Biomarker For Type 2 Diabetes Research Articles

Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery.

This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated with bioinformatics techniques. Univariate and multivariate analyses such as fold change, a receiver operating characteristic curve (ROC), and Partial Least-Squares Discriminant Analysis (PLS-DA) were used to identify biomarker metabolites that showed significant concentration in T2D patients. Three tree-based algorithms [eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Adaptive Boosting (AdaBoost)] that demonstrated robustness in high-dimensional data analysis were used to create a diagnostic model for T2D. As a result of the biomarker discovery process validated with three different approaches, Pyruvate, D-Rhamnose, AMP, pipecolate, Tetradecenoic acid, Tetradecanoic acid, Dodecanediothioic acid, Prostaglandin E3/D3 (isobars), ADP and Hexadecenoic acid were determined as potential biomarkers for T2D. Our results showed that the XGBoost model [accuracy = 0.831, F1-score = 0.845, sensitivity = 0.882, specificity = 0.774, positive predictive value (PPV) = 0.811, negative-PV (NPV) = 0.857 and Area under the ROC curve (AUC) = 0.887] had the slight highest performance measures. ML integrated with bioinformatics techniques offers accurate and positive T2D candidate biomarker discovery. The XGBoost model can successfully distinguish T2D based on metabolites.

Detection of type-Ⅱ diabetes using graphene-based biosensors

Diabetes is a global pandemic that increases the risk of various health complications, including heart attacks, renal failure, blindness, stroke, and peripheral neuropathy. Type-2 diabetes (T2D) results from an imbalance in lipid and glucose metabolism due to hostility to insulin action and insufficient insulin production response. Valine amino acid has been identified as a potential biomarker for T2D, but there have been no rigorous studies on its interaction with branch chain amino acids. In this study, we investigated the potential of graphene/modified graphene as a valine biosensor using density functional theory to examine the electronic properties and adsorption behaviour of graphene, Si-doped graphene (SiG), and P-doped graphene (PG). The adsorption of valine over the substrates was physisorption in nature, and the adsorption energies were in the order of SiG > G > PG. Density of states (DOS) and partial DOS calculations confirmed the molecule’s adsorption over the monolayers and indicated variations in the electronic properties. We also performed recovery time calculations to examine the reusability of the nano-surfaces as potential biosensors. Ultrafast recovery times were predicted for all three systems, with SiG showing the best results. Our study suggests that SiG could be used as a biosensor for valine, providing a real-time and efficient diagnostic tool for T2D.

Machine Learning and Augmented Intelligence Enables Prognosis of Type 2 Diabetes Prior to Clinical Manifestation.

The global incidence of type 2 diabetes (T2D) persists at epidemic proportions. Early diagnosis and/or preventive efforts are critical to attenuate the multi-systemic clinical manifestation and consequent healthcare burden. Despite enormous strides in the understanding of pathophysiology and on-going therapeutic development, effectiveness and access are persistent limitations. Among the greatest challenges, the extensive research efforts have not promulgated reliable predictive biomarkers for early detection and risk assessment. The emerging fields of multi-omics combined with machine learning (ML) and augmented intelligence (AI) have profoundly impacted the capacity for predictive, preventive, and personalized medicine. This paper explores the current challenges associated with the identification of predictive biomarkers for T2D and discusses potential actionable solutions for biomarker identification and validation. The articles included were collected from PubMed queries. The selected topics of inquiry represented a wide range of themes in diabetes biomarker prediction and prognosis. The current criteria and cutoffs for T2D diagnosis are not optimal nor consider a myriad of contributing factors in terms of early detection. There is an opportunity to leverage AI and ML to significantly enhance the understanding of the underlying mechanisms of the disease and identify prognostic biomarkers. The innovative technologies being developed by GATC are expected to play a crucial role in this pursuit via algorithm training and validation, enabling comprehensive and in-depth analysis of complex biological systems. GATC is an emerging leader guiding the establishment of a systems approach towards research and predictive, personalized medicine. The integration of these technologies with clinical data can contribute to a more comprehensive understanding of T2D, paving the way for precision medicine approaches and improved patient outcomes.

Association of PCSK-9 with the Biomarkers of Type-2 Diabetes and its Complications in the Indian Population: a Pilot Study.

