Liquid chromatography (LC) combined with mass spectrometry (MS) provides a high-throughput platform for large-scale protein analysis, enabling comprehensive investigation of protein expression, post-translational modifications, and interactions. The isobaric tags for relative and absolute quantitation (iTRAQ) method allows isotopic labeling and the simultaneous quantification of protein abundance from various sources. Hence, the iTRAQ-LC-MS/MS method is widely used in quantitative proteomics because of its efficiency in saving time and minimizing the number of experimental procedures when compared to traditional proteomic methods (46). Using this technique, researchers identified LAMA2, MLL4, and PLXDC2 as novel serum biomarkers for prediabetes, with 0-20% higher specificity and 20-40% greater sensitivity than FBG and HbA1c (47). MLL4 plays a key role in transcriptional activation, regulating islet β-cell function (48). LAMA2 deficiency is associated with impaired skeletal muscle metabolism, where muscle IR is a major driver of prediabetes (49). These proteins were absent or low in healthy individuals but significantly elevated in prediabetic subjects. Their combined use shows promise for developing a precise diagnostic tool for prediabetes, though further studies are needed to validate their clinical utility across diverse populations.

Selected Reaction Monitoring (SRM) is a key targeted proteomics technique for detecting and quantifying multiple proteins with high precision and specificity (50). Nano-LC/MS in SRM mode (SRM-MS) identified positive correlations between MASP1, THBS1, GPLD1, and prediabetes, while ApoA-IV showed a negative association. Bonferroni correction confirmed the positive association between MASP1, prediabetes, and fasting glucose levels. This link is likely mediated by elevated IR (51). Evidence suggests that THBS1 contributes to adipose tissue inflammation and metabolic dysregulation, explaining its elevated levels in prediabetes. A prospective cohort study using targeted SRM-MS found an independent association between MASP levels and the development of T2D and prediabetes, even after adjusting for known risk factors. Individuals with normal glucose levels who progressed to diabetes or prediabetes within 6.5 years showed significantly higher MASP levels, underscoring its predictive value (52). These proteins show promise as biomarkers, but further large-scale validation studies are essential. A label-free quantitative SRM-LC/MS/MS study revealed that when FPG ≥ 5.6 mmol/L, CD14 expression increased while CSF-1R decreased, suggesting early chronic inflammation (53). This indicates that CD14 and CSF-1R could be crucial biomarkers for prediabetes and potential new targets for therapy.

Two-dimensional electrophoresis (2-DE) combined with MS is a widely used technique to quantify disease-related protein alterations. This technique enables the simultaneous separation of hundreds to thousands of proteins from complex biological samples in a single run (54). A serum proteomics study using this method identified seven distinctive proteins: AACT, AAT, ApoA-I, HP, RBP4, TTR, and ZAG. These proteins show significant differences between individuals with normal glucose levels and those with prediabetes or diabetes (55). Most of these proteins play crucial roles in the transport, localization, and regulation of serum lipoprotein particles. Assessing the levels of these biomarkers may aid in the early diagnosis of prediabetes and diabetes.

2-DE is often regarded as an outdated technique, primarily due to its limitations in analytical depth, sample consumption, and time efficiency. As a derivative of 2-DE, two-dimensional differential gel electrophoresis (2D-DIGE) offers enhanced sensitivity, a wider linear range, more accurate quantification, and the ability to effectively separate and analyze protein isoforms, making it a more advanced alternative (56). Takahashi et al. performed a serum quantitative proteomics analysis in a prediabetic rat model utilizing 2D-DIGE and LC-multiple reaction monitoring (MRM). They successfully identified five differentially expressed proteins in prediabetes, namely A1I3, ApoE, MUG1, CRP, and SERPINA3N (57). The increased expression of SERPINA3N may lead to impaired insulin secretion in the pancreas by inhibiting Wnt/β-catenin signaling (58). These biomarkers may play a pivotal role in prediabetes pathogenesis and hold potential as diagnostic and therapeutic targets, though further replication and functional studies are needed to clarify their mechanisms.

Skeletal muscle IR is an early and critical abnormality in the development of T2D and prediabetes, making skeletal muscle a promising source for identifying biomarkers and therapeutic targets (59). Sequential window acquisition of all theoretical (SWATH) MS is an emerging MS technique that offers high quantitative accuracy, extensive proteomic coverage, reproducibility in proteome coverage, and sample throughput while minimizing the number of missing values. The application of this technology in proteomics studies has unveiled a notable decrease in proteins linked to mitochondrial energy metabolism and oxidative phosphorylation in the muscle tissues of IFG and IGT. The expression of SUPV3L1 protein is uniquely reduced in IFG muscles compared to NGT, while the expression of SNRPN is uniquely down-regulated in IGT muscles (60). These findings indicate that SUPV3L1 and SNRPN in skeletal muscle could serve as potential biomarkers for prediabetes, offering valuable insights into the pathogenesis of prediabetes from a skeletal muscle perspective. The biomarkers identified through proteomic analysis are presented in Table 1 .

