Major Depressive Disorder and Chronic Fatigue Syndrome Show Characteristic Heart Rate Variability Profiles Reflecting Autonomic Dysregulations: Differentiation by Linear Discriminant Analysis.

Major depressive disorder (MDD) and chronic fatigue syndrome (CFS) have overlapping symptoms, and differentiation is important to administer the proper treatment. The present study aimed to assess the usefulness of heart rate variability (HRV) indices. Frequency-domain HRV indices, including high-frequency (HF) and low-frequency (LF) components, their sum (LF+HF), and their ratio (LF/HF), were measured in a three-behavioral-state paradigm composed of initial rest (Rest), task load (Task), and post-task rest (After) periods to examine autonomic regulation. It was found that HF was low at Rest in both disorders, but was lower in MDD than in CFS. LF and LF+HF at Rest were low only in MDD. Attenuated responses of LF, HF, LF+HF, and LF/HF to task load and an excessive increase in HF at After were found in both disorders. The results indicate that an overall HRV reduction at Rest may support a diagnosis of MDD. HF reduction was found in CFS, but with a lesser severity. Response disturbances of HRV to Task were observed in both disorders, and would suggest the presence of CFS when the baseline HRV has not been reduced. Linear discriminant analysis using HRV indices was able to differentiate MDD from CFS, with a sensitivity and specificity of 91.8% and 100%, respectively. HRV indices in MDD and CFS show both common and different profiles, and can be useful for the differential diagnosis.

Acute eosinophilic pneumonia-like syndrome post-initiation of vortioxetine.

A man in his mid-30s presented to the emergency department with a 1-week history of fatigue, loss of appetite, fever and productive (yellow) cough. This progressed to requiring admission to intensive care needing a oxygen therapy via high-flow nasal cannula for acute hypoxaemic respiratory failure. He had recently started vortioxetine for major depressive disorder, and his acute symptoms correlated with an increase in the dose of vortioxetine. For more than 20 years, rare but consistent reports of serotonergic medications have been implicated in eosinophilic pulmonary conditions. During this same period, serotonergic medications have become a mainstay solution for a wide range of depressive symptoms and disorders. This is the first report of an eosinophilic pneumonia-like syndrome occurring while consuming the novel serotonergic medication vortioxetine.

Identification of major depression patients using machine learning models based on heart rate variability during sleep stages for pre-hospital screening.

With the COVID-19 pandemic causing challenges in hospital admissions globally, the role of home health monitoring in aiding the diagnosis of mental health disorders has become increasingly important. This paper proposes an interpretable machine learning solution to optimise initial screening for major depressive disorder (MDD) in both male and female patients. The data is from the Stanford Technical Analysis and Sleep Genome Study (STAGES). We analyzed 5-min short-term electrocardiogram (ECG) signals during nighttime sleep stages of 40 MDD patients and 40 healthy controls, with a 1:1 gender ratio. After preprocessing, we calculated the time-frequency parameters of heart rate variability (HRV) based on the ECG signals and used common machine learning algorithms for classification, along with feature importance analysis for global decision analysis. Ultimately, the Bayesian optimised extremely randomized trees classifier (BO-ERTC) showed the best performance on this dataset (accuracy 86.32%, specificity 86.49%, sensitivity 85.85%, F1-score 0.86). By using feature importance analysis on the cases confirmed by BO-ERTC, we found that gender is one of the most important factors affecting the prediction of the model, which should not be overlooked in our assisted diagnosis. This method can be embedded in portable ECG monitoring systems and is consistent with the literature results.

Treatment-Resistant Late-Life Depression: A Review of Clinical Features, Neuropsychology, Neurobiology, and Treatment.

Major depression is common in older adults (≥ 60 years of age), termed late-life depression (LLD). Up to 30% of these patients will have treatment-resistant late-life depression (TRLLD), defined as depression that persists despite two adequate antidepressant trials. TRLLD is challenging for clinicians, given several etiological factors (eg, neurocognitive conditions, medical comorbidities, anxiety, and sleep disruption). Proper assessment and management is critical, as individuals with TRLLD often present in medical settings and suffer from cognitive decline and other marks of accelerated aging. This article serves as an evidence-based guide for medical practitioners who encounter TRLLD in their practice.