Proprotein convertase subtilisin/kexin type-9 (PCSK-9) is a serine protease with profound effects on plasma LDL-C, the major risk factor for cardiovascular diseases (CVDs). However, plasma PCSK-9 level and its association with the biomarkers of CVDs, diabetes, and associated complications have not yet been reported in the northeastern population of India. Of the total cohort (n = 233), we analyzed healthy controls (HC; n = 50), freshly diagnosed type-2-diabetes mellitus (T2DM-FD; n = 46), T2DM treated (T2DM-T; n = 49), diabetic nephropathy (T2DM-N; n = 43), and diabetic dyslipidemia (T2DM-DL; n = 45) subjects. Plasma PCSK-9 and other biological determinants associated with T2DM, CVD, and nephrotic dysfunction were assessed. The level of plasma PCSK-9 in HC, T2DM-FD, T2DM-T, T2DM-N, and T2DM-DL groups was found to be 184.1 ± 13.83, 183.1 ± 24.4.3, 241.8 ± 75.42, 403.7 ± 85.94, and 641.3 ± 135.5 ng/mL, respectively, indicating its role in the severity of the here-mentioned complications. Moreover, plasma PCSK-9 levels further showed a significant correlation with the biomarkers of hyperglycemia, particularly HbA1c, as well as LDL-C in T2DM-FD, T2DM-N, and T2DM-DL subjects of the Indian population, while moderate association in T2DM-T subjects. Our first-of-its-kind clinical study aiming to quantify the circulatory PCSK-9 level in the Indian population concluded that elevated PCSK-9 was significantly associated with the severity of diabetes and associated complications. Moreover, such elevation in PCSK-9 might be attributed to the lipid- and glucoselowering medication-induced SREBP-2-dependent mechanisms. Since our conclusion is based on a pilot study, further cohort studies in larger populations of India are required to get a generalization regarding the role of PCSK-9 in DM and associated complications.

Alzheimer’s Disease Biomarkers: Roles of Type 2 Diabetes from the HABS‐HD Study

AbstractBackgroundAlzheimer’s disease (AD) disproportionately affects Mexican Americans, however, reasons underlying this health disparity remain unknown. Type 2 diabetes (T2D) is a risk factor for AD and may contribute to AD pathogenesis. The purpose of this study was to examine the associations between T2D and AD blood biomarkers among Mexican Americans and non‐Hispanic Whites.MethodsThis study analyzed baseline data from the Health and Aging Brain Study: Health Disparities (HABS‐HD) that investigated AD differences among Mexican Americans and non‐Hispanic White. HABS‐AD participants were excluded from this study if they had missing data or were outliers (z‐scores >|4|) on a given AD biomarker (N = 1,552 for Aβ42/40 ratio, 1,570 for t‐tau, and 1,553 for NfL). AD biomarkers included plasma Aβ42/42 ratio, total tau (t‐tau), and neurofilament light (NfL) analyzed using Simoa. Predictors were blood glucose, HbA1c, and T2D diagnosis. Data were analyzed with regression analyses controlling for covariates (e.g., demographics, smoking, BMI, health status, diseases, cognition) and treating missing predictor data using Bayesian MCMC estimation.ResultsMexican Americans differed significantly from non‐Hispanic Whites in age (66.6±8.7 vs. 69.5±8.6), sex (64.9% female vs. 55.1%), education (9.5±4.6 vs. 15.6±2.5), blood glucose (113.5±36.6 vs. 99.2±17.0), HbA1c (6.33±1.4 vs. 5.51±0.6), T2D diagnosis (35.3% vs. 11.1%), Aβ42/40 ratio (.051±.012 vs. .047±.011), t‐tau (2.56±.95 vs. 2.33±.90), and NfL (16.3±9.5 vs. 20.3±10.3). The models with the covariates and predictors accounted for 6% (p<.001), 15% (p<.001), and 36% (p<.001) of the variation in Aβ42/40 ratio, t‐tau, and NfL, respectively. Blood glucose, HbA1c, and T2D diagnosis were not related to Aβ42/40 ratio and t‐tau but explained 3.7% of the variation in NfL (p<.001). Among non‐Hispanic Whites, blood glucose, HbA1c, and T2D diagnosis were negatively (b = ‐0.09, p<.01, β = ‐0.26), not (b = 0.34, p = .71, β = 0.04), and positively (b = 3.32, p<.01, β = 0.33) associated with NfL, respectively. In contrast, blood glucose and T2D diagnosis were not, while HbA1c was positively (b = 2.31, p<.001, β = 0.26), associated with NfL among Mexican Americans.ConclusionBlood glucose, HbA1c, and T2D may contribute to differences in NfL levels, but not Aβ42/40 ratio and t‐tau, in an ethnicity‐specific manner. Future studies are needed to corroborate our findings in longitudinal cohorts.

