Emerging biomarkers for gestational diabetes mellitus and related pediatric outcomes

Abstract

Gestational diabetes mellitus (GDM) is a metabolic condition caused by chronic insulin resistance during pregnancy, affecting millions of women globally and causing significant health concerns. Its consequences are far-reaching, associated with poor feto-maternal outcomes. GDM has serious implications on metabolic health in both mother and child. Early diagnosis and management of GDM are crucial to prevent related consequences. Traditional diagnostic and predictive biomarkers for GDM, including oral glucose tolerance test, adiponectin, resistin, etc., have limitations. Recent advances in research have identified novel biomarkers for GDM, offering promising alternatives for early diagnosis and prediction to prevent the associated adverse pediatric outcomes. Emerging biomarkers include microRNAs, cell-free DNA, exosomes, glycolytic intermediates, inflammatory biomarkers (C-reactive protein and interleukin-6), metabolic biomarkers (Betatrophin, fetuin-A, etc.), etc. Emerging bidirectional communication pathway (gut microbiota gut-brain-axis) plays a crucial role in GDM pathophysiology, and could be a promising biomarker. Emerging technologies such as next-generation sequencing, metabolomics, and proteomics have enabled the discovery of novel biomarkers for GDM and related pediatric outcomes. This review aims to summarize the current state of knowledge on emerging biomarkers for GDM, including their diagnostic accuracy, predictive value, and potential clinical applications to improve feto-maternal outcomes by personalized medicine approaches.

Key Words: Emerging biomarkers; Gestational diabetes mellitus; Insulin resistance; Pediatric outcomes; Personalized medicine

Core Tip: Gestational diabetes mellitus (GDM) is a prevalent metabolic condition caused by chronic insulin resistance during pregnancy, affecting millions of women worldwide. It leads to poor maternal and neonatal outcomes. Traditional diagnostic and predictive biomarkers have limitations. Recent research has identified novel biomarkers, such as microRNAs, cell-free DNA, exosomes, glycolytic intermediates, inflammatory and metabolic biomarkers, amino acids, lipids, and gut microbiota metabolites, which offer promising alternatives for early diagnosis and prediction. These biomarkers may pave the way for personalized medicine, improving the prediction of GDM and related pediatric outcomes.

INTRODUCTION

Gestational diabetes mellitus (GDM), characterized by hyperglycemia that emerges or is first identified during pregnancy, is more prevalent alongside the onset of type 2 diabetes mellitus (T2DM) and obesity. GDM is a prevailing condition of pregnancy, affecting around 14% of women worldwide, with variations in frequency according to geographical regions and diagnostic criteria[1,2]. GDM is linked to both immediate and prolonged repercussions for the mother and the child[3,4]. Consequently, it is necessary to effectively detect all instances and initiate early treatment, thereby decreasing fetal exposure to hyperglycemia and mitigating adverse pregnancy outcomes associated with GDM[3,4]. Therefore, screening for GDM is advised as an integral component of standard prenatal treatment[4]. The traditional diagnostic and predictive measures play a crucial role to check the onset of GDM and related outcomes. Traditional biomarkers have certain limitations due to their limited sensitivity, specificity, and effectiveness of interventions, poor reproducibility, late diagnosis, and may not detect all cases of GDM[5,6]. There is a need for novel biomarkers that do not require a fasting sample for the rapid diagnosis of GDM[7]. There is a need for additional biomarkers that can detect GDM earlier and more accurately. Research is ongoing to develop new biomarkers that may improve diagnostic accuracy and predictive value. This comprehensive study aims to identify and characterize novel diagnostic and predictive biomarkers that fulfill the criteria for diagnosing and predicting GDM.

TRADITIONAL DIAGNOSTIC AND PREDICTIVE BIOMARKER

Traditional diagnostic and predictive biomarkers for GDM include glucose challenge test (GCT), fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), oral glucose tolerance test (OGTT), family history, previous history of GDM, body mass index (BMI), age, human chorionic gonadotropin (hCG), insulin growth factor-1 (IGF-1), adiponectin, leptin, resistin, etc. (Figure 1)[8,9].

Figure 1 Diagrammatic representation of traditional biomarkers (diagnostic and predictive biomarkers) for gestational diabetes mellitus. GDM: Gestational diabetes mellitus.

Diagnostic biomarkers for GDM

Diagnostic biomarkers for GDM help to identify pregnant women at risk. Precise diagnosis facilitates immediate intervention for managing GDM, hence lowering the risk of problems for both the mother and the infant[10]. Common diagnostic biomarkers include GCT, FPG, OGTT, HbA1c, and random plasma glucose for the diagnosis of GDM (Table 1)[4,11-24].

Table 1 Summarized details of traditional biomarkers for gestational diabetes mellitus diagnosis and associated pediatric outcomes.

Biomarker	Description	Diagnostic criteria	Sensitivity	Specificity	Notes	Ref.
Traditional biomarkers for GDM diagnosis
1-hour glucose challenge test	Measures glucose concentrations 1-hour post-glucose administration	≥ 140 mg/dL (7.8 mmol/L)	70%-90%	70%-80%	May require additional testing for diagnosis	Hosier et al[11], Ayesha et al[12], Zhao et al[13], Moon and Jang[14]
2-hour oral glucose tolerance test	Measures glucose concentrations 2-hour post-glucose administration	≥ 153 mg/dL (8.5 mmol/L)	80%-90%	90%-95%	Considered a gold standard for GDM diagnosis	Gautam et al[4], Jamieson et al[15], Prior et al[16], Madugalle et al[17]
Fasting plasma glucose	Measures glucose levels after an overnight fast	≥ 92 mg/dL (5.1 mmol/L)	70%-80%	80%-90%	May miss cases of GDM with normal fasting glucose	Ayesha et al[12], Beunen et al[18], Hasan et al[19]
Hemoglobin A1c	Evaluate mean glucose concentrations throughout the preceding 2-3 months	≥ 6.5% (48 mmol/moL)	40%-60%	80%-90%	May not be sensitive enough for GDM diagnosis	Valadan et al[20], Xiang et al[21], Tripathy et al[22]
Random plasma glucose	Monitors glucose levels at any time	≥ 200 mg/dL (11.1 mmol/L) with symptoms	Variable	Variable	Not recommended as a standalone diagnostic test	Jamieson et al[15], Huhn et al[23], Shaarbaf Eidgahi et al[24]

GDM: Gestational diabetes mellitus.