The effects of COVID-19 on European healthcare provision for working-age adults with major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a leading cause of disability worldwide, and yet delivery of care for this illness is rife with gaps. The COVID-19 pandemic has had far reaching implications for every facet of healthcare, and MDD is no exception. This scoping review aimed to ascertain the impacts of COVID-19 on the delivery of MDD care in Europe, as well as to evaluate any novel MDD care strategies trialled in this period. METHODS: We searched the PubMed and PsycINFO databases up to January 2022 with a strategy centred around COVID-19 and MDD. Full texts of eligible studies examining working-age adults and conducted in Europe were evaluated against several criteria. All outcomes were then extracted and a narrative synthesis was constructed to summarise identified themes. RESULTS: Of 1,744 records identified in our search, 11 articles were eligible for inclusion in the review. In general, these studies reported a decrease in treatment rates, access to care, and perceived access to care during the COVID-19 pandemic. In addition, digital interventions trialled during the pandemic were broadly well-received by users, though their efficacy in improving MDD care was ambiguous. CONCLUSIONS: Despite a limited number of pertinent studies, this scoping review identified a trend of exacerbated treatment gaps in MDD care during the pandemic. Several of our pre-specified gaps, including delays to detection or treatment of depression and rates of follow-up contacts, remained unexplored in the context of COVID-19. This highlights the need for further investigation to obtain a full understanding of the relationship between COVID-19 and MDD care in Europe.

Clinical experiences with intranasal esketamine for major depressive disorder resistant to treatment and with a psychiatric emergency: case presentations.

Recently, esketamine became availableas an intranasal formulation, proposed for treatment-resistant depression (TRD). Three cases of TRD are presented, two with features of a psychiatric emergency. The first case is a 35-year-old man with MDD onset at the age of 27 years, with five previous failed therapies. The second patient is a middle-aged man with a 21-year MDD onset and six previous antidepressant treatments discontinued for poor therapeutic effects and tolerability. He also presented suicidal ideation with intent and a history of a failed suicide attempt by self-cutting his forearms. The third case is a 28-year-old female with a first MDD episode in 2020, treated first with amitriptyline and then with intravenous clomipramine. She had a history of a previous suicide attempt by self-cutting and, by her admission, showed active suicidal ideation with intent. In all three cases, a rapid reduction of depressive symptoms was observed with a subsequent complete resolution of suicidal ideation and intent in the two patients with such risk. Intranasal esketamine treatment was carried out with concomitant oral antidepressant therapy. The third patient reported the only recorded side effect: dissociation 20 min after every esketamine administration. Our preliminary experience proved esketamine's effectiveness on TRD symptoms and successful outcomes in psychiatric emergencies such as suicide risk.

Prevalence of depression among students at a Sri Lankan University: A study using the Patient Health Questionnaire-9 (PHQ-9) during the COVID-19 pandemic.

BACKGROUND: The prevalence of mental health disorders is known to be high among university students globally. Currently there are only a few studies on depression among university students in Sri Lanka. The aim of this study was to screen for the prevalence of Major Depressive Disorder (MDD) and other forms of depression, and to evaluate the factors associated with MDD. METHODS: A cross sectional survey using the Patient Health Questionnaire (PHQ-9) was conducted among 637, second-year students from the faculties of Management Studies & Commerce, Science and Medicine at the University of Jaffna, during the Coronavirus (COVID-19) pandemic. Bivariate associations were assessed using chi-squared tests. Logistic regression was used to evaluate factors associated with any type of ragging. RESULTS: MDD was considered to have been experienced by 31% of the students. From all three faculties, 70% of the students claimed to have experienced some form of depression ranging from mild to severe. The factor associated with MDD was the students' ethnicity. CONCLUSION: Due to the high MDD risk among university students, it is imperative to develop psychosocial interventions to ensure early detection of mental health disorders and provide adequate support to safeguard this vulnerable population.