Abstract 16868: Genetic Regulation of Branched-Chain Amino Acid Levels Does Not Increase Type 2 Diabetes Risk

Introduction: The branched-chain amino acids (BCAAs) valine, leucine, and isoleucine are associated with incident type 2 diabetes (T2D). Prior genetic studies have provided conflicting results as to whether the BCAAs are causally related to T2D but are limited by a small number of genetic instruments associated with BCAA levels. Hypothesis: We hypothesized that polygenic predictors that capture a broad set of genetic mechanisms regulating BCAA levels would clearly define the etiological relationship with T2D. Methods: We constructed and validated instrumental variables for each BCAA using well-powered datasets and tested their association with T2D using two-sample inverse variance weighted (IWV) Mendelian randomization (MR). Sensitivity analyses were performed to ensure the accuracy of the findings. We also tested whether instrumental variables for T2D, fasting insulin, and body mass index (BMI) were associated with BCAA levels. Finally, logistic regression was used to test whether a polygenic risk score (PRS) for T2D was associated with measured BCAA levels among 1220 participants in the Framingham offspring study (FOS) without prevalent T2D. Results: There were no significant associations with T2D for valine (beta=0.17 change in log-odds per standard deviation change in valine, [95% CI, -0.28 - 0.62], p=0.45), leucine (beta=0.19 [-0.30 - 0.68] p=0.45) or isoleucine (beta=0.02 [-0.54 - 0.59], p=0.94). In contrast, T2D was associated with each BCAA (valine: beta=0.08 per standard deviation change in levels per log-odds change in type 2 diabetes, [0.05 - 0.10], p=1.8x10 -9 ), (leucine: beta= 0.06 [0.04 - 0.09], p=4.5x10 -8 ) and isoleucine (beta= 0.06 [0.04 - 0.08], p=2.8x10 -8 ). The T2D associations were replicated in an independent population but not in a second population where T2D cases were removed. Similarly, a T2D PRS was associated with measured BCAA levels in the FOS study but not when incident T2D cases were removed. Positive associations were seen for fasting insulin and BMI, and the BCAAs by MR, and, in multivariable MR analyses, T2D and fasting insulin had independent associations with each BCAA. Conclusions: These data suggest that BCAAs are not mediators of T2D risk but may rather serve as biomarkers of T2D and higher insulin levels.

1470-P: Novel Associations of Glycometabolic Traits in Ethnically Diverse Africans

Prior studies have identified genetic factors that impact an individual’s risk of developing type 2 diabetes (T2D). However, African ancestry is underrepresented in genetic studies despite the higher prevalence of T2D in African Americans compared to European or Asian Americans. To better understand risk factors for this health disparity, we used a unique dataset of T2D biomarkers (C-peptide and standing glucose) and ~5 million single nucleotide polymorphism genotypes from an ethnically diverse sampling of sub-Saharan Africans (N~1,250) to identify genetic variants associated with these traits. Because the individuals in our dataset practice traditional subsistence patterns and live in rural settings, there is less environmental heterogeneity than observed in urban settings. Between both traits, we uncovered hundreds of novel marginal associations (p<10−5) and three genome-wide significant associations (p<5×10−8), one with C-peptide (rs147703855, in DAAM1) and two with glucose (rs74085215, near KLC1; rs951516, intergenic). The minor alleles for these three associations are rare outside of Africa (<0.1%) but attain frequencies ~5-10% in at least one African ancestry group. Although none of these associations have been previously reported, the genes near the marginal C-peptide associations (<200kbp) are enriched for variants with previously reported T2D associations (GWAS catalogue), as well as chylomicron, VLDL, and triglyceride associations. The genes near our marginal glucose associations were not found to be enriched for previously reported T2D associations (p>0.05), though they were significantly enriched for body size, chylomicron, and triglyceride associations (p<0.006). We are following up this preliminary study with an expanded GWAS using a new proteomics dataset in ~500 subjects, and a metabolomics dataset in ~700 subjects. These data will be used to construct polygenic risk scores for biomarkers of T2D that are more effective in people of recent African descent. Disclosure M.Hansen: None. D.Harris: None. A.M.Harris: None. T.B.Nyambo: None. G.Belay: None. S.Mpoloka: None. C.Burant: Research Support; Rhythm Pharmaceuticals, Inc. S.A.Tishkoff: None. Funding American Diabetes Association (1-19-VSN-02 to S.A.T.); National Institutes of Health (R35GM134922)

Longitudinal Characterization of the Gut Microbiota in the Diabetic ZDSD Rat Model and Therapeutic Potential of Oligofructose.

