Introduction

Gestational diabetes mellitus (GDM)—defined as glucose intolerance with first recognition or onset during pregnancy [1]—is one of the most common pregnancy complications, affecting approximately 10% of the pregnancies in the USA [2]. While the absolute prevalence of GDM is relatively low, it is one of the fastest-growing pregnancy comorbidities in the USA, with an increase of over 50% between 2000 and 2010 [3]. These trends are concerning, because GDM is associated with significant morbidity for both the mother and the offspring [4]. For the mother, GDM is associated with maternal hypertensive disorders during pregnancy, C-section delivery, impaired lactogenesis, and difficulties breastfeeding [5]; future development of overt type 2 diabetes mellitus, cardiovascular disease, and metabolic syndrome [1, 6,7,8]. For the infant, GDM is a leading risk factor of macrosomia [9], neonatal hypoglycemia, jaundice, polycythemia and hypocalcemia, and preterm birth, which itself is associated with a range of adverse short- and long-term health consequences [10].

The central physiological disturbances of GDM revolve around increased insulin resistance and decreased insulin secretion, with most diagnoses made during the second trimester based on fasting glucose tolerance tests, e.g., the oral glucose tolerance test (OGTT), which is sometimes preceded by non-fasting screening glucose challenge test (GCT). Identification in early pregnancy of women with overt diabetes, as well as those at risk of developing GDM, is of interest to researchers and clinicians alike given the abovementioned morbidities of uncontrolled hyperglycemia during pregnancy. However, gaps and controversies surrounding knowledge of GDM disease etiology (e.g., risk factors, biological mechanisms underlying pathogenesis) and diagnostic criteria (e.g., type of assessment, appropriate cutoffs) present hurdles. In this review, we begin by providing a brief overview of the pathophysiology of GDM. Next, we introduce the technique of metabolomics and how it could improve current understanding of disease etiology and refine diagnostic criteria. Finally, we end with some suggestions for future directions, including ways in which metabolomics may aid in identification of women who are at risk for development of GDM-related postpartum metabolic conditions and the potential contribution of metabolomics to clinical risk assessment and practice.

Pathophysiology

During pregnancy, a woman’s body undergoes profound physiological changes to support fetal development. With respect to glucose metabolism, maternal insulin sensitivity typically decreases towards the end of the first trimester [11, 12]. This phenomenon is thought to favor glucose supply to the fetus, as a result of reduced insulin-mediated utilization of glucose in the mother, which switches her energy metabolism from the pre-dominant use of carbohydrates to lipids [13]. In parallel with the decrease in maternal insulin sensitivity, pancreatic β cell insulin secretion increases steadily from the first trimester, reaching a maximum in the third trimester before returning to normal values after delivery [14, 15].

GDM is caused by an imbalance between insulin resistance and insulin secretion during pregnancy which, historically, has been thought to occur when the pancreatic β cells fail to keep pace with the increasing insulin resistance that occurs during the second half of pregnancy [2]. However, a recent study by Powe et al. brought to light the heterogeneity in GDM pathogenesis [16]. In an analysis of 809 pregnant women, the researchers categorized participants into four subgroups: GDM with an insulin secretion defect (<25th percentile of the Stumvoll first-phase estimate [17, 18]; “GDM-secretion”), GDM with an insulin sensitivity defect (<25th percentile of the Matsuda index [19]; “GDM-sensitivity”), GDM with both defects (“GDM-mixed”), and normal glucose tolerance (NGT) based on results from a fasting 75-g OGTT administered at 24–30 gestational weeks. Compared to the NGT participants, the GDM-sensitivity defect group had greater odds of cesarean delivery and higher offspring birth weight, even after adjustment for maternal BMI (which was higher in GDM-sensitivity defect group). The GDM sensitivity defect group also had higher leptin and lower adiponectin levels. These findings bring to light the physiological heterogeneity within GDM subtypes, a concept that is not addressed by current methods of diagnoses (summarized in Table 1)—a controversial topic that has been reviewed in greater detail elsewhere [20, 21]. Metabolomic profiling could help us to parse out the heterogeneity and better understand the different pathophysiology of GDM subtypes.

Table 1 Commonly used criteria for diagnosis of gestational diabetes mellitus (GDM)
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Metabolomic Profiles of GDM and GDM-related Maternal/Offspring Outcomes

What is Metabolomics?

In recent years, advancements in high-throughput technologies have made it possible to systematically and comprehensively study associations of various biological conditions with differences in genetics (“genomics”), gene expression (“transcriptomics”), protein structure and function (“proteomics”), and metabolites (“metabolomics”). Of particular interest in this review is metabolomics, as it provides a snapshot of dynamic biochemical processes and, thus, may provide novel insights into disease onset, severity, and progression. In a review published in 2014, Huynh et al. summarized results of 17 studies exploring differences in metabolite profiles associated with GDM, several of which employed conventional methods of biomarker assessment, including the enzyme-linked immunosorbent assay (ELISA) and high-performance liquid chromatography (HPLC) [22]. In the present review, we focus on metabolomic studies that utilize high-throughput platforms—namely, mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy [23]—that allow for the systematic and comprehensive assessment of small molecules in biological tissues and fluids.

Metabolomic studies are broadly categorized as untargeted or targeted. In brief, untargeted assays provide a snapshot of relative concentrations of all measureable analytes within a biological sample. Following spectrographic analyses, a separate labor-intensive step is required to ascertain the chemical identities of statistically significant peaks via cross-reference to a chemical library; this process is described in greater detail elsewhere [24]. On the other hand, targeted assays, which quantify physiological levels of a specific list of compounds of a priori interest, include internal standards in order to derive absolute concentrations of each metabolite. Due to its broad and unbiased coverage, untargeted platforms are often used for biomarker discovery, particularly when there are no pre-existing hypotheses regarding specific biochemical pathways of interest [25], whereas targeted assays are more frequently (but not exclusively) used for hypothesis-driven investigations and/or for confirmatory studies.

Current Knowledge Regarding Metabolomics and GDM

Metabolomic Profiles of GDM

Current knowledge of metabolite patterns associated with GDM have arisen pre-dominantly from case-control studies comparing metabolite profiles of women with vs. without GDM [26,27,28], and findings generally indicate altered fatty acid and amino acid metabolism (Table 2). For example, using a targeted MS-based metabolomic approach, Chen et al. investigated the relationship between circulating fatty acids in pregnant women with GDM (failed 50-g GCT followed by ≥2 abnormal glucose values in the subsequent 100-g OGTT; n = 49), women with hyperglycemia (failed 50-g GCT, but fewer than 2 abnormal glucose values in the 100-g OGTT; n = 80), and healthy control gravidas (n = 98) and found a graded increase in fatty acids during the third trimester (e.g., linoleic, linolenic, arachidonic, eicosapentaenoic acid, and docosapentaenoic acid) across the spectrum of GDM severity [27]. Researchers have also found that women with higher fasting glucose levels tend to have higher serum levels of the amino acids alanine, proline, and leucine/isoleucine [28], which have previously been implicated in the pathogenesis of type 2 diabetes in non-pregnant adults [29, 30]. In a study of 400 women in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) cohort, a 5-year prospective observational study of pregnant women in 10 countries, Scholtens et al. carried out both targeted and untargeted metabolomic analyses on a MS-based platform and identified a small but distinct set of metabolites on gluconeogenesis and lipid metabolism pathways that were associated with maternal glycemia during pregnancy [31•]. Specifically, pre-OGTT fasting plasma glucose was positively associated with the gluconeogenic substrates alanine, lactate, hexitol, and fructose; and negatively correlated with medium-chain fatty acids and palmitoleic acid metabolites. One hour following the OGTT, the investigators observed similar positive associations between the plasma glucose and the abovementioned gluconeogenic compounds. Additionally, post-OGTT plasma glucose was also positively associated with non-esterified fatty acids (NEFAs), beta-hydroxybutyrate, triglycerides, glycerol, asparagine/aspartate, glutamine/glutamate, leucine/isoleucine, ornithine, phenylalanine, proline and serine, multiple acylcarnitines, and fatty acids. When the investigators explored associations of change in plasma glucose with change in metabolites, they found that all targeted amino acids, several long- and medium-chain fatty acids, and lipid metabolites, including acylcarnitines, glycerol, and beta-hydroxybutyrate, decreased after the OGTT, whereas triglycerides, carbohydrates, and energy cycle intermediates (e.g., pyruvate, citrate/isocitrate) increased. Together, these results suggest that poor glucose tolerance during pregnancy may be attributable to aberrances in energy and lipid metabolism pathways. On the other hand, in a study of 823 pregnant Norwegian women, Sasche et al. carried out untargeted metabolomic analyses via nuclear magnetic resonance (NMR) spectroscopy in urine collected at 8–20 gestational weeks, ~28 gestational weeks, and 10–16 weeks postpartum found no differences in metabolite profiles of women with GDM as compared to their normoglycemic counterparts at any of the time points [32]. Thus, although current studies shed light on potential mechanisms underlying abnormal glucose tolerance during pregnancy, the inconsistencies in tissue type used for metabolomic analyses (e.g., plasma vs. urine), the laboratory methods employed (MS vs. NMR), and the fundamental differences in the study populations make it challenging to synthesize and interpret findings.

