Dataset Information

RNA expression profiles of whole blood cells from a Han Chinese population with or without Type-2 Diabetes Mellitus or/and its complications in nephropathy and retinopathy

ABSTRACT: Our main purpose is to screen specific biomarkers for supporting diagnoses of type-2 diabetic nephropathy (T2DN), type-2 diabetic retinopathy (T2DR), and type-2 diabetes mellitus (T2D) without these complications. The RNA expression database was built to study T2D in humans with the performance of the Clariom™ D Assay, human. The notable highlight of the analysis results shows there are three main groups which include one control group and two diabetic groups. Interestingly, the diabetic groups share some aging-related pathways, especially fatty acid metabolism. On the other hand, B group with higher BMI coverage has many abundant coding genes in fatty acid degradation, while C group with lower BMI coverage involves in fatty acid elongation. It can be classified into obese and non-obese groups proportionally. In addition, the obese group consists of two T2DN-related cases without an independent T2DR_related case, which is contrary to the non-obese group has all three cases. It means that T2DR can be rarely found in overweight T2D-patients.

ORGANISM(S): Homo sapiens

PROVIDER: GSE189005 | GEO | 2023/07/07

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:The obese people with abnormal BMI are predisposed to insulin resistance and diabetes. At the same time, human subjects with obesity and high BMI that are otherwise insulin sensitive are an interesting group to study the underlying gene expression patterns which provide them with such protective phenotype. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between Î²-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the Î²-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation. Gene expression from muscle biopsies of Lean, Obese insulin sensitive (OIS), Obese insulin resistant (OIR) and obese T2D patients (T2D) were compared for differential expression between the groups (n=7 in each group)

Dataset Information

RNA expression profiles of whole blood cells from a Han Chinese population with or without Type-2 Diabetes Mellitus or/and its complications in nephropathy and retinopathy

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets