Open access peer-reviewed chapter

Biomarkers in GDM, Role in Early Detection and Prevention

Written By

Samar Banerjee

Submitted: 14 July 2021 Reviewed: 21 September 2021 Published: 23 November 2021

DOI: 10.5772/intechopen.100563

From the Edited Volume

Gestational Diabetes Mellitus - New Developments

Edited by Miroslav Radenković

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Gestational Diabetes Mellitus (GDM) happens to be a very frequent and major complication of pregnancy because of higher morbidity and mortality, both for the mother and the baby. After delivery, GDM carries the risk of higher maternal morbidity due to post pregnancy obesity, development of diabetes mellitus, obesity and also cardiovascular diseases in significant number in both the mother and child for future. As per current guidelines, GDM is diagnosed at the end of the second trimester by elevated blood glucose values when, foetal damages by metabolic and epigenetic changes had already started. As a result, treatments cannot be started before the late second or third trimester, when the process of high risk of foetal morbidity and mortality has been set in. If by any method we can predict development of GDM at earliest part of first trimester or even more overjealously, we can predict, before pregnancy, then and then only we can avoid many disasters induced by GDM. With this idea many biomarkers, both clinical and laboratory based like clinical, metabolic, inflammatory and genetic markers etc., related with early pregnancy metabolic alterations have been studied for their potential to help in the prediction of later pregnancy glucose intolerance. Though promises are seen with some biomarker-enhanced risk prediction models for GDM, but lack of external validation and translation into day-to-day clinical applications, cost effectiveness, with which they may be utilized in routine prenatal care has limited their clinical use. But future is very promising and incorporating the biomarkers which precede the onset of hyperglycaemia into a risk prediction model for GDM and may help us for earlier risk assessment, screening, and diagnosis of GDM and also prevention of its both the immediate and remote complications. This review highlights the current knowledge of the understanding of the candidacy and practical utility of these biomarkers for GDM with recommendations for further research.


  • Biomarkers
  • gestational diabetes mellitus (GDM)
  • macrosomia
  • foetal abnormalities

1. Introduction

Norman Freinkel once told that “No single period in human development, provides a greater potential (than pregnancy) for long – range ‘pay – off’ via a relatively short – range period of enlightened metabolic manipulation”.

During pregnancy, the body systems of the woman, must support nutrient and oxygen supply for the proper growth and development of the foetus and subsequently during lactation. Inability to adopt the changes in maternal physiology may lead to complications, such as gestational diabetes mellitus (GDM). The International Association of Diabetes and Pregnancy Study Groups (IADPSG) shows that, GDM may complicate 15–20% pregnancies, and has increased in the last 20 years in all ethnic groups as much as 27% [1].

GDM originates from interplay of factors like specific gene mutations, dysregulation of placental hormones and β-cell injury, favored by advanced age, gynecological alterations and diabetogenic factors. GDM mostly develop after the 2nd trimester of pregnancy, between the 24th and the 28th week of gestation. GDM may precipitate serious and long-term complications for foetal and maternal health, in particular, metabolism and cardiovascular in nature [2].

Currently, in most cases, the diagnosis of Gestational Diabetes Mellitus (GDM) is done around the late phase of second trimester, which may expose the foetus to the hazards of intrauterine metabolic alterations and also epigenetic changes for the period of exposure. Many documented evidences indicate that the metabolic alterations may subject the new born vulnerable to many long-term pathologies. Detection and management of GDM in pregnancy, can reduce the frequency of adverse pregnancy outcome. Hence, we need to predict and identify GDM earlier in pregnancy even if possible before the pregnancy, in order to limit the exposure to impaired glucose metabolism.

American Diabetes Association (ADA) recommends initial screening for GDM at 24–28 weeks [3]. But Seshiah V et al. from India has detected 62.1% cases of GDM before 24 weeks. Moreover, if we do not test before 24 weeks, we will miss earliest intervention for all the cases of undetected diabetes existing before pregnancy [4].

The aim of this review was to find out the useful and possible markers or guides to detect GDM early in pregnancy before rise of blood sugar and if possible, even before pregnancy to avoid all complication for mother and child arising from effects of GDM on gestation.

1.1 Search strategy and selection criteria

References for this review were identified by searching PubMed, Embase for articles in English with no language restrictions for articles published mainly from 2000 to 2021. The search terms used were GDM biomarkers, GDM pathogenesis, GDM prevention and epigenetics of GDM. The final reference list was prepared based on this search, supplemented with references from the authors’ own dataset.


2. Biomarkers

GDM develops when beta cell dysfunction coexists, and is complicated by further abnormalities in adipokine and cytokine profiles, increased free fatty acids (FFA), triglycerides (TG), low vitamin D and endothelial dysfunction. The identification of early biomarkers in pregnancy, who may develop GDM, may lead to an improved understanding of pathogenesis of GDM. Combination of biomarkers and different risk factors into a predictive model, may help in early prediction of GDM. This may also find out effective prevention strategies and finally can limit different complications related with GDM. The first-trimester biochemical predictors of GDM are shown in Table 1.

  • Glycemic markers

    • Fasting glucose

    • Post-load glucose

    • Hemoglobin A1C

    • Serum Insulin

    • Tests of insulin sensitivity (HOMA, QUICKI)

  • Lipid profile, with higher concentrations of total cholesterol and triglycerides

  • Insulin resistance markers

    • Fasting insulin

    • Sex hormone-binding globulin

  • Inflammatory markers

    • C-reactive protein

    • Tumor necrosis factor-alpha

    • IL-6

    • TNF-alfa

    • hsCRP

  • Genetic markers rs7957197 (HNF1A), rs10814916 (GLIS3), rs3802177 etc.

  • Urine biomarkers: l-tryptophan, l-urobilinogen, ceramide (d18:0/23:0), 21-deoxycortisol, cucurbitacin-C, aspartame etc.

  • Adipocyte-derived markers

    • Leptin

    • Adiponectin

    • Resistin

    • Visfatin

    • Omentin-1

    • Ghrelin

  • Placenta-derived markers

    • Follistatin-like 3

    • Placental growth factor

    • Placental exosomes

    • afamin,

    • fetuin-A,

    • fibroblast growth factors-21/23,

    • ficolin-3 and follistatin,

    • specific micro- RNAs

  • Others

    • Vitamin D

    • Glycosylated fibronectin

    • Soluble(pro)renin receptor

    • Alanine aminotransferase

    • Ferritin

    • Glucagon

    • PAI-1

    • Adipocyte fatty acid-binding protein

    • SNPs,

    • DNA methylation,

Table 1.

Showing the first-trimester biochemical predictors of GDM.


3. Epigenetic footprint

Metabolic alterations like impaired glucose control during the phase of foetal development, may result in functional and structural alterations in the developing foetus, and may result in a predispose to the development of chronic metabolic diseases in future life. These alterations are actually the ‘foetal programming’ and may trigger epigenetic changes [5]. The epigenetic changes are considered as different changes in the biochemical structure of DNA, which alters the gene expression in pregnancy as shown in Table 2.

  • DNA methylation,

  • Histone modification

  • Non-coding RNA processes.

Table 2.

Showing the epigenetic changes in pregnancy.

Maternal insulin resistance can also cause insulin resistance in the foetus [6]. Multiple studies have correlated maternal GDM, with the development of obesity and T2DM in children who are eight times more prone to develop T2DM than non-GDM children [7, 8]. This raises the strong need for early detection of GDM preceding the hyperglycaemia which might avoid subsequent harm.


