National and regional prevalence of gestational diabetes mellitus in India: a systematic review and Meta-analysis

Abstract

Background

Gestational diabetes mellitus (GDM) is frequently misdiagnosed during pregnancy. There is an abundance of evidence, but little is known regarding the regional prevalence estimates of GDM in India. This systematic review and meta-analysis aims to provide valuable insights into the national and regional prevalence of GDM among pregnant women in India.

Methods

We conducted an initial article search on PubMed, Scopus, Google Scholar, and ShodhGanga searches to identify quantitative research papers (database inception till 15th June,2022). This review included prevalence studies that estimated the occurrence of GDM across different states in India.

Results

Two independent reviewers completed the screening of 2393 articles, resulting in the identification of 110 articles that met the inclusion criteria, which collectively provided 117 prevalence estimates. Using a pooled estimate calculation (with an Inverse square heterogeneity model), the pooled prevalence of GDM in pregnant women was estimated to be 13%, with a 95% confidence interval (CI) ranging from 9 to 16%.. In India, Diabetes in Pregnancy Study of India (DIPSI) was the most common diagnostic criteria used, followed by International Association of Diabetes and Pregnancy Study Groups (IADPSG) and World Health Organization (WHO) 1999. It was observed that the rural population has slightly less prevalence of GDM at 10.0% [6.0–13.0%, I2=96%] when compared to the urban population where the prevalence of GDM was 12.0% [9.0–16.0%, I2 = 99%].

Conclusions

This review emphasizes the lack of consensus in screening and diagnosing gestational diabetes mellitus (GDM), leading to varied prevalence rates across Indian states. It thoroughly examines the controversies regarding GDM screening by analyzing population characteristics, geographic variations, diagnostic criteria agreement, screening timing, fasting vs. non-fasting approaches, cost-effectiveness, and feasibility, offering valuable recommendations for policy makers. By fostering the implementation of state-wise screening programs, it can contribute to improving maternal and neonatal outcomes and promoting healthier pregnancies across the country.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-024-18024-9.

Keywords: Gestational diabetes mellitus, Pregnancy complications, Diagnosis, IADPSG, Glucose tolerance test, DIPSI

Background

Manifestation of glucose intolerance in pregnancy, often, named Gestational Diabetes Mellitus (GDM) is emerging as a major public health problem. The World Health Organization 1999 report provides a fundamental definition which states “Gestational diabetes is a carbohydrate intolerance resulting in hyperglycemia of variable severity with onset or first recognition during pregnancy” [1]. Nevertheless, there has been substantial debate over how to characterize glucose in pregnancy, which has complicated clinical work and research over the past three decades. Additionally, it may start at the same time as pregnancy, which increases the risk of it going undetected and having adverse maternal and neonatal complications [2–6].

In 2015, the International Diabetic Federation (IDF) reported that 1 in 11 people worldwide have diabetes, with 75% of them residing in low and middle-income countries [7]. There is a huge variation in the prevalence of GDM globally from 10.1% (Eastern & Southeastern Asia) to 13.61% (Africa) depending on screening strategies, diagnostic criteria, and the background population’s ethnic composition [8, 9]. South East Asia region had 6.9 million live births being affected by hyperglycemia in pregnancy; with an estimated prevalence of 24.2% [10]. India, being the largest populous country in the world, shows the prevalence of GDM in the ranging from 3 to 35% [11–15].

Currently, the Diabetes in Pregnancy Study Group of India advocates for universal screening using a single non-fasting 2-h 75 g OGTT, with 2 h value > 140 mg/dL being diagnostic of GDM [16]. The International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria are based on the findings of the large-scale Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study and hence popular globally, [17] but its drawback is argued to be the large number of false-positive cases due to lower fasting cutoffs and hence adding to the burden of GDM [18, 19]. In addition, diagnosing the Indian population by international studies can be inconclusive as the HAPO study lacked Indian representativeness in its findings [17].

To solve the inconsistencies in diagnosis and management of GDM, a technical and operational guideline has been developed under the aegis of the Maternal Health Division, Ministry of Health and Family Welfare, Government of India in February 2018 [20]. However, subsequent studies have shown high variability in the prevalence, from rates as low as 0% in Manipur to 42% in Lucknow, Uttar Pradesh [21, 22]. A variety of factors may contribute to this variability, including differences in the genetics and population across India, as well as differences in screening practices.

A pan India prospective study (2021) conducted by FOGSI and DIPSI shows about one-third of the pregnant women are diagnosed with GDM during the first trimester and over a quarter of them have a history of fetal loss in the previous pregnancies [23]. Hence, GDM is a topic of considerable controversy when it comes to its screening, diagnosis and its cost-effectiveness.

With this aim, we conducted a systematic review to estimate the national and regional prevalence of GDM in pregnant women to foster the implementation of programs state-wise effectively. This analysis aims to investigate how various factors, such as different screening criteria, geographical locations (urban versus rural areas), techniques used for blood collection, and the timing of screening during pregnancy (early versus late), might influence the observed prevalence of GDM in pregnant women in India.

Methodology

Study protocol

This Systematic Review and Meta-Analysis is written in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines [24] and is registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (Ref.no. CRD42022335011).

Search strategy

We framed our research question using the PICO(S)(T) methodology (Population-pregnant women; Intervention-nil; Comparison-nil; Outcome-GDM; Study design-cross-sectional in India).

We performed searches in PubMed and Scopus using selected keywords. These results were supplemented by relevant studies from Google Scholar and ShodhGanga—Indian thesis repository (https://shodhganga.inflibnet.ac.in/). The last day fir performing the search was 15th June 2022. No date or language restrictions were imposed. The cross-references of the included studies were explored for additional studies. Keywords were identified by iterative discussion among reviewers, and a search query was developed separately for each database. The controlled descriptors (such as MeSH terms) and Boolean operators were used to develop a robust search strategy. (See Additional file 1: Search Strategy).

Eligibility criteria

The studies reporting the prevalence of GDM in pregnant women in India were included.

Inclusion criteria

1. Community or hospital-based studies.
2. Original published articles and short communications.
3. Studies providing the prevalence of GDM
4. Studies conducted in India
5. Type of studies: cross-sectional studies.

Exclusion criteria

1. Overviews, editorials, other review papers, or method protocols without results
2. Molecular or genetic studies, animal studies, Invitro studies.
3. Studies that did not differentiate between GDM and type 1 and/or type 2 diabetes
4. Studies that reported risk factors, associated biomarkers, or outcomes of GDM without reference to GDM prevalence
5. Studies which have not reported screening methods
6. Experimental studies
7. Three authors independently examined search results for inclusion. Disagreements, if any, were settled by consensus with a fourth author.

Study selection

A reviewer independently conducted searches on all information sources from various databases and uploaded to Rayyan QCRI online software [25]. Rayyan QCRI helped in ensuring a systematic and comprehensive search and selection process. A fourth reviewer managed Rayyan QCRI software, who identified and removed the duplicate citations. Three authors independently screen titles and abstracts with turned “blind” function on. The discrepancies between the three reviewers were discussed with a fourth author for making a consensus to select the articles. Full-text copies of all selected studies were obtained to find more details. We documented the reasons for the exclusion of studies explored as full text. The study inclusion process is presented using the PRISMA flowchart. The reference management software Mendeley Desktop (https://www.mendeley.com) for Windows was used to store, organize, cite, and manage all the included articles.