The complex development of type 2 diabetes (T2D) creates challenges for studying the progression and treatment of the disease in animal models. A newly developed rat model of diabetes, the Zucker Diabetic Sprague Dawley (ZDSD) rat, closely parallels the progression of T2D in humans. Here, we examine the progression of T2D and associated changes in the gut microbiota in male ZDSD rats and test whether the model can be used to examine the efficacy of potential therapeutics such as prebiotics, specifically oligofructose, that target the gut microbiota. Bodyweight, adiposity, and fed/fasting blood glucose and insulin were recorded over the course of the study. Glucose and insulin tolerance tests were performed, and feces collected at 8, 16, and 24 weeks of age for short-chain fatty acids and microbiota analysis using 16s rRNA gene sequencing. At the end of 24 weeks of age, half of the rats were supplemented with 10% oligofructose and tests were repeated. We observed a transition from healthy/nondiabetic to prediabetic and overtly diabetic states, via worsened insulin and glucose tolerance and significant increases in fed/fasted glucose, followed by a significant decrease in circulating insulin. Acetate and propionate levels were significantly increased in the overt diabetic state compared to healthy and prediabetic. Microbiota analysis demonstrated alterations in the gut microbiota with shifts in alpha and beta diversity as well as alterations in specific bacterial genera in healthy compared to prediabetic and diabetic states. Oligofructose treatment improved glucose tolerance and shifted the cecal microbiota of the ZDSD rats during late-stage diabetes. These findings underscore the translational potential of ZDSD rats as a model of T2D and highlight potential gut bacteria that could impact the development of the disease or serve as a biomarker for T2D. Additionally, oligofructose treatment was able to moderately improve glucose homeostasis.

Longitudinal characterization of the gut microbiota in the ZDSD rat model of diabetes

The complex development of type 2 diabetes (T2D) creates challenges for studying its progression in animal models, which often lack transition through the prediabetic state or fail to fully develop the multifaceted aspects of T2D. However, studying T2D development and progression in humans is costly with extensive limitations. A newly developed rat model of T2D, the Zucker Diabetic Sprague Dawley (ZDSD) rat, more closely parallels the progression of T2D in humans. Our goal was to examine T2D progression and the associated changes in the gut microbiota in the ZDSD model and test whether the model can be used to examine potential therapeutics targeting the gut microbiota. As physiological parameters like blood glucose and insulin levels can impact the gut microbiota and vice versa, we anticipate significant shifts in the gut microbiota as the ZDSD rats progress from a healthy to prediabetic to overt diabetic phenotype. Bodyweight, adiposity, and fed/fasting blood glucose and insulin were recorded throughout the progression of T2D in male ZDSD rats over 24 weeks. Glucose and insulin tolerance tests were performed, and feces collected for microbiota analysis using 16s rRNA gene sequencing at 8, 16, and 24 weeks. At the end of 24 weeks, half of the rats were supplemented with oligofructose (OFS) in their drinking water and tests repeated. Over the 24 weeks, we observed a transition from nondiabetic to prediabetic and overtly diabetic states, via worsened insulin and glucose tolerance and significant increases in fed/ fasted glucose values, followed by a significant decrease in circulating insulin. Microbiota analysis demonstrated alterations in the gut microbiota throughout the development of diabetes with shifts in alpha and beta diversity as well as alterations in specific bacterial genera in healthy compared to prediabetic and diabetic states. OFS treatment significantly increased body weight and adiposity, improved glucose tolerance, and shifted the cecal microbiota of the ZDSD rats during late-stage diabetes. These findings demonstrate the translational potential of ZDSD rats as a T2D model and highlight the potential for the gut microbiota to both impact the development of the disease, as well as serve as a biomarker for T2D. Additionally, OFS treatment was able to moderately improve glucose homeostasis in the ZDSD model. Arizona Biomedical Research Commission New Investigator Award (ADHS18-198857), National Institute of Food and Agriculture (67017-29252), and NIDDK (R01ES033993). This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Glycemic indices and effect of bitter leaf (Vernonia amygdalina) flavored non-alcoholic wheat beer (NAWB) on key carbohydrate metabolizing enzymes in high fat diet fed (HFD)/STZ-induced diabetic Wistar rats.