4. Obesity, inflammation and GDM

Now a days, more and more women are becoming pregnant, being either overweight or obese. The obese women show a three-fold risk for developing GDM. The global increase in GDM at present time is largely due to the on-going pandemic of obesity. Obesity is related to an altered production of proinflammatory cytokines from the adipocytes, which may lead to a state of chronic low-grade inflammation. It acts upon the expression and production of different proinflammatory cytokines e.g., TNF-alpha and IL-6 and also many anti-inflammatory cytokines. This also produces adipokines e.g., adiponectin, visfatin and leptin etc. Adipokines can modify insulin secretion & sensitivity, appetite, energy control and inflammation. Sound relationship is evident between obesity, chronic low-grade inflammation and development of T2DM. The normal pregnancy shows a balance between the productions of pro-inflammatory and anti-inflammatory cytokines.

Pregnancies in obese women, further may aggravate the proinflammatory markers and may lead to an imbalance and possible complications. It is now accepted that inflammation is also an associated feature of GDM [9]. During GDM, the increased production of proinflammatory cytokines disturbs the insulin signaling [10]. A down regulation of adiponectin and anti-inflammatory markers such as IL-4 and IL-10 and an enhanced production of proinflammatory cytokines such as IL-6 and TNF-α are usually observed in GDM [11].


5. Adipocyte-derived markers

5.1 Adipokines or Adiponectin’s

Adiponectin is actually an adipocyte protein and consists of anti-atherogenic, anti-inflammatory and also insulin-sensitizing effects [12]. Adiponectin is inversely correlated with the clinical conditions like hypertension, dyslipidaemia, obesity and also coronary artery disease. Diminished level of adiponectin are usually seen with an increased risk of T2DM [13]. During the normal pregnancies, adiponectin decrease progressively also, probably from a decrease in insulin sensitivity [14]. Many studies have indicated that reduced adiponectin levels during 24–28 weeks in GDM compared to non GDM women, probably corelate low levels of adiponectin with onset of insulin resistance and diminished beta cell function [15, 16]. In one study, adiponectin concentrations in 560 GDM patients and 781 controls revealed a significantly decreased adiponectin level in GDM patients vs. controls [17].

Adiponectin, an adipokine having anti-inflammatory, anti-atherosclerotic and insulin-sensitizing proprieties in another study, was constantly lower along the 1st–3rd trimester of GDM gestations [18]. Hypoadiponectinemia increases the risk of developing GDM by 4.6 times [19], and is inversely correlated with the insulin resistance, BMI and leptin [20]. The ratio of plasma adiponectin and leptin (< 0.33) is also considered as predictor of GDM as early as the period of 6th to 14th week of pregnancy [21]. But probably the assessment of the high molecular weight oligomeric-adiponectin may give better results [22].

Recent prospective studies have addressed the role of adiponectin as a possible early predictor of GDM. Lower levels of adiponectin in the first trimester of pregnancy are associated with a greater risk for developing GDM. This suggests that a down regulation of adiponectin may be a predictor of GDM [23]. In a systematic review and meta-analysis, adiponectin had a moderate effect for predicting future GDM [24]. Again, a case–control study found revealed that low adiponectin levels in pre-pregnancy period is associated with an increased risk of 5.0-fold for developing GDM [25].

This association was significant even when adjustment of known risk factors for GDM was done. This is important as it can identify a group of high-risk women, who might be not detected by conventional tests. Therapy with adiponectin in animal models of obesity improves glycaemia and also can reduce hyperinsulinaemia without any changes in body weight [26].

To summarize, a lower level of adiponectin is seen with type 2 diabetes, obesity and GDM. Adiponectin may influence the pathophysiology of GDM and also be a promising predictive biomarker for identifying GDM. Subsequent research for lifestyle interventions or adiponectin therapy should be done to finalize the role of adiponectin and diagnostic ability in cases of GDM particularly during the first trimester of GDM. Serum adiponectin in GDM, when is below <8.9 μg/ml shows an odds ratio of 3.3.

5.2 1,5 Alfa anhydroglucitrol, SHBG

Mean value of 1,5 Alfa anhydroglucitrol level is significantly lower in those destined to develop GDM. In the first trimester, higher SHBG levels are indicating the risk of GDM but this was no longer statistically significant when BMI, ethnicity and family history were considered. A measurement of CRP in the first trimester is not a useful marker of GDM [27].

5.3 Leptin

Leptin is an adipocyte-derived hormone, mostly produced by adipocytes but is also produced in ovaries and the placenta. It regulates energy balance through hypothalamic pathways. Increased leptin is associated with weight gain, obesity and hyperinsulinaemia.

Leptin is a proinflammatory adipokine and participate in immune responses. It also affects glucose metabolism by antagonistic action on appetite and insulin action. In addition, it can stimulate oxidative stress, atherogenesis and arterial stiffness [28]. Leptin levels is detected to be significantly higher in the 2nd half of pregnancy in both normal and overweight women with later diagnosis of GDM [29]. Menon M et al. did a prospective observational study with three study groups, with two-time points-first and second trimester to detect gestational diabetes mellitus as follows: [30]

  • Normal glucose tolerance (NGT)

  • Gestational diabetes mellitus 1 (GDM1), OGCT done at 1st trimester patients diagnosed as GDM in 1st trimester

  • Gestational diabetes mellitus 2 (GDM2), Repeat OGCT done at 2nd trimester patients diagnosed as GDM in 2nd trimester.

They found that found that out of the adipokines, leptin was found to be elevated in GDM2 compared to GDM1 and NGT group with a p value (0.11), adiponectin was reduced only in GDM1 group with p value (0.33), TNFα is almost the same in all the 3 study groups but IL-6 is elevated in first and second trimester GDM group.

Maternal leptin levels increase 2 to 3 times in pregnancy, as a placental secretion. Increased levels of leptin have been seen in GDM.

Inflammatory markers like IL-6 and TNF-α also are involved in the pathophysiology of GDM by promoting both the chronic low-grade inflammation and also leptin concentrations. A prospective study detected elevated values of leptin before 16 weeks of conception, regardless of presence of adiposity and this was accompanied by an increased risk of GDM [31]. In another study leptin was increased in all pregnant women, but with highest concentrations in obese GDM patients [32]. But due to confounding effects of the measures of adiposity, current evidence is limited. Leptin is probably involved in the pathophysiology of GDM but is a poor predictor of GDM.

5.4 Visfatin

Visfatin an adipokine mostly secreted from visceral fat. It possesses both endocrine, paracrine and autocrine effects. Increased level of visfatin is noted in obesity, metabolic syndrome and T2DM. During pregnancy, visfatin levels increase up to the 2nd trimester, then they decrease and persist in lowest concentrations in the third trimester. During GDM, studies on visfatin levels are is inconsistent, as both decreased and increased levels have been reported [33].

In addition to its insulin-like properties to bind to the insulin receptor-1 and promotion of hypoglycaemic effects, visfatin can activate NFκB signaling and chemotaxis and lead to the development of insulin resistance. In fact, visfatin was found increased at the late 1st trimester [34], but differentially expressed at the 3rd trimester of GDM [35].

One study observed, visfatin was better in the prediction of GDM in the first trimester than CRP, IL-6, adiponectin and leptin [36]. One case–control study found that, visfatin in the 1st trimester was higher in GDM, but when it was added to the other maternal risk factors, the GDM detection rate had no improvement [37]. At present, findings indicate that visfatin is a potential biomarker for GDM, but we need further prospective studies to further asses the relationship between visfatin and GDM.

5.5 Resistin

Resistin represents an adipose-derived hormone and is expressed from monocytes, macrophages and adipocytes. It is corelated with high LDL-c and pro-inflammatory molecules and is also positively associated with adiposity. It increases during pregnancy, probably from weight gain. A potential link might exist between resistin, adiposity and insulin resistance during pregnancy, but till now, remains inconclusive as because of conflicting reports from case–control studies [38]. Resistin, is found to be reduced or unchanged during GDM [39, 40].