Data extraction

After selecting eligible studies, we obtained full-text articles for all included studies. Two reviewers independently performed data extraction of relevant information. Data were extracted regarding author, year of publication, study location, site (hospital- or community-based or data-based), study type, trimester, sample size, diagnostic criteria, and prevalence of GDM. We recorded investigators’ definitions of GDM and screening and diagnostic criteria used for GDM.

When a study reports the prevalence of GDM in the same population using multiple diagnostic criteria, the most recent and up-to-date criteria was selected in the following sequence-.IADPSG/ WHO 2013 > DIPSI> WHO 1999 > ADA > NICE> Carpenter and Coustan > NDDG> O’Sullivan and Mahan’s Criteria as framed after the iterative discussion.

Bias reporting

The methodological quality of the studies was analyzed independently by two investigators using the AXIS tool which critically appraises study design and reporting quality as well as the risk of bias in cross-sectional studies. We assessed bias using the AXIS Tool for Prevalence Studies in our systematic review [26]. The AXIS tool has 20 components assessing the quality of the studies with special focus on the presented methods and results based on a combination of evidence, epidemiological processes, experience of the researchers and Delphi participants. The components included in this checklist are addressing study objective, design, sample size, sample population, sample frame, selection process, non-responders, risk factors and outcome measured, appropriateness of statistical methods, consistency of results, discussion justified, limitation of the study, ethical approval and any conflict of interest or funding received.

Data synthesis and analysis

The prevalence of GDM from different studies were pooled together using the Inverse variance heterogeneity method. Heterogeneity was assessed using I² Statistics. High heterogeneity in the study was analyzed using sub-group analysis and sensitivity analysis. MetaXL software was used for data synthesis [27]. Publication bias was determined using DoI plot and LFK index.

Results

On searching PubMed (n = 1883), Scopus (n = 345), Google Scholar (n = 92), and ShodhGanga—reservoir of Indian theses (n = 73), a total of 2393 articles were identified related to GDM (see Fig. 1: PRISMA flowchart) Thus, the full texts of 140 articles were assessed for eligibility. During this process, a total of 13 authors were contacted for full-text via email, out of which (n = 11) responded. Remaining 2 articles were included based on only abstract and in data extraction sheet, missing data were reported. Thus, a final 117 articles were included in the systematic review and meta-analysis for the analysis. (See Table 1: Data Extraction Sheet).

Fig. 1 PRISMA Flowchart (Figure not reproduced here; see the original article.)

A total of 13 studies were found to report the data in separate studies which was part of a large study. The studies by Punnose J et al. 2018 [28] and Punnose J et al. 2021 [29] and Agarwal MM et al. 2018 [30] was conducted in the same population (n = 36,530) during the time period January 2006–December 2016 and was also reported in multiple publication. Thus, data from these studies were considered as one data and the study with the longest time period (Punnose J et al. 2018) was included in the review. Similarly, a study was conducted in the South Indian pregnant women (n = 304) during July 2011 to August 2012 by Nayak PK et al. 2013 [31] and Mitra S et al. 2014 [32] and was reported as separate studies. Thus, we included the Mitra S et al. 2014 with the complete data for the analysis. Similarly, a project “Women in India with GDM Strategy (WINGS)” was carried out in Tamil Nadu between January 2013 and December 2015 in Pregnant women (n = 1459) and were reported as two separate studies by Bhavdharini et al. (2016 and 2017). We considered them as one data and included Bhavdharini et al. 2016 in our study.

Likewise, studies, namely, Rajput R et al. 2012, Tripathi R et al. 2012, Kumar CN et al., C R et al. 2014, Bhattacharya et al. 2002, Balaji V et al. 2006, Balaji V et al. 2012, and Seshiah V et a 2007, were reported as separate studies using data from a large study and hence, were excluded from the analysis.

Five studies were added using suffix (A, B and C) as they reported the prevalence of GDM using different sub-sets of population, but were otherwise reported in the same study. Taneja et al. 2020 in Punjab used the same criteria of screening GDM in women at different gestational age (26 to 28 weeks and after 34 weeks) [33]. These were considered as 2 separate studies and labelled as Taneja (A) and Taneja (B) respectively. Similarly, a study was conducted by Siddique et al. using ADA criteria in Saket, Muzzaffarpur and Bhilai area on different subset of population [34]. These studies were also considered as three different studies and labelled as Siddique (A), (B) and (C) respectively. Also, a community based study was conducted in urban, semi-urban and rural area of Chennai city on a different sub-set of population [35]. These were considered as three different studies and labelled as Seshiah V et al. 2009 (A), (B) and (C) respectively.

A total of 19 articles utilized a combination of criteria to estimate the prevalence of GDM [36–52].

The variation in diagnostic criteria during estimation of Glucose in pregnant women pose a challenge in data extraction. Thus, the most recent and up-to-date criteria was selected in the following sequence-IADPSG/ WHO 2013 > DIPSI>WHO 1999 > ADA > NICE> Carpenter and Coustan > NDDG> O’Sullivan and Mahan’s Criteria as framed after the iterative discussion by the subject experts.

Diagnostic criteria

A variety of diagnostic criteria were used in a total of 117 studies included in the review. (See Table 2: Different GDM Screening criteria).

Table 2 Different screening criteria used by societies for diagnosing GDM in Pregnant women

DIPSI (29 prevalence estimates) [23] was the most common diagnostic criteria used, followed by IADPSG / WHO 2013 (38 prevalence estimates) [53], WHO 1999 (24 prevalence estimates) [54], and ADA (11 prevalence estimates) [55]. Other criteria used were Carpenter and Coustan Criteria (6 prevalence estimates) [56], NDDG (1 prevalence estimate) [57], NICE (1 prevalence estimate) [58], and O’Sullivan and Mahan’s criteria (1 prevalence estimate) [59]. There was no clear description of study criteria used in 6 studies [33, 60–63].

Capillary versus venous blood

A total of 6 prevalence estimates used capillary blood glucose (CBG) or glucometer measurements rather than venous plasma glucose (VPG) [30, 64–68]. Three studies use capillary blood followed by venous blood glucose estimation [12, 48, 69]. In 3 studies, a comparative assessment of capillary and venous blood glucose estimation was done on the prevalence of the GDM in the pregnant women [70–72].

Two-step versus one-step procedure

A total of 93 studies (n = 93) uses one-step procedure to estimate the prevalence of GDM, whereas, only 19 studies (n = 19) used two-step procedure for the diagnosis of the GDM in the study population. There was no clear description of study criteria used in 5 studies.

Risk of Bias

We assessed the Risk of Bias using the AXIS tool [26]. Overall, 117 studies were included in the Risk of Bias assessment using the AXIS tool. A horizontal bar graph showing the Risk of bias tool result for each component is given in Fig. 2 Risk of Bias.

Fig. 2 Risk of Bias Assessment (Figure not reproduced here; see the original article.)

