Literature DB >> 34956073

Gestational Diabetes Mellitus in Europe: A Systematic Review and Meta-Analysis of Prevalence Studies.

Marília Silva Paulo1, Noor Motea Abdo1, Rita Bettencourt-Silva2,3, Rami H Al-Rifai1.   

Abstract

Background: Gestational Diabetes Mellitus (GDM) is defined as the type of hyperglycemia diagnosed for the first-time during pregnancy, presenting with intermediate glucose levels between normal levels for pregnancy and glucose levels diagnostic of diabetes in the non-pregnant state. We aimed to systematically review and meta-analyze studies of prevalence of GDM in European countries at regional and sub-regional levels, according to age, trimester, body weight, and GDM diagnostic criteria.
Methods: Systematic search was conducted in five databases to retrieve studies from 2014 to 2019 reporting the prevalence of GDM in Europe. Two authors have independently screened titles and abstracts and full text according to eligibility using Covidence software. A random-effects model was used to quantify weighted GDM prevalence estimates. The National Heart, Lung, and Blood Institute criteria was used to assess the risk of bias.
Results: From the searched databases, 133 research reports were deemed eligible and included in the meta-analysis. The research reports yielded 254 GDM-prevalence studies that tested 15,572,847 pregnant women between 2014 and 2019. The 133 research reports were from 24 countries in Northern Europe (44.4%), Southern Europe (27.1%), Western Europe (24.1%), and Eastern Europe (4.5%). The overall weighted GDM prevalence in the 24 European countries was estimated at 10.9% (95% CI: 10.0-11.8, I2 : 100%). The weighted GDM prevalence was highest in the Eastern Europe (31.5%, 95% CI: 19.8-44.6, I2 : 98.9%), followed by in Southern Europe (12.3%, 95% CI: 10.9-13.9, I2 : 99.6%), Western Europe (10.7%, 95% CI: 9.5-12.0, I2 : 99.9%), and Northern Europe (8.9%, 95% CI: 7.9-10.0, I2 : 100). GDM prevalence was 2.14-fold increased in pregnant women with maternal age ≥30 years (versus 15-29 years old), 1.47-fold if the diagnosis was made in the third trimester (versus second trimester), and 6.79- fold in obese and 2.29-fold in overweight women (versus normal weight). Conclusions: In Europe, GDM is significant in pregnant women, around 11%, with the highest prevalence in pregnant women of Eastern European countries (31.5%). Findings have implications to guide vigilant public health awareness campaigns about the risk factors associated with developing GDM. Systematic Review Registration: PROSPERO [https://www.crd.york.ac.uk/PROSPERO/], identifier CRD42020161857.
Copyright © 2021 Paulo, Abdo, Bettencourt-Silva and Al-Rifai.

Entities:  

Keywords:  Europe; GDM; Gestational Diabetes Mellitus; diabetes mellitus; meta-analysis; pregnancy complications; pregnancy hyperglycemia; systematic review

Mesh:

Year:  2021        PMID: 34956073      PMCID: PMC8698118          DOI: 10.3389/fendo.2021.691033

Source DB:  PubMed          Journal:  Front Endocrinol (Lausanne)        ISSN: 1664-2392            Impact factor:   6.055


Introduction

Hyperglycemia in pregnancy affects about one in every six pregnancies worldwide (1). Gestational Diabetes Mellitus (GDM) is defined as the type of hyperglycemia diagnosed for the first time during pregnancy (2, 3). This has been the widely used definition of GDM for many years, but it presents limitations in terms of the non-possible verification of the preexisting hyperglycemia (4). Hyperglycemia universal routine screening is not available for women at childbearing age before conception or in the first semester, so although GDM can take place at any time during pregnancy, it is more frequently diagnosed after the 24th week of gestation (1, 4). GDM is highly associated with obesity. Obesity is a growing major public health problem worldwide (5). In 2016, the estimated age-standardized prevalence of obesity and overweight among adult women of the European Region was 24.5% and 54.3%, respectively (6). This prevalence is expected to continue rising in the next years (7, 8). Being overweight (body mass index [BMI] 25.0-29.9 kg/m2) or obese (BMI ≥30.0 kg/m2) is the most important modifiable risk factor for GDM. The risk is up to 5-fold higher in morbidly obese women, when compared to women with normal body weight (9). Other modifiable risk factors for GDM comprise unhealthy dietary factors, physical inactivity, and cigarette smoking (10). Moreover, the gradual increase in the mean age at childbearing of women in Europe (from 28.8 years in 2013 to 29.3 years in 2018) has an important role in the prevalence of GDM, given that advanced maternal age is a well-known risk factor for GDM (11). The chances of developing GDM increment with previous history of GDM, macrosomia, excessive gestational weight gain, spontaneous abortion, fetal anomalies, preeclampsia, fetal demise, neonatal hypoglycemia, hyperbilirubinemia, and neonatal respiratory distress syndrome family history of type 2 diabetes mellitus (T2DM), polycystic ovary syndrome, parity, non-white ancestry also increment (10, 12). GDM has potentially serious short- and long-term consequences. The condition is associated with various adverse maternal, fetal, and perinatal outcomes, including but not limited to, preeclampsia, preterm delivery, cesarean section delivery, large for gestational age (LGA) newborns, neonatal hypoglycemia, and Neonatal Intensive Care Unit admission (13). The Hyperglycemia and Adverse Pregnancy Outcomes (HAPO) study reported a continuous association between maternal glucose levels and increased frequency of adverse outcomes, however, there was no obvious threshold at which risk increased (13). Furthermore, the gestational programming and intrauterine fetal exposure to hyperglycemia is an independent risk factor for obesity, hypertension and T2DM in the offspring (14, 15). GDM may play a crucial role in increasing the prevalence of T2DM in women. In the European Region, about 9.6% of women ≥ 25 years old have diabetes (16). A meta-analysis reported a 7-fold increased risk of T2DM in women with GDM compared with those without GDM (17). Comparing data on GDM is a challenge since there is a lack of universally accepted screening standards and diagnostic criteria. Diagnostic criteria have changed over time and remain controversial, but there has been a move towards the adoption of the International Association of Diabetes in Pregnancy Study Groups (IADPSG) recommendations (18–20). Using the systematic review and meta-analysis approach to understand the regional, sub-regional, and national prevalence of GDM will help the introduction of effective public health measures and enable highlighting the gaps in evidence, following the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) (21). The previously published meta-analysis on the GDM prevalence in Europe was limited to only developed countries in Europe excluding immigrants who did not originate from those developed countries (22). Also, the same meta-analysis was limited to only women tested for GDM in their second or third trimesters (22). To overcome these limitations and provide a more comprehensive and informative assessment on the GDM prevalence in Europe, the present systematic review included all countries in the European region according to the definition of the United Nations (UN) geoscheme and regardless of the original of the included pregnant women. In the present review, the literature search covers a wider range of countries (51 countries) in the European continent regardless of the development status, the origin of the study population, and pregnancy trimester. Moreover, our meta-analyses considered extracting, whenever possible, stratified estimates of the GDM rather than using the overall prevalence reported in the primary studies following a prioritized one-stratification scheme. Indeed, pooling stratified estimates would provide more precise findings on the national, sub-regional, and regional prevalence of GDM. As such, this systematic review and meta-analysis method quantifies the weighted prevalence of GDM in Europe, at regional, sub-regional, and national levels, between 2014 and 2019, according to and regardless of the maternal age, trimester, maternal weight, and GDM diagnostic criteria. It is believed that this study of the 51 countries of the European region regardless of their development will complement the scientific literature, providing more insights into the prevalence of GDM at the subregional level as countries within each subregion in the European continent might have not the same development status interpreted as a limitation in the previous systematic review (22).

Methods

Protocol and Registration

We have developed and registered our protocol on PROSPERO (registration number: CRD42020161857). This systematic review and meta-analysis follows the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement (23). The PRISMA checklist is provided elsewhere (see ). This systematic review and meta-analysis from prevalence studies in Europe is part of a major study that aims to estimate the prevalence of GDM in different regions in the world. From the same project, the first systematic review and meta-analysis providing findings on the prevalence of GDM in the Middle East and North Africa region has been already completed and submitted for a peer-reviewed journal (24).