Diagnostic biomarkers for pediatric outcomes

Diagnostic biomarkers are useful to find out the GDM-associated pediatric outcomes by helping to identify the risk to children. Precise diagnosis facilitates immediate intervention for managing and lowering the risk of problems for both the mother and the infant. Common diagnostic biomarkers[4,25,26] include birth weight, ponderal index, cord blood glucose, neonatal blood glucose, and anthropometric measures are biomarkers for the diagnosis of pediatric outcomes of GDM (Table 2)[23,27-38].

Table 2 Summarized details of traditional biomarkers for diagnosis of gestational diabetes mellitus associated pediatric outcomes.

Biomarker	Pediatric outcome	Description	Diagnostic criteria	Sensitivity	Specificity	Notes	Ref.
Traditional biomarkers for pediatric outcomes in GDM
Birth weight	Macrosomia	Measures fetal growth	> 4000 g	70%-80%	80%-90%	Linked with an increased risk of obesity and metabolic diseases	Huhn et al[23], Zheng et al[27], Hu[28], Bernea et al[29]
Ponderal index	Fetal growth restriction	Measures fetal growth	< 2.2 or > 2.8	60%-70%	70%-80%	May indicate fetal growth restriction or macrosomia	Powel et al[30], Teshome et al[31], Mirabelli et al[32]
Cord blood glucose	Neonatal hypoglycemia	Measures glucose levels at birth	< 40 mg/dL (2.2 mmol/L)	80%-90%	90%-95%	Associated with increased risk of neonatal hypoglycemia	Wang et al[33], Shao et al[34]
Neonatal blood glucose	Neonatal hypoglycemia	Measures glucose levels after birth	< 40 mg/dL (2.2 mmol/L)	80%-90%	90%-95%	May require monitoring and treatment	Kariniemi et al[35], García-Moreno et al[36]
Anthropometric measures	Childhood obesity	Measures body mass index	> 95th percentile	70%-80%	80%-90%	Linked with an increased risk of obesity and metabolic diseases	Huang et al[37], Castaneda et al[38]

GDM: Gestational diabetes mellitus.

The development of new biomarkers can predict pediatric outcomes in GDM. Biomarkers can help tailor treatment strategies to individual patients and improve pediatric outcomes. Long-term follow-up investigations are necessary to elucidate the correlation between GDM and pediatric outcomes.

Predictive biomarkers for GDM

Predictive biomarkers for GDM and pediatric outcomes can help identify high-risk pregnancies and improve feto-maternal outcomes[39]. Predictive Biomarkers for GDM include age, family history of diabetes, previous history of GDM, BMI, FPG, triglyceride, adiponectin, leptin, resistin, hCG, and IGF-1 (Table 3)[26,31,40-61].

Table 3 Summarized details of traditional biomarkers for gestational diabetes mellitus prediction.

Biomarker	Description	Predictive value	Sensitivity	Specificity	Notes	Ref.
Family history of diabetes	Diabetes prevalence among first-degree relatives	High risk	50%-70%	70%-80%	Important risk factor for GDM	Monod et al[40], Basil et al[41]
Body mass index ≥ 30	Obesity	Moderate risk	50%-70%	70%-80%	Increased risk of GDM with obesity	Teshome et al[31], Chen et al[42], Antoniou et al[43]
Age ≥ 35 years	Advanced maternal age	Moderate risk	40%-60%	60%-70%	Increased risk of GDM with advancing age	Deng et al[44], Guarga Montori et al[45], Machado-Gédéon et al[46]
Previous history of GDM	Previous diagnosis of GDM in a prior pregnancy	High risk	70%-90%	80%-90%	Strong predictor of GDM in subsequent pregnancies	Liang et al[47], Kouhkan et al[48]
Fasting plasma glucose in early pregnancy	Measures glucose levels in early pregnancy	High risk	70%-80%	80%-90%	May predict GDM development	Wang et al[49], Tong et al[50]
Triglycerides	Measures triglyceride levels	Moderate risk	50%-60%	70%-80%	May predict GDM development	Liang et al[47], Shi et al[51]
Adiponectin	Measures adiponectin levels, an adipokine involved in glucose regulation	Moderate risk	60%-70%	70%-80%	May predict GDM development	Mihai et al[52], Muntean et al[53], Moyce Gruber et al[54], Moyce Gruber et al[55]
Leptin	Measures leptin levels, an adipokine involved in energy balance	Moderate risk	50%-60%	70%-80%	May predict GDM development	Chico-Barba et al[56]
HCG	Measures elevated HCG levels	Moderate risk	50%-60%	70%-80%	May predict GDM development	Mandić-Marković et al[26], Kantomaa et al[57]
IGF-1	Measures IGF-1 levels, which may be associated with insulin resistance	Moderate risk	50%-60%	70%-80%	May predict GDM development	Tumminia et al[58], Alekseenkova et al[59]
Resistin	Measures resistin levels, which may be associated with insulin resistance	Moderate risk	50%-60%	70%-80%	May predict GDM development	Ferdousi et al[60], Saucedo et al[61]

GDM: Gestational diabetes mellitus; HCG: Human chorionic gonadotropin; IGF-1: Insulin growth factor-1.