Majority of the study components revealed a low risk of bias namely, objective of the study, appropriateness of the study design, study population defined, appropriateness of sample frame, risk factors measured according to the objectives and with the appropriate study tool, accuracy of choice of statistical methods, measures of replicability of the study, description of the basic data, results internally consistent, all results presented and justification of discussion and conclusion.

There was no clear description of response rate bias in 48 studies. Also, there was no description of Ethical consent in 22 studies. Only 9 studies reported funding, but there was no clarity of 28 studies on their funding sources keeping them in unclear risk of bias.

A high risk of bias was revealed in the sample size justification in 57 studies. Further, the results from 90 studies lacks generalizability to the general population marking them with high risk of bias. There was no description about non-responders and their information in 87 studies revealing the high risk of bias. Many studies (n = 63) which did not discuss their limitations were categorized as having high risk of bias.

Prevalence estimates of GDM in pregnant women in India

The final 117 studies were used for prevalence estimates of GDM in pregnant population in India. A total of 106 studies were conducted in a hospital-based setting and 11 were community-based studies.

We found a pooled estimate (with an Inverse square heterogeneity model) of the prevalence of overall GDM in pregnant women was 13% [95% CI, 9–16%, n = 117 studies] with the heterogeneity of the studies high at 99% which restricts the generalizability of the findings (Fig. 3 Forest Plot depicting the pooled prevalence of GDM in India) The possible reasons could be studies varied widely in population type, geography, as well as the diagnostic method used. (Table 3 Sub group Analysis) The publication date of the studies ranged from 1989 to 2022.

1. Geographical Zones

Fig. 3 Forest Plot depicting the pooled prevalence of GDM in India (Figure not reproduced here; see the original article.)

India has a union of 28 states and 8 Union territories, divided as “North,” “South,” “East,” “Central” or “West” based on the Inter-state council secretariat classification of geographic regions of India [73]. Therefore, region-wise subgroup analysis was also conducted to get estimates of the prevalence of GDM. North region includes Haryana, Himachal Pradesh, Punjab, Delhi, Chandigarh, Uttarakhand, Jammu and Kashmir and Ladakh. States like Gujarat, Rajasthan, Maharashtra, Goa, Daman and Diu and Dadara and Nagar Haveli comprises West Region of India. South India includes Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, Telangana, Andaman and Nicobar Islands, Lakshadweep and Puducherry. East and North-eastern states are Bihar, Jharkhand, Odisha, West Bengal, Arunachal Pradesh, Sikkim, Mizoram, Assam, Meghalaya, Manipur, Nagaland and Tripura. Central Zone of India includes Chhattisgarh, Uttar Pradesh and Madhya Pradesh.

The prevalence of GDM varies across the 5 zones of India. The highest prevalence of GDM was found in North region followed by South India. Areas of low prevalence include West, Central and Eastern zone of India. One of the confounding factors behind low prevalence could be lesser studies conducted in these zones to estimate the prevalence. (Fig. 4 Map of India showing the prevalence of GDM in 5 different zones of India).

Fig. 4 Map of India showing prevalence of GDM in 5 different zones of India (Figure not reproduced here; see the original article.)

The pooled prevalence of GDM in North Zone was found to be 16.1% [95% CI, 9.9–22.7, I² = 98.9%, n = 31 studies]. The maximum weightage (36.53) was by a study from Punnose J et al. conducted in 2018 [28].

Similarly, the pooled prevalence of GDM in West Zone was found to be 7% [95% CI, 3.3–11.2, I² = 98.9%, n = 17 studies]. The maximum weightage (50.24) was by a study from Naik RR et al. 2019 [74].

In Central Zone, the pooled prevalence of GDM was found to be 12.0% [95% CI, 4.3–21.1, I² = 99.29%, n = 13 studies]. A study by Jain R et al. conducted in 2016 has a maximum weightage of 66.55 [75].

The pooled prevalence of GDM in South Zone was 12.6% [95% CI, 7.8–17.8, I² = 98.38%, n = 47 studies]. The maximum weightage was held with study by Jeeyasalan L et al. conducted in 2016 [63].

In East and North-eastern Region, the pooled prevalence of 11.5% was found. [95% CI, 5.3–18.4, I² = 97.34%, n = 9 studies]. The maximum weightage (27.27) by a study done by Hussain et al. in 2020.) [76].

• Urban versus Rural Studies

A total of 92 studies were conducted in urban areas, 8 studies in semi-urban areas and 17 studies in rural areas. The pooled prevalence in the rural population was 10.0% [6.0–13.0%, I²₌96%, n = 10 studies], whereas, the pooled prevalence of 12.0% [9.0–16.0%, I² = 99%, n = 88 studies] was found in the urban population. A study conducted by Seshiah V et al. in 2009 included the study participants from urban, semi-urban and rural areas of Tamil Nadu [35].

• Diagnostic and Screening criteria

With the subgroup-analysis using diagnostic criteria, the prevalence of GDM using WHO 1999 criteria was 12.0% (9.0–16.0%), I²₌97% studies, n = 57 studies] which was slightly less than the prevalence of GDM with DIPSI criteria [23] 13.0% [3.0–24.0%, I²₌99%, n = 29 studies] The IADPSG/ WHO 2013 criteria detected a higher prevalence of GDM as 17.0% [12.0–22.0%, I² = 99%, n = 38 studies], while, ADA criteria pooled a lower prevalence of 7.0 [4.0–10.0%, I² = 86%, n = 11 studies]. There was prevalence range of 13.0% [3.0–24.0%, I₂ = 99%, n = 9 studies] was using other criteria like C&C criteria, NICE, NDDG and O′ Sullivan Criteria.

Small study effects

We evaluated the small study effects like publication bias using the DOI plot and LFK index. There was no asymmetry in the National pooled estimate [LFK index = − 0.67] and Zonal estimate except for the North zone and West zone. (See Fig. 5: DOI Plot for Publication bias).

Fig. 5 DOI plot for publication bias (Figure not reproduced here; see the original article.)

Discussion

Plethora of studies discussing the GDM prevalence in India are published, but there is a scarcity of studies discussing the regional estimates of GDM prevalence in India. A systematic review and meta-analysis conducted by Katherine T Li et al. quantitatively examined the prevalence of GDM across India based on 64 studies up to the year 2016 and explored the prevalence of GDM which ranged 0 to 41.9% [77].

This systematic review and meta-analysis included 110 studies reporting the prevalence of GDM ranging from 9 to 16% in pregnant women in India. We found a pooled estimate (with an Inverse square heterogeneity model) of the prevalence of overall GDM in pregnant women was 13% [95% CI, 9–16%] with the heterogeneity of the studies high at I₂ = 99%. The possible reasons behind this heterogeneity could be studies varying widely in population type, geography, study duration and the diagnostic method used. Our study also highlighted the discrepancy in prevalence estimates due to different screening criteria, gestational age of screening, capillary versus venous blood estimation and one-step versus two-step procedure used for diagnosing GDM.

Which diagnostic criteria is suitable for Indian pregnant women?