Eligibility Criteria

The search strategy was limited to English language publications between January 2006 and December 2019 and defined in accordance with our population, exposure, comparator, and outcome (PECO) criteria. The population included in this study were all pregnant women tested for GDM during their pregnancy, living in the European region according to the definition of the United Nations (UN) geoscheme (25). All included studies had at least ten pregnant women tested for GDM and reported the prevalence of GDM for their sample or have reported data that allowed us to calculate the GDM prevalence, regardless of the age, trimester, pregnancy status, or GDM ascertainment methodology. However, due to the high number of studies retrieved from databases, we restricted the inclusion criteria to only include studies published between 2014 and 2019. All studies reporting prevalence estimates on GDM were considered eligible. For this specific systematic review and meta-analysis focusing on the European region, we have excluded studies from the other regions of the globe and studies using unclear GDM diagnostic criteria, unless studies from medical records. These decisions made by the research team were due to the high volume of eligible studies and to produce less potentially biased and more precise estimates on the GDM prevalence.

Information Sources and Search

A specific search strategy was developed by the principal investigators and a medical librarian expert. The initial search was developed on PubMed-MEDLINE using varied Medical Subject Headings (MeSH) and free-text terms and then translated into EMBASE, Scopus, Web of Sciences, and Cochrane Library, comprising five electronic databases ( ).

Study Selection

We have used the Covidence software (26, 27) to perform study selection. All citations identified by our search strategy were uploaded into Covidence where duplicates were automatically removed. Two reviewers independently screened the studies for titles and abstracts and subsequently identified potential eligible full-text articles. Conflicts and discrepancies that emerged during the two stages of screening were solved by a third reviewer. The reference lists of eligible studies were also screened to identify additional studies that might have been missed.

Data Abstraction Process and Data Items

The data we have extracted include the study ID, article type, publication year, journal, country, city, study design, data collection period, population, sample size, sampling strategy, age, pregnancy trimester when GDM was tested, GDM criteria used for diagnosis ascertainment, strata used on the population of the study, the prevalence of GDM in the study sample and by strata whenever available. Furthermore, in research reports presenting more stratified GDM prevalence and at least ten tested subjects per strata, we have extracted the stratified prevalence of GDM following a priority list to avoid double counting: comorbidity, parity, age, pre-gestational BMI, ethnicity, year, placental location, nationality, and occupation. Where there was no stratification on the prevalence of GDM, the overall prevalence was extracted. All relevant data were introduced into a predesigned Excel sheet using string codes and numerical variables. We considered a research report a single publication that might contain data from several studies (each one on a specific population group). In reports where the main study design does not report a clear prevalence, we have extracted the original study design of the report and we have calculated the prevalence of GDM accordingly. In reports where the GDM was ascertained using more than one criterion, the most sensitive and reliable assessment (e.g., fasting glucose blood test vs. self-reported) was considered as well as the most recent criteria (e.g., The American Diabetes Association ADA 2010 vs. ADA 2006).

Summary Measures and Synthesis of Results

To estimate the weighted pooled prevalence of GDM and the corresponding 95% confidence interval (CI), we performed meta-analyses of the extracted data. The Freeman–Tukey double arcsine transformation method was applied to stabilize the variances of the prevalence measures (28). The inverse variance method was used to weight the estimated pooled prevalence measures (29). Dersimonian–Laird random-effects model was used to estimate the overall pooled GDM prevalence (30). Cochran’s Q statistic and the inconsistency index I-squared (I 2), were calculated to measure heterogeneity. Along with the pooled estimates, ranges and median were also reported to describe the dispersion of the GDM prevalence measures reported in the literature. The prediction interval, which estimates the 95% interval in which the true prevalence of GDM in a new study will lie, was also quantified and reported (31). The overall, country-level and sub-regional levels [Eastern Europe, Northern Europe, Western Europe, and Southern Europe (25)] pooled GDM prevalence was estimated. Moreover, within each sub-European region, the pooled GDM prevalence estimates were generated overall and based on age (<30, ≥30, or unclear age group), pregnancy trimester (first, second, third, or unclear trimester), BMI (normal, overweight, obsess, or unclear BMI), and GDM ascertainment criteria. The provision of pooled estimates regardless of the ascertainment guidelines was justified by the fact that the women were defined and treated as GDM patients following each specific ascertainment guideline. We conducted a synthesis of results including the above-described meta-analysis also comprises a description of the main findings relevant to the study.

Risk of Bias (RoB)

To test the robustness of the implemented methodology, quality of evidence criteria was also used GDM ascertainment method, sampling methodology, and precision of the estimate. The risk of bias (RoB) tool was performed for each research report and not for individual studies, using the six-quality items adapted from the National Heart, Lung, and Blood Institute (NIH) criteria (32). From the 14 items of the NIH RoB tool we used research question/objective, studied population, participation rate, recruitment, sample size justification, and outcome measures and assessment. Reports were considered to have “high” precision if at least 100 women were tested for GDM. We computed the overall proportion of research reports with potentially low RoB across each of these nine quality criteria and the proportion (out of nine) of quality items with a potentially low RoB for each of the included research reports.

Publication Bias

The small-study effect on the pooled GDM prevalence estimates was explored through plotting the funnel plot. In the funnel plot, each GDM prevalence measure was plotted against its standard error. The asymmetry of the funnel plot was tested using Egger’s test (33). Analyses were performed using the metaprop (34) and metareg packages in Stata/SE v15 (35).

Results

After de-duplication, 15,933 records were screened and 547 full-text research reports critically assessed for eligibility, 133 research reports were deemed eligible and included in the meta-analysis ( ).
Figure 1

PRISMA flowchart. • Reasons for full-text exclusion: 214 GDM or DM total population 78 Wrong setting 29 Not in Europe 24 GDM Prevalence was incalculable 23 reported an unclear ascertainment of GDM criteria (I report containing information front Albania, 3 from Denmark, 2 from Finland, 2 from Ireland, 5 front Italy, I from Netherlands, 2 f:om Poland, 2 from Portugal, 2 from Spain, and 3 from United Kingdom). 15 reports have duplicate data [I from Croatia (30), I from France (31), I from Italy (32), 2 from Netherlands (33,34),6 from Norway (35-40), and 4 from United Kingdom (41-44)], and only the report that first published the study data was used. 9 Conference abstract with not enough information 8 Case-control (GDM vs. non-GDM) 7 Duplicates 6 Wrong patient population 1 Year of GDM diagnosis is UNCLEAR (Not mentioned).

PRISMA flowchart. • Reasons for full-text exclusion: 214 GDM or DM total population 78 Wrong setting 29 Not in Europe 24 GDM Prevalence was incalculable 23 reported an unclear ascertainment of GDM criteria (I report containing information front Albania, 3 from Denmark, 2 from Finland, 2 from Ireland, 5 front Italy, I from Netherlands, 2 f:om Poland, 2 from Portugal, 2 from Spain, and 3 from United Kingdom). 15 reports have duplicate data [I from Croatia (30), I from France (31), I from Italy (32), 2 from Netherlands (33,34),6 from Norway (35-40), and 4 from United Kingdom (41-44)], and only the report that first published the study data was used. 9 Conference abstract with not enough information 8 Case-control (GDM vs. non-GDM) 7 Duplicates 6 Wrong patient population 1 Year of GDM diagnosis is UNCLEAR (Not mentioned).

Study Characteristics

The 133 research reports related to 24 countries in Europe and tested a total of 15,572,847 pregnant women for GDM and yielded 254 GDM prevalence studies. The majority of the research reports were reported from Northern Europe (59/133), followed by Southern Europe (36/133), Western Europe (32/133), and Eastern Europe (6/133). Across the four UN geoscheme sub-regions (25) the most studied countries were Italy (21 reports) and the United Kingdom (14 reports). – summarize basic characteristics of the included research articles in the four European sub-regions.
Table 1

Baseline studies characteristics from Eastern Europe.