Overall, insulin resistance and β-cell function regulation are the most common pathways affected by prediabetes biomarkers identified through proteomics. In the biological pathway of insulin resistance, the elevated levels of MASP1, THBS1, GPLD1, AAT, HP, RBP4, ZAG, and ApoA-I may be positively correlated with each other, synergistically exacerbating insulin resistance (51, 55). These biomarkers are closely associated with metabolic disturbances and may collectively influence insulin sensitivity. In contrast, the decreased levels of ApoA-IV, AACT, and TTR may also exhibit positive correlations, suggesting their potential role in alleviating insulin resistance through metabolic regulation (51, 55). On the other hand, in the pathway of β-cell function regulation, the increased levels of LAMA2, MLL4, PLXDC2, MASP, ApoE, MUG1, CRP, and SERPINA3N may act synergistically, contributing to β-cell damage and reflecting alterations in the pathway (47, 52, 57). Meanwhile, the reduced levels of A1I3 are negatively correlated with the elevated levels of other biomarkers, suggesting a protective role for A1I3 in β-cell function regulation (57).

Proteomic methods have identified several promising biomarkers for prediabetes, yet key challenges remain. The molecular mechanisms underlying these biomarkers remain unclear, and their sensitivity and specificity require further clarification (53). Clear concentration thresholds for diagnosing prediabetes are lacking, representing a critical area for future research (52). Many studies have relied on cross-sectional designs, limiting their ability to establish causation. Consequently, cohort studies may serve as an important approach to establishing causal relationships. Exploring biomarkers in other tissues, such as urine and saliva, offers promising avenues for future research.

Blood samples, including serum and plasma, are primary materials in metabolomics research due to their ability to reflect systemic physiological and pathological states. These samples are rich in metabolites and provide crucial indicators of disease onset, progression, and therapeutic response. Their accessibility and the maturity of analytical technologies make blood samples ideal for large-scale clinical and epidemiological research. Among carbohydrate biomarkers for prediabetes, mannose is the most prominent besides glucose (65). Prediabetes involves metabolic disruptions in carbohydrates, amino acids, and lipids, reflecting its complexity beyond glucose regulation. These metabolites are frequently integrated to construct a prognostic model, aiming to enhance the diagnostic accuracy of prediabetes. Elevated branched-chain amino acids (BCAAs) have been identified as early biomarkers, predicting T2D development up to ten years before onset (75, 76). Increased levels of BCAAs, including valine, leucine, and isoleucine, are frequently observed in prediabetes compared to individuals with normal glucose levels. This indicates their potential as important biomarkers for prediabetes and IR (65). Reduced tryptophan levels in prediabetes suggest its potential as a biomarker and highlight tryptophan metabolism as a promising therapeutic target (62). Other amino acids, such as glycine, glutamine, and alanine, also show distinct variations between prediabetic and normoglycemic individuals (64, 68, 70). These amino acids also possess the potential to serve as biomarkers for identifying prediabetes. Lipid metabolites, including lysophospholipids, acylcarnitine, and lysophosphatidylcholine, may serve as predictive indicators of prediabetes and T2D (64, 66). IR and mitochondrial dysfunction are key mechanisms driving these metabolic changes (61, 64–66). In summary, blood metabolites offer broad potential as biomarkers for prediabetes, and their integration into predictive models may provide novel strategies for prevention and diagnosis.

Most identified metabolites are associated with IR rather than direct changes in β-cell function. Identifying reliable biomarkers that reflect β-cell function is essential for detecting prediabetes. Using a combination of untargeted and targeted metabolomics, Li et al. employed both untargeted and targeted metabolomics to analyze the liver and plasma metabolome in two mouse models. They identified deoxyhexose 1,5-dehydrosorbitol as a biomarker reflecting the gradual loss of functional β-cell mass in asymptomatic prediabetes (67).

The liver is essential for regulating glucose homeostasis. In normal physiology, the liver regulates glycogenesis, glycogenolysis, glycolysis, gluconeogenesis, and lipogenesis in response to insulin during fasting and feeding (77). In pathological conditions, impaired hepatic insulin signaling, such as IR, disrupts metabolism, leading to hyperglycemia, inflammation, and adipose remodeling (78–80). Dahl salt-sensitive (SS) rats, which mimic prediabetic lesions, show impaired tricarboxylic acid (TCA) cycle function, reducing glucose utilization compared to salt-tolerant rats (81). Metabolomic analysis revealed higher levels of L-lactic acid and L-propionic acid in the livers of SS rats compared to salt-resistant SS.13BN rats. These findings suggest that L-lactic acid and L-propionic acid are potential hepatic biomarkers for prediabetes. Increased L-lactic acid may reflect lactic acid buildup due to impaired pyruvate oxidation, while elevated L-propionic acid suggests abnormal gluconeogenesis activation (69).

Urine offers a non-invasive and easily accessible sample material for metabolomics analysis. Despite these advantages, urine has been underutilized for identifying metabolic disease biomarkers in mass spectrometry-based metabolomics studies (82–84). Recent research developed sex-specific diagnostic models using urine metabolomics, incorporating a wide range of metabolic biomarkers. This model outperforms current blood-based parameters for IGT screening, highlighting the potential of sex-specific diagnostic models for urine-based prediabetes screening (73).

Skeletal muscle, one of the most insulin-sensitive tissues, is the primary site for insulin-stimulated glucose uptake (85). Along with the liver, skeletal muscle plays a key role in regulating glucose uptake and maintaining glucose homeostasis (86, 87). Szczerbinski et al. compared skeletal muscle metabolomics profiles in patients with varying glucose levels, revealing significant differences in glutamate, ornithine, carnosine, and sphingomyelins (41:1, 41:2) between normoglycemic and prediabetic individuals. These findings suggest these metabolites may serve as biomarkers for prediabetes and help prevent progression to T2D (74).

Insulin resistance is also the most frequently identified biological pathway in metabolomic biomarkers associated with prediabetes. Elevated levels of α-HB, L-GPC, oleic acid, 3-MOB, 4-MOP, α-KB, acetylcarnitine C2, BCKAs, acylcarnitines, lysophospholipids, phosphatidylcholines, C6DC, C14, C12, C14-OH, C14:1, C14:2, glutamate, ornithine, and carnosine may exhibit positive correlations within the biological pathways associated with insulin resistance (63, 64, 66, 71, 74). These biomarkers potentially act through multiple mechanisms, such as influencing BCKD activity, promoting the accumulation of lipid metabolites, impairing mitochondrial β-oxidation, and disrupting insulin signaling pathways, thereby exacerbating insulin resistance. Conversely, the downregulation of glycine, lysophosphatidylcholine (LPC) (18:2), leucine, C16, citrate, and sphingomyelins (41:1, 41:2) may also show positive correlations, suggesting their potential protective roles in maintaining metabolic homeostasis, which could contribute to the mitigation of insulin resistance (64, 71, 74).

Current metabolomics studies have identified distinct metabolic profiles across various samples, including blood, urine, liver, and skeletal muscle, demonstrating strong predictive potential for prediabetes risk. However, as with proteomics, most studies are cross-sectional, identifying metabolic differences between prediabetic and normoglycemic groups without validation through cohort studies. Limited sample sizes in current studies hinder the exploration of correlations and replication. The mechanisms underlying differential metabolites remain speculative and require further validation. Current research relies heavily on animal models, requiring clinical validation to assess diagnostic efficacy and therapeutic potential (69). Saliva, as a non-invasive, stable, and readily accessible sample, shows promise as a superior alternative to other bodily fluids for biomarker identification (88, 89). Saliva metabolomics holds significant potential for identifying biomarkers, presenting new opportunities for scientific research.

Most transcriptomic studies on prediabetes utilize blood samples due to their accessibility, minimal invasiveness, and reproducibility. A recent study identified four mRNAs—ZBP1, DDX58, NFKB1, and CHUK—with elevated expression in prediabetic individuals compared to healthy controls or T2D patients. These mRNAs are involved in the cGAS/STING and NOD-like receptor (NLR) pathways, which are critical in inflammation-driven IR (92). Lee et al. identified differential mRNA expression of inflammation and lipogenesis genes—such as FCGR2b, LGALS1, VCAM1, IGFBP5, and GAS6—in a high-fat diet-induced prediabetic mouse model (93). Microarray analysis revealed distinct mRNA expression profiles in prediabetic patients compared to normoglycemic controls. These mRNAs are involved in key biochemical pathways, including starch and sucrose metabolism, pantothenate and coenzyme A biosynthesis, and niacin metabolism (94).

The transcriptomic landscape of prediabetes extends beyond mRNA, with non-coding RNAs (ncRNAs) playing key roles. Long ncRNAs (lncRNAs), over 200 nucleotides in length, lack protein-coding capacity but regulate gene expression via epigenetic, transcriptional, and post-transcriptional processes (95, 96). Dysregulated lncRNAs have been implicated in various diseases, making them promising biomarkers and therapeutic targets (97). LncRNAs influence IR by regulating lipid, carbohydrate metabolism, and inflammation (98). Several lncRNAs identified via microarray and validated internally play crucial roles in the pathophysiology of prediabetes (94). LncRNA H19 regulates lipid, glucose, and immune metabolism, showing high sensitivity for identifying prediabetes (99). It promotes hyperglycemia by upregulating hepatic FoxO1 expression and enhancing gluconeogenesis (100). Additionally, lncRNA H19 shows diagnostic and predictive potential for diabetes-related microvascular complications (99). LncRNA HCG27_201, with 91% sensitivity and 64% specificity, is a promising biomarker for prediabetes, though its mechanism requires further study (101).

MicroRNAs (miRNAs), 19 to 22 nucleotides long, are small non-coding RNAs that regulate post-transcriptional gene expression (102). MiRNAs regulate key processes such as proliferation, differentiation, and apoptosis by modulating protein translation (103). Prediabetes involves hyperglycemia-induced stress, with miRNAs playing a key role in regulating this response (104). Changes in serum or plasma miRNA profiles are commonly observed in individuals with prediabetes (105). MiRNAs regulate glucose homeostasis, insulin production, and secretion (106, 107). MiR-126-3p outperforms HbA1c in detecting prediabetes, offering greater diagnostic precision (108). TaqMAN-based RT-qPCR identified elevated miR-375 and miR-9, two islet-specific miRNAs, in prediabetic patients (109). They play a crucial role in glucose homeostasis and the pathogenesis of diabetes by regulating insulin secretion mechanisms (110). While most prediabetes biomarkers are also present in diabetes, miR-192 and miR-193b levels are elevated exclusively in prediabetes, with no increase in T2D. This differential expression suggests that prediabetes reflects a dynamic adaptation to metabolic changes, potentially progressing to diabetes (111). Most studies have focused on male or mixed-sex cohorts, lacking female-specific miRNA profiles for prediabetes. Kovac et al. divided female patients into IFG and NGT groups, profiling plasma miRNAs and validating them in islet cells and adipocytes of female mice. They found elevated let-7i-5p levels under prediabetic conditions. Let-7i-5p may regulate systemic insulin sensitivity and serve as a biomarker for prediabetes in women by impairing insulin signaling and glucose homeostasis (112).