The most commonly used criteria were DIPSI followed by IADPSG/WHO 2013 and WHO 1999. With descriptive analysis, we found that the WHO 1999 criteria detected a high prevalence of GDM as compared to IADPSG and DIPSI which almost detected the pooled prevalence of 12–13%.

Das Mukhopadhyay et al. did not find any significant difference between the prevalence rates of GDM among DIPSI and IADPSG criteria [52]. But he concluded that DIPSI being simple in execution and patient friendly is close to the international consensus. In a study by Singh et al. in 2021, it was observed that DIPSI was only 37.1% sensitive as compared to IADPSG criteria [51]. Contrary to these findings, Seshiah et al. found a high concordance between DIPSI and IADPSG criteria [78]. The low sensitivity of DIPSI has been reported by studies such as Mohan et al.2014 [41]. and Herath et al. [79]. Sensitivity of DIPSI is quite low, hence to be used as screening and diagnostic tool at the same time is still questionable. This is the dire requirement of our country to have a better sensitive method for diagnosing GDM so that patients do not escape diagnosis (false-negatives cases) detected by DIPSI which later on crunch out the health system.

Indeed, in 2013, the WHO embraced the IADPSG criteria, replacing the earlier 1999 criteria. The DIPSI criteria were formulated based on the 2-hour post-glucose (PG) values of the WHO 1999 criteria, primarily focusing on the simplicity of assessing the 2-hour PG value independently. It’s important to note that the Fasting Plasma Glucose (FPG) parameter from the WHO 1999 criteria is considered outdated now, indicative of diabetes [53].

Further, IADPSG recommendation necessarily requires estimation of plasma glucose in three blood samples after administrating 75 g oral glucose load. Pregnant women resent this procedure, as they are pricked three times and feel too much of blood is drawn. Whereas, DIPSI criterion requires one blood sample drawn at 2-h for estimating the plasma glucose Future studies should compare the outcomes of the GDM cases diagnosed by different criteria as this would provide the final answer as to which criteria is more suitable for Indians.

Does sensitivity and Specifity of the diagnostic test matters?

A study by Mohan V et al. in 2014 compared the IADPSG, DIPSI and WHO 1999 criteria shows that the non-fasting OGTT has poor sensitivity compared to both the WHO 1999 criteria (27.7%) and the IADPSG criteria (22.6%) [41]. Thus, the current DIPSI guidelines of doing a single-step non-fasting OGTT using the 2-h VPG cut point of 140 mg/dl (7.8 mmol/l) to diagnose GDM would miss 72.3% of women with GDM diagnosed by the WHO 1999 criteria and 77.4% of women with GDM diagnosed by the IADPSG criteria. Similarly, a study by Tripathi R et al. 2017, a two-hour 75 g non-fasting DIPSI test was done and followed by OGTT [40]. Using OGTT as per the WHO 2013 /IADPSG criteria as gold standard, the sensitivity of 75 g non-fasting test was low. With this low sensitivity, about one quarter of women with GDM were missed. Missing such a large number is not acceptable for a diagnostic test, especially as GDM is associated with both maternal and perinatal complications. On contrary, in the study population, Seshiah V 2012, utilized both DIPSI and IADPSG criteria to ascertain the prevalence of GDM, which were 13.4 and 14.6% respectively [43].

Which is appropriate- early screening or risk-based screening?

There is a debate regarding the timing of screening for GDM, whether it should be done during the first prenatal visit or during the recommended period of 24–28 weeks of gestation. On the question of when to screen for GDM, a descriptive analysis by Li et al. 2018 showed that a substantial percentage of patients (11.4–60% of GDM cases) develop GDM in the first trimester, but that a similarly large percentage of patients (16–40% of GDM cases) are missed at the first visit [77]. Conducting the screening at later stages of pregnancy is linked to increased risks of maternal and perinatal morbidity and mortality. Many studies on GDM also suggest that early screening and dietary control of GDM can promote the curtailment of maternal and perinatal morbidities [80, 81]. Additionally, Raets et al. demonstrated that there is need for clear guidelines and criteria concerning early screening for GDM [82]. In line with the Flemish consensus of 2019 on screening for GDM, this review recommend to universally screen for diabetes in early pregnancy [83].

Therefore, the review findings indicates an early screening with an OGTT test at 24 weeks coupled with diet counselling and postpartum testing in pregnant women can improve perinatal outcomes [75]. However, this may not be a logistically feasible or cost-effective strategy for all patients, and screening may need to be risk-stratified in Low or Middle Income Country (LMIC).

How should pregnant women come for GDM screening- fasting or non-fasting?

In their study, Supraneni et al. conducted a comparative analysis of the diagnostic effectiveness of different fasting plasma glucose levels and the one-hour 75 g OGTT in diagnosing GDM [84]. The study found that fasting plasma glucose levels above 92 mg/dL exhibited better diagnostic effectiveness, but there was no significant difference when compared to the results obtained from the one-hour 75 g OGTT in distinguishing between pregnant women with and without GDM.

Additionally, the researchers observed that utilizing the International Association of Diabetes and Pregnancy Study Groups (IADPSG) cutoffs for fasting and one-hour 75 g OGTT demonstrated good diagnostic properties in the study population. By implementing an exit strategy based on a positive result at either the fasting or one-hour mark, it was estimated that the need for further testing could potentially be reduced in approximately one in five pregnant women. However, accessing antenatal care in a fasting state posed challenges in rural settings, as highlighted in a 2014 study by Mohan et al. [41]. On the other hand, the DIPSI (Diabetes in Pregnancy Study Group India) guidelines suggest that the GDM test can be conducted at any time during pregnancy, regardless of food intake [85]. Nevertheless, the DIPSI approach faces difficulties in effectively screening pregnant women for GDM due to low sensitivity and underdiagnosis [86].

Based on the findings of the review, it is clear that a significant need exists for well-designed and unified programs aimed at effectively managing GDM cases. Implementing such programs would be instrumental in reducing the escalating burden of diabetes in India.

Capillary versus venous blood – does it affect estimation?

There is contradictory evidence reporting varying results and conclusions regarding the accuracy and agreement between blood glucose estimation using venous plasma glucose (VPG) and capillary blood glucose (CBG) methods for diagnosing GDM.

The study by Balaji V in 2012 involving a significant number of cohorts indicated that the Accu-Chek glucometer, a CBG measurement device, provided accurate results that aligned well with laboratory measurements of VPG [72]. Similarly, another study reported that CBG values provided the closest approximation to VPG values in healthy individuals without diabetes or GDM [66]. On the other hand, Jadhav DS conducted a hospital-based clinical study in 2017 comparing VPG and CBG estimation using a glucometer based on the DIPSI criteria found a satisfactory level of agreement between the two methods with equal sensitivity. Additionally, the CBG estimation by glucometer demonstrated a small number of false positive cases due to its high specificity (99.46%) [70].

Indeed, it is interesting to note that in some studies, the capillary blood glucose (CBG) and venous plasma glucose (VPG) values were found to be similar at 1 hour (9.9 mmol/L vs. 9.6 mmol/L) and 2 hours (7.9 mmol/L vs. 7.7 mmol/L) after the glucose load [87]. These findings suggest a fair agreement between CBG and VPG measurements during the 2-hour OGTT test for (GDM.

