1. Introduction

According to the International Diabetes Federation, diabetes affects more than 463 million people, with type 2 diabetes (T2D) being the most common, accounting for around 90% of all diabetes worldwide in 2019 [1]. T2D was responsible for more than 4.2 million deaths in 2019, and is also a trigger for other non-communicable diseases, putting considerable pressure on national health systems [1]. T2D is associated with severe comorbidities, such as cardiovascular diseases (ischemic heart disease, myocardial infarction, peripheral arterial disease, heart failure, and stable angina being the most prevalent) [2], kidney diseases (such as glomerulosclerosis and glomerular hypertrophy inflammation/fibrosis, which ultimately lead to diabetic kidney disease) [3], and liver diseases (nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH), liver failure, cirrhosis, and hepatocellular carcinoma) [4], while it also increases the possibility of developing several types of cancer (such as breast cancer, bladder cancer, pancreatic cancer, non-Hodgkin lymphoma, etc.) [5]. Outcomes of such comorbidities can be reduced with early intervention in the development of type 2 diabetes. In recent decades, there has been a massive effort and investment to find biomarkers that can detect T2D early and support the implementation of prophylactic measures.

One of the most important clinical symptoms of diabetes mellitus is hyperglycemia. Thus, monitoring blood glucose levels via a glycated hemoglobin assessment remains the most common screening method. However, when glucose levels are elevated, the disease is already active. Significant investments in research have allowed the identification of biomarkers that can be used to describe the progression from a subclinical to a clinical stage, and some biomarkers have been described as having potential predictive value to differentiate between progressors and non-progressors.

The critical threshold is prediabetes. Prediabetes is an asymptomatic disorder of the normoglycemia–hyperglycemia transitional state, when plasma glucose is above normal range but below clinical diabetes. Prediabetic subjects present either impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or both, as well as an increased risk of developing type 2 diabetes. Such metabolic alterations are already mentioned as being responsible for microvascular problems (such as retinopathy, nephropathy, and neuropathy—persistent complications among the hyperglycemic community) [6]. Whether prediabetes justifies clinical identification and intervention is still continuously debated among international professional organizations, and overall criteria remain without consensus. However, the importance of targeting prediabetes is relevant considering that the risk of developing diabetes can decrease by 40 to 70% with lifestyle alterations in prediabetic patients [7]. The main problem associated with prediabetes is that it may lead to overdiagnosis and, therefore, overtreatment. The pharmacotherapy associated with prediabetes can include antidiabetic drugs such as biguanides (e.g., metformin) or thiazolidinediones (e.g., rosiglitazone), and others, such as GLP-1 analogs or α-glucosidase inhibitors. In addition to pharmacotherapy, bariatric surgery (such as gastric bypass or sleeve gastrectomy) has already been studied in prediabetic patients, with positive results, such as the reversion of IGT to normal values in 98% of individuals [7].

At a prediabetic stage, several metabolic imbalances are already established, occurring before the clinical manifestations. Identifying these imbalances with adequate and precise biomarkers can facilitate early intervention. In America, one in every three individuals have prediabetes, and 11% will develop diabetes [8]. Worldwide, prediabetes is increasing, and the expectation is that, by 2030, the number of people with prediabetes will increase to more than 470 million. Each year, 5–10% will progress to diabetes and develop diabetic comorbidities, such as hypertension [9].

2. Development and Findings

A total of 145 total cumulative records were retrieved from PubMed, 13 of which were duplicates and, hence, immediately excluded. The title and abstract were examined for the remaining 132 records, following concordance assessment of the inclusion criteria and objectives, resulting in an additional exclusion of 103 records. Thus, 29 studies were identified as being eligible and relevant. All manuscripts were carefully studied, and biomarkers were identified and counted. The analysis of the results identified two approaches to novel prediabetic biomarkers: metabolomics [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30], and microRNA studies [31,32,33,34,35,36,37,38]. The results are shown in Figure 1 . Further ahead, Table 1 and Table 2 summarize the most relevant biomarkers’ descriptions, outcomes, advantages, and disadvantages.

Metabolomics is a high-throughput technique that enables the identification and quantification of small molecules present in biological samples such as blood, urine, and tissue. Metabolomics is increasingly used to address metabolic dysregulation associated with prediabetes ( Figure 2 ). The method used in metabolomics combines analytical chemistry and data analysis with spectroscopic/spectrometric techniques (such as mass spectrometry or nuclear magnetic resonance) and separation techniques (such as gas chromatography, high-performance liquid chromatography (HPLC), ultra-HPLC, etc.) in a profitable, high-yield manner.

miRNAs are small, non-coding RNAs involved in post-transcriptional gene expression ( Figure 3 ); they can modulate important biological mechanisms such as growth and proliferation, differentiation, and cell death. Research on miRNAs is more recent than metabolomics research, leading to the belief that there is still much to be uncovered. miRNAs are becoming increasingly prominent in many pathologies, including prediabetic studies. Interestingly enough, different miRNA profiles were found in healthy, prediabetic, and diabetic individuals [38].

miRNAs associated with diabetic progression have different types of correlation according to their miRNA-specific function. Moreover, miRNAs can predict diabetic complications such as cardiovascular diseases, chronic renal disease, or retinopathy. They display consistent and reproducible circulating levels, and are stable and resistant to RNase activity—essential characteristics in biomarker assessment. Previous studies concluded that diabetes-related miRNA does not change dramatically in the prediabetic stage [38]. Moreover, due to a wide range of prediabetic-associated miRNAs, choosing a set of representative prediabetic biomarkers is challenging [92,93,94].

3. Conclusions

Diabetes is still one of the most challenging health problems worldwide. Programs for prevention and awareness of diabetes have proven to be insufficient to stop this pandemic; hence, clinical intervention could be the answer to avoid diabetic progression by targeting prediabetes. The growing attention to novel glycemic biomarkers is attributable to the limitations demonstrated by both HbA1c and OGTT.

From the present study, our interpretation is that these biomarkers are the ones that, so far, are at a more advanced research stage and, thus, are more promising for clinical implementation. However, many other biomarkers have been the target of research in diabetes (such as ophthalmate or galectin-3) with positive results, demonstrating the continuous effort of the academic community to find, comprehend, and interpret new and reliable molecules for the assessment of the (pre)diabetic pathology.

We believe that a biomarker multiplex is the most effective solution to achieve better sensitivity and specificity in predicting progressors in T2D. Such an achievement would improve patients’ health and decrease the national system’s burden regarding diabetes. Moreover, low-cost, effective interventions in the form of lifestyle changes would be sufficient to diminish drug/surgery-based clinical interventions.

This entry is adapted from the peer-reviewed paper 10.3390/biom11111589