Proteomics-based mortality prediction modeling in type 2 diabetes: new promise for personalized treatment and prevention

• Published in: BMC medicine Vol. 23; no. 1; pp. 17 - 3
• Main Authors: Gan, Lu, Wang, Changde, Liu, Xiaoying
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 21.01.2025; BioMed Central; BMC
• Subjects: Accuracy; Analysis; Biological activity; Biomarkers; Blood vessels; Cancer; Care and treatment; Comment; Complications and side effects; Customization; Damage assessment; Damage prevention; Deep learning; Diabetes; Diabetes mellitus (non-insulin dependent); Disease prevention; Growth factors; Health risks; Insulin resistance; Insulin-like growth factors; Medical research; Metabolism; Mortality; Mortality risk; Plasma; Prediction models; Prevention; Proteins; Proteomics; Risk factors; Type 2 diabetes; China
• ISSN: 1741-7015; 1741-7015
• DOI: 10.1186/s12916-024-03803-3

The study highlighted the role of immune and inflammatory processes in mortality across both cohorts. [...]the identification of a T2D-specific mortality pathway related to insulin-like growth factor (IGF) transporter regulation suggests that unique biological processes within T2D patients may contribute significantly to early mortality risk. [...]despite the study’s robust double-validation approach, analyses of certain cause-specific deaths such as cancer-related mortality may suffer from insufficient statistical power due to limited sample sizes, follow-up duration, diabetes severity, and blood vessel damage assessments. [...]proteomics-based mortality prediction models represent a promising avenue for personalized treatment and prevention strategies.