However, it is worth mentioning that other studies have reported a slight difference between VPG and CBG values, ranging from 0.28 to 0.5 mmol/L (5–9 mg/dL) specifically at the 2-hour mark, although the difference is relatively small [88]. These discrepancies in findings may be attributed to several factors, including the specific population under study, the glucose measurement methods used, and the performance characteristics of the glucose measurement devices employed [89]. The accuracy and agreement between CBG and VPG measurements can vary across different studies and settings.

A recent study by VidyaM Sree et al. demonstrated an excellent diagnostic accuracy (99.77%) of CBG estimation using a one-step OGTT based on DIPSI criteria for GDM in an Indian population. This study highlighted the feasibility and reliability of capillary blood estimation for GDM screening, particularly in countries with limited resources [71].

This review led to the conclusion that capillary blood estimation is a feasible and reliable method for screening GDM In countries with limited resources as this approach requires less technically trained manpower and equipment. It is important for further research to explore and address these differences in order to establish standardized guidelines and protocols for the diagnosis and management of GDM, particularly in terms of blood glucose estimation methods.

Cost-effectiveness and feasibility- what should be preferred?

The prevalence of GDM varies across different states in India, highlighting the country’s diversity. Even if a universally applicable, feasible, diagnostically accurate, and cost-effective test for GDM is discovered, the gravity of the problem remains consistent.

Supraneni et al. discovered in his study that the IADPSG criteria have good specificity, positive likelihood ratio and post-test probabilities for GDM in their study population [87]. However, the cost involved for performing IADPSG recommended procedure is high, as this procedure requires three blood tests compared to one blood test of DIPSI.

“DIPSI as one-step screening and diagnostic procedure for assessing GDM in pregnant women which is less time-consuming, economical and feasible” as stated by Mounika E et al. in her study conducted in south Indian Population [47]. But, the large extent of false negatives is a major limitation of DIPSI test which cannot be overlooked. Swaroop N et al. used one-step DIPSI criteria in his study and found it to be effective but larger studies are required to further validate its importance [90].

Thus, this review suggests that ideally, and whenever feasible, a single-step 75-g OGTT using the IADPSG criteria should be done in the fasting state as this is the accepted criteria worldwide and would help to bring about international standardization. However, in countries with less resources, DIPSI criteria may be used as a backup option in certain situations where it would be cost-effective without compromising the clinical equipoise: (a) inaccessible areas where pregnant females are not able to visit healthcare facility in fasting state in morning (b) epidemiological studies where fasting sample is unavailable (c) where OGTT is not feasible in some pregnant females due to certain specified reason.

Strength of the review

Our review raises a valid point regarding the challenges of implementing a universal screening program for GDM in India. We have taken into account unpublished literature from the Indian database ShodhGanga to gather comprehensive information about the current scenario of GDM in different zones of India. We have made efforts to contact authors to obtain full-text articles or any necessary information for our analysis, ensuring maximum data inclusion.

The review highlights the need for policymakers to reach a consensus on a universal screening test for diagnosing GDM in pregnant women, considering various key factors. These factors include the variation in diagnostic criteria, such as fasting or non-fasting, one-step or two-step approaches, and the use of capillary or venous blood estimation. Additionally, the review considers the sensitivity and specificity of the diagnostic test, the cost-effectiveness of the screening method, and its feasibility in real-world settings.

We also conducted an analysis to assess publication bias. However, since we have included prevalence studies, the results can be generalized to the population regardless of any bias. Furthermore, we performed a sub-group analysis to provide an overview of the current pooled prevalence of GDM in different geographic zones of India.

The authors suggest that implementing a uniform approach nationwide may not be practical. Instead, they propose adopting a more focused and region-specific strategy to maximize resources and efficiently detect and address cases of GDM.

Overall, our review aims to provide evidence-based insights and encourage policymakers to develop consensus guidelines for GDM screening in India. By considering the diverse factors and conducting thorough analyses, we hope to contribute to the formulation of effective strategies for GDM diagnosis and management across the country.

Limitations

Although we comprehensively searched four databases, we may have included a few more databases to include more GDM-related studies. Further, analyzing the risk factors involved in the prevalence of GDM was not in the scope of our review. Further, some studies did not provide detailed information about their population type, their GDM screening methods, trimester or the distribution between multiple different screening methods that were used. It is imperative to acknowledge the absence of a standardized screening strategy, which introduces a significant limitation to our analysis. Furthermore, we recognize the potential influence of evolving diagnostic criteria on variations in GDM prevalence. To address this concern, it would be beneficial to incorporate a comparative analysis of GDM prevalence across different regions, focusing on studies that employ consistent diagnostic criteria such as DIPSI or IADPSG (WHO 2013). Additionally, we acknowledge that differences in prevalence may be attributed to assessments conducted in distinct time periods. As a means to enhance the comprehensiveness of our review, we highlight the importance of exploring studies that specifically examine trends in GDM within a given population in India over time.

Conclusion

This review emphasizes the growing concern of GDM as a public health issue, particularly in resource-constrained settings like India, where the prevalence of GDM varies significantly among states. Numerous studies conducted in India have revealed poor agreement among existing diagnostic criteria for GDM. To enable prompt diagnosis and enhance the management of GDM in India, it is crucial to incorporate a diagnostic tool that is feasible, cost-effective, and reliable. Such a tool should seamlessly integrate with the existing public healthcare system and benefit the target population. Large-scale population-based studies are necessary to address the conflicts in GDM diagnosis and provide evidence-based criteria that are applicable to the Indian population. By tailoring the screening program based on regional variations, healthcare authorities can better allocate resources and implement interventions focused on areas with higher GDM prevalence or other risk factors.

Supplementary Information

Acknowledgements

Not Applicable.

Disclaimers

The views expressed in the submitted article are authors own views, and not an official position of the institution or funder.

Abbreviations

Authors’ contributions

NM designed the study; screened titles and abstracts; conducted a full-text review; assessed the quality of each study; interpreted the data and review the manuscript. ADG designed the study; screened titles and abstracts; conducted a full-text review; assessed the quality of each study; interpreted the data and review the manuscript. MP, PB and GS screened titles and abstracts. MKG and SS conducted a full-text review; assessed the quality of each study and reviewed the manuscript. VY screened titles and abstracts and reviewed the manuscript. PB designed the study; interpreted the data and reviewed the manuscript.MM reviewed the manuscript and provided inputs and read the final manuscript. All authors read and approved the final manuscript.

Funding

No funding support.

Availability of data and materials

Available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not required.

Competing interests

The authors declare no competing interests.

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

Available from the corresponding author on reasonable request.