Predictive biomarkers for pediatric outcomes

Fetal ultrasound and maternal glucose levels are valuable tools and important biomarkers for monitoring fetal development and predicting pediatric outcomes[5,39]. Fetal ultrasound enables early detection of potential issues, allowing for timely interventions and informing personalized care plans for pregnant women and their babies[62]. Whereas, maternal glucose levels may impact long-term pediatric outcomes, such as obesity and metabolic disorders[63]. Regular monitoring of maternal glucose levels is crucial for managing diabetes during pregnancy and inform personalized care plans that can reduce the risk of adverse pediatric outcomes (Table 4)[5,29,39,64-67].

Table 4 Summarized details of predictive biomarkers for pediatric outcomes of gestational diabetes mellitus.

Predictive biomarkers for pediatric outcomes of GDM		Ref.
Fetal ultrasound	Valuable tool for monitoring the fetal development and pediatric outcomes. Detect growth restriction, growth patterns, congenital anomalies, and fetal macrosomia. Predict neurodevelopmental outcomes, such as cerebral palsy. Enables early detection of potential issues. Timely interventions and informing personalized care plans	Rathnayake et al[5], Parsaei et al[39], David et al[64], Sodje[65], Debbink et al[66]
Maternal glucose level	High level can lead to fetal macrosomia, increasing the risk of birth injuries and complications. Impact long-term pediatric outcomes, such as obesity and metabolic disorders. Regular monitoring is crucial for managing GDM and inform personalized care plans for pregnant women	Rathnayake et al[5], Bernea et al[29], Parsaei et al[39], Ornoy et al[67]

GDM: Gestational diabetes mellitus.

Predictive biomarkers can identify high-risk pregnancies early, enable timely intervention to manage GDM and improve pediatric outcomes, and reduce the risk of complications for both mother and baby.

Traditional biomarkers have certain limitations due to their limited sensitivity, specificity, and effectiveness of interventions, late diagnosis, and may not detect all cases of GDM. There is a need for additional biomarkers that can detect GDM earlier and more accurately. Research is ongoing to develop new biomarkers that may improve diagnostic accuracy and predictive value.

PEDIATRIC OUTCOMES ASSOCIATED WITH GDM

GDM results in an elevated risk of maternal cardiovascular disease (CVD) and T2DM, along with macrosomia and complications during delivery for the newborns[67,68]. The newborn has a prolonged risk of obesity, T2DM, and metabolic disorders[4,69,70]. Premature delivery, macrosomia, neonatal hypoglycemia, and shoulder dystocia are many short-term effects for the infant that may arise from GDM (Table 5)[4,67,69-78]. Oral therapies for GDM management, like glyburide and metformin, have potential; nonetheless, concerns persist over their long-term safety for both the mother and the child[4]. New oral drugs and innovative biomarkers for the early diagnosis and prediction of GDM may be beneficial in mitigating its effects and occurrence. By highlighting gaps in the research, it recommends for further investigations and a multidisciplinary approach, finally aiming to improve the treatment strategies, early diagnosis, predictive approaches, and care for women with GDM.

Table 5 Pediatric outcomes associated with gestational diabetes mellitus can have short-term and long-term effects on the child's health.

Short-term outcomes		Ref.
Macrosomia	Excessive birth weight, which can increase the risk of complications during delivery	Gautam et al[4], Ornoy et al[67], Chen et al[71]
Neonatal hypoglycemia	Low blood sugar in newborns, which can be a complication of GDM	Gautam et al[4], Nakshine and Jogdand[69], Corcillo et al[72]
Respiratory distress syndrome	Difficulty breathing in newborns, which can be associated with GDM	Gautam et al[4], Chulkov et al[73], Cahen-Peretz et al[74]
Birth injuries	Elevated risk of birth injuries, including shoulder dystocia, linked to macrosomia	Gautam et al[4], Nakshine and Jogdand[69], Chen et al[71]
Long-term outcomes
Obesity	Children of mothers with GDM are at an increased risk of having obese in later life	Gautam et al[4], Ornoy et al[67], Nakshine and Jogdand[69], Semnani-Azad et al[70], Mantzorou et al[75]
T2DM	Children of mothers with GDM are at an elevated risk of getting T2DM in later life	Gautam et al[4], Nakshine and Jogdand[69], Semnani-Azad et al[70], Corcillo et al[72]
Metabolic syndrome	Children of mothers with GDM may have an elevated risk of developing metabolic syndrome, a group of disorders that enhance the risk of cardiovascular disease, cerebrovascular accidents, and T2DM	Gautam et al[4], Nakshine and Jogdand[69], Semnani-Azad et al[70], Corcillo et al[72], Pathirana et al[76]
Neurodevelopmental outcomes	Certain studies indicate that children delivered to mothers with GDM may have an elevated risk of neurodevelopmental delays or abnormalities	Ornoy et al[67], Hirata et al[77], Kim et al[78]

GDM: Gestational diabetes mellitus; T2DM: Type 2 diabetes mellitus.

EMERGING BIOMARKERS

Emerging biomarkers for GDM are being researched to improve diagnosis and prediction. Here are some potential biomarkers that enable early intervention and better outcomes for mothers and babies by improving the diagnosis and prediction strategies. However, more research is needed to validate their effectiveness and practicality in clinical settings (Figure 2).

Figure 2 Diagrammatic representation of emerging biomarkers for gestational diabetes mellitus, such as epigenetic biomarker, genetic biomarker, metabolic biomarker, inflammatory biomarker, protein biomarker, cell-free DNA, and exosomes.

