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*

Depression ; Depressive Disorder, Major ; Employment ; Humans

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

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.

Cognitive deficits in major depressive disorder (MDD) patients have been described in numerous studies. However, few reports have aimed to describe cognitive deficits in the remitted state of MDD and the mediational effect of cognitive deficits on occupational outcome. The aim of the current review is to synthesize the literature on the mediating and moderating effects of specific domains of cognition on occupational impairment among people with remitted MDD. In addition, predictors of cognitive deficits found to be vocationally important will be examined. Upon examination of the extant literature, attention, executive function and verbal memory are areas of consistent impairment in remitted MDD patients. Cognitive domains shown to have considerable impact on vocational functioning include deficits in memory, attention, learning and executive function. Factors that adversely affect cognitive function related to occupational accommodation include higher age, late age at onset, residual depressive symptoms, history of melancholic/psychotic depression, and physical/psychiatric comorbidity, whereas higher levels of education showed a protective effect against cognitive deficit. Cognitive deficits are a principal mediator of occupational impairment in remitted MDD patients. Therapeutic interventions specifically targeting cognitive deficits in MDD are needed, even in the remitted state, to improve functional recovery, especially in patients who have a higher risk of cognitive deficit.
Cognition ; Comorbidity ; Depression ; Depressive Disorder, Major* ; Education ; Executive Function ; Humans ; Learning ; Memory ; Negotiating ; Occupations

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.