References

1. Diagnostic Criteria and Classification of Hyperglycaemia First Detected in Pregnancy. :1–63.
2. Kapoor N, Sankaran S, Hyer S, Shehata H. Diabetes in pregnancy: a review of current evidence. Curr Opin Obstet Gynecol. 2007;19:586–590. doi: 10.1097/GCO.0b013e3282f20aad.
3. Metzger B, Lowe L, Dyer A, Trimble E, et. a. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med. 2009;358:1991–2002. doi: 10.1056/NEJMoa0707943.
4. Spellacy WN, Miller S, Winegar A, Peterson PQ. Macrosomia-maternal characteristics and infant complications. Obstet Gynecol. 1985;66:158–161.
5. Alfadhli EM, Osman EN, Basri TH, Mansuri NS, Youssef MH, Assaaedi SA, et al. Gestational diabetes among Saudi women: prevalence, risk factors and pregnancy outcomes. Ann Saudi Med. 2015;35:222–230. doi: 10.5144/0256-4947.2015.222.
6. Perkins JM, Dunn JP, Jagasia SM. Perspectives in gestational diabetes mellitus: a review of screening, diagnosis, and treatment. Clin Diabetes. 2007;25:57–62.
7. International Diabetes Federation. IDF Diabetes Atlas. Seventh Edition. [Internet]. 2015. Available from: http://www.diabetesatlas.org/.
8. Nguyen CL, Pham NM, Binns CW, Duong D Van, Lee AH. Review article prevalence of gestational diabetes mellitus in eastern and southeastern Asia : A Systematic Review and Meta-Analysis 2018;2018.
9. Muche AA, Olayemi OO, Gete YK. Prevalence and determinants of gestational diabetes mellitus in Africa based on the updated international diagnostic criteria : a systematic review and meta-analysis. Archiv Pub Health. 2019:1–20.
10. International Diabetes Federation. IDF Diabetes Atlas, 8th Ed. 2017. p. 1–150.
11. Kalra P, Kachhwaha C, Singh H. Prevalence of gestational diabetes mellitus and its outcome in western Rajasthan. Indian J Endocrinol Metab. 2013;17:677. doi: 10.4103/2230-8210.113760.
12. Arora GP, Thaman RG, Prasad RB, Almgren P, Brøns C, Groop LC, et al. Prevalence and risk factors of gestational diabetes in Punjab, North India: results from a population screening program. Eur J Endocrinol. 2015;173:257–267. doi: 10.1530/EJE-14-0428.
13. Ambrish M, Beena B, Sanjay K. Gestational diabetes in India: science and society. Indian J Endocrinol Metab. 2015;19:701–704. doi: 10.4103/2230-8210.164031.
14. Rajput R, Yadav Y, Nanda S, Rajput M. Prevalence of gestational diabetes mellitus & associated risk factors at a tertiary care hospital in Haryana. Indian J Med Res. 2013;137:728–733.
15. Seshiah V, Balaji V, Balaji MS, Sanjeevi CB, Green A. Gestational diabetes mellitus in India. J Assoc Physicians India. 2004;52:707–711.
16. Seshiah V. Fifth national conference of diabetes in pregnancy study group. India J Assoc Physicians India. 2010;58:329–330.
17. Coustan DR, Lowe LP, Metzger BE, Dyer AR. The HAPO study: paving the way for new diagnostic criteria for GDM. Am J Obstet Gynecol. 2010;202:654.e1–654.e6. doi: 10.1016/j.ajog.2010.04.006.
18. Reddi Rani P, Begum J. Screening and diagnosis of gestational diabetes mellitus, where do we stand. J Clin Diagnostic Res. 2016;10:QE01–QE04. doi: 10.7860/JCDR/2016/17588.7689.
19. Koning SH, van Zanden JJ, Hoogenberg K, Lutgers HL, Klomp AW, Korteweg FJ, et al. New diagnostic criteria for gestational diabetes mellitus and their impact on the number of diagnoses and pregnancy outcomes. Diabetol. 2018;61:800–809. doi: 10.1007/s00125-017-4506-x.
20. Venkatesh SRKD. National Guidelines for Diagnosis & Management of viral hepatitis. Natl Heal Mission. 2018:1–80.
21. Vanlalhruaii RS, Prasad L, Singh N, Singh T. Prevalence of gestational diabetes mellitus and its correlation with blood pressure in Manipuri women. Indian. J Endocrinol Metab. 2013;17:957. doi: 10.4103/2230-8210.122597.
22. Gopalakrishnan V, Singh R, Pradeep Y, Kapoor D, Rani AK, Pradhan S, et al. Evaluation of the prevalence of gestational diabetes mellitus in north Indians using the International Association of Diabetes and Pregnancy Study groups (IADPSG) criteria. J Postgrad Med. 2015;61:155–158. doi: 10.4103/0022-3859.159306.
23. Das A K, Kapur Anil, Anjalakshi C, Balaji V, Diwakar Hema, Chawla Rajeev, et al. Diagnosis & Management of Gestational Diabetes Mellitus(2021) by-Diabetes in Pregnancy study Group India. 2021;1–39.
24. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 Statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372.
25. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5:1–10. doi: 10.1186/s13643-016-0384-4.
26. Downes MJ, Brennan ML, Williams HC, Dean RS. Development of a critical appraisal tool to assess the quality of cross-sectional studies (AXIS) BMJ Open. 2016;6:1–7. doi: 10.1136/bmjopen-2016-011458.
27. Doi SAR, Barendregt JJ, Onitilo AA. Methods for the bias adjustment of meta-analyses of published observational studies. J Eval Clin Pract. 2013;19:653–657. doi: 10.1111/j.1365-2753.2012.01890.x.
28. Punnose J, Malhotra RK, Sukhija K, Mathew A, Sharma A, Choudhary N. Bimodal glucose distribution in Asian Indian pregnant women: relevance in gestational diabetes mellitus diagnosis. J Clin Transl Endocrinol. 2018;13:20–25. doi: 10.1016/j.jcte.2018.06.001.
29. Punnose J, Malhotra RK, Sukhija K, Sharma A, Vij P, Rijhwani RM, et al. Diabetes res Clin Pract. Elsevier B.V; 2021. Prevalence of ‘hyperglycemia in pregnancy’ remained stable between 2006 and 2015, despite rise in conventional risk factors: a hospital based study in Delhi, North India; p. 108872.
30. Agarwal MM, Punnose J, Sukhija K, Sharma A, Choudhary NK. Gestational diabetes mellitus: using the fasting plasma glucose level to simplify the International Association of Diabetes and Pregnancy Study Groups Diagnostic Algorithm in an adult south Asian population. Can J diabetes. 2018;42:500–504. doi: 10.1016/j.jcjd.2017.12.009.
31. Nayak PK, Mitra S, Sahoo JP, Daniel M, Mathew A, Padma A. Feto-maternal outcomes in women with and without gestational diabetes mellitus according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) diagnostic criteria. Diabetes Metab Syndr Clin Res Rev. 2013;7:206–209. doi: 10.1016/j.dsx.2013.10.017.
32. Mitra S, Nayak PK, Sahoo J, Mathew A, Padma A, Kamalanathan S, et al. Predictors for antenatal insulin requirement in gestational diabetes. Gynecol Endocrinol. 2014;30:565–568. doi: 10.3109/09513590.2014.911274.