But, nested case–control studies, investigating resistin levels in early pregnancy, found no differences in resistin levels between GDM and controls (adjusted for BMI) [41]. Currently, there is no solid evidence that resistin is involved in the pathophysiology or prediction of GDM.

5.6 Omentin

Omentin-1, is an adipokine produced in non-fat cells from the adipose tissues (stromal vascular cells). It is involved in vascular tone relaxation due to the production of endothelial nitric oxide and lowering of both hs-CRP and TNFα signaling [42]. Omentin-1 was lower at the 2nd trimester of GDM similar to adiponectin, and in contrast to IL-6 [43].

5.7 Ghrelin

Hungarian study reported that fasting serum ghrelin levels were lower in women with GDM compared to non-pregnant healthy controls and pregnant controls without GDM in the 1st trimester and 3rd trimester [44].


6. Inflammatory markers

6.1 TNFα

TNFα a proinflammatory cytokine produced by monocytes and macrophages affects insulin sensitivity and secretion. These occurs from impairment of B-cell function and insulin signaling and results in insulin resistance and possibly GDM [45]. Multiple studies showed increased maternal TNFα levels in GDM, predominantly during late pregnancy [46]. Increased TNF-α levels in GDM than controls have been shown. Subgroup analysis detected this relationship to remain significant when they are compared with BMI-matched controls [47].

These increased levels are due to increased oxidative stress and inflammation arising from impaired glucose metabolism [48]. A small case–control study 0f 14 cases and 14 controls to address the predictive value of TNFα found no differences between women with GDM and without [49]. In one study of GDM and controls, TNFα levels measured pre-gravid, at 12–14 weeks and 34–36 weeks were increased at 34–36 weeks of gestation. These were inversely correlated with the insulin sensitivity [50]. We need more prospective studies to assess the predictive value of TNFα during GDM, with due adjustment for measures of adiposity.

6.2 Il-6

IL-6 is one of the proinflammatory cytokines and is increased in obesity and associated with indices of adiposity and insulin resistance, such as body mass index (BMI). The relationship between IL-6 and insulin action appears to be regulated via adiposity. However, in a case–control study, plasma IL-6 levels were elevated when adjusted for BMI in women with GDM [51].

6.3 High-sensitivity C-reactive protein (hsCRP)

Wolf and co-workers had found that the first-trimester CRP levels were significantly raised among them who later on developed GDM than the control subjects (3.1 vs. 2.1 mg/L, P < 0.01) [52]. After the adjustment for age, race/ethnicity, blood pressure smoking, parity, and age at gestation at CRP sampling, the increased risk of developing GDM among women was seen in the highest tertile than the lowest tertile and was 3.6 times higher (95% CI: 1.2–11.4). But when adjusted for BMI, this relation was not seen anymore. But Berggren and co-workers examined whether first-trimester hs CRP could predict the third-trimester impaired glucose tolerance (IGT). The hs CRP was positively correlated to (hs)CRP and GDM appears to be partly mediated by BMI.

Another study found that elevated plasma insulin and reduced adiponectin levels during first trimester may improve GDM identification rates than by clinical factors alone [53]. Maternal risk factors alone offer a prediction rate of 61% for GDM, but addition of adiponectin and SHBG, improved detection rates to 74% [54].


7. Glycaemic markers

7.1 Serum insulin and C-peptide

O’Malley E G et al. found that, both the serum insulin and C-peptide levels in the third tertile were correlated with GDM development (p < 0.001 if adjusted for maternal obesity). Higher values of ghrelin were showing a lower odd of development of GDM, even after adjustment for maternal obesity. The conclusion of the study was though 3 of the 10 biomarkers were statistically indicating an increased risk of GDM, but the presence of large overlap in values between women with normal and abnormal glucose tolerance reflect that the biomarkers (alone or in combination) were not clinically helpfull [55].

7.2 Glucagon and PAI-1

Two small studies of 54 and 51 women reported higher levels of glucagon and PAI-1 respectively in women with GDM [56, 57].


8. Serum lipids

Li et al. compared 379 women in the first trimester who developed GDM subsequently with 2166 healthy women. They found that lipid profile was different between the groups. The GDM patients had higher concentrations of Triglyceride, LDL-Cholesterol and total cholesterol but lower concentrations of HDL [58]. The lipid values at first trimester in the cohort of Correa et al. was altered even when glycaemia and glycated hemoglobin were normal. The first trimester insulin concentration was seen to be also higher in women who developed GDM. Both theses indicate that there is a role of lipid metabolism in the pathogenesis of the disease [59].


9. Placenta-related factors

Placenta-Related Factors such as sex hormone-binding globulin, afamin, fetuin-A, fibroblast growth factors-21/23, ficolin-3 and follistatin, or specific micro- RNAs may be involved in GDM progression and may help in its recognition [60].

In GDM, some adipose-derived factors such as TNFα, visfatin, omentin and FABP4 may be also expressed and expressed from placenta, resulting to their elevated plasma levels [10]. The sex hormone binding globulin (SHBG) from placenta acting as a regulator of sex steroid hormones had been linked with inversely insulin resistance, metabolic syndrome, obesity and T2DM [61]. A lower level of plasma SHBG in the 1st trimester was a true biomarker for GDM [62, 63].

Nanda et al. showed reduced SHBG in parallel to adiponectin in GDM during 11–13th week of pregnancy, in presence of previous macrosomia, BMI > 30 kg/m2, and family history of DM [63, 64]. Similarly, an hepatokine promoter of insulin resistance, known as fetuin-B, is raised at the 3rd trimester of GDM, but returns after delivery [65]. Again, at the late 1st trimester, a reduction of plasma fetuin-A levels (and elevated hs-CRP) is also noted [66].

FGF-21, responsible for browning of white adipose tissue and an upstream effector of adiponectin, was increased in GDM at the 24th week of gestation [67]. Afamin, a glycoprotein member of the albumin family found in liver and placenta, may be a first trimester biomarker for pathological glucose and lipid metabolism [68].

The decreased levels of ficolin-3 (an activator of the lectin pathway of the complement system expressed in liver and placenta) and the increased ratio of ficolin-3/adiponectin are predictive of GDM at the 16–18th week of gestation [18]. Follistatin, a gonadal regulator of follicular-stimulant hormone and activin-A, having angiogenic, anti-inflammatory and cardioprotective properties, were lower in the 3rd trimester of GDM pregnancy [69].

The non-coding RNAs such as micro-RNAs (miR) can be released from placenta to maternal circulation as early as the 6th week of gestation and may be involved in placenta development, insulin signaling and cardiovascular homeostasis [70]. These miR can regulate trophoblasts proliferation, apoptosis, migration and invasion, and angiogenesis [71].

A significant downregulation of miR-29a, miR-132 and miR-222 had been reported in plasma at the 16th week of pregnant women who developed GDM [72]. Similarly, during the 7th–23rd week of gestation, elevated plasma levels of miR-21-3p were seen with GDM [73].

9.1 Sex hormone-binding globulin (SHBG)

SHBG a glycoprotein regulates the transport of sex hormones. In vitro, this is a marker in insulin resistance as insulin and insulin-like growth factor inhibit SHBG secretion. Indeed, a relation of low levels of SHBG and T2DM has been observed [74]. A study found its concentrations to be significantly lower in GDM [75]. Moreover, women treated with insulin showed even lower SHBG levels. Probably SHBG may help to differentiate or predict who will require insulin therapy or not.