Development and validation of a machine learning-based vocal predictive model for major depressive disorder.

BACKGROUND: Variations in speech intonation are known to be associated with changes in mental state over time. Behavioral vocal analysis is an algorithmic method of determining individuals' behavioral and emotional characteristics from their vocal patterns. It can provide biomarkers for use in psychiatric assessment and monitoring, especially when remote assessment is needed, such as in the COVID-19 pandemic. The objective of this study was to design and validate an effective prototype of automatic speech analysis based on algorithms for classifying the speech features related to MDD using a remote assessment system combining a mobile app for speech recording and central cloud processing for the prosodic vocal patterns. METHODS: Machine learning compared the vocal patterns of 40 patients diagnosed with MDD to the patterns of 104 non-clinical participants. The vocal patterns of 40 patients in the acute phase were also compared to 14 of these patients in the remission phase of MDD. RESULTS: A vocal depression predictive model was successfully generated. The vocal depression scores of MDD patients were significantly higher than the scores of the non-patient participants (p < 0.0001). The vocal depression scores of the MDD patients in the acute phase were significantly higher than in remission (p < 0.02). LIMITATIONS: The main limitation of this study is its relatively small sample size, since machine learning validity improves with big data. CONCLUSIONS: The computerized analysis of prosodic changes may be used to generate biomarkers for the early detection of MDD, remote monitoring, and the evaluation of responses to treatment.

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset.

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi's fractal dimension, and Katz's fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

Social and Economic Factors Associated With Subthreshold and Major Depressive Episode in University Students During the COVID-19 Pandemic.

Depression is one of the most frequent mental health disorders in college students and variations according to social and economic factors have been reported, however, whether social and economic variations also exist in subthreshold depression is still unknown, especially during the COVID-19 pandemic. The aim of this study was to estimate the prevalence of subthreshold depressive episode (SDE) and major depressive episode (MDE) and to examine the association between social and economic factors with SDE and MDE in undergraduate students during the COVID-19 pandemic. The participants were 1,577 college students from a university in the south of Chile (64.6% females, 22 years old on average). The participants took an online survey in November 2020 which collected information about social and economic variables, depressive symptoms, and perceived social support. Bivariate and multinomial logistic regression analysis were used. The results showed a high prevalence of SDE (14.3%) and MDE (32.3%) in the sample. Belonging to a social group and perceiving positive social support were the only variables examined that were associated with SDE. Instead, female sex, poorer quintiles, living with other relatives but not parents, economic difficulties due to the pandemic, being a parent, and perceiving positive social support were associated with MDE. Subthreshold and threshold depressive symptoms are frequent in college students, and associations with social and economic factors differ according to the level of such symptoms. These results should be considered in the development of tailored preventive and early interventions for depression in college students.

The prevalence and incidence of major depressive disorder in 8079 Chinese university freshmen before and during COVID-19.

BACKGROUND: COVID-19 pandemic may impact the prevalence and incidence of depression in college students. However, there is no longitudinal study focusing on major depressive disorder (MDD) before and during COVID-19 pandemic. METHODS: A cohort study was carried out among 8079 Chinese college freshmen. The baseline survey was conducted in 2018 (T0) and annual follow-ups were in 2019 (T1, before COVID-19) and in 2020 (T2, during COVID-19). CIDI-3.0 was used to diagnose MDD. Random effects logistic models of panel data analysis were used for the risk of MDD incidence. STATA 15.1 was used for all analysis. RESULTS: The weighted 12-month prevalence of MDD at T2 (2.10%) was significantly lower than that at T1 (2.67%) (p < 0.001). However, among students who reported exposure to the COVID-19 events, the annual prevalence of MDD at T2 was higher than that at T1 (4.21% vs. 2.79%, p < 0.001). The incidence from T0 to T1, incidence from T1 to T2, and the 2-year incidence was 2.23%, 1.34% and 3.75%, respectively. Only 8.93% of MDD students had chosen to seek professional help during the COVID-19 pandemic period. LIMITATIONS: The computer assisted CIDI may not be as sensitive and specific as the diagnosis made by a psychiatrist and may have caused report bias. CONCLUSIONS: Although the MDD incidence of college students was decreasing over time, the impact of the pandemic on student mental health may depend on exposure to COVID-19 events. Not seeking professional help in the Chinese college students is still an important issue.