In a bid to make the use of functional food easier in the management and prevention of diseases, product development and fortification from/with functional foods have become the recent focus of research. This study, therefore, sought to exploit the recent trend in the brewing industry on the production of non-alcoholic beers by investigating the possibility of having a non-alcoholic beer flavored with bitter leaf, a known plant widely reported to have a strong hypoglycemic effect, as against the traditional use of hops, and the effect of the produced beer on the glycemic indices and various diabetic biochemical parameters that serve as biomarkers for type-2 diabetes (T2D). The glycemic indices, as well as the inhibitory potentials of bitter leaf-flavored Non-alcoholic wheat beer (NAWB) in ratios of 100%HP, 100%BL, 75:25BL, 50:50BL, and 25:75BL, on enzymes linked to a high-fat diet/streptozocin (HFD/STZ)-induced T2D albino Wistar rats were investigated. There were no significant difference (p > .05) between the starch (1.72-1.77 mg/100 mL), amylose (0.22-0.24 mg/100 mL), and amylopectin (1.49-1.53 mg/100 mL) contents of the various samples. The Glycemic Index (GI) of the samples ranged from 36 to 73 with 75:25Bl and 50:50BL have the lowest (36) values. The samples reduced blood glucose levels and inhibited pancreatic α-amylase, lipase, and intestinal α-glucosidase activity. The inhibitory potentials of these beer samples on α-amylase and α-glucosidase as well as their ability to reduce blood glucose levels in diabetic rats thus making the bitter leaf flavored NAWB a suitable healthy beverage for better glycemic control in diabetics. PRACTICAL APPLICATIONS: This study revealed the potential of producing non-alcoholic wheat beer flavored with bitter leaves as a possible substitute for hops. The potential inherent in bitter leaf in the management of type 2 diabetes can thus be made available through a far-reaching beverage medium such as non-alcoholic beer to help in the treatment/management of T2D. The results of this research could be an eye-opener to the possible utilization of bitter leaf and by extension other plants that have been reported in the management of T2D. The use of the bitter leaf as a substitute for hops in the production of non-alcoholic beer in the brewing industry could help in a health-oriented campaign for safe drinks that could be helpful in the control of blood glucose levels of diabetic patients.

Untargeted approach to investigating the metabolomics profile of type 2 diabetes emiratis

Type 2 Diabetes (T2D) is expected to be the seventh most significant cause of death worldwide by 2030. Although research into its mechanism has received the attention it deserves, our understanding of T2D is still limited. This case-control study employs untargeted metabolomics to explore novel T2D plasma biomarkers in the Emirati population. Ninety-two UAE nationals were included in the cohort, with fifty T2D and forty-two non-T2D profiles. Participants were then stratified into three groups based on metabolic profiles, clinically verified diabetic status, and current HbA1c values: namely controlled diabetics,uncontrolled diabetics and prediabetics, and non-diabetics. The study identified fifteen significant differentially abundant metabolites between the uncontrolled diabetics group and the prediabetics or controlled diabetics group. Interestingly, some metabolites essential for the corticosteroid and thyroid signaling pathways were found to be significantly elevated in poorly controlled T2D, including cortisol, glycocholic acid, bile acids, thyroxine, and the tryptophan metabolite, 5-hydroxyindoleacetic acid. These findings align with those from prior western cohorts and suggest an intriguing linkage between T2D glycemic control and thyroid and adrenal signaling that may provide new diagnostic and prognostic indicators. Research significanceThis study investigates the underlooked metabolomic role and correlation with T2D in the UAE population. The report indicates fifteen significant differentially abundant metabolites between on diabetics, uncontrolled diabetics and or controlled diabetics or prediabetics. This panel of metabolites such as thyroxine and corticosteroids should be considered further as potential diagnostic or prognostic biomarkers for T2D in the region.

Exploring biomarkers and transcriptional factors in type 2 diabetes by comprehensive bioinformatics analysis on RNA-Seq and scRNA-Seq data.

BackgroundType 2 diabetes (T2D) is a prevalent chronic disease with elusive. Combining transcriptome and single-cell sequencing data to explore biomarkers of T2D could provide new insights into the in-depth understanding of the molecular mechanisms and diagnosis of T2D.MethodsThe GSE41762 dataset including RNA-seq data for healthy and T2D patients, was obtained from the Gene Expression Omnibus (GEO) database. The potential functions of the differentially expressed genes (DEGs) were revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Moreover, biomarkers were screened out by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and receiver operating characteristic (ROC) analysis. Furthermore, single-cell RNA (sc-RNA)-seq data in the “E-MTAB-5061” dataset was downloaded from the ArrayExpress (European Bioinformatics Institute, EBI) database. Principal components analysis (PCA) and t-distributed stochastic neighbor embedding (tSNE) were used for dimensionality reduction analysis and cell clustering. The FindAllMarkers function was used annotate different cell clusters, and key cell clusters were screened by the expression levels of the biomarkers. Finally, the transcription factors (TFs) of the biomarkers were recognized.ResultsA total of 111 DEGs were screened in the GSE41762 dataset, which were mainly related to hormone secretion, specialized postsynaptic membrane, pancreatic secretion, JAK-STAT signaling pathway, and Ras signaling pathway. In addition, SLC2A2, SERPINF1, RASGRP1, and CHL1 were screened out as biomarkers of T2D, which possessed potential diagnostic value as AUC value greater than 0.8. A total of 1,515 T2D group cells and 1,817 healthy cohort cells were screened as core cells in the “E-MTAB-5061” dataset. Following tSNE dimensionality reduction cluster analysis, the core cells were divided into 13 cell clusters. According to the marker genes, the 13 cell clusters were annotated into six types of cells. Notably, SERPINF1 was highly expressed in fibroblasts and might be regulated by NR2F2 (nuclear receptor subfamily2, group F, and member 2).ConclusionsThis study identified four biomarkers (SLC2A2, SERPINF1, RASGRP1, and CHL1) for T2D, which provided new markers for the clinical diagnosis of T2D. Among them, SERPINF1 might be regulated by NR2F2, which provides valuable insight into the pathogensis of T2D.