Epigenetic biomarkers

Epigenetic biomarkers play a crucial role in understanding the pathophysiology of GDM[3]. Epigenetic biomarkers are involved in glucose regulation, insulin signaling, cholesterol efflux, regulating the cell cycle, and energy metabolism[79]. They can aid in early detection, prediction of GDM risk and complications, enable timely interventions, and develop personalized treatment strategies to facilitate preventive measures. Epigenetic biomarkers include DNA methylation, miRNA, and histone modification. Among them, miRNA could be a promising emerging biomarker[80]. Emerging microRNAs (miRNAs) as biomarkers for GDM are being researched for their potential in early detection and diagnosis. miRNAs play a crucial role in regulating gene expression, and their dysregulation has been linked to various diseases[81,82], including GDM[3,63]. The relationship between miRNA biomarkers and GDM is still being researched to investigate the underlying mechanisms and to refine their application in GDM diagnosis and treatment. Further studies are needed to fully understand their potential and effectiveness in diverse populations (Table 6)[83-104].

Table 6 Summarized details of emerging epigenetic (microRNAs) biomarkers for diagnosis and early prediction of gestational diabetes mellitus and associated outcomes.

MicroRNAs	Expression level	Potential role	Ref.
MiR-16	Upregulated	Involved in insulin resistance and glucose metabolism	Hocaoglu et al[83], Alimoradi et al[84], Sørensen et al[85]
MiR-29a	Upregulated	Involved in glucose metabolism and insulin signaling	Hocaoglu et al[83], Li et al[86], Dalgaard et al[87]
MiR-335	Upregulated	Regulating insulin resistance and pancreatic islet β-cell secretion	Gezginci-Oktayoglu et al[88], Li et al[89]
MiR-132	Upregulated	Associated with insulin resistance and GDM	Carr et al[90], Sałówka and Martinez-Sanchez[91]
MiR-222	Need for further research	Contributing to estrogen-induced insulin resistance	He et al[92], Valerio et al[93]
MiR-17	Upregulated	Involved in insulin signaling pathways	Ejaz et al[94], Jiang et al[95]
MiR-19a	Upregulated	Enhance β-cell function by targeting suppressor of cytokine signaling 3, contribute to pancreatic β-cell dysfunction and insulin resistance	Holvoet[96], Du et al[97]
MiR-19b	Upregulated	Involved in insulin signaling pathways	Chao et al[98], He et al[99]
MiR-20a	Upregulated	Involved in insulin signaling pathways	He et al[92], da Silva et al[100]
MiR-223	Downregulated	Associated with insulin sensitivity and GDM	He et al[92], da Silva et al[100], Masete et al[101]
MiR-330-3p	Upregulated	Potential biomarker for GDM diagnosis	He et al[92], da Silva et al[100]
MiR-144	Downregulated	Associated with insulin sensitivity and GDM	Juchnicka et al[102], Zhang et al[103]
MiR-195	Upregulated	Associated with insulin resistance and GDM	He et al[92], da Silva et al[100], Masete et al[101]
MiR-21	Upregulated	Potential biomarker for GDM prediction	Silva et al[100], Kunysz et al[104]

GDM: Gestational diabetes mellitus.

Genetic biomarkers

Genetic biomarkers are characteristics derived from DNA or RNA that reflect normal or abnormal biological processes, including disease conditions. Genetic biomarkers include single-nucleotide polymorphisms (SNPs), copy number variations (CNVs), restriction fragment length polymorphisms, microsatellites, and mutations. They identify, diagnose, and monitor illnesses, predict therapeutic responses, and comprehend disease aetiology[105]. Among them, SNPs could be a promising genetic biomarker for the prediction and diagnosis of GDM and associated pediatric outcomes (Table 7)[106-119].

Table 7 Summarized details of emerging genetic biomarker (single nucleotide polymorphisms) for diagnosis and early prediction of gestational diabetes mellitus and associated outcomes.

Gene	Variant/mutation	Potential role	Ref.
TCF7 L2	Rs7903146 (C/T)	Associated with GDM risk and insulin secretion	Shalabi et al[106], Fang et al[107]
KCNQ1	Rs2237892 (C/T)	Regulating insulin secretion and glucose metabolism	Ortega-Contreras et al[108], Alshammary et al[109]
CDKAL1	Rs7754840 (C/G)	Associated with GDM risk and insulin secretion	Mahdizade et al[110], Wang et al[111]
HHEX	Rs1111875 (C/T)	Associated with GDM risk and pancreatic function	Zeng et al[112], Xie et al[113]
SLC30A8	Rs13266634 (C/T)	Regulating zinc transport and insulin secretion	Xie et al[113], Zeng et al[114]
GCK	Rs1799884 (A/T)	Regulating glucose sensing and insulin secretion	Hu et al[115], Popova et al[116]
MTNR1B	Rs10830963 (G/C)	Associated with GDM risk and insulin secretion	Chen et al[117], Bai et al[118]
PPARγ	Rs1801282 (C/G)	Regulating glucose metabolism, insulin sensitivity, and adipogenesis	Chen et al[117], Wu et al[119]

GDM: Gestational diabetes mellitus.

Inflammatory biomarkers

Inflammatory biomarkers represent inflammation, the body's innate reaction to an injury or disease. These indicators may consist of proteins, enzymes, or other chemicals released into the blood circulation or tissues during the inflammatory process[120]. Inflammatory biomarkers can help to identify the presence and severity of inflammation in various diseases, including infections, autoimmune conditions, and even some cancers[121]. Inflammatory biomarkers may alter insulin signalling pathways, resulting in insulin resistance and elevated blood glucose levels, which are fundamental characteristics of GDM[3,4,63]. They may be used to monitor disease progression, assess therapy efficacy, and anticipate the outcomes of certain disorders. They serve as an essential tool in research to elucidate the fundamental causes of disease and to develop novel therapeutics (Table 8)[21,61,122-130].

Table 8 Summarized details of emerging inflammatory biomarker for diagnosis and early prediction of gestational diabetes mellitus and associated outcomes.

Biomarker	Description	Expression level	Potential role	Ref.
IL-6	Pro-inflammatory cytokine	Upregulated	Associated with insulin resistance and GDM	Srivastava et al[122], Hosseini et al[123], Tutar et al[124]
IL-1β	Pro-inflammatory cytokine	Upregulated	Involved in inflammation and GDM	Zgutka et al[125], Yousif et al[126]
IL-8	Pro-inflammatory cytokine	Upregulated	Associated with inflammation and GDM	Vilotić et al[127]
CRP	Acute-phase protein	Upregulated	Exacerbating insulin resistance and glucose metabolism dysregulation	Quansah et al[128], Chakraborty et al[129]
Tumor necrosis factor-alpha	Pro-inflammatory cytokine	Upregulated	Involved in inflammation and GDM	Saucedo et al[61], Hosseini et al[123]
High-sensitivity CRP	Sensitive marker of inflammation	Upregulated	Potential biomarker for GDM prediction	Xiang et al[21], Tao et al[130]

CRP: C-reactive protein; GDM: Gestational diabetes mellitus; IL: Interleukin.

Metabolic biomarkers

Amino acid metabolism (tryptophan, phenylalanine, histidine, and branched-chain amino acids), fatty acid (omega-6-fatty acids) and glycerolipid metabolism, and Inositol phosphate metabolism are the metabolic pathways that can play a role in insulin resistance[131–133]. Other metabolites, such as bile acids, steroids, acylcarnitine, and N-Acetylproline, may also be associated with GDM (Table 9)[61,132,134-161]. Emerging metabolic biomarkers for GDM are being researched for their potential in early diagnosis and prediction, improving patient outcomes. Metabolic biomarkers can help tailor treatment strategies in individual patients.

Table 9 Summarized details of emerging metabolic biomarkers for diagnosis and early prediction of gestational diabetes mellitus and associated outcomes.

Biomarker	Amino acid/metabolic pathway	Potential role	Ref.
Metabolic biomarkers
Amino acid metabolites
Branched-chain amino acids	Leucine, isoleucine, valine	Associated with insulin resistance and GDM	Li et al[134], Ademolu[135]
Tryptophan	Tryptophan	Involved in various metabolic processes and altered tryptophan metabolism have been linked to insulin resistance and GDM	Zhou et al[136], Özdemir et al[137]
Phenylalanine	Phenylalanine	Elevated phenylalanine levels have been linked to insulin resistance and GDM	Yang et al[138]
Histidine	Histidine	Modified histidine concentrations have been associated with insulin resistance and GDM	Zhou et al[136], Yang et al[138]
Glutamic acid	Glutamic acid	Potential biomarker for GDM diagnosis	Yang et al[138], Kong et al[139]
N-Acetylproline	Proline	Altered N-Acetylproline levels have been linked to insulin resistance and GDM	Aleidi et al[140]
Alanine	Alanine	Potential biomarker for GDM diagnosis	Zhou et al[141], Spanou et al[142]
Tyrosine	Tyrosine	Potential biomarker for GDM prediction	Yang et al[138], Spanou et al[142]
Arginine	Arginine	Employed in glucose metabolism and GDM	Spanou et al[142], Zhan et al[143]
Glycine	Glycine	Associated with insulin sensitivity and GDM	Yang et al[138], Zhou et al[141], Spanou et al[142]
Lipid metabolites
Triacylglycerols	Insulin resistance-related lipid metabolites	Potential biomarkers for GDM prediction	Zhang et al[144], Balachandiran et al[145]
Inositol phosphate	Insulin resistance and glucose metabolism	Insulin resistance and glucose intolerance in GDM may be exacerbated by altered IP3 signaling	Pillai et al[146], Mazzera et al[147]
Glycerolipid	GDM and insulin resistance may exacerbate by elevated triacylglycerols	Predictive biomarker for GDM risk	Zhan et al[143], Zhang et al[144]
Omega-6-fatty acid	Changes in amino acid levels might be a factor in GDM and insulin resistance	Predictive biomarker for GDM risk	Egalini et al[132], Hosseinkhani et al[148]
Phosphatidylcholines	Phospholipids involved in glucose metabolism	Potential biomarkers for GDM diagnosis	Zhou et al[141], Wang et al[149]
Sphingomyelins	Sphingolipids associated with insulin resistance	Potential biomarkers for GDM prediction	Fakhr et al[150], Pinto et al[151]
Lysophosphatidylcholines	Phospholipid metabolites associated with GDM	Potential biomarkers for GDM diagnosis	Zhou et al[141], Zhan et al[143], Hung et al[152]
Ceramides	Sphingolipids associated with insulin resistance	Potential biomarkers for GDM prediction	Mustaniemi et al[153], Lantzanaki et al[154]
Free fatty acids	Lipid metabolites associated with insulin resistance	Potential biomarkers for GDM prediction	Kong et al[139], Hosseinkhani et al[148]
Acylcarnitines	Fatty acid metabolites associated with insulin resistance	Potential biomarkers for GDM prediction	Zhan et al[143], Pinto et al[151]
Glycolytic intermediates
1,5-Anhydroglucitol	Glycolysis	Marker of glycemic control and GDM diagnosis	Xu et al[155], Lin et al[156]
Pyruvate	Glycolysis	Potential biomarker for GDM diagnosis	Zhou et al[141], Bhushan et al[157]
Glyceraldehyde-3-phosphate	Glycolysis	Potential biomarker for GDM prediction	Saucedo et al[61]
Lactate	Glycolysis	Associated with insulin resistance and GDM	Zhou et al[141]
Fructose-1,6-bisphosphate	Glycolysis	Potential biomarker for GDM prediction	Wei et al[158], Wang et al[159]
Glucose-6-phosphate	Glycolysis	Potential biomarker for GDM diagnosis	Wei et al[158]
Phosphoenolpyruvate	Glycolysis	Potential biomarker for GDM prediction	Lai et al[160], Xu et al[161]

GDM: Gestational diabetes mellitus.

