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TRUVACE RECORD VERSION
record: TRV-2026-1164
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-22T06:53:30.592519Z
status: published
lens: g_space
sector: health
headline: Serum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder
dek: The diagnosis of major depressive disorder (MDD) currently relies on subjective clinical assessment, underscoring the need for objective biomarkers. Glycosylation, a common post-translational modification, is involved in neuroinflammation and immune regulation, both implicated in MDD pathogenesis. This study aimed to identify serum protein glycopatterns as biomarkers for MDD diagnosis and severity stratification. Serum samples from 150 individuals, including healthy volunteers (HV, n = 38), mild-to-moderate MDD…
gain_title: Serum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder: These features were used to train seven machine-learning models, among which K-nearest neighbours (KNN) performed best, achieving 95.3% ± 2.7% accuracy and an AUC (Micro) of 0.990 ± 0.010 in a nested 5-fold cross-validation framework.
problem_title: (none)
trace_subject: (none)
gain_reading: Serum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder: These features were used to train seven machine-learning models, among which K-nearest neighbours (KNN) performed best, achieving 95.3% ± 2.7% accuracy and an AUC (Micro) of 0.990 ± 0.010 in a nested 5-fold cross-validation framework.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: The diagnosis of major depressive disorder (MDD) currently relies on subjective clinical assessment, underscoring the need for objective biomarkers. Glycosylation, a common post-translational modification, is involved in neuroinflammation and immune regulation, both implicated in MDD pathogenesis.

These features were used to train seven machine-learning models, among which K-nearest neighbours (KNN) performed best, achieving 95.3% ± 2.7% accuracy and an AUC (Micro) of 0.990 ± 0.010 in a nested 5-fold cross-validation framework. These results demonstrated that serum glycopatterns combined with machine learning provided a promising objective tool for MDD diagnosis and severity assessment.
limitation: 
tag: Evidence-backed gain
key_points: The diagnosis of major depressive disorder (MDD) currently relies on subjective clinical assessment, underscoring the need for objective biomarkers. | Glycosylation, a common post-translational modification, is involved in neuroinflammation and immune regulation, both implicated in MDD pathogenesis. | This study aimed to identify serum protein glycopatterns as biomarkers for MDD diagnosis and severity stratification.
rundown: The diagnosis of major depressive disorder (MDD) currently relies on subjective clinical assessment, underscoring the need for objective biomarkers. Glycosylation, a common post-translational modification, is involved in neuroinflammation and immune regulation, both implicated in MDD pathogenesis.

This study aimed to identify serum protein glycopatterns as biomarkers for MDD diagnosis and severity stratification. Serum samples from 150 individuals, including healthy volunteers (HV, n = 38), mild-to-moderate MDD (M-MDD, n = 52), and severe MDD (S-MDD, n = 60), were analyzed using lectin microarrays.
sources:
- peer_reviewed | Glycobiology | https://doi.org/10.1093/glycob/cwag080 | 2026-09-21
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