A prospective study evaluated several biomarkers before 15 weeks of gestation and observed that low levels of SHBG were indicating an increased risk of GDM. Adding hs-CRP increases the specificity to 75.46% [76]. However another prospective cross-sectional study, revealed that low levels of SHBG assessed between 13 and 16 weeks of gestation were positively associated with the development of GDM (n = 30) (P < 0.01) [77]. A case–control study also found that SHBG in the non-fasting state in first trimester had a consistent association with an increased GDM risk [78].


10. Other potential biomarkers

AFABP or Adipocyte fatty acid-binding protein may be one of the risk predictors for cardiovascular disease, metabolic syndrome and T2DM [79]. Two studies have established its increased levels in GDM. Gestational diabetes mellitus causes changes in the concentrations of adipocyte fatty acid-binding protein and other adipo-cytokines in cord blood [80, 81]. Studies investigating the predictive value of AFABP in GDM have not been performed to date, however.

The fatty acid-binding protein 4 (FABP4) correlates with obesity markers e.g., fat mass and high BMI. FABP4 act on lipid and glucose metabolism via fatty acid transport and uptake [82]. The retinol-binding protein 4 (RBP4) is one of the circulating retinol transporters and id correlated with cardiometabolic markers in inflammatory chronic diseases like T2DM, metabolic syndrome obesity, and atherosclerosis process [83]. Higher levels of FABP4 can predict GDM from the 1st and 3rd trimester of [84, 85]. Upregulated values of plasma RBP4 in the 1st and 2nd trimester may modestly indicate GDM risk, especially among women with obesity and advanced age [18, 86].

10.1 Molecular biomarkers

Growing evidence suggests the use of SNPs, DNA methylation, and miRNAs as biomarkers that could help in the early detection of GDM. In presence of their potential, these molecular biomarkers pose several challenges that need to be addressed before they can become clinically applicable [87].

Decreased levels of first trimester pregnancy-associated plasma protein A (PAPP-A) and increased levels of second trimester unconjugated estriol (uE3) and dimeric inhibin A (INH) were associated with GDM [88].

10.2 Vitamin D

Lower levels of vitamin D have been seen in both obesity and type 2 diabetes and also in pregnancy very often. Low levels of Vitamin D levels during first trimester also carry a higher risk for GDM as seen in recent meta-analyses [89]. As the mentioned studies all were not randomized controlled studies, we need future RCTs to confirm the predictive role of vitamin D [90].

10.3 Candidate proteins

Zhao et al. studied maternal blood prospectively from pregnant women at 12–16 weeks of pregnancy. Among these, 30 women were subsequently diagnosed with GDM at 24 to 28 weeks and were selected as case studies along with 30 normoglycemic women as controls. They found that, four proteins, apolipoprotein E, coagulation factor IX, fibrinogen alpha chain, and insulin-like growth factor-binding protein 5, with a high sensitivity and specificity, may provide effective early screening for GDM. The panel of four candidate proteins could distinguish women subsequently developed with GDM from controls with high sensitivity and specificity [91].

10.4 Genetic markers

For the first time, Ding M et al. detected 8 variants to be associated with GDM, They are rs7957197 (HNF1A), rs3802177 (SLC30A8), rs10814916 (GLIS3), rs34872471 (TCF7L2), rs9379084 (RREB1), rs7903146 (TCF7L2), rs11787792 (GPSM1) and also rs7041847 (GLIS3). They also confirmed 3 other variants e.g., rs1387153 (MTNR1B), rs10830963 (MTNR1B), and rs4506565 (TCF7L2), which had been earlier identified by them or significant association with GDM risk [92].

10.5 Urine biomarkers

The study of urine metabolome profile in GDM during the 3rd trimester found relation of 14 metabolites with the steroid hormone biosynthesis and tryptophan metabolism, which were significantly high. They are l-urobilinogen, l-tryptophan, 21-deoxycortisol, cucurbitacin-C, ceramide (d18:0/23:0) and aspartame [93]. Upregulation of these pathways could aggravate insulin resistance and respond to oxidative stress and inflammation during GDM. Earliest at 12th–26th week of pregnancy, augmented levels of AHBA, 3-hydroxybutanoic acid (BHBA), valine, alanine, serotonin and related metabolites like l-tryptophan levels were observed in urine (and plasma) from GDM mothers [94].

11. Clinical prediction models incorporating biomarkers

Clinical risk prediction models’ wave has been investigated in GDM. For example, the development of GDM can be predicted from the ethnicity, family history, history of GDM and body mass index. One large prospective study (n = 7929), found that, based on BMI, ethnicity, family history of diabetes and past history of GDM, there was a sensitivity, specificity and AUC of 73% [66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79], 81% [80, 81, 82] and 0.824 (0.793–0.855), respectively, for the identification of GDM patients who required insulin therapy [95].

The introduction of biomarkers if added to a set of clinical risk factors are supposed to increase the predication rates of GDM. In particular, low HDL cholesterol and tissue plasminogen activator (t-PA) appeared as independent significant predictors of GDM. The addition of these 2 biomarkers to a group of clinical and demographic risk factors enhances the ROC (area under the curve) from 0.824 to 0.861 [96]. The t-PA not only is a predictor of GDM, it is also associated with a higher risk of T2DM [97].

Addition of maternal adiponectin and visfatin to a bunch of maternal risk factors, reached a detection rate of 68% [98]. The clinical implementation of these multi-parametric prediction models is determined by factors like practical acceptability, significant reduction in adverse pregnancy outcomes and cost-effectiveness. But these models need prospective validation studies and also further identification of predictive threshold values for the said biomarkers.

12. Metabolomic profiling

In one study, women with GDM (n = 96) were matched to women with NGT (n = 96) by age, BMI, gravidity and parity and the levels of 91 metabolites measured. Six metabolites (anthranilic acid, alanine, glutamate, creatinine, allantoin and serine) were found to have significantly different levels between the two groups in conditional logistic regression analyses (p < 0.05). Metabolic markers identified as being predictive of type 2 diabetes may not have the same predictive power for GDM [99].

Endogenous galanin as a novel biomarker to predict gestational diabetes mellitus is also observed [100]. The higher level of galanin observed in GDM may represent an adaptation to the rise of glucose, weight, GGT associated with GDMs thriving for clinically useful thresholds [101].

Mean 1,5 AG levels are significantly lower in those that go on to develop GDM. Hs-CRP and SHBG are important early predictors of GDM. Adding SHBG to hs-CRP improves specificity and serves good overall accuracy. Uric acid, creatinine and albumin have no role in GDM prediction [102].

Bivariate logistic regression analysis had shown that both adiponectin and insulin highlight future development of gestational diabetes. Both of them measured at 11 weeks, may predict oncoming GDM. But we need further studies to assess the reliability of these biomarkers [103].

Placental growth factor (PLGF), a vascular endothelial growth factor-like protein, is highly expressed in the placenta. About three studies suggest that higher early pregnancy PLGF levels are associated with GDM [104, 105, 106]. Recently, ALT, a liver enzyme, a marker of hepatocellular damage, has been examined as a first-trimester predictor of GDM [107].

One moderate-sized study (N = 182) showed that glycosylated fibronectin measured in the first trimester could predict GDM with high accuracy [108]. Watanabe et al. assessed the soluble (pro)renin receptor levels in 716 Japanese women at less than 14 weeks of gestation and found increased levels in women who developed subsequent GDM [109]. In a case–control study of 1000 women from the UK, Syngelaki et al. found that maternal serum TNF-alpha measured at 11–13 weeks gestation was associated with subsequent GDM [110].

Donovan et al. in their study, indicated that women diagnosed with GDM have lower first trimester levels of both pregnancies associated free β-hCG and plasma protein-A (PAPP-A) than normoglycemic pregnant women. These two markers may indicate the presence of abnormal glucose metabolism at the beginning of pregnancy and may help for identification of future development of GDM [111].

