Integrated Causal Inference and Transcriptomics Data for Diabetic Kidney Disease Research

A 16.6 KB research document by Mingliang Liu, last updated on 2026-04-15. It integrates genetic inference, kidney transcriptomic stratification, network analyses, and experimental validation to investigate the role of the transcription factor ChREBP (MLXIPL) in diabetic kidney disease. The study uses data from Mendelian randomization, the GSE30529 kidney transcriptomics dataset, and validation experiments in db/db mice and primary proximal tubular epithelial cells.

Use Cases

• Conducting causal inference analysis based on Mendelian randomization results for MLXIPL expression, T2D risk, and urinary albumin-to-creatinine ratio.
• Performing gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) based on MLXIPL-high/low stratification within diabetic kidney disease transcriptomes.
• Investigating metabolic and profibrotic pathway activity based on the described transcriptomic signatures and network analyses.
• Validating computational findings based on experimental data from db/db mice and primary proximal tubular epithelial cells exposed to high glucose.

Strengths

• The study integrates multiple analytical methods: two-sample Mendelian randomization, transcriptomic stratification, network analysis, and experimental validation.
• Data includes a non-diabetic stratum for urinary albumin-to-creatinine ratio analysis, mentioned in the description.
• The document is openly licensed under CC-BY-4.0.

Limitations

• The dataset is a 16.6 KB DOCX file, indicating a very limited scope likely containing summary results and descriptions rather than raw data.
• Column-level documentation and sample data are unavailable, requiring manual inspection after download.
• Row count and the structure of any underlying data are unknown.

Provenance

Source

figshare

Collection Method

Integrated analysis of public genetic and transcriptomic datasets (e.g., GSE30529) with original experimental validation.

Time Range

The temporal coverage of the underlying datasets is not specified.

Freshness

Last updated 2026-04-15 05:43:03.

Geography

The geographic coverage of the underlying datasets is not specified.