Autoantibodies Directed Toward a Novel IA-2 Variant Protein Enhance Prediction of Type 1 Diabetes

Castillo

Ann Pediatr Endocrinol Metab

et al. 2022

Purpose: Type 1 diabetes (T1D) is the most common type of diabetes in children, but the frequency of type 2 diabetes (T2D) is increasing rapidly. Classification of diabetes is based on a constellation of features that are typical of each type. We aimed to compare demographic, clinical and laboratory characteristics at diabetes diagnosis in pediatric T1D and T2D. Methods:We studied children who attended a large academic hospital in Houston, Texas (USA) with a new diagnosis of T2D (n=753) or T1D (n=758). We compared age, sex, race/ethnicity, presence of obesity, glucose, hemoglobin A1c, islet autoantibody positivity, C-peptide, and presence of diabetic ketoacidosis (DKA) at diabetes diagnosis.Results: At diagnosis of diabetes, children with T2D, compared with those with T1D, were older (13.6 vs 9.7 years old), more likely females (63.2% vs 47.8%), of racial/ethnic minority (91.1% versus 42.3%) and obese (90.9% vs 19.4%), and were less likely to have DKA (7.8% vs 35.0%) and diabetes autoantibodies (5.5% vs 95.4%). Children with T2D also had significantly less marked elevation of glucose and hemoglobin A1c, and lower C-peptide levels (all comparisons, p<0.0001). In multiple logistic regression analysis, older age, racial/ethnic minority, obesity, higher C-peptide and negative islet autoantibodies were independently associated with T2D (all, p<0.05) while sex, glucose, hemoglobin A1c and DKA were not (model p<0.0001).

Section: Discussionmentioning

confidence: 99%

Demographic and diagnostic markers in new onset pediatric type 1 and type 2 diabetes: differences and overlaps

Castillo

Ann Pediatr Endocrinol Metab

et al. 2022

“…Many members of this family have been reported to be closely related to tumors (Duś-Szachniewicz et al, 2015;Zhang et al, 2018;Bloch et al, 2019). PTPRN has been identified as an autoantigen that reacts with insulin-dependent diabetes mellitus (IDDM) patient sera (Lan et al, 1994;Acevedo-Calado et al, 2019). Only a few studies have reported a relationship with tumors, such as human midgut carcinoids and small cell lung cancer (Cunningham et al, 2000;Xu et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Identification of a Five-mRNA Signature as a Novel Potential Prognostic Biomarker for Glioblastoma by Integrative Analysis

Zhang

Xia

et al. 2022

Front. Genet.

Despite the availability of advanced multimodal therapy, the prognosis of patients suffering from glioblastoma (GBM) remains poor. We conducted a genome-wide integrative analysis of mRNA expression profiles in 302 GBM tissues and 209 normal brain tissues from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and the Genotype-Tissue Expression (GTEx) project to examine the prognostic and predictive value of specific mRNAs in GBM. A total of 26 mRNAs were identified to be closely related to GBM patients’ OS (p < 0.05). Utilizing survival analysis and the Cox regression model, we discovered a set of five mRNAs (PTPRN, ABCC3, MDK, NMB, and RALYL) from these 26 mRNAs that displayed the capacity to stratify patients into high- and low-risk groups with statistically different overall survival in the training set. The model of the five-mRNA biomarker signature was successfully verified on a testing set and independent sets. Moreover, multivariate Cox regression analysis revealed that the five-mRNA biomarker signature was a prognostic factor for the survival of patients with GBM independent of clinical characteristics and molecular features (p < 0.05). Gene set enrichment analysis indicated that the five-mRNA biomarker signature might be implicated in the incidence and development of GBM through its roles in known cancer-related pathways, signaling molecules, and the immune system. Moreover, consistent with the bioinformatics analysis, NMB, ABCC3, and MDK mRNA expression was considerably higher in four human GBM cells, and the expression of PTPRN and RALYL was decreased in GBM cells (p < 0.05). Our study developed a novel candidate model that provides new prospective prognostic biomarkers for GBM.

“…Islet autoantibodies were present in the vast majority (95.4%) of the children with T1D but also in 5.5% of the children with T2D. Potential reasons for the absence of autoantibodies in individuals with otherwise undistinguishable T1D could be the expression of autoantibodies that are not currently measured (12) or unrecognized forms of diabetes (13). Positivity for islet autoantibodies in a small percentage of children with clinically diagnosed T2D has been previously reported although its significance is still unclear (7; 14).…”

Section: Discussionmentioning

confidence: 99%

Comparison of Demographic and Diagnostic Markers in New Onset Pediatric Type 1 and Type 2 Diabetes

Castillo²,

Nieto³

et al. 2021

Preprint

Purpose Type 1 diabetes (T1D) is the most common type of diabetes in children, but the frequency of type 2 diabetes (T2D) is increasing rapidly. Classification of diabetes is based on a constellation of features that are typical of each type. We aimed to compare demographic, clinical and laboratory characteristics at diabetes diagnosis in pediatric T1D and T2D. Methods We studied children who attended a large academic hospital in Houston, Texas (USA) with a new diagnosis of T2D (n=753) or T1D (n=758). We compared age, sex, race/ethnicity, presence of obesity, glucose, hemoglobin A1c, islet autoantibody positivity, C-peptide, and presence of diabetic ketoacidosis (DKA) at diabetes diagnosis. Results At diagnosis of diabetes, children with T2D, compared with those with T1D, were older (13.6 vs 9.7% years old), more likely females (63.2% vs 47.8%), of racial/ethnic minority (91.1%% versus 42.3%) and obese (90.9% vs 19.4%), and were less likely to have DKA (7.8% vs 35.0%) and diabetes autoantibodies (5.5% vs 95.4%). Children with T2D also had significantly less marked elevation of glucose and hemoglobin A1c, and lower C-peptide levels (all comparisons, p<0.0001). In multiple logistic regression analysis, older age, racial/ethnic minority, obesity, higher C-peptide and negative islet autoantibodies were independently associated with T2D (all, p<0.05) while sex, glucose, hemoglobin A1c and DKA were not (model p<0.0001). Conclusions There are important demographic, clinical and laboratory differences between T1D and T2D in children with T1D. However, none of the characteristics was unique to either diabetes type, which poses challenges to diabetes classification at diagnosis.