Table 2 Summary of studies characterizing metabolite patterns associated with gestational glucose tolerance and pregnancy/offspring outcomes
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Metabolomic Profiles of GDM in Relation to Infant Outcomes

In attempt to understand the impact of maternal hyperglycemia on the infant, researchers have also compared metabolite concentrations in cord blood of mother-infant dyads with vs. without GDM (Table 2). In a study of 30 term infants born to women with GDM and 40 control newborns, Dani et al. carried out untargeted metabolomic assays in cord artery serum [26] and found that as compared to their healthy counterparts, mother-infant pairs affected by GDM exhibited lower cord artery serum glucose—which the authors posited was due to fetal hyperinsulinemia—in conjunction with higher concentrations of metabolites indicative of defective placental amino acid transportation including pyruvate, histidine, alanine, valine, methionine, arginine, lysine, and hypoxanthine [26]. Similarly, in another study of 400 women in the HAPO study, Scholtens et al. carried out both targeted and untargeted metabolomic analysis in maternal serum collected at ~28 weeks and found disturbances in similar metabolic pathways, namely, those involved in carbohydrate and amino acid metabolism [31•]. The consistency in these findings, despite the fact that Dani et al. analyzed cord serum while Scholtens et al. evaluated maternal serum, points towards the relevance of these two biochemical processes in the etiology of GDM. It is worth noting, however, that in the study by Dani et al., the authors observed no differences in clinical indicators of newborn health (e.g., Apgar score; prevalence of hypocalcemia, hypoglycemia, or hyperbilirubinemia; prolonged hospitalization after birth), which could be related to the fact that cord artery blood represents blood of the fetus directed towards the placenta as opposed to cord vein blood indicative of blood directed towards the fetus, and thus, may not capture the gestational milieu experienced by the infant. Such nuances highlight the complexities of maternal/fetal nutrient exchange and the caution with which metabolomic data must be interpreted.

The Potential Role of Metabolomics in Improving Knowledge of GDM Pathophysiology

Here, we propose that metabolomics offers the potential to improve understanding of GDM pathophysiology, and potentially earlier characterization of pregnancy-associated hyperglycemia. The novelty of capturing the metabolome is in its representation of a “real-time” portrait of a cell or organism, and its functional significance as it reflects an integration of multiple physiological (or pathophysiological) processes [33]. For example, given that defects in insulin secretion vs. defects in insulin sensitivity have different root causes, it is likely that the physiological disturbances may manifest as unique metabolic profiles that, if replicable over time and in multiple populations, could be used to refine the definition, criteria, and methods of treatment for GDM.

While we do not explicitly discuss genetic determinants of GDM in this review, metabolomic analyses may also be useful to identify metabolic signatures associated with genetic variants implicated in GDM risk. In a study of 284 male German participants of the Cooperative Health Research in the Augsburg Region (KORA) study, Gieger et al. [34] examined associations of genetic variants involved in metabolic homeostasis with serum concentrations of metabolites previously implicated in type 2 diabetes pathogenesis [35] quantified by targeted assays. The researchers found that the genetic variants accounted for a significant portion of variance in metabolites of corresponding metabolic pathways, suggesting that common genetic polymorphisms induce major differences in metabolic phenotype. This study points towards the feasibility of metabolomics to unveil differences in metabolism with respect to genetic variants that have been associated with GDM risk [36, 37] for more timely identification of at-risk women.