First-onset major depression during the COVID-19 pandemic: A predictive machine learning model.

BACKGROUND: This study longitudinally evaluated first-onset major depression rates during the pandemic in Italian adults without any current clinician-diagnosed psychiatric disorder and created a predictive machine learning model (MLM) to evaluate subsequent independent samples. METHODS: An online, self-reported survey was released during two pandemic periods (May to June and September to October 2020). Provisional diagnoses of major depressive disorder (PMDD) were determined using a diagnostic algorithm based on the DSM criteria of the Patient Health Questionnaire-9 to maximize specificity. Gradient-boosted decision trees and the SHapley Additive exPlanations technique created the MLM and estimated each variable's predictive contribution. RESULTS: There were 3532 participants in the study. The final sample included 633 participants in the first wave (FW) survey and 290 in the second (SW). First-onset PMDD was found in 7.4% of FW participants and 7.2% of the SW. The final MLM, trained on the FW, displayed a sensitivity of 76.5% and a specificity of 77.8% when tested on the SW. The main factors identified in the MLM were low resilience, being an undergraduate student, being stressed by pandemic-related conditions, and low satisfaction with usual sleep before the pandemic and support from relatives. Current smoking and taking medication for medical conditions also contributed, albeit to a lesser extent. LIMITATIONS: Small sample size; self-report assessment; data covering 2020 only. CONCLUSIONS: Rates of first-onset PMDD among Italians during the first phases of the pandemic were considerable. Our MLM displayed a good predictive performance, suggesting potential goals for depression-preventive interventions during public health crises.

Effects of workplace measures against COVID-19 and employees' worry about them on the onset of major depressive episodes: A 13-month prospective study of full-time employees.

BACKGROUND: Workplace measures against COVID-19 may prevent the onset of major depressive episode (MDE) in the working population. This 13-month prospective study aimed to investigate the association of the number of workplace measures against COVID-19 and employees' worry about the measures on the onset of MDE during COVID-19 outbreaks in Japan. METHODS: Data were collected from employees by using online questionnaires at baseline (May 2020) and the 7th survey (June 2021). The onset of MDE during the follow-up was retrospectively measured at the 7th survey, with a self-report scale based on the Mini-International Neuropsychiatric Interview according to the DSM-IV/DSM-5 criteria. Participants were asked to report the number of workplace measures against COVID-19 in their companies/organizations and their worry about these measures (scored 0-3). Multiple logistic regression was conducted of MDE on the number of workplace measures and worry about these, adjusting for demographic and work-related covariates and psychological distress at baseline. RESULTS: Among 968 respondents employed in May 2020, 827 completed the 7th survey in June 2021 (80%). We excluded 75 respondents who reported they had an MDE in May 2020 or earlier. Worry about workplace measures was significantly associated with the onset of MDE after adjusting for the covariates (OR for 1 score increase, 1.53; 95% CI, 1.02-2.32; p = 0.042). No significant association was found between the number of workplace measures and the onset of MDE. CONCLUSIONS: Worrying about workplace measures taken by company/organization may be a risk factor for the onset of an MDE among employees during the COVID-19 pandemic.

Accuracy in detecting major depressive episodes in older adults using the Swedish versions of the GDS-15 and PHQ-9.

