Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The present study was performed to investigate the validity and reliability of the Korean version of the THINC-it tool (THINC-it-K) in adult patients with major depressive disorder (MDD). Methods: Subjects aged 19−65 years with recurrent MDD experiencing moderate to severe major depressive episode (n = 44) were evaluated and compared to age and sex matched healthy controls (n = 44). Subjects completed the THINC-it-K which includes variants of the Identification Task (IDN) using Choice Reaction Time, One-Back Test, Digit Symbol Substitution Test, Trail Making Test-Part B, and the Perceived Deficits Questionnaire for Depression-5-item (PDQ-5-D). Results: A total of 75.0% of patients with MDD exhibited cognitive performance 1 standard deviation or below. The differences in Spotter (p = 0.001), Codebreaker (p = 0.001), PDQ-5-D (p ＜ 0.001) and objective THINC-it-K composite score (p = 0.002) were significant between the two groups. Concurrent validity of the THINC-it-K based on a calculated composite score was good (r = 0.856, p ＜ 0.001), and ranges for each component tests were from 0.076 (IDN) to 0.928 (PDQ-5-D). Conclusion The THINC-it-K exhibits good reliability and validity in adults with MDD. It could be a useful tool for the measurement of cognitive deficits in persons with MDD and should be implemented in clinical practice.

Depression ; Depressive Disorder, Major ; Employment ; Humans

Humans ; COVID-19 ; Pandemics ; Grief ; SARS-CoV-2

COVID-19/psychology* ; Global Health/statistics & numerical data* ; Health Personnel/statistics & numerical data* ; Humans ; Mental Disorders/etiology* ; Mental Health/statistics & numerical data* ; Occupational Diseases/psychology*

OBJECTIVE: The purpose of this study was to compare the efficacy and safety of escitalopram, paroxetine and venlafaxine in Korean patients with major depressive disorder (MDD). METHODS: A total of 449 Korean MDD patients were recruited in a six-week, randomized, rater-blinded, active-controlled trial and were evenly randomized to paroxetine, venlafaxine, or escitalopram treatment. RESULTS: When comparing the mean difference for the Montgomery-Åsberg Depression Rating Scale (MADRS) and the Hamilton Depression Rating Scale (HDRS) total scores during six weeks, paroxetine (−6.4±0.4, and −5.4±0.4, respectively) was found to be significantly superior to escitalopram (−3.7±0.5 and −3.1±0.4, respectively). Venlafaxine had a significantly lower MADRS total score (−5.4±0.4) than escitalopram. When adjusting baseline variables, the response, according to the MADRS and HDRS scores, in the paroxetine group was greater than that for the escitalopram group (odds ratio [OR]=2.43, 95% confidence interval [CI]=1.42–4.16 for MADRS; and OR=2.32, 95% CI=1.35–3.97 for HDRS) and the venlafaxine group (OR=1.94, 95% CI=1.17–3.21 for MADRS; and OR=1.71, 95% CI=1.03–2.83 for HDRS). Despite that the overall tolerability was high and similar among the three groups, a total of 268 subjects (59.7%) prematurely discontinued treatment, representing the main limitation of the present study. CONCLUSION: Although a low study completion rate limits generalizability, our findings suggest that paroxetine might be superior to escitalopram in Korean MDD patients. Further studies should be conducted to draw a definite conclusion.
Citalopram* ; Depression ; Depressive Disorder, Major* ; Humans ; Paroxetine* ; Venlafaxine Hydrochloride*