Author (Ref)Duration of data collectionCitySampling strategyPopulationAscertainment methodTested sampleGDM PositivePrev. (%)
Hungary
Renes L. et al. (36)01/2014 – 12/2014Hungary, SzegedConsecutiveGeneral populationWHO 1999149315510.1%
Kun A. et al. (37)01/2009 – 12/2017Hungary (Western)ConsecutiveGeneral populationWHO 20139469150514.9%
Poland
Mac-Marcjanek K. et al. (38)06-2011 – 06/2013Poland, LodzUnclearCaucasian pregnant womenPDA 201114511378%
PDA 201410471.7%
Kosinska-Kaczynska K. et al. (39)01/2007 – 06/2016Poland, WarsawUnclearWomen with dichorionic twin pregnancies at <14 weeks of pregnancyPolish Gynaecological Society Guidelines2012713.4%
Szymusik I. et al. (40)07/2013 – 12/2016Poland, WarsawConsecutiveGeneral populationPolish Gynaecological Society Guidelines368318%
Republic of Moldova
Brankica K. et al. (41)01/2013 – 06/2013Republic of Moldova, SkopjeConsecutiveGeneral populationIADPSG1187866.1%

IADPSG, International Association of Diabetes in Pregnancy Studies Group; PDA, Polish Diabetes Association; WHO, World Health Organization.

Table 4

Baseline studies characteristics from Southern Europe.

Author (Ref)Duration of data collectionCitySampling strategyPopulationAscertainment methodTested sampleGDM PositivePrev. (%)
Croatia
Djakovicí I. et al. (132)2011 – 2012Croatia, ZagrebConsecutiveGeneral populationHAPO study guidelines64075939.3%
Djelmis J. et al. (133)2012 -2014CroatiaUnclearSingleton pregnanciesIADSPG4646107423.1%
NICE 2015464682617.8%
Erjavec K. et al. (134)2010Croatia, NationalConsecutiveGeneral populationWHO 1999426569532.2%
2014IADSPG3909218294.6%
Vince K, et al. (135)2011Croatia, NationalConsecutiveGeneral populationIADSPG4064111812.9%
Cyprus
Inancli SS et al. (136)11/2013 – 04/2014Cyprus, NationalConsecutiveTurkish CypriotNational Diabetes Data Group2304519.6%
Greece
Vassilaki M. et al. (137)02/2007 – 02/2008Greece, CreteConvenienceGeneral populationCarpenter-Coustan11221029.1%
Italy
Trotta F. et al. (138)10/2009 – 09/2010Italy, LombardyWhole populationGeneral populationMedical records8617119212.3%
Pintaudi B. et al. (139)05/2010 – 10/2011Italy, MessinaConsecutiveCaucasian womenIADSPG101511311.1%
Caserta D. et al. (140)01/2007 – 06/2011Italy, RomeWhole populationTwin pregnanciesMedical records20762.9%
Twin pregnancies with assisted conception1381410.1%
Lacaria E. et al. (141)01/2012 – 13/2013Italy, Pisa and LivornoConsecutiveCaucasian womenIADSPG249727911.1%
D’Anna R. et al. (142)01/2011 – 04/2014Italy, Messina and ModinaRandom samplingObese womenIADSPG2415123.8%
Pinzauti S. et al. (143)01/2010 – 12/2014Italy, Florence, and SienaWhole populationTwin pregnancies with assisted conceptionMixed method430306.9%
Capula C. et al. (144)08/2011 – 01/2015Italy, CatanzaroConvenienceHealthy pre-pregnancy womenIADSPG3974106626.8%
Santamaria A. et al. (145)01/2012 – 12/2014Italy, Messina and ModenaConvenienceOverweight CaucasianADA 20111022827.5%
Overweight Caucasian receiving Myo-inositol951111.6%
Bianchi C. et al. (146)01/2010 – 03/2015Italy, PisaUnclearGeneral populationMedical records119847639.7%
Di Cianni G. et al. (147)01/2015 – 12/2015Italy, TuscanyWhole populationGeneral populationMedical records17606200011.4%
Bordi et al. (148)01/2001 – 06/2015Italy, RomeWhole populationTwin pregnancies with assisted conceptionMedical records450388.4%
Twin pregnancies647182.8%
Chiefari E. et al. (149)08/2011 – 12/2016Italy, CantazaroUnclearGeneral populationItalian Minister Guidelines5473155928.5%
Cozzolino M, et al. (150)01/2010 – 01/2016Italy, FlorenceWhole populationMultiple pregnanciesIADSPG6569915.1%
Bruno R. et al. (151)02/2013 – 06/2014Italy, ModenaUnclearSingleton pregnancies of overweight/obese women with prescribed personalized dietary interventionIADSPG622337.1%
Singleton pregnancies of obese women691318.8%
Bianchi C. et al. (152)01/2013 – 12/2015Italy, PisaWhole populationGeneral populationItalian National Guidelines133853439.95
Meregaglia M. et al. (153)01/2014 – 12/2014Italy, NationalWhole populationGeneral populationMedical records444021154010.9%
Quaresima P. et al. (154)01/2015 – 12/2016Italy, CatanzaroConsecutiveGeneral populationIADSPG141345131.8%
Gerli S. et al. (155)01/2011 – 12/2013Italy, NationalWhole populationWomen in Robson class 1 according to the Ten Group Classification SystemIADSPG76931321.7%
Women in Robson class 3 according to the Ten Group Classification System4919951.9%
Masturzo B. et al. (156)01/2011 – 12/2015Italy, TurinWhole populationSingleton pregnanciesMedical records2780723088.3%
Visconti F. et al. (157)08/2011 – 12/2016Italy, CalabriaConsecutiveSingleton pregnanciesIADSPG242459624.7%
Marozio L. et al. (158)2009 - 2015Italy, TurinWhole populationPregnant women < 40 years oldADA 20145241314302.7%
Pregnant women between 40-44 years old35412035.7%
Pregnant women > 45 years old257218.2%
Malta
Xuereb S. et al. (159)01/2009 – 12/2009Malta, NationalConsecutiveGeneral populationWHO 20062034321.2%
Slovenia
Kek T. et al. (160)05/2013 – 09/2015Slovenia, LjubljanaUnclearGeneral populationSelf-reported4504310.0%
Spain
Goni L. et al. (161)11/2009 – 03/2010Spain, NavarraConvenienceGeneral populationMedical records59873977.8%
Ruiz-Gracia T. et al. (162)04/2011 – 03/2012Spain, MadridConsecutiveGeneral populationCarpenter-Coustan175018510.5%
Berglund SK. et al. (163)2008 - 2012Spain, GranadaConvenienceOverweight and Obese womenSpanish Society of Gynecology and Obstetrics3334613.8%
Benaiges D. et al. (164)04/2013 – 09/2015Spain, BarcelonaConsecutiveSingleton pregnanciesNational Diabetes Data Group115815213.1%
Assaf-Balut C. et al. (165)01/2015 – 12/2015Spain, MadridConsecutiveSingle pregnancy following standard Med-Diet supplemented with EVOO and pistachiosIADSPG4347417.1%
Single pregnancy following standard Med-Diet4407423.4%
Gortazar L. et al. (166)2006 – 2015Spain, CataloniaWhole populationSingleton pregnanciesMedical records739877357294.8%
Mane L. et al. (167)2010 - 2013Spain, BarcelonaWhole populationGeneral populationSelf-reported563357210%

ADA, American Diabetes Association; EVOO, extra virgin olive oil; HAPO, Hyperglycemia and Adverse Pregnancy Outcomes; IADPSG, International Association of the Diabetes and Pregnancy Study Groups; WHO, World Health Organization.

Eastern Europe

From the Eastern Europe countries (Belarus, Bulgaria, Czech Republic, Hungary, Poland, Republic of Moldova, Romania, Russian Federation, Slovakia, and Ukraine), our search has just captured six reports that tested a total of 12,122 pregnant women for GDM from Hungary (two reports), Poland (three reports), and Republic of Macedonia (one report). In two out of the eight GDM prevalence studies reported in these three countries, GDM ascertainment was based on the Polish Gynecological Society Guidelines ( ). Baseline studies characteristics from Eastern Europe. IADPSG, International Association of Diabetes in Pregnancy Studies Group; PDA, Polish Diabetes Association; WHO, World Health Organization.