The Potential Role of Metabolomics in Early Identification and Treatment of GDM

Early Identification

In addition to identifying women with overt GDM, early recognition of those at risk for developing GDM is critical to take advantage of GDM risk-reduction strategies and to minimize the detrimental consequences of this pregnancy complication for mother and offspring. Therefore, although clinical assessment of gestational glycemia typically occurs during the second trimester, research efforts target first trimester detection or prediction, which could eventually be integrated into clinical practice given that blood is collected for other assessments during the first trimester as part of typical clinical practice. For example, in addition to sociodemographic predictors like race/ethnicity, family history, body mass index (BMI), and prior history of GDM, lower levels of adiponectin and sex-hormone-binding globulin and higher circulating C-reactive protein (CRP) during the first trimester have been identified as potential biomarkers of GDM risk [38, 39]. Metabolomics offers a way in which the varying physiological states of hyperglycemia and GDM might be studied, identified, and classified. Outside of pregnancy, researchers are currently using metabolomics to predict and diagnose type 2 diabetes and pre-diabetes [33]. The BCAAs leucine, isoleucine, and valine have been consistently linked to both conditions, and through the use of metabolomics, the identification of elevated levels of these compounds has been found nearly 14 years ahead of the clinical manifestation of disease [33]. Because metabolomics can detect relatively small differences in circulating compounds, it could aid in identification of abnormal glucose tolerance or other relevant alterations in metabolism earlier in pregnancy.

So far, a few studies have attempted to characterize metabolite patterns in maternal serum as a potential indicator of GDM risk (Table 3) [40,41,42,43,44,45,46,47,48]. Despite variability in the type of analytical platform used (e.g., untargeted vs. targeted platforms, NMR vs. MS-based instrumentation), timing of blood collection (first trimester vs. second trimester), study population composition, and the statistical methods employed, current evidence points towards altered amino acid metabolism as a potential predictor of GDM risk. For example, using a case-control design, Pinto et al. carried out untargeted metabolomic assays via NMR spectroscopy in plasma of 32 Portuguese women without clinical signs of GDM at up to 21 gestational weeks, but who developed GDM 2–22 weeks later (“pre-diagnosis group”), 12 pregnant women with a confirmed GDM diagnosis at 18 to 37 gestational weeks (“post-diagnosis group”), and 35 control gravidas [49]. The investigators found that in comparison to controls, the pre-diagnosis group exhibited higher plasma levels of metabolites on amino acid (valine) and glucose (pyruvate, lactate, and glucose) metabolism pathways, and lower levels of glutamine, creatine, dimethyl sulfone, trimethylamine N-oxide (TMAO), betaine, proline, methanol, and 1,5-anhydroglucitol [49]. While the difference in concentrations of some of these compounds (e.g., those on amino acid [valine, alanine] and glucose metabolism [glucose, lactate] pathways) between the post-diagnosis and control groups did not align with that of the pre-diagnosis group, these results point towards the relevance of these biochemical pathways in GDM etiology and also highlight their potential to identify apparently healthy women at risk for developing this pregnancy complication [22]. In another study, Bentley-Lewis et al. [41] compared concentrations of amino acids, biogenic amines, and other polar metabolites quantified via MS-based targeted assays in first trimester fasting serum of 96 GDM cases vs. 96 normoglycemic controls selected from a Boston-area cohort of white women. The investigators observed higher levels of several compounds involved in amino acid metabolism, namely alanine and serine, as well as elevated anthranilic acid, glutamate, and allantoin; and lower levels of creatinine in first trimester serum of women who went on to develop GDM, as compared to their normoglycemic counterparts [41]. Although there are discrepancies in the exact compounds identified in these studies, the consistency in the relevance of amino acid pathways point towards the feasibility of using metabolomic technologies for early identification of GDM cases. Additional studies are warranted in larger and more diverse populations.

