Recent studies have led to considerable advances in the identification of genetic variants associated with type 1 and type 2 diabetes. An approach for converting genetic data into a predictive measure of disease susceptibility is to add the risk effects of loci into a polygenic risk score. In order to summarize the recent findings, we conducted a systematic review of studies comparing the accuracy of polygenic risk scores developed during the last two decades. We selected 15 risk scores from three databases (Scopus, Web of Science and PubMed) enrolled in this systematic review. We identified three polygenic risk scores that discriminate between type 1 diabetes patients and healthy people, one that discriminate between type 1 and type 2 diabetes, two that discriminate between type 1 and monogenic diabetes and nine polygenic risk scores that discriminate between type 2 diabetes patients and healthy people. Prediction accuracy of polygenic risk scores was assessed by comparing the area under the curve. The actual benefits, potential obstacles and possible solutions for the implementation of polygenic risk scores in clinical practice were also discussed. Develop strategies to establish the clinical validity of polygenic risk scores by creating a framework for the interpretation of findings and their translation into actual evidence, are the way to demonstrate their utility in medical practice.

be used in semiquantitative (fluorometric) and qualitative (β-galactosidase activity) studies of different xenobiotic substances and pollutants.

Type 2 diabetes (T2D) is a deficiency in how the body regulates glucose. Uncontrolled T2D will result in chronic high blood sugar levels, eventually resulting in T2D complications. These complications, such as kidney, eye, and nerve damage, are even harder to treat. Identifying individuals at high risk of developing T2D and its complications is essential for early prevention and treatment. Numerous studies have been done to identify biomarkers for T2D diagnosis and prognosis. This review focuses on recent T2D biomarker studies based on circulating nucleic acids using different omics technologies: genomics, transcriptomics, and epigenomics. Omics studies have profiled biomarker candidates from blood, urine, and other non-invasive samples. Despite methodological differences, several candidate biomarkers were reported for the risk and diagnosis of T2D, the prognosis of T2D complications, and pharmacodynamics of T2D treatments. Future studies should be done to validate the findings in larger samples and blood-based biomarkers in non-invasive samples to support the realization of precision medicine for T2D.

Prediabetes is an intermediate state of hyperglycemia during which glycemic parameters are above normal levels but below the T2D threshold. T2D and its precursor prediabetes affect 6.28% and 7.3% of the world’s population, respectively. The main objective of this paper was to create and compare two polygenic risk scores (PRSs) versus changes over time (Δ) in metabolic parameters related to prediabetes and metabolic complications. The genetics of 446 prediabetic patients from the Polish Registry of Diabetes cohort were investigated. Seventeen metabolic parameters were measured and compared at baseline and after five years using statistical analysis. Subsequently, genetic polymorphisms present in patients were determined to build a T2D PRS (68 SNPs) and an obesity PRS (21 SNPs). Finally, the association among the two PRSs and the Δ of the metabolic traits was assessed. After a multiple linear regression with adjustment for age, sex, and BMI at a nominal significance of (p < 0.05) and adjustment for multiple testing, the T2D PRS was found to be positively associated with Δ fat mass (FM) (p = 0.025). The obesity PRS was positively associated with Δ FM (p = 0.023) and Δ 2 h glucose (p = 0.034). The comparison of genotype frequencies showed that AA genotype carriers of rs10838738 were significantly higher in Δ 2 h glucose and in Δ 2 h insulin. Our findings suggest that prediabetic individuals with a higher risk of developing T2D experience increased Δ FM, and those with a higher risk of obesity experience increased Δ FM and Δ two-hour postprandial glucose. The associations found in this research could be a powerful tool for identifying prediabetic individuals with an increased risk of developing T2D and obesity.