Northern Europe

From Northern Europe sub-region (Denmark, Estonia, Finland, Iceland, Ireland, Latvia, Lithuania, Norway, Sweden, and United Kingdom), there were 59 reports presenting estimates on GDM prevalence. None of those reports were from Estonia or Latvia. Seven reports reporting 17 GDM prevalence studies were from Denmark, 10 reports with 22 GDM prevalence studies were from Finland, one report with three GDM prevalence studies were from Iceland, seven reports with 10 GDM prevalence studies were from Ireland, two reports with three GDM prevalence studies were from Lithuania, nine reports with 19 GDM prevalence were studies from Norway, nine reports with 20 GDM prevalence were studies from Sweden and 14 reports with 28 GDM prevalence studies were from the United Kingdom. In the 122 GDM prevalence studies that tested a total of 10,278,921 pregnant women reported in the Northern European countries, the IADPSG (in 15 out of 122 studies) followed by the WHO 2013 (in 14 out of 122 studies) were the most common used GDM diagnostic ( ).
Table 2

Baseline studies characteristics from Northern Europe.

Author (Ref)Duration of data collectionCitySampling strategyPopulationAscertainment methodTested sampleGDM PositivePrev. (%)
Denmark
Bonnesen B. et al. (42)01/2009-12/2010Denmark, HvidovreConsecutivePrimiparous women with a spontaneous singleton pregnancyMedical Records3,440431.3%
Medek H. et al. (43)05/2012 – 10/2013Denmark, ReykjavikConsecutiveGeneral populationIADPSG1172212.4%
Holst S. et al. (44)01/2006 – 12/2010Denmark, NationalWhole populationWomen with singleton pregnanciesMedical Records26453957812.2%
Jeppesen C. et al. (45)01/2012 – 12/2012Denmark, NationalWhole populationWomen aged 15-49 years oldMedical Records5689417213.0%
McIntyre HD. et al. (10)01/2010 –12/2012Denmark, NationalWhole populationGeneral populationWHO 2013151662040.1%
Hamann CR. et al. (46)01/1997 –12/2014Denmark, NationalWhole populationGeneral populationMedical Records10441019141.8%
Women with atopic dermatitis any time prior to birth104411751.7%
Women with atopic dermatitis 24 months prior to birth1064151.4%
Women with atopic dermatitis during pregnancy31930.9%
Strand-Horlm KM. et al. (47)01/2004 – 12/2012Denmark, AarhusWhole populationWomen with singleton pregnanciesWHO 2013425719282.2%
Finland
Koivusalo SB. et al. (48)01/2008 – 12/2014Finland, LappeenrantaRandom selectionWomen with a history of GDM or pre-pregnancy obesityADA 20072694717.4%
Ellenberg A. et al. (49)01/2006 – 12/2008Finland, NationalWhole populationWomen with singleton pregnanciesMedical records3446025227.2%
01/2010 –12/201236331412811.3%
Koivunen S. et al. (50)01/2006 – 12/2006Finland, NationalConsecutivePregnant at gestational age ≥ 22 weeks or a birthweight ≥ 500 gThe Finnish Current Care guidelines1568251799.1%
01/2012 – 12/201030365667911.3%
Meinilä J. et al. (51)01/2008 – 12/2014Finland, Helsinki Metropolitan area and LappeenrantaUnclearWomen at high risk of GDM due to obesity, history of GDM, or bothADA 20082514618.3%
Laine MK. et al. (52)01/2009 – 12/2015Finland, VantaaWhole populationPrimiparous womenThe Finnish Current Care guidelines7750128116.5%
Laine MK. et al. (53)01/2009 – 12/2015Finland, VantaaWhole populationPrimiparous women with height < 159 cmThe Finnish Current Care guidelines68919828.7%
Primiparous women Primiparous women with height between 164-167 cm122124319.9%
Girchenko P. et al. (54)01/2011 – 12/2012Finland, NationalWhole populationGeneral populationMedical records25042489.9%
Kong L. et al. (55)01/2004 – 12/2014Finland, NationalWhole populationGeneral populationMedical records6490439856815.2%
Ellfolk M. et al. (56)01/1996 – 12/2016Finland, NationalWhole populationWomen exposed to antipsychoticsMedical records21125304714.4%
Ijas H. et al. (57)01/2009 – 12/2009Finland, NationalWhole populationWomen with singleton pregnanciesMedical records24555565823.4%
Iceland
Tryggvadottir EA. et al. (58)04/2012 – 10/2013Iceland, ReykjavikConsecutiveNon-smoking women and without GDM risk factorsWHO 20131681710.1%
Ireland
Lindsay KL. et al. (59)03/2012 – 03/2013Ireland, DublinRandom samplingObese womenCarpenter and Coustin13864.3%
Daly N. et al. (60)04/2014 – 08/2014Ireland, DublinConvenienceObese European womenWHO 2013241666.7%
Mone F. et al. (61)01/2011 – 09/2012Ireland, DublinWhole populationGeneral populationWHO 201372521401.9%
Moore R. et al. (62)2007 -2013Ireland, DublinUnclearHIV womenCarpenter and Coustin14232.1%
O’Dea A. et al. (63)01/2013 – 12/2013Ireland, GalwayConvenienceGeneral populationIADPSG690487.0%
Farren N. et al. (64)01/2014 – 01/2016Ireland, DublinConsecutiveWomen with family history of DMIADPSG2404016.6%
Daly N. et al. (65)11/2013 – 04/2016Ireland, DublinConsecutiveWomen with BMI ≥ 30 that participated in the interventionIADPSG432558.1%
Women with BMI ≥ 30 that did not participate in the intervention432148.8%
Lithuania
Ramoniene G. et al. (66)01/2010 – 12/2010Lithuania, KhaunasConsecutiveObese women with singletonsWHO 19991403323.6%
Normal weight women with singletons31071605.1%
Malakauskiene L. et al. (67)01/2005 – 12/2015Lithuania, NationalWhole populationPregnant after bariatric surgeryMedical records13032.31%
Norway
Rasmussen S. et al. (68)01/2007 – 12/2010Norway, NationalWhole populationGeneral populationMedical records7729410861.4%
Sommer C. et al. (69)05/2008 – 05/2010Norway, OsloUnclearGeneral populationIADPSG72822931.5%
Helseth R. et al. (70)04/2007 – 06/2009Norway, Trondheim, and StavangerUnclearNordic Caucasian womenWHO 2013687426.1%
Leirgul E. et al. (71)01/2006 – 12/2009Norway, NationalWhole populationGeneral populationMedical records23330334841.5%
Garnæs KK. et al. (72)11/2012 – 03/2013Norway, TrondheimUnclearWomen with BMI ≥ 28 that participated in the interventionWHO 201346818.2%
Women with BMI ≥ 28 that did not participate in the intervention451329.5%
Sorbye LM. et al. (73)01/2006 – 12/2014Norway, NationalWhole populationWomen in their second pregnancyNorwegian Society of Gynecology and Obstetrics241984391.8%
Lehmann S. et al. (74)01/1967 – 12/2014Norway, NationalWhole populationWomen who trial labor after caesarean sectionMedical records111968663.0%
Sole KB. Et al. (75)01/1999 – 12/2014Norway, NationalWhole populationWomen with singleton pregnanciesMedical records907048142001.57%
Magnus MA. et al. (76)01/2009 – 12/2013Norway, NationalWhole populationGeneral populationMedical records16234359383.7%
Sweden
Lindqvist M. et al. (77)2011 – 2012Sweden, NationalWhole populationGeneral populationMedical records18129225481.4%
Nilsson C. et al. (78)2012 – 2013Sweden, NationalWhole populationGeneral populationWHO 199974912102.8%
Stokkeland K. et al. (79)2006 – 2011Sweden, NationalWhole populationGeneral populationMedical records57664263431.0%
Sundelin HEK. et al. (80)2006 – 2014Sweden, NationalWhole populationGeneral populationMedical records87774299191.1%
Stogianni A. et al. (81)2009 – 2012Sweden, KronobergWhole populationGeneral populationMedical records2809734.6%
Crump C. et al. (82)1973 - 2014Sweden, NationalWhole populationGeneral populationMedical records4186615342550.8%
Hilden K. et al. (83)1998 – 2012Sweden, NationalWhole populationGeneral populationMedical records1294006148331.0%
Khashan AS. et al. (84)1982 – 2012Sweden, NationalWhole populationGeneral populationMedical records129279249670.4%
Liu C. et al. (85)2014 – 2017Sweden, NationalWhole populationRefugeesMedical records3189711483.6%
United Kingdom
Farrar D. et al. (86)2008 – 2010UK, BradfordWhole populationGeneral populationWHO 199911516113210%
West J et al. (87)03/2007 – 12/2010UK, BradfordConsecutiveCaucasian British/Irish womenWHO 199935031724.9%
Pakistani women265640615.3%
Syngelaki A. et al. (88)03/2006 – 07/2013UK, London and GillinghamUnclearGeneral populationMixed methods7516118272.4%
Poston L. et al. (89)03/2009 – 06/2014UK, London, Bradford, Glasgow, Manchester, Newcastle, SunderlandRandom samplingObese womenIADPSG128033226%
Sovio U. et al. (90)08/2008 – 07/2012UK, CambridgeUnclearNulliparous womenMixed methods40691714.2%
Murphy NM. et al. (91)05/2007 – 02/2011UK, London, Manchester, Cork, LeedsUnclearWomen at high risk of GDMMixed methods395358.9%
Pregnant women without risk of GDM261207.7%
White SL. et al. (92)2009 – 2014UKUnclearObese womenIADPSG130333725.9%
Hanna FW. et al. (93)02/2010 – 12/2013UKUnclearGeneral populationNICE 2015693096713.7%
Panaitescu AM. et al. (94)03/2006 – 11/2015UK, LondonUnclearGeneral populationWHO 199910778825422.4%
Hall E. et al., (95)05/2017 – 08/2017UK, LondonWhole populationGeneral populationNICE 2015126726421%
Balani J. et al. (96)2010 – 2011UK, SurreyUnclearObese womenWHO 19993027223.8%
Nzelu D. et al. (97)2011 – 2016UK, LondonConsecutivePregnant women with pregnancy induced hypertensionNICE 20157739312%
Vieira MC. et al. (98)03/2009 – 06/2014UK, LondonWhole populationObese womenIADPSG82424129.6%
Wagnild JM. et al. (99)02/2017 – 08/2017UK, Northeast EnglandConsecutiveWomen at high risk of GDMNICE 20153263116.5%