OBJECTIVES: The purpose of this study was to evaluate the diagnostic accuracy at different cut-off values for the Swedish versions of the 15-item Geriatric Depression Scale (GDS-15) and Patient Health Questionnaire (PHQ-9) compared with a structured clinical psychiatric interview in older adults. METHODS: Community-dwelling participants (N = 113) aged 65 years or older completed the Swedish versions of the GDS-15 and PHQ-9 and were then interviewed using the Mini International Neuropsychiatric Interview (MINI) to establish the presence or absence of current major depressive episodes (MDEs). Areas under the curve (AUC) were calculated for each scale, as well as the sensitivity, specificity, and Youden's index for different cut-off values. RESULTS: Seventeen participants met the criteria for MDEs. The AUC was 0.97 for the GDS-15 and 0.95 for the PHQ-9. A cut-off of ≥6 on the GDS-15 yielded a sensitivity of 94%, a specificity of 88%, and a Youden's index of 0.82. A cut-off of ≥5 on the PHQ-9 yielded a sensitivity of 100%, a specificity of 81%, and a Youden's index of 0.81. The proposed cut-off of ≥10 on the PHQ-9 produced excellent specificity of 95% but a lower sensitivity of 71%. CONCLUSIONS: This study indicates that the Swedish versions of the GDS-15 and PHQ-9 have comparable accuracy as screening instruments for older adults with MDEs. However, the proposed cut-off of 10 on the PHQ-9 might be too high when applied to older individuals in Sweden, and further investigations in larger samples in different healthcare settings are warranted.

Depression Symptoms and Olfactory-related Quality of Life.

OBJECTIVES: Patients with olfactory dysfunction (OD) frequently report symptoms of depression. The objective of this study was to determine how clinical characteristics and olfactory-related quality of life (QoL) measures associate with the likelihood for major depressive disorders (MDDs). METHODS: A total of 192 OD patients were included. Olfactory function was measured using all three subtests of the Sniffn' Sticks test. Olfactory-related quality of life (QoL) was evaluated using the Questionnaires of Olfactory Dysfunction (QOD)-negative (NS) and -positive statement (PS). The likelihood for MDD was assessed using the Patients Health Questionnaire-2 (PHQ-2). Demographics and disease-specific variables (etiology and duration of OD) were collected. Univariate and multivariable analyses were used to associate disease-specific variables and the QOD with the outcome of the PHQ-2. Additionally, the predictive ability of the QOD-NS to predict depressive symptoms was calculated. RESULTS: In univariate analysis, COVID-19 related smell loss, the QOD-NS, and the QOD-PS were significantly associated with the PHQ-2. In multivariable analyses adjusting for QoL measures, the QOD-NS (ß = 0.532, p < 0.001) and sinonasal OD (compared with postinfectious OD) were significantly associated with the PHQ-2 (ß = 0.146, p = 0.047). When omitting QoL measures from multivariable analyses, only COVID-19 related OD (compared with postinfectious OD) was significantly associated with the PHQ-2 (ß = 0.287, p = 0.009). A QOD-NS score > 20.5 had 70.13% sensitivity and 76.32% specificity for detecting symptoms of depression. CONCLUSION: Our results suggest that COVID-19 related OD might be associated with a higher likelihood for MDD. Furthermore, we showed that the QOD-NS score might be helpful to predict symptoms of depression in OD patients. LEVEL OF EVIDENCE: 4 Laryngoscope, 132:1829-1834, 2022.

Validation of the Malay Self-Report Quick Inventory of Depressive Symptomatology in a Malaysian Sample.

Depression is ranked as the second-leading cause for years lived with disability worldwide. Objective monitoring with a standardized scale for depressive symptoms can improve treatment outcomes. This study evaluates the construct and concurrent validity of the Malay Self-Report Quick Inventory of Depressive Symptomatology (QIDS-SR16) among Malaysian clinical and community samples. This cross-sectional study was based on 277 participants, i.e., patients with current major depressive episode (MDE), n = 104, and participants without current MDE, n = 173. Participants answered the Malay QIDS-SR16 and were administered the validated Malay Mini-International Neuropsychiatric Interview (MINI) for DSM-IV-TR. Factor analysis was used to determine construct validity, alpha statistic for internal consistency, and receiver operating characteristic (ROC) analysis for concurrent validity with MINI to determine the optimal threshold to identify MDE. Data analysis provided evidence for the unidimensionality of the Malay QIDS-SR16 with good internal consistency (Cronbach's α = 0.88). Based on ROC analysis, the questionnaire demonstrated good validity with a robust area under the curve of 0.916 (p < 0.000, 95% CI 0.884-0.948). A cut-off score of nine provided the best balance between sensitivity (88.5%) and specificity (83.2%). The Malay QIDS-SR16 is a reliable and valid instrument for identifying MDE in unipolar or bipolar depression.