33. Taneja A, Gupta S, Kaur G, Jain NP, Kaur J, Kaur S. Vitamin D : Its Deficiency and Effect of Supplementation on Maternal Outcome 2020;68:47–50.
34. Siddiqui S, Waghdhare S, Panda M, Sinha S, Singh P, Dubey S, et al. Regional Prevalence of Gestational Diabetes Mellitus in 2022;25–8.
35. Seshiah V, Balaji V, Balaji M, et. a. Gestational diabetes mellitus in all trimester of pregnancy. Diabetes Res Clin Pract. 2009;77:482–484. doi: 10.1016/j.diabres.2007.01.001.
36. Saxena P, Shubham T, Puri M, Jain A. Diagnostic Accuracy of Diabetes in Pregnancy Study Group of India with Carpenter–Coustan and National Diabetes Data Group Criteria for Diagnosis of Gestational Diabetes Mellitus and Correlation with Fetomaternal Outcome. J Obstet Gynecol India.; 2022;72:154–159 10.1007/s13224-021-01486-x.
37. Todi S, Sagili H, Kamalanathan SK. Arch Gynecol Obstet. Berlin Heidelberg: Springer; 2020. Comparison of criteria of International Association of Diabetes and Pregnancy Study Groups (IADPSG) with National Institute for health and care excellence (NICE) for diagnosis of gestational diabetes mellitus; pp. 47–52.
38. Material and Methods. Acta Physiol Scand 1958;45:9–19.
39. Saxena P, Verma P, Goswami B. Comparison of diagnostic accuracy of non-fasting DIPSI and HbA1c with fasting WHO criteria for diagnosis of gestational diabetes mellitus. J Obstet Gynecol India Springer India. 2017;67:337–342. doi: 10.1007/s13224-017-0962-y.
40. Tripathi R, Verma D, Gupta VK, Tyagi S, Kalaivani M, Ramji S, et al. Evaluation of 75 g glucose load in non-fasting state [diabetes in pregnancy study group of India (DIPSI) criteria] as a diagnostic test for gestational diabetes mellitus. Indian J Med Res. 2017:209–14. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23144490
41. Mohan V, Mahalakshmi MM, Bhavadharini B, Maheswari K, Kalaiyarasi G, Anjana RM, et al. Comparison of screening for gestational diabetes mellitus by oral glucose tolerance tests done in the non-fasting (random) and fasting states. Acta Diabetol. 2014;51:1007–1013. doi: 10.1007/s00592-014-0660-5.
42. Surapaneni T, Nikhat I, Nirmalan PK. Diagnostic effectiveness of 75 g oral glucose tolerance test for gestational diabetes in India based on the international association of the diabetes and pregnancy study groups guidelines. Obstet Med. 2013;6:125–128. doi: 10.1177/1753495X13482895.
43. Seshiah V, Balaji V, Shah SN, Joshi S, Das AK, Sahay BK, et al. Diagnosis of gestational diabetes mellitus in the community. J Assoc Physicians India. 2012;60:15–16.
44. Somani BL, Arora MM, Bhatia K, Arora D, Banerjee M. A comparative study of the different diagnostic criteria of gestational diabetes mellitus and its incidence. Med J Armed Forces India [Internet] Director General, Armed Forces Medical Services; 2012;68:6–11 10.1016/S0377-1237(11)60124-X.
45. Balaji V, Balaji M, Anjalakshi C, Cynthia A, Arthi T, Seshiah V. Diabetes res Clin Pract [internet] Elsevier Ireland Ltd; 2011. Inadequacy of fasting plasma glucose to diagnose gestational diabetes mellitus in Asian Indian women; pp. e21–e23.
46. Wani AI, Bashir MI, Masoodi SR, Sheikh MI, Zargar AH. Relationship of prevalence of gestational diabetes mellitus with maternal hemoglobin [1] J Assoc Physicians India. 2005;53:1077–1078.
47. Mounika E, Loke C. Screening and diagnosis of gestational diabetes mellitus with diabetes in pregnancy study Group of India Criteria-a Prospective Study in south Indian. AcademiaEdu. 2018;5:332–336.
48. Balagopalan N, Pathak R, Islam F, Nigam A, Kapur P, Agarwal S. Diagnostic accuracy of diabetes in pregnancy study Group of India criteria for the screening of gestational diabetes mellitus in primary care setting. Indian J Community Fam Med. 2021;7:25.
49. Rudra S, Yadav A. Efficacy of diabetes in pregnancy study group India as a diagnostic tool for gestational diabetes mellitus in a rural setup in North India. J SAFOG. 2019;11:349–352.
50. Tahmina S, Daniel M. A comparison of pregnancy outcomes using two diagnostic criteria for gestational diabetes mellitus-carpenter coustan criteria and international association of the diabetes and pregnancy study groups (IADPSG) criteria. J ASEAN Fed Endocr Soc. 2017;32:27–31. doi: 10.15605/jafes.032.01.05.
51. Singh A, Yadav R, Kunwar S, Kumari S, Shrivastava K. Comparative evaluation of diabetes in pregnancy study Group of India and International Association of diabetes and pregnancy study groups: criteria for the diagnosis of gestational diabetes mellitus. J SAFOG. 2021;13:212–215.
52. DasMukhopadhyay L, Bhattacharya SM, Dey A. Prevalence of gestational diabetes mellitus utilizing two definitions. J Obstet Gynecol India. 2020;70:245–247. doi: 10.1007/s13224-019-01271-x.
53. Metzger BE. International Association of Diabetes and Pregnancy Study Groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33:676–682. doi: 10.2337/dc09-1848.
54. Definition, diagnosis and classification of Diabetes Mellitus and its complications. Part I:Diagnosis and Classification of Diabetes mellitus. World Health Organization Geneva WHO/NCD/NCS/99.2 ed; 1999 1–59.
55. Standards of medical care in diabetes. Diabetes management guidelines. American Diabetes Association. Diabetes Care. 2007:30.
56. Carpenter MW, Coustan DR. Criteria for screening tests for gestational diabetes. Am J Obstet Gynecol. 1982;144:768–773. doi: 10.1016/0002-9378(82)90349-0.
57. Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes. 1979;28:1039–57.
58. National Institute for Health and Care Excellence (NICE). Diabetes in pregnancy, NICE guideline NG3, 2015. 2020;640. Available from: https://www.nice.org.uk/guidance/ng3
59. O’sullivan Jb, Mahan Cm. Criteria for the Oral glucose tolerance test in pregnancy. Diabetes. 1964;13:278–85.
60. Menon U, Ranjan M, Jasper P, Oommen A. Evaluation of Plasma Fructosamine as a Screening Test for Gestational Diabetes. :25–6.
61. Tellapragada C, Eshwara VK, Bhat P, Acharya S, Kamath a. Risk Factors for Preterm Birth and Low Birth Weight Among Pregnant Indian Women : A Hospital-based Prospective Study 2016;165–75.
62. Ghosh S, Ghosh K. Maternal and neonatal outcomes in gestational diabetes mellitus 2013;111:24765693.
63. Jeyaseelan L, Yadav B, Silambarasan V, Vijayaselvi R, Jose R, Jose R. Large for gestational age births among south Indian Women : temporal trend and risk factors from 1996 to 2010. J Obstet Gynecol. 2016;66:42–50. doi: 10.1007/s13224-015-0765-y.
