Supplementary Data for Diabetic Retinal Neurodegeneration Proteomics Study

Supplementary data supporting a multi-cohort study on diabetic retinal neurodegeneration (DRN). The data likely contains proteomic and clinical variables used to develop a machine learning-based prediction model (Pro-DRN). It was authored by Huangdong Li and last updated on June 2, 2026.

Use Cases

• Developing predictive models for diabetic retinal neurodegeneration based on plasma protein signatures.
• Validating proteomic biomarkers for diabetic eye disease across independent cohorts.
• Analyzing associations between plasma proteins and retinal nerve fiber layer thinning rates.

Strengths

• Data supports findings from a large discovery cohort of 1,492 participants.
• Model performance metrics are reported, including a C-index of 0.860 for the proteomics model.
• Includes cross-ethnic external validation in the UK Biobank (n=502).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Proteomic assessment was performed at a single time point, a noted methodological limitation.

Provenance

Source

figshare

Collection Method

Integrated high-throughput plasma proteomics with longitudinal optical coherence tomography (OCT) in two independent populations.

Freshness

Last updated 2026-06-02 17:40:40; freshness should be verified.

Geography

Includes participants from the Guangzhou Diabetic Eye Study (GDES) and the UK Biobank.