Extreme phenotype-derived machine learning reveals susceptibility and resilience signatures for severe diabetic retinopathy.

doi:10.3389/fendo.2026.1933878

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Article

Extreme phenotype-derived machine learning reveals susceptibility and resilience signatures for severe diabetic retinopathy.

Frontiers in endocrinology - 1 Jan 2026

Zhang Jing-Yi, Shu Yue-Ming, Yang Fang-Yuan, Cai Si-Wei, Xie Rong-Rong, Sun Ran, Tian Wei, Zhu Xiao-Rong

Abstract excerpt

Background: The mechanisms underlying why some individuals with diabetes develop severe diabetic retinopathy (DR), whereas others remain free of retinal complications despite long-standing disease, remain incompletely understood. We aimed to identify systemic susceptibility and resilience signatures associated with severe DR using an extreme phenotype-derived machine learning framework. Methods: An extreme...

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Topics

Humans
Diabetic Retinopathy
Female
Phenotype
Machine Learning
Male

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Middle Aged

Disease Susceptibility

Diabetes Mellitus, Type 2

Aged

Severity of Illness Index

Predictive Learning Models

Cohort Studies

Source

Open in PubMed Open full text

Identifiers

PMID: 42723850
DOI: 10.3389/fendo.2026.1933878
PMCID: PMC13557971

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