64. Chanda S, Dogra V, Hazarika N, Bambrah H, Sudke AK, Vig A, et al. Prevalence and predictors of gestational diabetes mellitus in rural Assam: a cross-sectional study using mobile medical units. BMJ Open. 2020:10.
65. Dave VR, Rana BM, Sonaliya KN, Chandwani SJ, Sharma SV, Khatri SO, et al. Screening of gestational diabetes and hypertension among antenatal women in rural West India. Cent Asian J Glob Heal. 2014:3.
66. Kumar N, Das V, Agarwal A, Pandey A, Agrawal S. Does two hour post 75-gram sugar test levels for diagnosis of gestational diabetes correlate with type of intervention required? An audit from tertiary care center of India. J Clin Diagnostic Res. 2018;12:QC06–QC09.
67. Rajput M, Bairwa M, Rajput R. Prevalence of gestational diabetes mellitus in rural Haryana: a community-based study. Indian J Endocrinol Metab. 2014;18:350–354. doi: 10.4103/2230-8210.131176.
68. Sawant AP, Naik SS, Nagarkar VD, Shinde AV. Screening for gestational diabetes mellitus (GDM) with oral glucose tolerance test (OGTT) in sai shirdi rural area of Maharashtra state. Biomed Res. 2011;22:203–206.
69. Chudasama RK, Kadri AM, Ratnu A, Jain M, Chandrakant P. Magnitude of Gestational Diabetes Mellitus , its Influencing Factors and Diagnostic Accuracy of Capillary Blood Testing for its Detection at a Tertiary Care Centre , Rajkot , Gujarat. 2019;6–10.
70. Jadhav DS, Wankhede UN. Original ResearchArticle Comparative study of capillary blood glucose estimation by glucometer and venous plasma glucose estimation in women undergoing the one step DIPSI test ( diabetes in pregnancy study group India ) for screening and diagnosis of gest 2017;6:1488–92.
71. SreeVidhya M. Comparative study of capillary blood glucose estimation by glucometer and venous plasma glucose estimation in women undergoing one-step DIPSI test (Diabetes in Pregnancy study group) for screening and diagnosis of gestational diabetes mellitus. [Internet] ePrints@Tamilnadu Med Univ Available from http//repository-tnmgrmu.ac.in/14256/Available from http//repository-tnmgrmu.ac.in/14256/. 2020;
72. Arthi T, Seshiah V. Comparison of Venous Plasma Glucose and Capillary Whole Blood Glucose in the Diagnosis of Gestational Diabetes Mellitus : A Community-Based Study 2012;14:131–4.
73. INTER-STATE COUNCIL SECRETARIAT.ISCS. Ministry of Home Affairs , Government of India; 2019. p. 2–3.
74. Naik RR, Pednekar G, Cacodcar J. Incidence and risk factors of gestational diabetes mellitus in antenatal mothers in Goa , India. 2019;8:586–90.
75. Jain R, Davey S, Davey A, Raghav SK, Singh J V. O riginal article can the management of blood sugar levels in gestational diabetes mellitus cases be an indicator of maternal and fetal outcomes ? The results of a prospective cohort study from India. 2016;
76. Hussain T, Das S, Parveen F, Samanta P, Bal M, Yadav VS, et al. Prevalence , risk factors and morbidities of gestational diabetes among pregnant women attending a hospital in an urban area of Bhubaneswar. Odisha. 2020:5327–33.
77. Li KT, Naik S, Alexander M, Mathad JS. Screening and diagnosis of gestational diabetes in India: a systematic review and meta-analysis. Acta Diabetol. 2018;55:613–625. doi: 10.1007/s00592-018-1131-1.
78. Seshiah V, Das AK, Balaji V, Joshi SR, Parikh MN, Gupta S. Gestational diabetes mellitus--guidelines 2006;2006.
79. Herath M, Weerarathna T, Umesha D. Is non-fasting glucose challenge test sensitive enough to diagnose Gestational Diabetes Mellitus? Int Archiv Med. 2015;8:93.
80. Thomas T, Prabhata S, Valsangkar S. Diabetes screening and the distribution of blood glucose levels in rural areas of North India. J Fam Community Med. 2015;22:140–144. doi: 10.4103/2230-8229.163026.
81. Schaefer UM, Songster G, Xiang A, Berkowitz K, Buchanan TA, Kjos SL. Congenital malformations in offspring of women with hyperglycemia first detected during pregnancy. Am J Obstet Gynecol. 1997;177:1165–1171. doi: 10.1016/s0002-9378(97)70035-8.
82. Raets L, Beunen K, Benhalima K. Screening for gestational diabetes mellitus in early pregnancy: what is the evidence? J Clin Med. 2021;10:1–16. doi: 10.3390/jcm10061257.
83. Benhalima K, Minschart C, Van Crombrugge P, Calewaert P, Verhaeghe J, Vandamme S, et al. The 2019 Flemish consensus on screening for overt diabetes in early pregnancy and screening for gestational diabetes mellitus. Acta Clin Belgica Int J Clin Lab Med. 2020;75:340–347. doi: 10.1080/17843286.2019.1637389.
84. Surapaneni T, Fernandez E. Obesity in gestational diabetes: emerging twin challenge for perinatal care in India. Int J Infertil Fetal Med. 2010;1:35–39.
85. Seshiah V, Balaji V, Balaji MS, Sekar A, Sanjeevi CB, Green A. One step procedure for screening and diagnosis of gestational diabetes mellitus. J Obs Gynecol India. 2005;55:525–529.
86. Goldberg RJ, Ye C, Sermer M, Connelly PW, Hanley AJG, Zinman B, et al. Circadian variation in the response to the glucose challenge test in pregnancy: implications for screening for gestational diabetes mellitus. Diabetes Care. 2012;35:1578–1584. doi: 10.2337/dc11-2217.
87. Weiss PAM, Haeusler M, Kainer F, Purstner P, Haas J. Toward universal criteria for gestational diabetes: relationships between seventy-five and one hundred gram glucose loads and between capillary and venous glucose concentrations. Am J Obstet Gynecol. 1998;178:830–835. doi: 10.1016/s0002-9378(98)70500-9.
88. Colagiuri S, Sandbæk A, Carstensen B, Christensen J, Glumer C, Lauritzen T, et al. Comparability of venous and capillary glucose measurements in blood. Diabet Med. 2003;20:953–956. doi: 10.1046/j.1464-5491.2003.01048.x.
89. Bhavadharini B, Mahalakshmi MM, Anjana RM, Maheswari K, Uma R, Deepa M, et al. Prevalence of gestational diabetes mellitus in urban and rural Tamil Nadu using IADPSG and WHO 1999 criteria (WINGS 6). Clin Diabetes Endocrinol. 2016:2. 10.1186/s40842-016-0028-6.
90. Swaroop N, Rawat R, Lal P, Pal N, Kumari K, Sharma P. Gestational diabetes mellitus: study of prevalence using criteria of diabetes in pregnancy study group in India and its impact on maternal and fetal outcome in a rural tertiary institute. Int J Reprod Contracept Obstet Gynecol. 2015;4:1950–1953.