Integrated Causal Inference and Transcriptomics Data for Diabetic Kidney Disease

A 19.3 KB DOCX file by Mingliang Liu, last updated April 15, 2026, under a CC-BY-4.0 license. The document presents research integrating Mendelian randomization, kidney transcriptomic stratification, and experimental validation to identify MLXIPL/ChREBP as a driver of metabolic remodeling in diabetic kidney disease.

Use Cases

• Validate causal gene-disease links based on Mendelian randomization analysis described in the document.
• Analyze metabolic pathway activity based on GSEA/GSVA stratification of kidney transcriptomes.
• Study gene regulatory networks in diabetic kidney disease based on the described network inference.
• Correlate genetic susceptibility with renal injury markers like UACR based on the reported findings.

Strengths

• File is small (19.3 KB), facilitating quick download and review.
• Research integrates multiple methodologies: Mendelian randomization, transcriptomics, network analysis, and experimental validation.
• Document is recent, last updated in April 2026.

Limitations

• Row count and column-level data are unknown, limiting suitability assessment.
• The dataset is a DOCX document, not a structured data file; analysis requires extracting information from text.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

figshare

Collection Method

Research data and analysis from an integrated study of diabetic kidney disease.

Time Range

Publication date suggests data likely from studies prior to 2026.

Freshness

Last updated 2026-04 15 05:42:59.

Geography

Global, as the study uses genetic and transcriptomic data from public repositories.