Circular RNAs (circRNAs) are closed-loop non-coding RNAs involved in key biological processes across eukaryotes and prokaryotes (113, 114). CircRNAs act as miRNA sponges, sequestering miRNAs and regulating their target genes by competing for miRNA binding sites (115–117). CircRNAs such as hsa_circ_0063425, hsa_circ_0056891, hsa_circ_0111707, and hsa_circ_0071336 regulate target gene expression by sequestering miRNAs, highlighting their potential as prediabetes biomarkers (118–120). Table 3 delineates the potential biomarkers identified through transcriptomic methodologies.

Insulin resistance and dysregulation of pancreatic β-cell function are the predominant pathways implicated in transcriptomics-based biomarkers of prediabetes. In the biological pathways underlying insulin resistance, the upregulation of let-7b, miR-144, miR-29a, and miR-148b-3p may be positively correlated, collectively contributing to the exacerbation of insulin resistance through various mechanisms (122, 125). In contrast, the downregulation of miR-27a-3p and miR-142 may also be positively correlated, suggesting a protective role in modulating insulin resistance, potentially alleviating its effects. In the context of pancreatic β-cell dysfunction, the upregulation of miR-30a-5p, miR-182-5p, miR-375, and miR-9 may interact synergistically, adversely affecting β-cell function and further exacerbating β-cell damage (109, 126). These changes in miRNA expression highlight their involvement in the complex regulatory networks associated with both insulin resistance and β-cell dysfunction.

The current research is limited by the lack of large-sample validation, multi-population studies, and prospective trials to confirm these biomarkers’ role in diabetes risk screening strategies. Many mechanisms remain unclear, requiring further experimental studies to explore the regulatory roles of ncRNAs in downstream gene expression and their entry into the bloodstream (118, 124). Differential expression of prediabetic RNAs is mainly observed in serum and plasma, indicating potential for future exploration in diverse tissues.

Genomic approaches have elucidated numerous biomarkers of considerable significance associated with prediabetes ( Table 4 , Figure 2 ). Feng et al.’s study indicated that allele C of SNP rs867529 at the PERK gene locus is associated with an increased risk of prediabetes, whereas allele G of SNP rs10986663 in the BIP gene is inversely correlated with prediabetes risk. Genetic variations in these genes may play a role in the development of endoplasmic reticulum stress (130). A GWAS-based on changes in prediabetes status has identified five novel genes associated with changes in prediabetes status, including SGCZ at 8p22, HPSE2 at 10q24.2, ADGRA1 at 10q26.3, GLB1L3 at 11q25, and PCSK6 at 15q26.3 (131). Sparsø et al. discovered that the G allele of MTNR1B rs10830963 is associated with an increased risk of isolated IFG in a large sample of European populations (134).

The correlation between genetic susceptibility markers and the prediabetes phenotype has been a prominent focus of research in recent years. Lin et al. utilized GWAS technology to investigate the Chinese Han population, revealing a significant association between genetic variations of CTAGE11P, MARCHF2, KRT71, ABO and other genes with fasting proinsulin (FPI), fasting insulin (FI), 2 hours postprandial proinsulin (2hPI) and 2 hours postprandial insulin (2hI). These genetic variants may be indicative of IR and abnormal insulin secretion, holding promise as potential biomarkers for prediabetes prevention (129). The MRPS6/SLC5A3 gene rs13052524 and rs62212118 sites were found to be correlated with 2hPG levels in prediabetic patients from Hainan, China, according to a study conducted there. Six loci within four prediabetic genes (LINC01648, MATN1, CRAT37, and SLCO3A1) exhibited associations with HbA1C. This indicates that these loci may play a role in the regulation of 2hPG and HbA1C levels (135). Genetic loci associated with waist-hip ratio, such as rs6795735 (ADAMTS9), rs984222 (TBX15-WARS2) and rs1011731 (DNM3-PIGC), were found to be linked to the risk of IFG, indicating a potential association between genetic predisposition to central obesity and impaired islet function (133). Wang et al. conducted a GWAS and identified two independent significant sites (rs9457733 and rs11243373) as well as 37 candidate genes associated with prediabetic 25 (OH) D3, indicating its importance as a risk factor for prediabetes (137). Choi et al. discovered that GCK and YKT6 were susceptibility loci associated with prediabetes in a GWAS study involving two population cohorts, a control group, and a prediabetic group. Additionally, polymorphisms of these genetic variants were independently linked to an elevated risk of microalbuminuria. This suggests that the genetic mechanism of prediabetes may serve as a predictor for the risk of renal microvascular complications to some extent, thereby laying the groundwork for early detection and management of high-risk patients (136).

Prediabetes often arises from a combination of minor variations in multiple genes and the interplay between genetic factors and environmental influences, which presents challenges in interpreting and applying genetic test results. Most studies do not stratify SNPs by age/sex to investigate age/sex-specific differences in SNP effects (130, 131, 134). Furthermore, environmental factors such as lifestyle, diet, and physical activity significantly impact the onset and progression of prediabetes (138). However, genomics struggles to comprehensively encompass the influence of these factors. Studies based on GWAS can only identify correlations, not causations. Therefore, it is essential to validate the association between genetic loci or candidate genes and prediabetes through molecular experiments. Current genomic studies are limited by relatively small sample sizes. Future large-scale genomic research will be essential to validate the identified SNPs and uncover additional genetic loci associated with prediabetes (131).

