevalence of GDM. Second, the analyses only took major diagnostic criteria and screening strategies into consideration. Some less used diagnostic criteria and screening strategies could only be classified as “others” due to a small number of studies that used those practices. Therefore, the standardization procedure is suboptimal.

      5. Conclusion

      Using the newly developed standardization methods, i.e., PRRs, our analysis generated global, IDF region and country income level specific prevalence of GDM identified by a universal OGTT strategy and use of IADPSG's diagnostic criteria. We found that the practice- & age- standardized global prevalence of GDM was high at 14.0%, with the highest prevalence of GDM in MENA and SEA, and the lowest prevalence of GDM in EUR and NAC. We also found a major difference by country income levels, with LIC and HIC having a higher practice- & age- standardized prevalence of GDM while MIC had a lower practice- & age- standardized prevalence of GDM. The comparable and increased prevalence of GDM with implementing of the standardization to IADPSG's diagnostic criteria and a universal OGTT strategy provides an insight into the understanding of the global picture of GDM. Our findings call for allocation of more medical resources to cope with the rising prevalence of GDM.

      Declaration of Competing Interest

      The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

      Acknowledgement

      The authors of this manuscript espress their deep thanks to those authors of the included original studies for provision of additional data for the work.

      Final support

      This project was funded by the Pfizer-MSD Alliance, with the additional support of Sanofi and Novo Nordisk.

      Appendix A. Supplementary material

      The following are the Supplementary data to this article:

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