Abstract
Since optimal treatment at an early stage leads to remission of symptoms and recovery of function, putative biomarkers leading to early diagnosis and prediction of therapeutic responses are desired. The current study aimed to use a metabolomic approach to extract metabolites involved in both the diagnosis of major depressive disorder (MDD) and the prediction of therapeutic response for escitalopram. We compared plasma metabolites of MDD patients (n = 88) with those in healthy participants (n = 88) and found significant differences in the concentrations of 20 metabolites. We measured the Hamilton Rating Scale for Depression (HRSD) on 62 patients who completed approximately six-week treatment with escitalopram before and after treatment and found that kynurenic acid and kynurenine were significantly and negatively associated with HRSD reduction. Only one metabolite, kynurenic acid, was detected among 73 metabolites for overlapped biomarkers. Kynurenic acid was lower in MDD, and lower levels showed a better therapeutic response to escitalopram. Kynurenic acid is a metabolite in the kynurenine pathway that has been widely accepted as being a major mechanism in MDD. Overlapping biomarkers that facilitate diagnosis and prediction of the treatment response may help to improve disease classification and reduce the exposure of patients to less effective treatments in MDD.
Highlights
Since optimal treatment at an early stage leads to remission of symptoms and recovery of function[9], putative biomarkers leading to early diagnosis and prediction of therapeutic responses are desired
This study demonstrated the potential of metabolomics to provide information on the early efficacy of sertraline[17] and another study (2013) reported that good therapeutic outcomes of major depressive disorder (MDD) were associated with low levels of branched-chain amino acids[18]
Based on the above points of view, the present study aims to extract markers that overlap diagnostic biomarkers of MDD and predictive biomarkers of treatment of escitalopram from many metabolites obtained by metabolomics
Summary
Since optimal treatment at an early stage leads to remission of symptoms and recovery of function, putative biomarkers leading to early diagnosis and prediction of therapeutic responses are desired. The current study aimed to use a metabolomic approach to extract metabolites involved in both the diagnosis of major depressive disorder (MDD) and the prediction of therapeutic response for escitalopram. Kynurenic acid was lower in MDD, and lower levels showed a better therapeutic response to escitalopram. Overlapping biomarkers that facilitate diagnosis and prediction of the treatment response may help to improve disease classification and reduce the exposure of patients to less effective treatments in MDD. Since optimal treatment at an early stage leads to remission of symptoms and recovery of function[9], putative biomarkers leading to early diagnosis and prediction of therapeutic responses are desired. Recent advances in analytical chemistry have made this approach possible
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