Although genomic biomarkers have been identified in European and Chinese populations, their applicability in underrepresented populations remains to be fully explored, given the genetic diversity across different ethnic and geographic groups. For example, cross-population GWAS could validate the relevance of these biomarkers in other populations and potentially identify novel genetic markers specific to these groups. Such efforts would contribute to a better understanding of the genetic heterogeneity of prediabetes across diverse populations and support the development of precision medicine and global prediabetes diagnostic strategies.

As a common epigenetic modification, DNA methylation involves adding methyl groups to the fifth carbon of cytosine in cytosine-phosphate-guanine (CpG) dinucleotides, catalyzed by DNA methyltransferase (141, 142). Hypermethylation in gene promoters impedes transcription factor binding, inhibiting transcription and silencing gene expression. In contrast, hypomethylation enhances transcription factor binding, often increasing gene expression. Using methylated DNA immunoprecipitation (MeDIP) technology, Matsha et al. identified distinct DNA methylation patterns in South African individuals of mixed ancestry, showing both hypermethylation and hypomethylation in gene promoters. These changes affect genes involved in immune function, signal transduction, glucose transport, and pancreatic development. The affected pathways include linoleic acid and arachidonic acid metabolism, both showing hypomethylation in prediabetes (143). This study lays a foundation for exploring epigenetic mechanisms in prediabetes and identifying potential biomarkers.

Testosterone and estrogen directly affect skeletal muscle, liver, and adipose tissue, influencing carbohydrate metabolism. They also indirectly influence metabolism by modulating body fat quantity and distribution (144, 145). These hormones thus play a pivotal role in glucose regulation and prediabetes pathogenesis. Peripheral blood DNA methylation analysis in a case-control study found a positive correlation between AR gene methylation and IFG in females. Serum testosterone negatively modulated the link between AR gene methylation and IFG, suggesting that low testosterone and AR methylation levels reduce IFG risk (146). This phenomenon may be attributed to the capacity of testosterone to ameliorate IR and dysregulated glucose metabolism resulting from AR gene methylation by modulating the expression or activity of the AR, thereby reducing the risk of IFG. Feng et al. examined how estrogen receptor α (ESRα) methylation and progesterone influence glucose metabolism disorders in a case-control study. In men and postmenopausal women, CpG 1 methylation in the ESRα gene and elevated progesterone levels were positively associated with IFG. Individuals with both high CpG 1 methylation and progesterone levels showed a higher IFG risk (147). This mechanism likely involves IR caused by ESRα methylation, progesterone levels, and pancreatic secretory cell apoptosis. These factors may act synergistically through the PI3K/AKT pathway, but further research is needed to clarify the underlying mechanisms (148–150).

In late pregnancy, hormonal fluctuations increase IR, requiring greater insulin secretion by pancreatic β cells to maintain normal blood glucose (151). If β-cell secretion fails to meet increased demand, hyperglycemia occurs. A cohort study using DNA methylation assays identified differential methylation at three CpG sites—LINC00917, TRAPPC9, and LEF1—in women with abnormal glucose tolerance. LINC00917 and TRAPPC9 sites were independently linked to glucose tolerance status four years postpartum, suggesting their role in postprandial prediabetes (152).

Modifications to histones alter their structure and function, influencing chromatin architecture and gene expression. Histone methylation is a key post-translational modification; for example, H3K4me3 refers to the trimethylation of histone H3’s fourth lysine residue. The myeloid/lymphoid or mixed-lineage leukemia (MLL) gene encodes a histone methyltransferase that methylates H3K4, modulating gene transcription (153). Yoshino et al. demonstrated the crucial role of the MLL gene in pancreatic β cells. In βHC-9 cells with MLL knockout, insulin secretion declined, along with reduced expression of glucose-sensitive genes SLC2A1 and SLC2A2. Moreover, MLL heterozygous knockout mice showed impaired glucose tolerance and reduced insulin secretion (154). These results suggest that MLL genes contribute to prediabetes pathogenesis through histone modifications. Castellano-Castillo et al. provided the first analysis of H3K4me3 markers on gene promoters involved in lipogenesis, lipid metabolism, and inflammation in human visceral adipose tissue. They found significant enrichment of H3K4me3 at the promoters of E2F1, LPL, SREBF2, SCD1, PPARG, and IL6 in lean individuals with normal glucose compared to obese individuals with prediabetes. This suggests that H3K4me3 plays a critical role in prediabetes onset and progression (155). Table 5 presents potential biomarkers identified through epigenomic analysis.

In the field of epigenomics related to prediabetes, current research has predominantly focused on 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). However, emerging epigenetic modifications, such as N6-methyladenine (6mA) and other non-canonical DNA modifications, remain underexplored in this context. Future studies should investigate these additional modifications to gain a more comprehensive understanding of the epigenomic landscape and their potential roles in prediabetes pathogenesis. MeDIP cannot distinguish between 5mC and 5hmC or identify differentially methylated CpG sites. Advanced techniques are needed for more precise DNA methylation analysis in future research. Although some studies used peripheral blood leukocytes, future research should focus on tissues more relevant to prediabetes, such as pancreatic β cells, due to variations in methylation across blood cell types (143, 146). Moreover, future studies should control for confounding factors like smoking and obesity to reduce potential interference. Since many studies are observational case-control studies, cohort studies are needed to establish causality and provide further validation (146). Animal study findings must be validated in clinical settings.