ADA, American Diabetes Association; BMI, body mass-index; DM, diabetes mellitus; GDM, gestational diabetes mellitus; IADPSG, International Association of the Diabetes and Pregnancy Study Groups; NICE, National Institute for Health and Care Excellence; UK, United Kingdom; WHO, World Health Organization.

Baseline studies characteristics from Northern Europe. ADA, American Diabetes Association; BMI, body mass-index; DM, diabetes mellitus; GDM, gestational diabetes mellitus; IADPSG, International Association of the Diabetes and Pregnancy Study Groups; NICE, National Institute for Health and Care Excellence; UK, United Kingdom; WHO, World Health Organization.

Western Europe

From Western Europe sub-region (Austria, Belgium, France, Germany, Liechtenstein, Luxembourg, Monaco, Netherlands, and Switzerland). In this sub-region, the majority of the 32 research reports were in France (34.4%) followed by Germany (18.8%), Austria (15.6%), and Switzerland (15.6%). Our study did not find any prevalence studies on GDM from three countries (Liechtenstein, Luxembourg, and Monaco) in this sub-region reported between 2014 and 2019. In the 55 GDM prevalence studies that tested a total of 4,212,723 pregnant women in the Western European countries, the IADPSG (in 14 studies) was the most commonly used GDM diagnostic ( ).
Table 3

Baseline studies characteristics from Western Europe.

Author (Ref)Duration of data collectionCitySampling strategyPopulationAscertainment methodTested sampleGDM PositivePrev. (%)
Austria
Bozkurt L. et al. (100)2010 – 2014Austria, ViennaUnclearGeneral population with OGTT at 16 weeksIADPSG2218138.3%
Tramontana A. et al. (101)01/2010- 11/2013Austria, ViennaWhole populationGeneral populationIADPSG49482094.2%
Tramontana A. et al. (102)Austria, ViennaWhole populationWomen with high-risk pregnanciesIADPSG38217044.5%
Koninger A. et al. (103)2009 – 2018Austria, EssenWhole populationWomen with polycystic ovary syndromeGDDD632946%
Weiss C. et al. (104)01/2013 – 12/2015Austria, LinzWhole populationSingleton pregnanciesWHO 2013329355316.8%
Belgium
Benhalima K. et al. (105)01/2010 – 12/2013Belgium, Leuven, AalstWhole populationGeneral populationCarpenter-Coustan146616014.1%
De Munck N. et al. (106)03/2010 – 08/2014Belgium, BrusselsWhole populationOcyte recipient with use of closed vitrificationMix method1121311.6%
France
Grunewald D. et al. (107)2008-2013France, ParisUnclearPregnant women with cystic fibrosisMedical records2328.7%
Miailhe G. et al. (108)04/2011 – 02/2012France, ParisWhole populationSingleton pregnanciesIADPSG218730914%
Goueslard K. et al. (109)2007 – 2013France, NationalWhole populationGeneral populationMedical records1515387629584.14%
Regnault N. et al. (110)2013France, BondyWhole populationGeneral populationMedical records788494678108.6%
Mortier I. et al. (111)01/2011 – 07/2012France, MarseilleWhole populationSingleton pregnanciesIADPSG4446013.5%
Boudet-Berquier J. et al. (112)01/2012 – 04/2014France, NationalRandom samplingGeneral populationMixed methods32042477.7%
Billionnet C. et al. (113)2012France, NationalWhole populationGeneral populationMedical records796346576297.24%
Mitanchez D. et al. (114)08/2010 – 03/2013France, ParisUnclearSingleton pregnancy in obese womenIADPSG2269943.8%
Singleton pregnancy in normal weight women2224118.4%
Marie C. et al. (115)2006Auvergne, FranceWhole populationGeneral populationCarpenter-Coustan1175736.2%
201028401565.5%
Preaubert L. et al. (116)01/2010 – 12/2016France, ParisWhole populationOcyte recipient with use of closed vitrificationIADPSG2473915.8%
Soomro MH. et al. (117)03/2003 – 01/2006France, Poitiers and NancyWhole populationWomen with blood-biomarkers to study heavy metalsCarpenter-Coustan62347.1%
Germany
Stuber TN. et al. (118)2006 – 2011Germany, WurzburgWhole populationGeneral populationMedical records28102649.4%
Beyerlein A. et al. (119)2008 – 2014Germany, BavariaConsecutiveGeneral populationMedical records17371864273.7%
Tamayo T. et al. (120)07/2012 – 06/2013Germany, North RhineConsecutiveGeneral populationIADPSG15330292296.0%
07/2013 – 06/2014158839108176.8%
Melchior H. et al. (121)01/2014 – 12/2015Germany, NationalWhole populationGeneral populationMedical records5671917486913.2%
Köninger A. et al. (122)2014 -2016Germany, EssenUnclearSingleton pregnanciesGerman Diabetes Association1052927.64
Pahlitzsch TMJ. et al. (123)2001 – 2017Germany, SolingenWhole populationMothers of macrocosmic newbornsMedical records2277873.8%
Netherlands
Lamain-de-Ruiter ML (124).12/2010 – 01/2014NetherlandsUnclearGeneral populationMixed method37231814.9%
Koning SH. et al. (125)01/2011 – 09/2016Netherlands, GroningenWhole populationPregnant women with at least one risk factors for GDMWHO 201310642336431.6%
De Wilde MA. et al. (126)04/2008 – 04/2012NetherlandsUnclearWomen with polycystic ovarian syndromeADA 20041884323.%
12/2012 – 12/2013Singleton pregnanciesWHO 199928891294.5%
Switzerland
Mosimann B. et al. (127)01/2014 – 12/2014Switzerland, BernConsecutiveGeneral populationMixed method3285115.5%
Amylidi S. et al. (128)06/2011 – 11/2012Switzerland, BernWhole populationPregnant women with at least one risk factors for GDMADA 20162183214.7%
Ryser Rüetschi J. et al. (129)10/2010 – 04/2012Switzerland, Geneva and BaselConsecutiveGeneral populationIADSPG229825110.9%
Horsch A. et al. (130)11/2012 * 07/2013Switzerland, LausanneWhole populationGeneral populationMixed method2033919.2%
Savopol H. et al. (131)01/2014 – 12/2015Whole populationGeneral populationIADSPG50215931.7%

