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Abstract #4937

Predictive diagnosis of Major Depressive Disorder using integrated NMR based metabolomics and logistic regression model approach

Ritu Tyagi1, Vishwa Rawat1, Gagan Hans2, Pratap sharan2, S Senthil Kumaran1, and Uma Sharma1
1Department of NMR, All India Institute of Medical Sciences (AIIMS), New Delhi, India, 2Department of Psychiatry, All India Institute of Medical Sciences (AIIMS), New Delhi, India

Synopsis

Keywords: Psychiatric Disorders, Spectroscopy, NMR based metabolomics, Logistic regression analysis, blood serum, neuroinflammationThe current diagnosis for Major Depressive disorder (MDD) is dependent on symptomatic clusters and resulting high error rates. The study identifies a panel of biomarkers using 1H NMR spectroscopy and logistic regression prediction modelling. The VIP score of >1.5 and S-plot based on OPLS-DA depicted 4 significant metabolites (phosphocreatine, phosphocholine, glycerophosphocholine and glutamine) indicating abnormalities in energy and lipid metabolism. Phosphocreatine showed the highest AUC of 0.875 with 90% sensitivity and specificity, while with a combination of 4 metabolites, the AUC increased to 0.927 with 96.3% sensitivity and 87.5% specificity, which may act as a supplementary diagnostic tool for MDD.

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