Cell-free DNA

Cell-free DNA (cfDNA) refers to DNA that is freely circulating in the bloodstream or other bodily fluids, rather than being contained within cells[162]. The cfDNA typically consists of short fragments of DNA, often around 160-180 base pairs in length. The cfDNA can originate from various sources, including apoptotic cells, necrotic cells, and actively released DNA[163]. The cfDNA is relatively stable in circulation, allowing for its detection and analysis. The cfDNA can be used for non-invasive prenatal testing, such as screening for fetal aneuploidies[164]. The cfDNA can be used to detect genetic or epigenetic changes associated with various diseases. The cfDNA is emerging as a potential biomarker for GDM. The cfDNA can be isolated from maternal blood or other bodily fluids and may enable early detection of GDM. The cfDNA can provide information on specific genetic or epigenetic changes associated with GDM[165]. Changes in cfDNA fragment size and concentration may also be indicative of GDM.

Exosomes

Exosomes carry specific proteins, lipids, and nucleic acids that can reflect the biological state of the mother and fetus[166]. Exosomes can be isolated from maternal blood or other bodily fluids, making them a potential non-invasive biomarker. Exosomes may enable early detection of GDM[167]. Exosomes are emerging as potential biomarkers for GDM. Specific miRNAs carried by exosomes may be associated with GDM[168]. Exosomal proteins, such as insulin signaling proteins, may be altered in GDM. Exosomes may also carry other molecules, such as lipids and metabolites that can serve as biomarkers[166,169]. Exosomal biomarkers may improve the diagnosis and prediction of GDM and help tailor treatment strategies for individual patients. Exosomes may also be used to monitor disease progression and treatment response.

Protein biomarkers

Protein biomarkers are protein molecules that serve as indicators of specific processes or conditions inside an organism, their altered levels play a crucial role in the pathophysiology of various diseases such as cancer, CVDs, and other metabolic disorders. Several proteins, including fetuin-A, insulin-like growth factor binding protein-1, retinol-binding protein 4, etc., could be emerging biomarkers for GDM. These proteins are linked to insulin resistance, inflammation, and other metabolic changes associated with GDM. These emerging protein biomarkers can help identify women at risk of developing GDM earlier in pregnancy, monitor the effectiveness of treatment, and predict complications. Studying protein biomarkers helps researchers understand the mechanisms underlying GDM (Table 10)[6,7,59,145,169-194].

Table 10 Summarized details of emerging protein biomarkers for diagnosis and early prediction of gestational diabetes mellitus and associated outcomes.

Biomarker	Protein function	Association with GDM	Potential role	Clinical utility	Advantages	Ref.
Fetuin-A	Insulin resistance	Elevated levels of fetuin-A have been associated with GDM	Fetuin-A may play a role in insulin resistance and glucose metabolism during pregnancy	Fetuin-A may be used to identify women at high risk of developing GDM, allowing for early intervention and prevention strategies	Fetuin-A is a relatively stable protein that can be measured in maternal serum or plasma, making it a potentially useful biomarker for GDM	Bogdanet et al[6], Ruszała et al[7], Wu et al[170], Cai et al[171]
IGFBP-1	Glucose metabolism	Decreased levels of IGFBP-1 have been associated with GDM	IGFBP-1 may play a role in glucose metabolism and insulin sensitivity during pregnancy	IGFBP-1 may be used to identify women at high risk of developing GDM, allowing for early intervention and prevention strategies	IGFBP-1 is a relatively stable protein that can be measured in maternal serum or plasma, making it a potentially useful biomarker for GDM	Alekseenkova et al[59], Hivert et al[172], Hong et al[173], Martín-Estal et al[174]
SHBG	Sex hormone transport	Low levels of SHBG have been associated with GDM	Associated with insulin resistance and GDM	SHBG may be used to identify women at high risk of developing GDM, allowing for early intervention and prevention strategies	SHBG is a relatively stable protein that can be measured in maternal serum or plasma, making it a potentially useful biomarker for GDM	Bruno et al[175], Sharmin et al[176], Liu et al[177]
RBP4	Retinol transport	Elevated levels of RBP4 have been associated with GDM	RBP4 may play a role in insulin resistance and glucose metabolism during pregnancy	RBP4 may be used to identify women at high risk of developing GDM, allowing for early intervention and prevention strategies	RBP4 is a relatively stable protein that can be measured in maternal serum or plasma, making it a potentially useful biomarker for GDM	Kučerová et al[178], Leca et al[179], Mousavi et al[180]
Afamin	Vitamin E transport	Elevated levels of Afamin have been associated with GDM	Afamin may play a role in glucose metabolism and insulin sensitivity during pregnancy	Afamin may be used to identify women at high risk of developing GDM, allowing for early intervention and prevention strategies	Afamin is a relatively stable protein that can be measured in maternal serum or plasma, making it a potentially useful biomarker for GDM	Eroğlu et al[181], Atakul et al[182], Wang et al[183]
Fibronectin	Cell adhesion and migration	Altered levels of fibronectin have been associated with GDM	Fibronectin may play a role in placental development and function, and its dysregulation may contribute to GDM	Fibronectin may be used to identify women at high risk of developing GDM, allowing for early intervention and prevention strategies	Fibronectin is a relatively stable protein that can be measured in maternal serum or plasma, making it a potentially useful biomarker for GDM	Ruszała et al[7], Karoutsos et al[184]
Betatrophin	Glucose and lipid metabolism	Altered levels of Betatropin have been associated with GDM	Betatropin may play a role in regulating glucose metabolism and insulin sensitivity during pregnancy	Betatropin may be used to identify women at high risk of developing GDM, allowing for early intervention and prevention strategies	Betatropin is a relatively novel biomarker that may provide new insights into GDM pathophysiology	Xu et al[185], Guo et al[186], Kirlangic et al[187], Melekoglu and Celik[188]
PAPP-A	Regulating placental function and fetal growth	Altered levels of PAPP-A have been associated with GDM	PAPP-A may play a role in regulating insulin-like growth factor bioavailability and glucose metabolism during pregnancy	PAPP-A may be used to identify women at high risk of developing GDM, allowing for early intervention and prevention strategies	PAPP-A is a well-established biomarker for placental function and may provide insights into GDM pathophysiology	Conover and Oxvig[189], Yanachkova et al[190]
SFlt-1	Regulating angiogenesis and vascular function	Altered levels of sFlt-1 have been associated with GDM	SFlt-1 may play a role in regulating angiogenesis and vascular function in GDM	SFlt-1 may be used to identify women at high risk of developing GDM, allowing for early intervention and prevention strategies	SFlt-1 is a well-established biomarker for preeclampsia and may provide insights into GDM pathophysiology	Joshi et al[191], Liao et al[192], Gul Kara et al[193]
PlGF	Promoting angiogenesis, regulating placental development and enhancing vascular function	Altered levels of PlGF have been associated with GDM	PlGF may play a role in regulating angiogenesis and placental function in GDM	PlGF may be used to identify women at high risk of developing GDM, allowing for early intervention and prevention strategies	PlGF is a well-established biomarker for placental function and may provide insights into GDM pathophysiology	Yanachkova et al[190], Bolatai et al[194]
Apo	Lipid metabolism	Altered levels of Apo (e.g., ApoA1, ApoB) have been associated with GDM	Apo may play a role in regulating lipid metabolism and insulin sensitivity in GDM	Apo may be used to identify women at high risk of developing GDM, allowing for early intervention and prevention strategies	Apo are well-established biomarkers for cardiovascular disease and may provide insights into GDM pathophysiology	Balachandiran et al[145], Bernea et al[169]