The gut microbiome, consisting of trillions of microbes residing in the gastrointestinal tract, has been recognized as a virtual organ that interacts with the gut and other organs in the host’s body to facilitate various physiological processes (156). Multiple research has shown that gut microbiota and its metabolites are essential in controlling inflammation, maintaining energy equilibrium, and regulating lipid and glucose metabolism (157–159). Emerging evidence reveals significant gut microbiota dysbiosis in individuals with prediabetes. High-throughput 16S rRNA sequencing, widely used in microbiome research, provides insights into bacterial diversity and community structure in health and disease.

Prediabetes alters gut microbial structure, reducing both diversity and relative abundance (160). Compared to individuals with NGT, there is a notable decrease in the presence of bacteria that produce short-chain fatty acids (SCFAs) in the gut microbiota of those with prediabetes, including genera such as Anaerostipes, Enterococcus, Intestinibacter, Faecalibacterium prausnitzii, Roseburia and others (161–164). The primary SCFAs present in the intestinal tract are acetate, propionate, and butyrate. The population of butyrate-producing bacteria in the intestines of individuals with prediabetes is notably reduced, including species such as Roseburia, Faecalibacterium prausnitzii, Clostridium spp., Alistipes spp., Pseudoflavonifractor spp., Oscillibacter spp., and others (161, 163, 165, 166). In contrast, an increased abundance of some opportunistic pathogenic bacteria was observed, including species from Enterobacteriaceae, Streptococcus, Pseudomonadota, and Shigella spp./Escherichia spp., among others (162, 164, 166–168). The increased presence of opportunistic pathogens and bacteria-derived lipopolysaccharides can lead to the development of low-grade inflammation and IR (169, 170), both of which are key characteristics of prediabetes. Table 6 summarizes the potential biomarkers for prediabetes identified through gut microbiota profiling.

As the main product of dietary fiber fermentation in the large intestine, butyrate provides a critical energy source for colon cells (178). Butyrate and the bacteria that produce it promote anti-inflammatory properties, maintain intestinal immune homeostasis, and can also enhance insulin sensitivity and glucose tolerance (179, 180). The mechanisms through which SCFAs like butyrate regulate blood glucose may include several key aspects ( Figure 3 ).

Butyrate supplementation has been demonstrated to elevate levels of glucagon-like peptide-1 (GLP-1) and significantly enhance glycemic control in individuals with T2D (181). The hormone GLP-1, produced in the intestines, acts to stimulate pancreatic β cells and induce the secretion of insulin for the regulation of glucose levels. Butyrate and other SCFAs stimulate the secretion of GLP-1 by binding to FFAR2 receptors on intestinal L cells (182). The GLP-1R (receptor) gene is also expressed in β cells, and butyrate can induce activation of the GLP-1R gene and subsequent release of GLP-1 (183). The potential mechanism involves GLP-1 promoting the upregulation of cAMP (cyclic adenosine monophosphate), leading to postprandial insulin secretion through acceleration of glucose-dependent closure of ATP-regulated potassium channels (184–186). In addition to its pro-insulin effect, GLP-1 also maintains pancreatic β cell mass by promoting β cell proliferation and inhibiting β cell apoptosis, and has been utilized in the transdifferentiation of insulin-producing cells (187–189).

The impact of SCFAs extends beyond the gastrointestinal tract, as they are absorbed into the peripheral circulation and contribute to tissue metabolism. Butyrate signaling plays a crucial role in islet function. Butyrate and other SCFAs can serve as ligands for G protein-coupled receptors (GPR), binding to free fatty acid receptor 3 (FFAR3) (GPR41) and FFAR2 (GPR43), among others. Both GPR41 and GPR43 are expressed in pancreatic islets (190, 191). Through GPR41 signaling, butyrate can enhance β cell metabolism and inhibit islet cell apoptosis (192). On the other hand, by activating GPR43, SCFAs such as butyrate can stimulate insulin secretion, β cell proliferation, and the expression of β cell differentiation genes, indicating that GPR43 holds promise as a target for future research (193).

SCFAs, primarily butyrate, function as a natural inhibitor of histone deacetylase (HDAC) and regulate gene expression through epigenetic mechanisms (194). Animal experiments have demonstrated that sodium butyrate may ameliorate IR, dyslipidemia, fat accumulation, and gluconeogenesis in rats by inducing hyperacetylation of histone H3, thereby enhancing glucose homeostasis (195). Furthermore, it has been demonstrated that sodium butyrate can enhance insulin sensitivity in skeletal muscle cells exposed to long-term palmitate by promoting hyperacetylation of insulin receptor substrate-1 (196). Therefore, the reduction of butyrate may lead to IR through epigenetic mechanisms, subsequently causing disorders in blood glucose metabolism.

SCFAs binding receptors, such as GPR109A, are also present in adipose tissue. A study demonstrated that the administration of a GPR109A agonist inhibited lipolysis to improve glucose homeostasis in T2D patients (197). Therefore, it can be hypothesized that the deficiency of SCFAs, such as butyrate, may lead to impaired blood glucose regulation by accelerating lipolysis; however, further confirmation is still required through human cohort studies.