Table 3 Summary of studies characterizing metabolite patterns as potential predictors of GDM risk
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Treatment

Metabolomics not only offers the opportunity to more accurately characterize and diagnose maternal glucose intolerance and GDM but it may also refine GDM treatment. A large prospective cohort study of ~800 women in the Genetics of Glucose regulation in Gestation and Growth (Gen3G) cohort in Canada found that nearly half of the women who were diagnosed with GDM had an insulin sensitivity defect, 30% had a defect in insulin secretion, and 20% had a mix of both a defect in insulin sensitivity and secretion [16]. In comparison to women who had a normal glucose tolerance and after controlling for BMI, women with GDM with insulin sensitivity defect were at greater risk for complications at delivery. Specifically, women with GDM were more likely to deliver a large-for-gestational age infant, to experience hypoglycemia after birth, and to deliver via cesarean section despite receiving similar clinical care to their normoglycemic counterparts [16]. These results suggest a role for small metabolites in their contribution to these risks and complications (such as inflammatory cytokines, adipokines, or lipid fractions), and the potential for metabolomics to refine current understanding of different GDM subtypes. Ultimately, improvements in this area could lead to more tailored treatment regimens than that of what is currently available.

Future Directions

Advancements in the field of metabolomics have expanded our understanding of the etiology of metabolic disease [50]. In addition to continued research aimed at replicating and validating metabolite patterns reported in the current literature, we envision three key avenues for growth within the realm of metabolomics and GDM.

First, metabolomics offers the potential to identify women afflicted by GDM who are at risk of developing overt type 2 diabetes postpartum—an important research endeavor given that up to 50% of the women affected by GDM progress to type 2 diabetes within 5 years [51, 52]. When reviewing the literature, we identified one study that has attempted to do this. In retrospective cohort study of 1010 women with GDM during pregnancy, Allalou et al. [53•] used targeted metabolomic assays to quantify free fatty acids and amino acids in fasting plasma drawn at 6–9 weeks postpartum and identified elevations in several amino acids, including isoleucine, leucine, threonine, tryptophan, tyrosine, and valine, and proprionylcarnitine (aka acylcarnitine C3) among women who went on to develop type 2 diabetes within the next 2 years, as compared to those who did not. Again, while these seminal findings shed light on etiological underpinnings of the transition from GDM to over type 2 diabetes and point towards a role for metabolomics in identification of at-risk women, there is need for validation of results in an independent population.

Second, given the rapid advancements in high-throughput technologies, studies that combine metabolomics with other ‘omics will provide a more holistic view of complex metabolic phenotypes. For example, proteomic analyses have identified amino acids and low-molecular-weight peptides that are differentially expressed in GDM vs. control patients [54]. Integration of these data with metabolomics could serve as a way to validate biological pathways involved in GDM pathogenesis, while also providing insight into temporality of physiological alterations leading to development of overt disease.

Finally, in the long term, we foresee opportunities for metabolomics in clinical risk assessment and practice. Such applications have already begun for chronic diseases with distinct metabolic characteristics like Alzheimer’s disease, hepatocarcinoma, chronic kidney disease, and ovarian endometriosis [55]. Major challenges to achieving clinical impact include accurate identification of perturbed pathways relevant to GDM (e.g., given the current high costs of high-throughput assays, most studies have carried out metabolomic analyses at a single point in time, precluding the ability to evaluate metabolic flux) and replication/validation of not only the utility but also the performance (e.g., sensitivity, specificity, positive predictive value, negative predictive value) of metabolomic biomarkers of GDM in multiple populations.

Conclusions

In the past decade, metabolomics has demonstrated its utility to identify metabolic aberrances, including those associated with GDM, and offers promise as a clinical tool. In the era of personalized medicine characterized by the development of increasingly specific treatment therapies, there is need for reliable and sensitive biomarkers to shed light on disease etiology, monitor disease risk, and develop treatment plans. We envision that collaborative efforts from multiple cohorts and consortia with metabolomic data will improve the power and generalizability of results, eventually leading to a better understanding of risk factors, physiological perturbations, and strategies for management of GDM and its related comorbidities. Ultimately, in addition to improving clinical care, findings from the field of metabolomics have great potential to improve GDM prevention and management, especially because pregnancy is a life stage when women not only have frequent and consistent interaction with the health care system but also because it is a time when a woman may be more receptive to diet and lifestyle changes to reduce the risks posed to her child.