Progression of early diagnostic markers for diabetic kidney disease: From single-indicator detection to multi-omics integration modeling.
Level 5 - mechanism / opinion, no new human data
Narrative review without systematic review methodology or primary data
PubMed 42633262 · doi:10.1016/j.crphys.2026.100190
What was done
This narrative review synthesized recent advances in early diagnostic markers for diabetic kidney disease. The authors examined the limitations of conventional indicators such as urinary albumin and glomerular filtration rate, reviewed emerging omics biomarkers, and outlined machine learning strategies for multi-omics integration.
What was found
The abstract reports no numerical findings, sensitivity, specificity, or performance statistics. It qualitatively concludes that traditional markers have insufficient sensitivity and susceptibility to interference, while multi-omics integration provides a framework for early risk assessment.
Why it matters
The paper summarizes the rationale for shifting from single-indicator clinical tests to multi-omics predictive modeling to identify diabetic kidney disease before significant renal impairment occurs.
Limits
This is a narrative review presenting no primary empirical data, study counts, or quantitative diagnostic accuracy metrics. The absence of systematic review methodology precludes assessment of search bias or certainty of evidence.
Cited by
- supports Diabetes is the leading cause of kidney destruction and kidney disease.