13. First trimester biomarkers for prediction of gestational diabetes mellitus

Tenenbaum-Gavish et al. in a cohort of GDM group found that, compared to the normal group BMI and insulin (P = 0.003) were higher (both P < 0.003). The soluble (s)CD163 and multiples of median values of uterine artery pulsatility index (UtAPI) were high (p for both <0.01) but, pregnancy associated plasma protein A, tumor-necrosis factor alpha and placental protein 130, were low (p for all <0.005). There was no significant difference between the groups in placental growth factor, leptin, interleukin 6, soluble mannose receptor or peptide YY. For screening GDM in obese pregnancy a combination of high BMI, TNFα, insulin and sCD163 reached an AUC of 0.95, and the detection rate of 89% with a 10% false positive rate. For nonobese pregnancy, the combination of TNFα, PP13,sCD163 and PAPP-A showed an AUC of 0.94 and the detection rate was 83% at 10% false positive rate [112].

14. Conclusion

By blood sugar estimation when GDM is diagnosed, adverse foetal changes have already set in. So, we will have to attempt to diagnose GDM, before the foetal changes take place. It would be more rewarding if we can diagnose impending GDM and alert the person even when she plans for pregnancy.

Different biomarkers e.g., glycemic, insulin resistance, inflammatory, adipocyte and placenta-derived, had been evaluated as the first-trimester predictors of GDM. The majority of these studies are smaller in size and was based on case–control designs. But some large studies of glycemic markers indicated that hemoglobin A1C and/or fasting glucose help in detecting women without diagnosis of previous diabetes and they may be benefited from early detection and treatment of GDM, though these observations should be confirmed by interventional studies.

The improvement of GDM development and outcomes is possible by earlier and more specific identification of GDM accompanied by metabolic and cardiovascular risks. In line with these, first or second trimester-related biomarkers seen in maternal plasma like adipose tissue-derived factors like adiponectin, omentin-1, visfatin, fatty retinol binding-protein-4 and acid-binding protein-4 reflect correlations with development of GDM. In addition, placenta-related factors e.g., sex hormone-binding globulin, afamin, fetuin-A, ficolin-3 and follistatin, fibroblast growth factors-21/23 and specific micro-RNAs may be important in detecting progression of GDM and its recognition. Finally, urinary metabolites related to non-polar amino-acids and ketone bodies, serotonin system, may help in completing a predictive or early diagnostic group of GDM biomarkers.

To transform the observations obtained from observational studies into clinical practice, we need also more clinical trials or cost-effectiveness analyses of screening and treatment c.onsidering the first-trimester biochemical GDM predictors. Further studies should examine the first-trimester biochemical markers for adverse outcomes in GDM by prospective trials to find its prevention or early treatment.

GDM involves a significant proportion of pregnant women and is becoming more prevalent as rates of obesity rise globally. Its development and complications could be arrested if accurately predicted in early pregnancy even if possible before conception and effective interventions initiated. Many Several biomarkers have been studied to understand pathogenesis of GDM, but till date none are showing adequate robustness to be used for clinical algorithms for prediction of GDM.

Application of the high methodologies gives novel insights about the role of genetic variants, metabolomics and epigenetics regarding the pathogenesis of GDM. This option for using a predictive model during the subclinical phase of GDM appears to be promising as an important arena of future research and development. These modern technologies are off course complex and not applicable to mass level screening. There are also issues related to validity across populations, reproducibility, and selectivity. We will have to find out methods with cost-effectiveness and universal access, otherwise the present complex biomarkers are likely to prove invaluable in the diagnosis of GDM.

The emerging evidences suggest that the assessment at eleven and thirteen weeks of gestation, should be the platform towards a new approach in antenatal care. The data from the maternal history should be added to the results of biochemical and biophysical tests to examine the patient-specific risk related to a wide variety of pregnancy complications. Ideal GDM biomarkers appears to be a combination of several molecular biomarkers to balance the lack of sensitivity and specificity of individual factors. But targeted rapid technological advances will overcome these challenges and develop a quick, cost-effective point-of-care test that can accurately identify women at high risk for GDM during early pregnancy even if before conception.