Apo: Apolipoproteins; GDM: Gestational diabetes mellitus; IGFBP-1: Insulin-like growth factor binding protein-1; PAPP-A: Pregnancy-associted plasma protein-A; PlGF: Placental growth factor; RBP4: Retinol-binding protein 4; SFlt-1: Soluble fms-like tyrosine kinase-1; SHBG: Sex hormone-binding globulin.

Further research is needed to validate the utility of emerging proteomic biomarkers for GDM. The development of point-of-care tests for proteomic biomarkers may improve accessibility and convenience. Combining proteomic biomarkers with clinical risk factors may improve predictive value.

EMERGING BIDIRECTIONAL COMMUNICATION PATHWAY: GUT MICROBIOTA GUT-BRAIN-AXIS IN GDM

The gut microbiota-gut-brain axis is a bidirectional communication pathway that plays a crucial role in glucose metabolism and insulin sensitivity, influencing GDM development. Gut microbiota produces metabolites that signal to the brain, and neural pathways facilitate bidirectional communication between the gut and brain. Neurotransmitters and hormones involved in glucose homeostasis, regulated by gut microbes. Gut dysbiosis plays a major role in the development of insulin resistance, exacerbating the inflammation and oxidative stress, which leads to GDM. Alterations in the gut microbiota play a significant role in the development of GDM. Key microbial metabolites such as butyrate (a short-chain fatty acid) and mevalonate may act as intermediaries linking gut dysbiosis to impaired glucose metabolism and hormonal imbalance during pregnancy. Reduced butyrate levels are associated with inflammation and insulin resistance, while altered mevalonate metabolism may affect the synthesis of pregnancy-related hormones. Additionally, increased levels of lipopolysaccharides derived from gut bacteria can trigger systemic inflammation, further contributing to insulin resistance.

These findings suggest that microbial metabolites could serve as early biomarkers and therapeutic targets for the prevention and management of GDM[195,196]. Importantly, GDM-induced dysbiosis not only affects maternal health but also alters the fetal gut microbial environment, potentially impacting fetal neurodevelopment. These findings underscore the gut microbiota as a central player in GDM pathophysiology and support the potential of microbial metabolites as early biomarkers and intervention targets to improve outcomes for both mother and child[197,198]. Emerging research indicates that GDM disrupts maternal metabolic health and adversely affects both maternal and fetal microbiomes, leading to gut dysbiosis. This microbial imbalance has been associated with an increased risk of neurodevelopmental disorders in offspring, including attention-deficit/hyperactivity disorder, schizophrenia, intellectual disabilities, anxiety, and depression. The underlying mechanisms involve changes in microbial composition and function, which may impact fetal brain development through the microbiota–gut–brain axis[197]. Gut microbiota profiles and exploring the potential of gut microbiota modulation may improve GDM management and outcomes and serve as emerging potential biomarkers for GDM risk assessment.

EMERGING TECHNOLOGIES FOR BIOMARKER DISCOVERY

Next-generation sequencing

Next-generation sequencing (NGS) is a powerful tool for discovering novel biomarkers for GDM. NGS allows for the analysis of entire genomes, transcriptomes, and epigenomes, providing a comprehensive understanding of the genetic and molecular mechanisms underlying GDM[199,200]. Whole-genome sequencing, whole-exome sequencing, RNA sequencing, and epigenome sequencing are fundamental approaches for NGS[201,202]. NGS can identify genetic variants such as SNPs[203] and CNVs[204], genes that are differentially expressed, discover novel transcripts, such as long non-coding RNAs and circular RNAs, and epigenetic biomarkers, such as DNA methylation and histone modification, providing insights into the molecular mechanisms underlying the disease[205-207]. NGS may enable early detection, personalized medicine for GDM women. NGS is still being researched for discovering the biomarkers, and more studies are needed to fully understand their potential (Figure 3).