ADA, American Diabetes Association; DM, diabetes mellitus; GDDD, Deutsche Gesellschaft fur gynakologie und Geburtshilfe; HBV, Hepatitis B virus, HIV, Human Immunodeficiency virus; IADPSG, International Association of the Diabetes and Pregnancy Study Groups; OGTT, oral glucose tolerance test; WHO, World Health Organization.

Baseline studies characteristics from Western Europe. ADA, American Diabetes Association; DM, diabetes mellitus; GDDD, Deutsche Gesellschaft fur gynakologie und Geburtshilfe; HBV, Hepatitis B virus, HIV, Human Immunodeficiency virus; IADPSG, International Association of the Diabetes and Pregnancy Study Groups; OGTT, oral glucose tolerance test; WHO, World Health Organization.

Southern Europe

From Southern Europe sub-region (Albania, Andorra, Bosnia and Herzegovina, Croatia, Greece, Italy, Malta, Montenegro, North Macedonia, Portugal, San Marino, Serbia, Slovenia, and Spain), there were 36 research reports, of which, the majority were from Italy (58.3%) followed by 19.4% were from Spain. Between 2014 and 2019, there were no prevalence studies on GDM from Albania, Andorra, Bosnia and Herzegovina, Montenegro, North Macedonia, Portugal, San Marino, and Serbia. In the 69 GDM prevalence studies that tested a total of 1,069,081 pregnant women, the IADPSG was the most common GDM ascertainment criteria used (30.4%) ( ). Baseline studies characteristics from Southern Europe. ADA, American Diabetes Association; EVOO, extra virgin olive oil; HAPO, Hyperglycemia and Adverse Pregnancy Outcomes; IADPSG, International Association of the Diabetes and Pregnancy Study Groups; WHO, World Health Organization.

Weighted GDM Prevalence

In the 15,572,847 pregnant women tested for GDM the weighted GDM prevalence estimated was 10.9% (95% CI: 10.0–11.8%, I, 100%) in the 24 countries out of a total of 48 countries in Europe. Of the tested pregnant women, 76.6% were from three countries: Sweden (48%), France (20.0%), and Norway (8.6%). From the represented countries in our analysis, Sweden (Northern Europe) shows the lowest weighted GDM prevalence of 1.8% (95% CI: 1.5–2.2, I, 99.9%) ( ). The highest observed national-based prevalence of 66.1% from a single study in the Republic of Moldova has contributed to the observed highest weighted GDM prevalence in the Eastern Europe sub-region ( ).
Table 5

Weighted national, sub–regional, and regional GDM prevalence in Europe.

CountryNo. of studiesTested sampleGDMGDM prevalenceHeterogeneity measures
Range (%)Median(%)Weighted prev. %95% CIQ (p−value)1 I2 (%)2 95% PI (%)3 P–value4(fixed)
Eastern Europe p<0.001(p<0.001)
 Hungary210,9621,66010.1–14.912.515.114.4–15.8
 Poland51,0422988.0–78.013.434.18.8– 65.8427.8 (p<0.001)99.10.00–100
 Republic of Moldova11187866.157.2–74.0
Overall Eastern 812,1222,0368.0–78.014.231.519.8–44.6665.8 (p<0.001)98.90.8–79.0
Northern Europe p<0.001(p<0.001)
 Denmark17474,09419,3500.9– 40.112.06.33.7–9.322,782.0(p<0.001)99.90.00–24.1
 Finland22749,342129,0624.9–36.317.318.416.7–20.26,728.1(p<0.001)99.710.6–27.8
 Iceland3168172.3–28.99.111.00.6–29.717.5(p<0.001)88.6
 Ireland108,5723091.8–58.49.318.910.0–29.9376.6(p<0.001)97.60.0–64.1
 Lithuania33,3771962.3–23.65.18.51.4–20.245.1(p<0.001)95.6
 Norway191,332,09225,0921.1–63.02.04.63.8–5.56,094.2(p<0.001)99.71.6–8.9
 Sweden207,479,06274,0730.2–34.61.51.81.5–2.218,241.0(p<0.001)99.90.6–3.8
 United Kingdom28232,21410,1131.9–29.811.211.79.4–14.46,947.8(p<0.001)99.61.8–28.6
Overall 12210,278,921258,2120.2–63.07.58.97.9–10.0365,513.4(p<0.001)100.01.0–23.4
Western Europe p<0.001(p<0.001)
 Austria58,8971,0424.2–46.038.327.313.0–44.3796.0(p<0.001)99.50.0–90.4
 Belgium214,7736144.1–11.67.93.93.6–4.3
 France163,109,492189,1731.2–43.87.58.05.9–10.422,936.1(p<0.001)100.02.7–17.0
 Germany181,058,242101,7243.4–27.67.07.35.1–9.961,693.8(p<0.001)99.90.8–21.3
 Netherlands417,4423,7174.5–31.614.013.91.9–34.12,340.4(p<0.001)99.90.0–100.0
 Switzerland103,87758310.0–31.716.117.011.3–23.4120.3(p<0.001)92.51.7–41.4
Overall Western 554,212,723296,8531.2–46.08.610.79.5–12.073,483.9(p<0.001)99.93.4–21.4
Southern Europe p<0.001(p<0.001)
 Croatia1388,0864,6761.1–23.14.75.83.2–9.23,635.5(p<0.001)99.70.0–24.0
 Cyprus12304519.615.0–25.2
 Greece41,1221027.6–17.09.310.06.4–14.369.6(p=0.02)9.90.1–31.3
 Italy32222,80913,4971.7–47.611.514.511.1–18.113,663.2(p<0.001)99.80.9–39.8
 Malta12034321.216.1–27.3
 Slovenia1450439.67.2–12.6
 Spain17756,18137,7864.8–39.611.415.011.0–19.41,838.4(p<0.001)99.11.7–37.6
Overall Southern 691,069,08156,1921.1–47.610.712.310.9–13.919,346.8(p<0.001)99.63.0–28.0
OVERALL Europe8 25415,572,847613,2930.2–78.09.910.910.0–11.8674,742.8(p<0.001)100.01.4–27.3

1 Q: Cochran’s Q statistic is a measure assessing the existence of heterogeneity in estimates of GDM prevalence.

2 I2: a measure assessing the percentage of between−study variation that is due to differences in GDM prevalence estimates across studies rather than chance.

3 PI: Prediction intervals: estimates the 95% confidence interval in which the true GDM prevalence estimate in a new study is expected to fall.

4 Heterogeneity between subgroups using random effects model (fixed effect model).

5 Overall pooled GDM prevalence in 4 countries in Europe regardless of the tested population, sample size, and data collection period, using the most updated criteria when GDM ascertained using different criteria in the same population.

8 Overall pooled GDM prevalence in ALL Europe

CI, confidence interval calculated using the exact binomial method. GDM: gestational diabetes mellitus.

