MLXIPL/ChREBP Causal Inference Data for Diabetic Kidney Disease

11.3 KB of integrated genetic and transcriptomic data supporting MLXIPL/ChREBP as a driver of diabetic kidney disease. The dataset, authored by Mingliang Liu and last updated in April 2026, includes results from Mendelian randomization, kidney transcriptomic stratification, and network analyses. It was generated to disentangle the causal role of the glucose-responsive transcription factor in renal injury and maladaptive metabolic remodeling.

Use Cases

• Validate causal gene-disease relationships based on Mendelian randomization results for MLXIPL, T2D, and UACR.
• Analyze metabolic and profibrotic pathway enrichment based on MLXIPL-high/low stratification within diabetic kidney disease transcriptomes.
• Study gene regulatory networks based on the inferred dense metabolic interaction landscape centered on MLXIPL/ChREBP.
• Correlate experimental validation findings from db/db mice and PTECs with the human genetic and transcriptomic data.

Strengths

• Integrated multi-method evidence from Mendelian randomization, transcriptomics, network analysis, and experimental validation.
• Dataset is openly licensed under CC-BY-4.0.
• Specific focus on confounding-aware transcriptomic stratification within diabetic kidney disease.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset is very small at 11.3 KB, indicating limited scope, likely containing summary results rather than raw data.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Integrated analysis of two-sample Mendelian randomization, kidney transcriptomics (GSE30529), network inference, and experimental validation in db/db mice and PTECs.

Time Range

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Freshness

Last updated 2026-04-15 05:43:02.

Geography

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