Investigating the Molecular miRNAs as Biomarkers of Type 2 Diabetes

Introduction: Biomarkers would significantly improve the early detection of the disease and identification of individuals at risk of emerging complications. Diabetes mellitus is a group of diverse and complex metabolic disorders. Both type 1 diabetes (T1D) and type 2 diabetes (T2D) mellitus are associated with distinct alterations in the profile of MicroRNAs (miRNAs) in the blood, which are sometimes detectable several years before the disease manifests. Lately, considerable attention has been paid to the role of miRNAs as biomarkers for T2D. The aim of this study was to review the expression of different miRNAs in pre-diabetic (per-T2D), T2D and healthy groups.
 Conclusion After evaluating several articles, including main articles, meta-analysis and review studies, it was found that the expression of micRNAs was statistically different in healthy, pre-T2D and T2D groups. In addition, the expression of specific miRNAs is useful in preventing disease and modifying gene structure.
 This study indicated that the plasma expression level of miRNAs could be considered as a non-invasive and fast tool for the separation of pre-T2D individuals from their healthy counterparts.

CircRNA hsa_circ_0071336 is associated with type 2 diabetes through targeting the miR-93-5p/GLUT4 axis.

Glucose transporter 4 (GLUT4) is a dominant regulator of whole-body glucose homeostasis. Accumulating evidence has shown that circular RNAs (circRNAs) play significant roles in the pathogenesis of disease. The aim of the present study was to identify the circRNA that can be used as a novel biomarker for type 2 diabetes (T2D) through regulating GLUT4. Based on previous microarray analysis comparing T2D cases and healthy controls, hsa_circ_0071336, which was predicted to be a regulator of GLUT4 by acting as a competitive endogenous RNAs (ceRNA) to sponge miR-93-5p, was selected for further validation. The clinical significance of circulating hsa_circ_0071336 was investigated in a large independent cohort. The results showed that circulating hsa_circ_0071336 was significantly downregulated in blood in T2D and had a high diagnostic accuracy for discriminating T2D and impaired fasting glucose (IFG) from healthy controls. Low expression of circ_0071336 was an independent predictor of T2D, IFG and insulin resistance. A luciferase reporter assay and western-blot analysis indicated that miR-93-5p was a direct target of hsa_circ_0071336, and miR-93-5p may negatively regulate the expression of GLUT4. The expression levels of hsa_circ_007136 were negatively related to miR-93-5p expression and positively correlated with the mRNA expression of GLUT4 in adipose tissues. In conclusion, hsa_circRNA_0071336 can be considered as a potential novel and stable biomarker for T2D and its early detection. hsa_circ_0071336 regulates the GLUT4 expression by sponging miR-93-5p and maybe involved in the pathogenesis of T2D. These findings may unveil new targets for the prevention, diagnosis and treatment of T2D.

Circulating Proteome and Progression of Type 2 Diabetes.

Circulating proteomes may provide intervention targets for type 2 diabetes (T2D). We aimed to identify proteomic biomarkers associated with incident T2D and assess its joint effect with dietary or lifestyle factors on the T2D risk. We established 2 nested case-control studies for incident T2D: discovery cohort (median 6.5 years of follow-up, 285 case-control pairs) and validation cohort (median 2.8 years of follow-up, 38 case-control pairs). We integrated untargeted mass spectrometry-based proteomics and interpretable machine learning to identify T2D-related proteomic biomarkers. We constructed a protein risk score (PRS) with the identified proteomic biomarkers and used a generalized estimating equation to evaluate PRS-T2D relationship with repeated profiled proteome. We evaluated association of PRS with trajectory of glycemic traits in another non-T2D cohort (n = 376). Multiplicative interactions of dietary or lifestyle factors with PRS were evaluated using logistic regression. Seven proteins (SHBG, CAND1, APOF, SELL, MIA3, CFH, IGHV1-2) were retained as the proteomic biomarkers for incident T2D. PRS (per SD change) was positively associated with incident T2D across 2 cohorts, with an odds ratio 1.29 (95% CI, 1.08-1.54) and 1.84 (1.19-2.84), respectively. Participants with a higher PRS had a higher probability showing unfavored glycemic trait trajectory in the non-T2D cohort. Red meat intake and PRS showed a multiplicative interaction on T2D risk in the discovery (P = 0.003) and validation cohort (P = 0.017). This study identified proteomic biomarkers for incident T2D among the Chinese populations. The higher intake of red meat may synergistically interact with the proteomic biomarkers to exaggerate the T2D risk.