Weighted national, sub–regional, and regional GDM prevalence in Europe. 1 Q: Cochran’s Q statistic is a measure assessing the existence of heterogeneity in estimates of GDM prevalence. 2 I2: a measure assessing the percentage of between−study variation that is due to differences in GDM prevalence estimates across studies rather than chance. 3 PI: Prediction intervals: estimates the 95% confidence interval in which the true GDM prevalence estimate in a new study is expected to fall. 4 Heterogeneity between subgroups using random effects model (fixed effect model). 5 Overall pooled GDM prevalence in 4 countries in Europe regardless of the tested population, sample size, and data collection period, using the most updated criteria when GDM ascertained using different criteria in the same population. 8 Overall pooled GDM prevalence in ALL Europe CI, confidence interval calculated using the exact binomial method. GDM: gestational diabetes mellitus.

Sub-Regional Weighted GDM Prevalence

The highest sub-regional weighted GDM prevalence observed in the three Eastern European countries (31.5%, 95% CI: 19.8–44.6, I, 98.9%), followed by 12.3% (95% CI:10.9–13.9, I, 99.6%) in Southern Europe, 10.7% (95% CI: 9.5–12.0, I, 99.9%) in Western Europe, and 8.9% (95% CI: 7.9–10.0, I, 100.0%) in Northern Europe.

Sub-Group Analysis

The weighted prevalence of GDM was significantly higher in pregnant women ≥30 years old (15.4%, I2, 99.8%) compared with 15–29 years old women (7.2%, I, 99.6%), in their third (18.4%, I2, 99.8%) compared with second trimester (12.5%, I, 99.9%) of pregnancy, in obese (23.1%, I, 98.3%) and overweight (7.8%, I2, 99.5%) compared with normal weight (3.4%, I, 99.4%) pregnant women. This observation was comparable in the four sub-regions, whenever data was available. In the Northern European sub-region that comprised 48.0% of the GDM prevalence studies and tested 66.0% of the pregnant women in Europe, the weighted prevalence of GDM was 1.86-time higher in pregnant women ≥30 years old (13.4%, I2, 99.7%) compared with younger women (7.2%, I, 99.7%), 1.83-time higher in the third trimester (18.0%, 95% CI: 10.0–27.7, I, 99.8%) compared with the second trimester (9.8%, 95% CI: 7.6–12.2, I, 99.9%), 4.2-time and 14.1-time higher in obese (31.1%, 95% CI: 26.5–35.8, I, 0.0%) compared with overweight (7.4%) and normal weight (2.2%) women, respectively. In all sub-regions, there was a significant variation (p<0.001) in the weighted GDM prevalence between the used GDM ascertainment guidelines ( ). The results of the four RoB domains assessed and the six quality of evidence items from NIH are presented in ( ). Overall, the RoB and quality of evidence showed a significant low RoB with domains like the study population and research question having 100% of high quality of evidence. Recruitment and outcomes measurement were also rated with high quality of evidence in 97%, while sample size justification was unclear for 70% of the studies. Regarding RoB, GDM ascertainment and precision were low for 4% and 5%, respectively. While the response rate and sampling methodology were considered high for 14% and 10%, respectively ( ).
Figure 2

Risk of Bias assesment of the 132 reviewed research reports on GDM. RoB1: GDM ascertainment (1: biological assay/medical records; 2: self-reported; 3: unclear) RoB2: Sampling methodology (1: probability-based ''random, consecutive, or whole population within a specified period of time''; 2: non-probability based; 3: unclear) RoB3: Response rate (1:<80%; 2:80%) RoB4: Precision (1: tested sample size100; 2: tested sample size <100) NIH-1: Was the research question or objective in this paper clearly stated? 1: Low risk of bias (ROB), 2: High ROB, 3: Unclear ROB NIH-2: Was the study population clearly specified and defined? 1: Low ROB, 2: High ROB, 3: Unclear ROB NIH-3: Was the participation rate of eligible persons at least 50%? 1: Low ROB, 2: High ROB, 3: Unclear ROB NIH-3: Was the participation rate of eligible persons at least 50%? 1: Low ROB, 2: High ROB, 3:Unclear ROB prespecified and applied uniformly to ail participants? 1: Low ROB, 2: High ROB, 3: Unclear ROB NIH-5: Was a sample size justification, power description, or variance and effect estimates provided? 1: Low ROB, 2: High ROB, 3: Unclear ROB NIH-11: Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? 1: Low ROB, 2: High ROB, 3: Unclear ROB.

Risk of Bias assesment of the 132 reviewed research reports on GDM. RoB1: GDM ascertainment (1: biological assay/medical records; 2: self-reported; 3: unclear) RoB2: Sampling methodology (1: probability-based ''random, consecutive, or whole population within a specified period of time''; 2: non-probability based; 3: unclear) RoB3: Response rate (1:<80%; 2:80%) RoB4: Precision (1: tested sample size100; 2: tested sample size <100) NIH-1: Was the research question or objective in this paper clearly stated? 1: Low risk of bias (ROB), 2: High ROB, 3: Unclear ROB NIH-2: Was the study population clearly specified and defined? 1: Low ROB, 2: High ROB, 3: Unclear ROB NIH-3: Was the participation rate of eligible persons at least 50%? 1: Low ROB, 2: High ROB, 3: Unclear ROB NIH-3: Was the participation rate of eligible persons at least 50%? 1: Low ROB, 2: High ROB, 3:Unclear ROB prespecified and applied uniformly to ail participants? 1: Low ROB, 2: High ROB, 3: Unclear ROB NIH-5: Was a sample size justification, power description, or variance and effect estimates provided? 1: Low ROB, 2: High ROB, 3: Unclear ROB NIH-11: Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? 1: Low ROB, 2: High ROB, 3: Unclear ROB. Graphically, the funnel plot shows a potential of publication bias and small-study effect (Egger’s test, p < 0.001) on the estimated pooled prevalence ( ).

Discussion

Summary of Evidence

This systematic review and meta-analysis research summarizes the prevalence of GDM in Europe based on 133 reports comprising data of 254 single studies reported between 2014 and 2019 in 24 countries. Most of these studies were from Italy and the United Kingdom. The overall estimated prevalence of GDM in the 24 countries from the entire European Region was lower (10.9%, 95% CI: 10.0–11.8, I: 100%) than the estimates reported by the International Diabetes Federation (IDF) for 2019 (16.3%) (168) and higher than a previous meta-analysis (5.4%, 95% CI: 3.8–7.8) conducted by Eades and colleagues (22). Differences in the population estimates (and countries) might explain the variation between the reports. IDF has included data of 39 countries and only for women aged 20-45 years old (168) and Eades and colleagues included only 12 countries (22). A descriptive study revising the global GDM prevalence points to Europe as the region with the lowest GDM prevalence with a median of 6.1 (range 1.8%-31.0%) (169), in our study, the median estimate was 9.9 (range 0.2%-78%). Considering the four sub-regions of Europe, the Eastern region presented the highest GDM prevalence (31.5%, 95% CI: 19.8–44.6, I: 98.9%), followed by Southern Europe (12.3%, 95% CI: 10.9–13.9, I: 99.6%), Western Europe (10.7%, 95% CI: 9.5–12.0, I: 99.9%), and Northern Europe (8.9%, 95% CI: 7.9–10.0, I: 100). A review of the literature from 2000-2009 is consistent with these results presenting the lowest GDM prevalence for the European northern or Atlantic seaboard countries in comparison with the Southern or Mediterranean countries (170). The Eastern (and Southern regions were also the two regions with the smallest number of studies included, 4.5% and 27.1% respectively, due to the lack of identified reports from these countries. These results highlight the need for good quality and standardized epidemiological studies in these two regions, not to mention the 25 countries that are not represented in our study. We have assessed full-text studies from some countries like Albania and Portugal that were potentially eligible to be considered but as the GDM ascertainment criteria was not clear, therefore they were excluded for not meeting our criteria. The Republic of Moldova has the highest GDM prevalence across the entire region (66.1%, 95% CI 19.8-44.6%, I2: 98.9%), followed by Poland, Austria, Cyprus, and Malta. Sweden has the lowest GDM prevalence followed by Belgium, Norway, Croatia, and Denmark. The IDF 2019 Diabetes Atlas presents GDM prevalence for 12 countries in the region and their estimated prevalence is within our confidence interval for France, Ireland, Netherlands, Poland, and Sweden (168). For Norway, Spain, and the UK their estimates are higher than ours. These findings may suggest the recent higher reported rates for GDM prevalence compared with previous years as our review comprises data from 2014-2019 and there is just for 2019. In women with a history of GDM, lifestyle interventions and medical treatment decreased the progression of T2DM by up to 40% (171). Therefore, GDM becomes a public health priority issue as it poses a significant health burden, not only to these pregnancies but also to the future health of both mothers and offspring. In this way, the diagnosis and management of GDM can represent an opportunity for intervention to reduce the burden of T2DM. Strategies to prevent T2DM may incorporate hyperglycemia screening 4 to 12 weeks after the post-partum as recommended by the most recent guidelines from ADA (12). Differences in the GDM criteria used in the different countries and sub-regions also play an important role in the differences of prevalence reported and most importantly in the heterogeneity of our meta-analysis estimations. It is known that there is a poor consensus and uniformity in the diagnosis of GDM, as our study demonstrates, by having 24 different criteria used. This fact is to be considered as well with the recent criteria updates, specifically from the WHO in 2013. The differences in GDM criteria allied with the different countries’ screening guidelines (e.g., universal GDM screening vs screening for women with risk factors) introduce heterogeneity to the meta-analysis and increases the challenge of comparing the prevalence across countries and regions. Standardized studies and policies across the European region would help to tackle the GDM public health burden.

