Diabetic Neuropathy Biomarker Analysis: Mendelian Randomization and Bioinformatics Results

Seven plasma proteins were identified as significantly associated with diabetic neuropathy (DN) through Mendelian randomization analysis of 4,907 proteins. The dataset, authored by Jiannan Li and last updated on 2026-04-27, contains results from bioinformatics analyses, including differential gene expression from a cohort of 53 DN patients and 53 healthy donors. It integrates findings from machine learning algorithms and validation in a DN mouse model.

Use Cases

• Validate causal protein biomarkers for diabetic neuropathy based on Mendelian randomization results.
• Perform functional enrichment analysis on key genes like HSPB1, KRT14, and SFN mentioned in the description.
• Train diagnostic models for diabetic neuropathy using the identified biomarker signatures.
• Investigate molecular mechanisms in pathways such as the Wnt Signaling Pathway or MAPK Cascade based on the enrichment results.

Strengths

• Analysis is based on Mendelian randomization of 4,907 plasma proteins, providing a broad screening scope.
• Findings are validated through multiple methods, including Bayesian colocalization, two machine learning algorithms (LASSO and Random Forest), and an animal model.
• The underlying gene expression data (GSE148061) includes 53 diabetic neuropathy patients and 53 healthy controls.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset is small at 139.1 KB, indicating limited scope, likely containing summary results rather than raw data.
• Last updated 2026-04-27 04:14:47; freshness should be verified as the date is in the future relative to the current time.

Provenance

Source

figshare, author Jiannan Li

Collection Method

Mendelian randomization using GWAS data, bioinformatics analysis of GEO dataset GSE148061, and validation via machine learning and animal models.

Freshness

2026-04-27 04:14:47