Figure 3 Revolutionizing biomarker discovery, offering new ways/technologies to identify and validate potential disease indicators.

Metabolomics

Metabolomics, the study of small molecules in biological systems, is a promising approach for discovering biomarkers for GDM. By analyzing metabolic profiles, researchers can identify potential biomarkers that may aid in the early detection, diagnosis, and treatment of GDM[208]. Targeted and untargeted metabolomics approaches are involved; targeted metabolomics mainly focuses on specific metabolites or pathways, such as glucose, amino acids, or lipids. Whereas, untargeted metabolomics explores the entire metabolic profile, identifying novel metabolites and pathways associated with GDM[140,209]. Altered levels of amino acids (glutamine, glutamate, and arginine), lipid profiles (triglycerides, phospholipids, and sphingolipids), organic acids (citrate, isocitrate, and alpha-ketoglutarate), and novel metabolites (bile acids, steroids, and xenobiotics) have been linked to GDM[139,210-212]. Further research is required to identify the role of metabolomics biomarkers in GDM pathophysiology to explore the new insights of GDM diagnosis and treatment, and to find out the new approaches by combining metabolomics with other omics, to provide a more comprehensive understanding of GDM.

Proteomics

Proteomics, the study of proteins and their functions, is a promising approach for discovering biomarkers for GDM. Mass spectrometry, two-dimensional gel electrophoresis, and protein microarrays are tools which involved in proteomics approaches[213,214]. Proteomics may enable early identification of GDM risk, allowing for timely interventions and helping to tailor treatment strategies to individual patients[215]. The relationship between proteomics and GDM is still being researched, and more studies are needed to fully understand their potential.

FUTURE DIRECTIONS

Validation of emerging biomarkers

Emerging biomarkers for GDM must undergo validation to ensure their accuracy, reliability, and therapeutic value[62,216]. For the validation, those steps are followed by the researchers: (1) Discovery phase (various omics approaches, such as genomics, proteomics, and metabolomics are used to identify the potential biomarkers); (2) Verification phase (identified biomarkers validate in a separate cohort of patients to confirm their association with GDM); (3) Validation phase (performance of the biomarkers evaluate in a larger, more diverse population to assess their sensitivity, specificity, and predictive value); and (4) Clinical validation (clinical utility of the biomarkers assess in real-world settings, including their impact on patient outcomes and healthcare costs)[217].

Validation criteria for novel biomarkers are important, and are as follows[217,218]: (1) Sensitivity (ability of the biomarker to detect GDM women); (2) Specificity (ability of the biomarker to exclude women without the GDM); (3) Positive predictive value (proportion of patients with a positive biomarker result who have GDM); (4) Negative predictive value (proportion of patients with a negative biomarker result who do not have GDM); and (5) Area under the receiver operating characteristic curve (measurement of the biomarker's ability to discriminate between patients with and without GDM).

Collaborative research is essential to validate emerging biomarkers for GDM. Standardization of analytical methods and criteria for biomarker validation will facilitate comparison of results across studies.

Development of biomarker panels

Development of biomarker panels for GDM involves combining multiple biomarkers to improve diagnostic accuracy and predictive value. Biomarker panels can improve the sensitivity and specificity of GDM diagnosis, provide a more accurate prediction of GDM risk and outcomes, and help to expand the personalized medicine approaches[5,24]. Metabolic, inflammatory, proteomic, and genetic/epigenetic biomarker panels are important and involved in GDM early prediction and diagnosis strategies[219,220]. Discovery, verification, panel development, and clinical validation are the key steps for the development of biomarker panels[221,222]. Collaboration among researchers, standardization of methods, and integration with clinical risk factors are the major topics that need further research.

Personalized medicine

Personalized medicine for GDM entails customizing treatment plans for each patient according to their distinct traits. Emerging diagnostic and predictive biomarkers play a crucial role in this approach[215,223]. Biomarkers and epigenetic changes reflecting insulin resistance, chronic inflammation, and altered placental function highlight the complex interplay between genetic and environmental factors[224]. Emerging diagnostic and predictive biomarkers help to identify women at high risk for GDM. Biomarkers may complement existing clinical risk factors to identify women at high risk of developing GDM and may help to distinguish women who may benefit from targeted strategies to reduce GDM development[225]. Personalized medicine approaches may lead to better outcomes for mothers and offspring by tailoring treatment to individual needs[4].

CONCLUSION

GDM is a complex condition that requires early detection and prediction to prevent adverse outcomes for both mother and fetus. This review has identified and described emerging biomarkers that could potentially replace the traditional biomarkers and be used to both predict and diagnose GDM. Various emerging biomarkers, including metabolic, protein, genetic, epigenetic, inflammatory biomarkers, and gut microbiota metabolites, hold promise for improving GDM diagnosis and prediction. These biomarkers may enable healthcare providers to identify high-risk patients, monitor disease progression, and develop personalized treatment plans. Further research is needed to validate these biomarkers and translate them into clinical practice, ultimately improving outcomes for women with GDM.

ACKNOWLEDGEMENTS

The authors are thankful to the Department of Biotechnology, New Delhi, and Maulana Azad National Fellowship, University Grants Commission, New Delhi, respectively, for fellowships. They are also grateful to the Department of Biotechnology, Department of Science and Technology, Indian Council of Medical Research, New Delhi, and Centre of Excellence, Higher Education, Government of Uttar Pradesh, Molecular and Human Genetics Laboratory, Department of Zoology, University of Lucknow, India.