  1. 1. Ferrara A. Increasing prevalence of gestational diabetes mellitus: a public health perspective. Diabetes Care. 2007;30: S141–S6.5
  2. 2. Pedersen J. Diabetes and pregnancy; blood sugar of new born infants during fasting and glucose administration. Ugeskr Laeger.1952;114(21):68.4
  3. 3. American Diabetes Association. Management of Diabetes in Pregnancy: Standards of Medical Care in Diabetes 2021.Diabetes Care 2021;44(Suppl. 1):S200–S210
  4. 4. Seshiah V, Cynthia A, Balaji V, Balaji M S, Arthi T. Detection and care of women with gestational diabetes mellitus from early weeks of pregnancy results in birth weight of new born babies appropriate for gestational age. Diabetes research and clinical practice, 2008, vol. 80, 2, pp. 199-202
  5. 5. Hanson M A and Gluckman P. D. Early Developmental Conditioning Of Later Health And Disease: Physiology Or Pathophysiology? Physiological Reviews 2014 94 1027-1076.)
  6. 6. Cardozo E, Pavone M E,Jennifer E. et al. Metabolic syndrome and oocyte quality. Trends in Endocrinology and Metabolism 2011 22 103-109
  7. 7. Crume TL, Ogden L, Daniels S et al. The impact of in utero exposure to diabetes on childhood body mass index growth trajectories: the EPOCH study Journal of Pediatrics 2011 158 941-946
  8. 8. Clausen T D, Mathiesen E R, Hansen T et al. High Prevalence of Type 2 Diabetes and Pre-Diabetes in Adult Offspring of Women with Gestational Diabetes Mellitus or Type 1 Diabetes – The Role of Intrauterine Hyperglycemia. Diabetes Care 2008 31 340-346
  9. 9. Qiu C, Sorensen TK, Luthy DA et al. A prospective study of maternal serum C-reactive protein (CRP) concentrations and risk of gestational diabetes mellitus. Paediatric and Perinatal Epidemiology 2004 18 377-384
  10. 10. Kirwan J P, Mouzon S H, Lepercq J et al. TNF-α is a predictor of insulin resistance in human pregnancy. Diabetes 2002 51 2207-2213
  11. 11. Georgiou H M, Lappas M, Georgiou GM et al. Screening for biomarkers predictive of gestational diabetes mellitus. Acta Diabetologica 2008 45 157-165
  12. 12. Chandran M, Phillips SA, Ciaraldi T et al. Adiponectin: more than just another fat cell hormone? Diabetes Care 2003 26 2442-2450
  13. 13. Nakashima R, Kamei N, Yamane K al. Decreased Total and High Molecular Weight Adiponectin Are Independent Risk Factors for the Development of Type 2 Diabetes in Japanese-Americans. Americans. Journal of Clinical Endocrinology and Metabolism 2006 91 3873-3877
  14. 14. Galic S, Oakhill JS, Steinberg GR Adipose tissue as an endocrine organ. Molecular and Cellular Endocrinology 2010 316 129-139
  15. 15. Tsai PJ, Yu CH, Hsu SP et al Maternal plasma adiponectin concentrations at 24 to 31 weeks of gestation: negative association with gestational diabetes mellitus. Nutrition 2005 21 1095-1099
  16. 16. Soheilykhah S, Mohammadi M, Mojibian M et al. Maternal serum adiponectin concentration in gestational diabetes. Gynecological Endocrinology 2009 25 593-596
  17. 17. Xu J, Zhao YH, Chen YP et al. Maternal circulating concentrations of tumor necrosis factor-alpha, leptin, and adiponectin in gestational diabetes mellitus: a systematic review and meta-analysis. Scientific World Journal 2014 2014 926-932
  18. 18. Yuan X-S, Shi H, Wang H-Y, Yu B, Jiang J. Ficolin-3/adiponectin ratio for the prediction of gestational diabetes mellitus in pregnant women. J Diabetes Investig. 2018; 9:403-410
  19. 19. Williams MA, Qiu C, Muy-Rivera M, Vadachkoria S, Song T, Luthy DA. Plasma adiponectin concentrations in early pregnancy and subsequent risk of gestational diabetes mellitus. J Clin Endocrinol Metab. 2004;89:2306-2311
  20. 20. Cseh K, Baranyi E, Melczer Z, Kaszás E, Palik E, Winkler G. Plasma adiponectin and pregnancy-induced insulin resistance. Diabetes Care.2004;27:274-275
  21. 21. Thagaard IN, Krebs L, Holm J-C, Lange T, Larsen T, Christiansen M. Adiponectin and leptin as first trimester markers for gestational diabetes mellitus: a cohort study. Clin Chem Lab Med. 2017;55:1805-1812
  22. 22. Abell SK, Shorakae S, Harrison CL, Hiam D, Moreno-Asso A, Stepto NK, et al. The association between dysregulated adipocytokines in early pregnancy and development of gestational diabetes. Diabetes Metab Res Rev. 2017;33(8):e2926
  23. 23. Wójcik M, Chmielewska-Kassassir M, Grzywnowicz K et al. The relationship between adipose tissue-derived hormones and gestational diabetes mellitus (GDM). Endokrynologia Polska 2014 65 134-142
  24. 24. Iliodromiti S, Sassarini J, Kelsey TW et al. Accuracy of circulating adiponectin for predicting gestational diabetes: a systematic review and meta-analysis. Diabetologia 2016 59 692-699.)
  25. 25. Hedderson M M, Darbinian J, Havel P J et Low Prepregnancy Adiponectin Concentrations Are Associated With a Marked Increase in Risk for Development of Gestational Diabetes Diabetes Care 2013 36 3930-3937
  26. 26. Ukkola O, Santaniemi M. Adiponectin: a link between excess adiposity and associated comorbidities? Journal of Molecular Medicine 2002 80 696-702
  27. 27. Corcoran SM, Achamallah N, Loughlin JO et al. First trimester serum biomarkers to predict gestational diabetes in a high-risk cohort: Striving for clinically useful thresholds. Eur J Obstet Gynecol Reprod Biol. 2018 Mar; 222: 7-12. Epub 2018 Jan 1
  28. 28. Katsiki N, Mikhailidis DP, Banach M. Leptin, cardiovascular diseases and type 2 diabetes mellitus. Acta Pharmacol Sin. 2018; 39:1176-1188
  29. 29. López-Tinoco C, Roca M, Fernández-Deudero A, García-Valero A, Bugatto F, Aguilar-Diosdado M, et al. Cytokine profile, metabolic syndrome and cardiovascular disease risk in women with late-onset gestational diabetes mellitus. Cytokine. 2012; 58:14-19
  30. 30. Menon M, Alaganandha M, Mohan J et al. Int J Reprod Contracept Obstet Gynecol. 2017 Oct;6(10):4402-4406
  31. 31. Qiu C, Williams MA, Vadachkoria S. Increased maternal plasma leptin in early pregnancy and risk of gestational diabetes mellitus. Obstetrics & Gynecology 2004 103 519-525
  32. 32. Kirwan JP, Mouzon S H, Lepercq J et al. TNF-α is a predictor of insulin resistance in human pregnancy. Diabetes 2002 51 2207-2213
  33. 33. Lewandowski KC, Stojanovic N, Press M et al. Elevated serum levels of visfatin in gestational diabetes: a comparative study across various degrees of glucose tolerance. Diabetologia 2007 50 1033-1037
  34. 34. Ferreira AFA, Rezende JC, Vaikousi E, Akolekar R, Nicolaides KH. Maternal serum visfatin at 11-13 weeks of gestation in gestational diabetes mellitus. Clin Chem. 2011; 57:609-613
  35. 35. Rezvan N, Hosseinzadeh-Attar MJ, Masoudkabir F, Moini A, Janani L, Mazaherioun M. Serum visfatin concentrations in gestational diabetes mellitus and normal pregnancy. Arch Gynecol Obstet. 2012; 285:1257-1262
  36. 36. Mastorakos G, Valsamakis G, Dimitrios al. The Role of Adipocytokines in Insulin Resistance in Normal Pregnancy: Visfatin Concentrations in Early Pregnancy Predict Insulin Sensitivity. Clinical Chemistry 2007 53 1477-1483
  37. 37. Fatima a, Ferreira Juliana C. et al. Maternal Serum Visfatin at 11-13 Weeks of Gestation in Gestational Diabetes Mellitus. Clinical Chemistry 2011 57 609-613
  38. 38. Kuzmicki M, Telejko B, Szamatowicz J et al. High resistin and interleukin-6 levels are associated with gestational diabetes mellitus. Gynecological Endocrinology 2009 25 258-263
  39. 39. Cortelazzi D, Corbetta S, Ronzoni S, Pelle F, Marconi A, Cozzi V, Cetin I, Cortelazzi R, Beck-Peccoz P, Spada A. Maternal and foetal resistin and adiponectin concentrations in normal and complicated pregnancies. Clin Endocrinol (Oxf). 2007;66(3):447-453