Organ fat in Latino youth at risk for type 2 diabetes.

Obesity in youth increases the risk for type 2 diabetes (T2D) and elevated abdominal adipose tissue and organ fat may be particularly deleterious. The purpose of this study was to examine associations among measures of adiposity including total, visceral, and organ fat (hepatic and pancreatic) and whether these measures were independently associated with glycemia in Latino youth at risk for diabetes. Latino adolescents (47 boys and 32 girls, 13.7 ± 1.4 years) with obesity (BMIz 2.3 ± 0.3) were assessed for total fat by DXA and visceral and organ fat by 3 T magnetic resonance imaging. Glycemic indicators included HbA1c, fasting glucose (FG), and 2-h glucose (2-HrG) following an oral glucose tolerance test. Pearson correlations and stepwise linear regression analyses controlling for age and sex were used to examine independent associations between adiposity and glycemia. Total fat was associated with visceral (r=0.66, p=0.001) and hepatic fat (r=0.34, p < 0.01) while visceral fat was associated with hepatic (r=0.42, p < 0.001) and pancreatic fat (r=0.36, p < 0.001). In stepwise linear regression analysis, hepatic and pancreatic fat were significant predictors of FG, explaining 4.7% and 5.2% of the variance, respectively (total R2 =0.14, p=0.02). Hepatic fat was the only significant predictor of 2-HrG explaining 9.9% of the variance in the model (total R2 =0.12, p=0.03). No measure of adiposity was retained as a significant predictor of HbA1c. Hepatic and pancreatic fat were the only adiposity measures independently associated with glycemia but the small amount of variance explained underscores the need for additional T2D biomarkers in high risk youth.

Markers, Pathways, and Current Evidence for Periodontitis-associated Insulin Resistance: A Narrative Review.

The aim of the present paper is to provide a narrative review of the markers and pathways of periodontitis-associated insulin resistance (IR). Research papers published in peer-reviewed scientific journals from 2000 to 2021 were searched systematically in Online Cochrane Library, Google Scholar, and MedLine/PubMed database. The medical subject headings (MeSH) terms used for literature search were "diabetes AND periodontal disease," "diabetes AND periodontitis," "inflammation AND insulin resistance," "Insulin resistance AND periodontal disease," and "insulin resistance AND periodontitis." Manual search for applicable work in review article peer-reviewed print journals, and latest editions of standard textbooks of pharmacology and pathology were searched for updated additional information. Relevant papers in English language on the topic and abstracts of pertinent articles after excluding the duplicates, animal studies, and in-vitro studies were also scrutinized thoroughly and finally included as required in this narrative review. Literature search in MedLine/PubMed with MeSH words mentioned above revealed 4,621, 4,993, 19,349, 414, and 434 papers, respectively. Seven out of 13 systematic reviews and a total of 18 randomized clinical trials to evaluate periodontitis-induced IR were short-listed to update current evidences. The current literature in the past two decades has evaluated the effect of periodontal therapy on various type-2 diabetes (T2D) biomarkers following periodontal therapy. These indicators of periodontal disease activity and surrogate biomarkers of T2D in periodontitis may be an important diagnostic tool for the early prediction of complications due to IR. This increased systemic burden of proinflammatory cytokines by periodontitis can be reduced by periodontal therapy, thus improving the patient's overall systemic condition. The inflammatory response in periodontitis is characterized by dysregulated secretion of host-derived mediators of inflammation and tissue breakdown that may lead to IR. It can be comprehended that periodontal disease is a recognized amendable risk factor for T2D.

Omics era in type 2 diabetes: From childhood to adulthood.