Both salivary glucose levels and pH influence the composition of the oral microbiome. Studies show that individuals with diabetes have lower salivary pH compared to non-diabetic individuals, and salivary glucose levels are closely linked to hyperglycemia (198–200). Thus, individuals with hyperglycemia are expected to display a distinct oral microbiome composition compared to healthy individuals. Significant structural changes in the oral microbiome have been observed in individuals with diabetes (201, 202). Although the oral bacterial population is significantly smaller than that of the gut, it has the potential to disseminate to other body sites, leading to localized and systemic inflammation (203, 204). These potentially harmful pathogens may have a notable influence on the development of IR, akin to the effect of the microbiota in the gut. Thus, oral microbiota dysbiosis is not only a marker of hyperglycemia but may also drive disease progression. The findings indicate that the traits displayed by the oral microbiota could serve as potential indicators of disease status. The analysis of bacterial microbiome composition in saliva has the potential to serve as a valuable non-invasive approach for diagnosing prediabetes.

The limited research in this area has resulted in no consensus on whether oral microbial abundance and diversity increase or decrease in prediabetes. One study analyzing the salivary microbiome based on HbA1c and FBG reported that prediabetic individuals exhibit greater microbial richness and diversity compared to normoglycemic individuals (177). However, a study by Saeb et al. reached the opposite conclusion, finding a significant reduction in the biological and phylogenetic diversity of the prediabetic oral microbiota (205). Rungrueang et al. discovered that the ratio of firmicutes to bacteroides in the oral microbiota of prediabetic patients was higher compared to healthy controls, consistent with previous findings in T2D. The abundance of Rothia significantly decreased in prediabetes, irrespective of whether HbA1c or FPG was used as the classification criterion (177). The mechanism may be that nitrate reductase in Rothia metabolizes nitrate to nitrite, which can be further reduced to nitric oxide (NO) in blood and muscle. NO has been shown to have a substantial impact on the control of metabolic processes, and it has the ability to enhance insulin secretion and promote muscle glucose uptake (206). Therefore, a reduction in the abundance of oral bacteria Rothia may serve as a significant indicator for the presence of prediabetes. The potential biomarkers for prediabetes, identified through oral microbiome analysis, are outlined in Table 6 .

Due to the multifactorial nature of the impact on intestinal flora, it is challenging for many studies to effectively control for potential confounding variables, which may somewhat limit the generalizability of study findings. Among the various factors influencing gut microbiome composition and diversity, diet stands out as one of the most significant. For instance, a high-carbohydrate diet has been linked to elevated levels of Prevotella, while a high-fat/protein diet is associated with a predominant presence of Bacteroides (207). By adopting a plant-based diet, you can enhance the abundance of beneficial bacteria in your gut, such as Bacteroidetes, thereby supporting gastrointestinal and overall health (208). Furthermore, the administration of hypoglycemic agents, antibiotics, probiotics, and other pharmaceuticals may influence the diversity of gut microbiota. For instance, metformin treatment elevates Firmicutes while reducing Bacteroidetes (209). The use of antibiotics often results in damage to the gut microbiota. For instance, broad-spectrum antibiotics like ampicillin disrupt the microbial community and lead to a reduction in the diversity of the gut microbiota (210). Therefore, minimizing the impact of confounding factors to the greatest extent possible may be crucial for enhancing the reliability of changes observed in gut microbiota outcomes. Since some studies were cross-sectional and focused on single-race cohorts, further longitudinal studies across diverse populations are needed.

Visceral adipose tissue (VAT) serves as a critical predictor of IR and is prominently featured as a biomarker in imaging studies related to diabetes and prediabetes (215). The increased metabolic activity of VAT promotes the release of free fatty acids into circulation, thereby inducing IR in adjacent tissues (216). A German population-based study employed quantitative MRI to evaluate the link between hepatic steatosis and prediabetes. Results showed that MRI-defined hepatic steatosis was significantly higher in individuals with prediabetes and positively correlated with several glucose metabolism markers, such as HbA1c, fasting glucose, and 2hPG (217). The underlying cause may be associated with IR induced by an accumulation of lipid metabolites in the liver (218), indicating that hepatic steatosis could serve as a potential biomarker for prediabetes. Bamberg et al. evaluated liver proton density fat fraction (PDFF) along with subcutaneous and visceral abdominal fat using MRI techniques. The findings indicated that PDFF levels, as well as total and visceral fat quantities, were significantly elevated in individuals with prediabetes, suggesting their potential role as indirect biomarkers (219). Borel et al. conducted simultaneous measurements of visceral fat and liver fat using CT imaging, revealing that liver fat content exhibited a positive correlation with isolated IGT (ilIGT), whereas visceral adiposity demonstrated a positive correlation with both ilIFG and ilIGT (220). Similarly, research measuring VAT volume through CT suggests it could serve as a reliable tool for assessing metabolic risk factors in individuals with prediabetes (221). Taking liver adipose tissue as an example, radiomics quantifies hepatic fat accumulation while metabolomics elucidates changes in metabolites associated with hepatic lipid metabolism, such as L-lactic acid and L-propionic acid (69). The integration of these two approaches not only facilitates the early identification of prediabetes risk but also enhances our understanding of its metabolic mechanisms, thereby providing a more precise foundation for personalized treatment and intervention.