  40. 40. Megia A, Vendrell J, Gutierrez C, Sabaté M, Broch M, Fernández-Real J-M, et al. Insulin sensitivity and resistin levels in gestational diabetes mellitus and after parturition. Eur J Endocrinol. 2008; 158:173-178
  41. 41. Lain KY, Daftary A R, Ness R B et al. First trimester adipocytokine concentrations and risk of developing gestational diabetes later in pregnancy. Clinical Endocrinology 2008 69 407-411
  42. 42. Yang RZ, Lee MJ, Hu H, Pray J, Wu HB, Hansen BC, Shuldiner AR, Fried SK, McLenithan JC, Gong DW. Identification of omentin as a novel depot-specific adipokine in human adipose tissue: possible role in modulating insulin action. Am J Physiol Endocrinol Metab. 2006;290(6):E12
  43. 43. Abell SK, Shorakae S, Harrison CL, Hiam D, Moreno-Asso A, Stepto NK, et al. The association between dysregulated adipocytokines in early pregnancy and development of gestational diabetes. Diabetes Metab Res Rev. 2017;33(8):e2926
  44. 44. Supák D, Melczer Z, Cseh K. Elevated serum acylated (biologically active) ghrelin and resistin levels associate with pregnancy-induced weight gain, insulin resistance and antropometric data in the fetus. Eur J Obstet Gynecol Reprod Biol 2016;206:e111
  45. 45. Cawthorn WP, Sethi JK. TNF-alpha and adipocyte biology. FEBS Letters 2008 582 117-131
  46. 46. Gao XL, Yang HX, Zhao Y. Variations of tumor necrosis factor-alpha, leptin and adiponectin in mid-trimester of gestational diabetes mellitus. Chinese Medical Journal 2008 121 701-705
  47. 47. Xu J, Zhao Y H, Chen Y P et al. Maternal Circulating Concentrations of Tumor Necrosis Factor-Alpha, Leptin, and Adiponectin in Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis. Scientific World Journal 2014 2014 926-932
  48. 48. Briana D D, Malamitsi-Puchner A et al Adipocytokines in Normal and Complicated Pregnancies. Reproductive Sciences 2009 16 921-937.)
  49. 49. Georgiou H M, Lappa M, Georgiou GM, Marita A et al. Screening for biomarkers predictive of gestational diabetes mellitus. Acta Diabetologica 2008 45 157-165
  50. 50. Kirwan J P, Mouzon S H, Lepercq J et al. TNF-α Is a Predictor of Insulin Resistance in Human Pregnancy. Diabetes 2002 51 2207-2213
  51. 51. Morisset AS, Dubé MC, Côté JA et al. Circulating interleukin-6 concentrations during and after gestational diabetes mellitus. Acta Obstetricia et Gynecologica Scandinavica 2011 90 524-530
  52. 52. Wolf M, Sauk J, Shah A et al. Inflammation and glucose intolerance: a prospective study of gestational diabetes mellitus. Diabetes Care 2004 27 21-27
  53. 53. Georgiou H, Lappas M, Georgiou G M et al. Screening for Biomarkers Predictive of Gestational Diabetes Mellitus. Acta Diabetologica 2008 45 157-165
  54. 54. Prenatal Diagnosis 2011 31 135-141.). Adding maternal visfatin and adiponectin to a set of maternal risk factors resulted in a detection rate of 68% (95% CI: 58.3-76.3%)
  55. 55. O’Malley E G, Ciara Reynoldsa C M E, Killaleab A et al. /European Journal of Obstetrics & Gynecology and Reproductive Biology 250 (2020) 101-106
  56. 56. Beis C, Grigorakis SI, Philippou G, Alevizaki M, Anastasiou E. Lack of suppression of plasma glucagon levels in late pregnancy persists postpartum only in women with previous gestational diabetes mellitus. Acta Diabetol 2005;42(1):31-35
  57. 57. Akinci B, Demir T, Saygili S, Yener, Alaccioglu I, Saygili F, et al. Gestational diabetes has no additional effect on plasma thrombin-activatable fibrinolysis inhibitor antigen levels beyond pregnancy. Diabetes Res Clin Pract 2008;81 (1):93-96
  58. 58. Li G, Kong L, Zhang L, Fan L, Su Y, Rose J, et al. Early pregnancy maternal lipid profiles and the risk of gestational diabetes mellitus stratified for body mass index. Reprod Sci. 2014;22:712-717
  59. 59. Correa P J, Venegas P, Palmeiro Y, Albers D, Rice G, Roa J et al. First trimester prediction of gestational diabetes mellitus using plasma biomarkers: a case-control study. J. Perinat. Med. 2018; aop,1-8
  60. 60. Lorenzo-Almorós A, Hang T, Peiró C et al. Predictive and diagnostic biomarkers for gestational diabetes and its associated metabolic and cardiovascular diseases Cardiovasc Diabetol (2019) 18:140
  61. 61. Ding EL, Song Y, Malik VS, Liu S. Sex Differences of endogenous sex hormones and risk of type 2 diabetes: a systematic review and metaanalysis. JAMA. 2006;295(11):12
  62. 62. Smirnakis KV, Plati A, Wolf M, Thadhani R, Ecker JL. Predicting gestational diabetes: choosing the optimal early serum marker. Am J Obstet Gynecol. 2007;196:410.e1-6 (discussion 410.e6-7)
  63. 63. Zhang T, Du T, Li W, Yang S, Liang W. Sex hormone-binding globulin levels during the first trimester may predict gestational diabetes mellitus development. Biomark Med. 2018;12(3):239
  64. 64. Nanda S, Savvidou M, Syngelaki A, Akolekar R, Nicolaides KH. Prediction of gestational diabetes mellitus by maternal factors and biomarkers at 11 to 13 weeks. Prenat Diagn. 2011;31(2):135-141
  65. 65. Kralisch S, Hoffmann A, Lössner U, Kratzsch J, Blüher M, Stumvoll M, et al. Regulation of the novel adipokines/hepatokines fetuin A and fetuin B in gestational diabetes mellitus. Metab Clin Exp. 2017; 68:88-94
  66. 66. Kansu-Celik H, Ozgu-Erdinc AS, Kisa B, Findik RB, Yilmaz C, Tasci Y. Prediction of gestational diabetes mellitus in the first trimester: comparison of maternal fetuin-A, N-terminal proatrial natriuretic peptide, high-sensitivity C-reactive protein, and fasting glucose levels. Arch Endocrinol Metab. 2019; 63:121-127
  67. 67. Bonakdaran S, Khorasani ZM, Jafarzadeh F. Increased serum level of FGF21 in gestational diabetes mellitus. Acta Endocrinol (Buchar).2017;13:278-81
  68. 68. Köninger A, Mathan A, Mach P, Frank M, Schmidt B, Schleussner E, et al. Is afamin a novel biomarker for gestational diabetes mellitus? A pilot study. Reprod Biol Endocrinol. 2018;16:30
  69. 69. Näf S, Escote X, Ballesteros M, Yañez RE, Simón-Muela I, Gil P, Albaiges G, Vendrell J, Megia A. Serum activin A and follistatin levels in gestational diabetes and the association of the activin a-follistatin system with anthropometric parameters in offspring. PLoS ONE. 2014;9(4): e92175
  70. 70. Poirier C, Desgagné V, Guérin R, Bouchard L. MicroRNAs in pregnancy and gestational diabetes mellitus: emerging role in maternal metabolic regulation. Curr Diabetes Rep. 2017; 17:35
  71. 71. Morales-Prieto DM, Ospina-Prieto S, Schmidt A, Chaiwangyen W, Markert UR. Elsevier trophoblast research award lecture: origin, evolution and future of placenta miRNAs. Placenta. 2014;35(Suppl): S39–S45
  72. 72. Zhao C, Dong J, Jiang T, Shi Z, Yu B, Zhu Y, Chen D, Xu J, Huo R, Dai J, Xia Y, Pan S, Hu ZSJ. Early second-trimester serum miRNA profiling predicts gestational diabetes mellitus. PLoS ONE. 2011;6(8): e2392
  73. 73. Wander PL, Boyko EJ, Hevner K, Parikh VJ, Tadesse MG, Sorensen Teat al. Circulating early- and mid-pregnancy microRNAs and risk of gestational diabetes. Diabetes Res Clin Pract. 2017; 132:1-9
  74. 74. Hu J, Zhang A, Yang S et al. Combined effects of sex hormone-binding globulin and sex hormones on risk of incident type 2 diabetes. Journal of Diabetes 2015 8 508-515
  75. 75. Bartha JL, Comino-Delgado R, Romero-Carmona R et al. Sex hormone-binding globulin in gestational diabetes. Acta Obstetricia et Gynecologica Scandinavica 2000 79 839-845
  76. 76. Maged AM, Moety GAF, Mostafa W A et al. Comparative study between different biomarkers for early prediction of gestational diabetes mellitus. Journal of Maternal-Fetal & Neonatal Medicine 2014 27 1108-1112