Parallel to the dramatic rise of pediatric obesity, estimates reported an increased prevalence of type 2 diabetes (T2D) already in childhood. The close relationship between obesity and T2D in children is mainly sustained by insulin resistance (IR). In addition, the cardiometabolic burden of T2D including nonalcoholic fatty liver disease, cardiovascular disease and metabolic syndrome is also strictly related to IR. Although T2D pathophysiology has been largely studied in an attempt to improve therapeutic options, molecular mechanisms are still not fully elucidated. In this perspective, omics approaches (including lipidomics, metabolomics, proteomics and metagenomics) are providing the most attractive therapeutic options for T2D. In particular, distinct both lipids and metabolites are emerging as potential therapeutic tools. Of note, among lipid classes, the pathogenic role of ceramides in T2D context has been supported by several data. Thus, selective changes of ceramides expression might represent innovative therapeutic strategies for T2D treatment. More, distinct metabolomics pathways have been also found to be associated with higher T2D risk, by providing novel potential T2D biomarkers. Taken together, omics data are responsible for the expanding knowledge of T2D pathophysiology, by providing novel insights to improve therapeutic strategies for this tangled disease. We aimed to summarize the most recent evidence in the intriguing field of the omics approaches in T2D both in adults and children.

Abstract 11848: Biomarkers of Glucose-Insulin Homeostasis, Randomized Treatment With Omega-3 and Vitamin D Supplementation, and Incident Type 2 Diabetes: Prospective Analysis From the Vitamin D and Omega-3 Trial (VITAL)

Introduction: Insulin resistance is central to the development of type 2 diabetes (T2D). Association between specific biomarkers of glucose-insulin homeostasis and T2D have been inconsistent. Whether vitamin D or omega-3 fatty acids (n-3 FA) can modify these associations is also unclear. Hypothesis: We hypothesized that higher insulin, C-peptide, insulin resistance score (IRS), and HbA1c are each associated with higher T2D risk, and that these associations would be attenuated among participants who received vitamin D or n-3 FA. Methods: VITAL Diabetes (NCT01633177) was a pre-specified ancillary 2x2 factorial study in the VITAL RCT to assess whether vitamin D (cholecalciferol, 2000 IU/d) or n-3 FA (EPA + DHA, 1 g/d) could prevent T2D. Subjects without baseline reported T2D and with HbA1c &lt;6.5% were included. Incident cases were matched 1:1 on age and sex to healthy controls (N=359 pairs). Conditional logistic regression models with adjustment for demographics, risk factors including BMI, and randomized treatment were used to estimate the adjusted odds ratio (aOR) and 95% CI between baseline measurements of insulin, C-peptide, IRS (Insulinх0.0265+C-peptideх0.00511+Creatinineх-3.2641), HbA1c, and T2D. Interaction was tested between each biomarker and treatment groups (placebo vs vitamin D or n-3 FA). Results: Mean age of participants was 66 years, approximately 45% were female, and 16% were African American. Each of the glucose-insulin homeostasis biomarkers was associated with T2D risk ( Table 1 ), aOR (95% CI) per SD increment, insulin: 1.46 (1.10, 1.94), C-peptide: 1.72 (1.28, 2.31), IRS: 1.59 (1.26, 2.01), and HbA1c: 80.43 (25.90, 249.77). Biomarker associations did not differ by randomized treatment ( P interaction &gt;0.05). Conclusions: In VITAL Diabetes, insulin, C-peptide, IRS, and HbA1c were each associated with an increased risk of T2D, independent of other risk factors. Randomization to either vitamin D or n-3 FA did not modify these associations.

Identification of pathognomonic purine synthesis biomarkers by metabolomic profiling of adolescents with obesity and type 2 diabetes

The incidence of type 2 diabetes is increasing more rapidly in adolescents than in any other age group. We identified and compared metabolite signatures in obese children with type 2 diabetes (T2D), obese children without diabetes (OB), and healthy, age- and gender-matched normal weight controls (NW) by measuring 273 analytes in fasting plasma and 24-hour urine samples from 90 subjects by targeted LC-MS/MS. Diabetic subjects were within 2 years of diagnosis in an attempt to capture early-stage disease prior to declining renal function. We found 22 urine metabolites that were uniquely associated with T2D when compared to OB and NW groups. The metabolites most significantly elevated in T2D youth included members of the betaine pathway, nucleic acid metabolism, and branched-chain amino acids (BCAAs) and their catabolites. Notably, the metabolite pattern in OB and T2D groups differed between urine and plasma, suggesting that urinary BCAAs and their intermediates behaved as a more specific biomarker for T2D, while plasma BCAAs associated with the obese, insulin resistant state independent of diabetes status. Correlative analysis of metabolites in the T2D signature indicated that betaine metabolites, BCAAs, and aromatic amino acids were associated with hyperglycemia, but BCAA acylglycine derivatives and nucleic acid metabolites were linked to insulin resistance. Of major interest, we found that urine levels of succinylaminoimidazole carboxamide riboside (SAICA-riboside) were increased in diabetic youth, identifying urine SAICA-riboside as a potential biomarker for T2D.