Strengths, Implications, and Limitations

This study has used a comprehensive search strategy to review all the studies of GDM in Europe at the regional, sub-regional, and national levels. The study includes a huge number of reports and single estimates that were combined. Estimating a weighted GDM prevalence based on a huge number (over 15 million) of tested pregnant women provides the best-precise estimation of the burden of GDM in the included European countries. Additionally, estimating the pooled GDM prevalence among various pregnant women population groups according to age, trimester of GDM diagnosis, maternal body weight, also provides specific estimates in this population group to priorities action and screening strategies. As mentioned above, the range of GDM per country varied widely therefore we are not able to extrapolate the reported GDM prevalence for the European countries not represented in our estimates, the sub-regions itself and even within the countries, as the case of the Republic of Moldova, Iceland, and Malta that are included in our analysis with one single report. Another potential limitation is the lack of or small number of studies from specific countries which might not reflect the reality of the region. Therefore, interpreting the present findings should be exercised in the light of this important potential limitations.

Conclusions

The overall GDM prevalence in Europe is considerable, particularly for pregnant women in Eastern European countries. Epidemiological studies focusing on GDM and using standardized GDM criteria would be crucial to better estimate the national, sub-regional, and regional GDM of Europe as GDM has serious public health implications for the life of the mothers and newborns. This systematic review and meta-analysis findings highlight these implications and aim to contribute to the vigilant public health awareness campaigns about the risk factors associated with developing GDM in Europe and globally.

Data Availability Statement

The original contributions presented in the study are included in the article/ . Further inquiries can be directed to the corresponding author.

Ethics Statement

There are no primary data used in this review. There is no need for any ethical approval or an exemption letter according to the United Arab Emirates University-Human Research Ethics Committee.

Author Contributions

RHA conceptualized and designed the study. MSP assessed the eligibility of the retrieved citations in the titles/abstracts and full-text screening phases. RHA, NA, and MSP critically assessed the eligible studies and extracted data. RHA and NA performed the analysis. MSP and RB-S wrote the initial draft of the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This systematic review was funded by the Summer Undergraduate Research Experience (SURE) PLUS-Grant of the United Arab Emirates University, 2017 (Research grant: 31M348). The funder had no role in the study design, collection, analysis, or interpretation of the data, nor in writing and the decision to submit this article for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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1.  Associations of Maternal Diabetes and Body Mass Index With Offspring Birth Weight and Prematurity.

Authors:  Linghua Kong; Ida A K Nilsson; Mika Gissler; Catharina Lavebratt
Journal:  JAMA Pediatr       Date:  2019-04-01       Impact factor: 16.193

2.  Maternal 75-g OGTT glucose levels as predictive factors for large-for-gestational age newborns in women with gestational diabetes mellitus.

Authors:  Krstevska Brankica; Velkoska Nakova Valentina; Simeonova Krstevska Slagjana; Jovanovska Mishevska Sasha
Journal:  Arch Endocrinol Metab       Date:  2016-02       Impact factor: 2.309

3.  Italian national guidelines for the screening of gestational diabetes: Time for a critical appraisal?

Authors:  C Bianchi; G de Gennaro; M Romano; L Battini; M Aragona; M Corfini; S Del Prato; A Bertolotto
Journal:  Nutr Metab Cardiovasc Dis       Date:  2017-06-23       Impact factor: 4.222

4.  Prevalence of gestational diabetes and risk of complications before and after initiation of a general systematic two-step screening strategy in Germany (2012-2014).

Authors:  T Tamayo; M Tamayo; W Rathmann; P Potthoff
Journal:  Diabetes Res Clin Pract       Date:  2016-03-05       Impact factor: 5.602

5.  Myo-inositol may prevent gestational diabetes onset in overweight women: a randomized, controlled trial.

Authors:  Angelo Santamaria; Antonino Di Benedetto; Elisabetta Petrella; Basilio Pintaudi; Francesco Corrado; Rosario D'Anna; Isabella Neri; Fabio Facchinetti
Journal:  J Matern Fetal Neonatal Med       Date:  2015-12-23

6.  Abstracts of 52nd EASD Annual Meeting.

Authors: 
Journal:  Diabetologia       Date:  2016-08       Impact factor: 10.122

7.  Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study: associations with neonatal anthropometrics.

Authors: 
Journal:  Diabetes       Date:  2008-11-14       Impact factor: 9.461

8.  Epidemiology of gestational diabetes mellitus according to IADPSG/WHO 2013 criteria among obese pregnant women in Europe.

Authors:  Aoife M Egan; Akke Vellinga; Jürgen Harreiter; David Simmons; Gernot Desoye; Rosa Corcoy; Juan M Adelantado; Roland Devlieger; Andre Van Assche; Sander Galjaard; Peter Damm; Elisabeth R Mathiesen; Dorte M Jensen; Liselotte Andersen; Annuziata Lapolla; Maria G Dalfrà; Alessandra Bertolotto; Urszula Mantaj; Ewa Wender-Ozegowska; Agnieszka Zawiejska; David Hill; Judith G M Jelsma; Frank J Snoek; Christof Worda; Dagmar Bancher-Todesca; Mireille N M van Poppel; Alexandra Kautzky-Willer; Fidelma P Dunne
Journal:  Diabetologia       Date:  2017-07-12       Impact factor: 10.122

9.  Role of maternal age and pregnancy history in risk of miscarriage: prospective register based study.

Authors:  Maria C Magnus; Allen J Wilcox; Nils-Halvdan Morken; Clarice R Weinberg; Siri E Håberg
Journal:  BMJ       Date:  2019-03-20

10.  Is Afamin a novel biomarker for gestational diabetes mellitus? A pilot study.

Authors:  Angela Köninger; Annette Mathan; Pawel Mach; Mirjam Frank; Boerge Schmidt; Ekkehard Schleussner; Rainer Kimmig; Alexandra Gellhaus; Hans Dieplinger
Journal:  Reprod Biol Endocrinol       Date:  2018-03-27       Impact factor: 5.211

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  2 in total

1.  Association Between History of Gestational Diabetes Mellitus and the Risk of Arthritis in Women.

Authors:  Yuanyuan Mao; Wenbin Hu; Bin Xia; Li Liu; Qin Liu
Journal:  Front Public Health       Date:  2022-05-27

Review 2.  Adverse pregnancy and perinatal outcomes in Latin America and the Caribbean: systematic review and meta-analysis.

Authors:  Estela Blanco; Marcela Marin; Loreto Nuñez; Erika Retamal; Ximena Ossa; Katherine E Woolley; Tosin Oludotun; Suzanne E Bartington; Juana Maria Delgado-Saborit; Roy M Harrison; Pablo Ruiz-Rudolph; María Elisa Quinteros
Journal:  Rev Panam Salud Publica       Date:  2022-05-02
  2 in total

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