Peripheral insulin sensitivity in humans largely depends on glucose utilization within skeletal muscle tissue. Consequently, alterations in skeletal muscle composition regarding fat content and lipid distribution may serve as critical determinants of IR (222). Kiefer et al. conducted a quantitative analysis of intracellular lipids (IMCLs) and extracellular lipids (EMCLs) in abdominal skeletal muscle using MRI, revealing that the levels of IMCLs and EMCLs in prediabetic patients were significantly elevated compared to those in normoglycemic control groups. This finding suggests that the distribution patterns of IMCLs and EMCLs may serve as promising biomarkers for prediabetes (223). Lipid metabolism abnormalities in skeletal muscle are closely associated with insulin resistance. Radiomics quantifies the distribution of IMCLs and EMCLs, revealing changes in lipid accumulation, while metabolomics analyzes shifts in metabolites such as glutamate, carnosine, and sphingomyelins, which are closely linked to lipid metabolism disturbances (74). The integration of these approaches provides a more comprehensive understanding of skeletal muscle metabolism, thereby facilitating the early identification and diagnosis of prediabetes.

Ectopic fat accumulation in the pancreas, known as pancreatic steatosis or fatty pancreas, increases the risk of prediabetes and diabetes (224). A fatty pancreas is typically characterized by hyperechoic pancreas (HP) when assessed via US. Research utilizing abdominal ultrasound has demonstrated that the risk ratio for glucose progression escalates with the severity of hyperechoic pancreatitis (225). Furthermore, moderate to severe HP serves as a reliable predictor for prediabetes and diabetes (226). Research has demonstrated that pancreatic fibrosis can lead to the destruction of pancreatic tissue, resulting in impaired secretion of pancreatic β cells and subsequent insulin deficiency (227). The hyperglycemic milieu induced by diabetes can lead to the aberrant activation of pancreatic stellate cells. These cells are capable of secreting a diverse array of pro-inflammatory and growth factors, which not only facilitate the synthesis of α-smooth muscle actin but also expedite the deposition of extracellular matrix components such as collagen, ultimately resulting in pancreatic fibrosis (228, 229). An extracellular volume fraction (fECV) derived from contrast-enhanced CT was employed to evaluate the extent of pancreatic fibrosis in prediabetic patients. The findings indicated that pancreatic fECV was significantly elevated in prediabetic individuals compared to non-diabetic counterparts and exhibited a moderate correlation with HbA1c levels (230). These findings suggest that pancreatic fECV could be a valuable biomarker for monitoring glucose dysregulation.

The investigation conducted by Askani et al. examined the correlation between adrenal volume measurements derived from MRI and impaired glucose metabolism. The findings revealed a significant increase in adrenal volume among prediabetic patients compared to healthy controls. However, this association weakened after adjusting for BMI (231). Therefore, adrenal volume could serve as an indirect biomarker of prediabetes, offering valuable insights for future research into its pathophysiology and the discovery of reliable biomarkers. Normally, perirenal fat appears hyperechoic or moderately echogenic on ultrasound imaging. In diabetes and prediabetes, IR and metabolic disorders often disrupt lipid metabolism and alter adipose tissue composition, resulting in a hypoechoic appearance on ultrasound. Shen et al. conducted an evaluation of 240 renal ultrasound cases and discovered that hypoechoic perinephric fat (HPF) demonstrated exceptionally high specificity and positive predictive value in patients diagnosed with prediabetes and diabetes (232). This finding indicates that HPF serves as a significant imaging biomarker closely associated with metabolic syndrome and IR.

Hyperechoic deltoid indicates that ultrasound imaging of the shoulder reveals an echo intensity greater than normal for this muscle. In individuals with diabetes or prediabetes, fatty infiltration and IR may lead to diminished glycogen levels within the deltoid muscle, resulting in a hyperechoic appearance. Research utilizing ultrasound assessments of the shoulder has demonstrated that the characteristic hyperechoic deltoid appearance serves as a robust predictor for prediabetes and can function as an adjunctive tool for early detection (233). The biomarkers associated with prediabetes, identified through radiomics analysis, are summarized in Table 7 .

Overall, insulin resistance is the most prevalent pathway implicated in radiomics-based biomarkers of prediabetes. Radiomics biomarkers, including adrenal volume, hepatic steatosis, IMCLs, EMCLs, liver PDFF, abdominal fat mass, VAT volume, HPF, HP, and hyperechoic deltoid, may be positively correlated with one another, working synergistically to promote the development of insulin resistance (217, 219, 221, 223, 225, 231–233). These biomarkers may interact through various mechanisms, such as influencing fat distribution, lipid accumulation, liver function, the endocrine role of adipose tissue, and local inflammatory responses, thereby contributing to the exacerbation and progression of insulin resistance.

Cross-sectional designs pose challenges in establishing causality, requiring future validation through larger cohort studies to confirm their clinical applicability (217). Many of these investigations have ambiguous underlying mechanisms, requiring further exploration for confirmation. Manual image segmentation is prone to subjective bias (231). However, integrating artificial intelligence (AI) into radiology could enable automated segmentation, facilitating data extraction from large datasets and identifying markers associated with disease risk profiles. Current radiomics research often lacks histopathological validation, and future investigations in this domain could be fortified to improve the robustness of their conclusions (230). Most imaging studies currently operate independently. Future efforts should integrate multiple imaging modalities to gain more comprehensive insights. Concurrently, the incorporation of advanced deep learning algorithms, such as convolutional neural networks and recurrent neural networks, is essential for enhancing diagnostic accuracy and predictive capabilities.