  77. 77. Caglar GS, Ozdemir ED, Cengiz SD et al. Sex-hormone-binding globulin early in pregnancy for the prediction of severe gestational diabetes mellitus and related complications. Journal of Obstetrics and Gynaecology Research 2012 38 1286-1293
  78. 78. Smirnakis K V, Plati A, Wolf M et al. ,Predicting gestational diabetes: choosing the optimal early serum marker. American Journal of Obstetrics and Gynecology 2007 196 410.e1-410
  79. 79. Kralisch S, Fasshauer M. Adipocyte fatty acid binding protein: a novel adipokine involved in the pathogenesis of metabolic and vascular disease? Diabetologia 2013 56 10-21
  80. 80. Ortega-Senovilla H, Schaefer-Graf U, Meitzner K et al. Serum levels of adipocyte fatty acid binding protein are increased in gestational diabetes mellitus. Diabetes Care 2011 34 2061-2066
  81. 81. Kralisch S, Stepan H, Kratzsch J, Verlohren M et al. Serum levels of adipocyte fatty acid binding protein are increased in gestational diabetes mellitus. European Journal of Endocrinology 2009 160 33-38
  82. 82. Wu LE, Samocha-Bonet D, Whitworth PT, Fazakerley DJ, Turner N, Biden TJ, et al. Identification of fatty acid binding protein 4 as an adipokine that regulates insulin secretion during obesity. Mol Metab. 2014;3:465-473
  83. 83. Zabetian-Targhi F, Mahmoudi MJ, Rezaei N, Mahmoudi M. Retinol binding protein 4 in relation to diet, inflammation, immunity, and cardiovascular diseases. Adv Nutr. 2015;6:748-762
  84. 84. Li YY, Xiao R, Li CP, Huangfu J, Mao JF. Increased plasma levels of FABP4 and PTEN is associated with more severe insulin resistance in women with gestational diabetes mellitus. Med Sci Monit. 2015;8(21):426-431
  85. 85. Ning H, Tao H, Weng Z, Zhao X. Plasma fatty acid-binding protein 4 (FABP4) as a novel biomarker to predict gestational diabetes mellitus. Acta Diabetol. 2016; 53:891-898
  86. 86. Du C, Kong F. A prospective study of maternal plasma concentrations of retinol-binding protein 4 and risk of gestational diabetes mellitus. Ann Nutr Metab. 2019; 74:1-8
  87. 87. Dias S, Pheiffer C, Abrahams Y et al. Int. J. Mol. Sci. 2018, 19, 2926; doi:10.3390/ijms19102926
  88. 88. Snyder B M, Baer R J, Oltman S C et al. Early pregnancy prediction of gestational diabetes mellitus risk using prenatal screening biomarkers in nulliparous women. Diabetes Research and Clinical Practice 163(2020)108139
  89. 89. Lacroix M, Battista MC, Doyon M. et al. Lower vitamin D levels at first trimester are associated with higher risk of developing gestational diabetes mellitus. Acta Diabetologica 2014 51 609-616
  90. 90. Zhang M, Pan G, Guo. et al Vitamin D Deficiency Increases the Risk of Gestational Diabetes Mellitus: A Meta-Analysis of Observational Studies. Nutrients 2015 7 8366-8375
  91. 91. Zhao D, Liming Shen L, Wei Y, Xie J, Chen S, Liang Y et al. Identification of candidate biomarkers for the prediction of gestational diabetes mellitus in the early stages of pregnancy using iTRAQ quantitative proteomics. Proteomics Clin. Appl. 11, 7-8, 2017, 1600152
  92. 92. Ding M, Chavarro J, Olsen S et al. Genetic variants of gestational diabetes mellitus: a study of 112 SNPs among 8722 women in two independent populations. Diabetologia. 2018 Aug;61(8):1758-1768
  93. 93. López-Hernández Y, Herrera-Van Oostdam AS, Toro-Ortiz JC, López JA, Salgado-Bustamante M, Murgu M, et al. Urinary metabolites altered during the third trimester in pregnancies complicated by gestational diabetes mellitus: relationship with potential upcoming metabolic disorders. Int J Mol Sci. 2019; 20:1186
  94. 94. Leitner M, Fragner L, Danner S, Holeschofsky N, Leitner K, Tischler S, Doerfler H, Bachmann G, Sun X, Jaeger W, Kautzky-Willer A. Combined metabolomic analysis of plasma and urine reveals AHBA, tryptophan and serotonin metabolism as potential risk factors in gestational diabetes mellitus (GDM). Front Mol Biosci. 2017; 21(4):84
  95. 95. Thériault S, Forest J, Massé J et al. Validation of early risk-prediction models for gestational diabetes based on clinical characteristics. Diabetes Research and Clinical Practice, Volume 103, Issue 3, March 2014, Pages 419-425
  96. 96. Savvidou M, Nelson S M, Makgoba M et al. First-Trimester Prediction of Gestational Diabetes Mellitus: Examining the Potential of Combining Maternal Characteristics and Laboratory Measures. Diabetes 2010 59 3017-3022
  97. 97. Wannamethee S G, Sattar N, Rumley A et al. Tissue Plasminogen Activator, von Willebrand Factor, and Risk of Type 2 Diabetes in Older Men Diabetes Care 2008 31 995-1000
  98. 98. Ferreira F, Rezende J C, Vaikousi E et al. Maternal Serum Visfatin at 11-13 Weeks of Gestation in Gestational Diabetes Mellitus. Clinical Chemistry 2011 57 609-613
  99. 99. Bentley-Lewis R, Huynh J, Xiong G et al. Metabolomic profiling in the prediction of gestational diabetes mellitus. Diabetologia (2015) 58:1329-1332
  100. 100. Zhanga Z, Gu C, Fang P et al. Endogenous galanin as a novel biomarker to predict gestational diabetes mellitus. Peptides 54 (2014) 186-189
  101. 101. Siobhan M, Achamallaha M, O’Loughlin J et al. et al. First trimester serum biomarkers to predict gestational diabetes in a high-risk cohort: Striving for clinically useful thresholds. European Journal of Obstetrics & Gynecology and Reproductive Biology 222 (2018) 7-12
  102. 102. Maged AM, Moety GA, Mostafa WA et al. Comparative study between different biomarkers for early prediction of gestational diabetes mellitus. J Matern Fetal Neonatal Med, 2014; 27(11): 1108-1112
  103. 103. Georgiou HM, Lappas M, Georgiou GM et al. Screening for biomarkers predictive of gestational diabetes mellitus. Harry M. Georgiou et al. Acta Diabetol (2008) 45:157-165
  104. 104. Syngelaki A, Kotecha R, Pastides A et al. First-trimester biochemical markers of placentation in screening for gestational diabetes mellitus. Metabolism. 2015;64(11):1485-1489
  105. 105. Ong CY, Lao TT, Spencer KJ et al. Maternal serum level of placental growth factor in diabetic pregnancies. Reprod Med. 2004;49(6):477-480
  106. 106. Eleftheriades M, Papastefanou I, Lambrinoudaki I et al. Elevated placental growth factor concentrations at 11-14 weeks of gestation to predict gestational diabetes mellitus. Metabolism. 2014;63(11):1419-1425
  107. 107. Yarrington CD, Cantonwine DE, Seely EW et al. The Association of Alanine Aminotransferase in Early Pregnancy with Gestational Diabetes. Metab Syndr Relat Disord.2016;14(5):254-258
  108. 108. Rasanen J1, Snyder CK, Rao PV et al. Glycosylated fibronectin as a first-trimester biomarker for prediction of gestational diabetes. Obstet Gynecol. 2013;122(3):586-94
  109. 109. Watanabe N, Morimoto S, Fujiwara T et al Prediction of gestational diabetes mellitus by soluble (pro)renin receptor during the first trimester. J Clin Endocrinol Metab. 2013;98(6):2528-2535
  110. 110. Syngelaki A, Visser GH, Krithinakis K et al. First trimester screening for gestational diabetes mellitus by maternal factors and markers of inflammation. Metabolism. 2016;65(3):131-137
  111. 111. Donovan BM, Nidey NL, Jasper EA, Robinson JG, Bao W, Saftlas AF, et al. (2018) First trimester prenatal screening biomarkers and gestational diabetes mellitus: A systematic review and meta-analysis. PLoS ONE 13(7): e0201319.
  112. 112. Tenenbaum-Gavish K, Sharabi-Nov A, Binyamin D, Møller H J, Danon D, Rothman L et al. First trimester biomarkers for prediction of gestational diabetes mellitus. Placenta 101 (2020) 80-89

Written By

Samar Banerjee

Submitted: 14 July 2021 Reviewed: 21 September 2021 Published: 23 November 2021