Truace
Subscribe

Blood test plus AI sorted depression severity with 95.3% accuracy in a 150-person study

Study title: Serum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder

The diagnosis of major depressive disorder (MDD) currently relies on subjective clinical assessment, which leaves a need for objective biomarkers. Glycosylation, a common post-translational modification, is involved in neuroinflammation and immune regulation, both implicated in MDD pathogenesis.

Read the study abstract

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…

Truvace record1 min read1 sourceEvidence: Moderate · 60/100 — Observational study · n = 150 · 1 independent sourcePeer-reviewedTRV-2026-1164 · cite or check it
Serum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder
Hospital Universitari Doctor Peset, València 08 by 19Tarrestnom65. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

In brief

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.

Main points

  1. The diagnosis of major depressive disorder (MDD) currently relies on subjective clinical assessment, which leaves a need for objective biomarkers.
  2. Glycosylation, a common post-translational modification, is involved in neuroinflammation and immune regulation, both implicated in MDD pathogenesis.
  3. This study aimed to identify serum protein glycopatterns as biomarkers for MDD diagnosis and severity stratification.

The gain

In a 150-person study, an AI model using blood sugar-protein patterns told healthy people from mild-to-moderate and severe depression with 95.3% accuracy in cross-validation.

The full story

The diagnosis of major depressive disorder (MDD) currently relies on subjective clinical assessment, which leaves a 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

  1. Peer-reviewedGlycobiology2026-09-21

The debate