Measurement invariance of the Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder scale (GAD-7) across four European countries during the COVID-19 pandemic.

BACKGROUND: The Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder scale (GAD-7) are self-report measures of major depressive disorder and generalised anxiety disorder. The primary aim of this study was to test for differential item functioning (DIF) on the PHQ-9 and GAD-7 items based on age, sex (males and females), and country. METHOD: Data from nationally representative surveys in UK, Ireland, Spain, and Italy (combined N = 6,054) were used to fit confirmatory factor analytic and multiple-indictor multiple-causes models. RESULTS: Spain and Italy had higher latent variable means than the UK and Ireland for both anxiety and depression, but there was no evidence for differential items functioning. CONCLUSIONS: The PHQ-9 and GAD-7 scores were found to be unidimensional, reliable, and largely free of DIF in data from four large nationally representative samples of the general population in the UK, Ireland, Italy and Spain.

The impact of physical conditions on the incidence of major depressive disorder in Chinese university students: Results from a longitudinal study.

BACKGROUND: Major depressive disorder (MDD) is prevalent, and highly comorbid with physical illnesses. Few longitudinal studies have investigated the relationship between physical health conditions and MDD. The objectives of this study were to investigate the comorbid relationship between physical conditions and MDD, and the association between physical conditions and the 2-year risk of MDD. METHODS: A study was conducted in first-year Chinese university students (n = 8,079) over two and half years, using a longitudinal design. An adapted version of the Composite International Diagnostic Interview (CIDI - 3.0) was used to assess for MDD. The presence of physician diagnosed physical conditions was assessed using ten self-report questions. Cross-sectional and longitudinal associations between self-reported physical conditions and MDD were estimated, adjusting for possible confounders. RESULTS: The most frequently reported physical conditions were migraines, chronic rhinitis, and gastritis. We found that migraines, gastritis, and stomach ulcers were associated with a significantly higher lifetime prevalence of MDD than those without any physical health conditions. In those without a lifetime MDD, migraines, gastritis and stomach ulcers were also found to be significant predictors for 2-year risk of new onset MDD. LIMITATIONS: Recall and selection biases are possible when using self-reporting measures. Additionally, the COVID-19 outbreak impacted the response rate at the second follow-up assessment. Lastly, the severity of the physical conditions was not measured. CONCLUSIONS: Physical conditions and MDD are highly prevalent and comorbid in university students. Migraines, gastritis and stomach ulcers are associated with the risk of developing MDD. Future studies should further investigate how this information can be used to prevent MDD.

The impact of self-efficacy on first onset and prognosis of major depressive disorder: findings from a longitudinal study in a sample of Chinese first-year university students.

BACKGROUND: Self-efficacy is a pivotal factor in the etiology and prognosis of major depression. However, longitudinal studies on the relationship between self-efficacy and major depressive disorder (MDD) are scarce. The objectives were to investigate: (1) the associations between self-efficacy and the 1-year and 2-year risks of first onset of MDD and (2) the associations between self-efficacy and the 1-year and 2-year risks of the persistence/recurrence of MDD, in a sample of first-year university students. METHODS: We followed 8079 first-year university students for 2 years from April 2018 to October 2020. MDD was ascertained by the Chinese version of the Composite International Diagnostic Interview (CIDI-3.0) based on self-report. Self-efficacy was measured by the 10-item General Self-efficacy (GSE) scale. Random effect logistic regression modeling was used to estimate the associations. RESULTS: Among participants without a lifetime MDD, the data showed that participants with high baseline GSE scores were associated with a higher risk of first onset of MDD over 2 years [odds ratio (OR) 1.04, 95% confidence interval (CI) 1.01-1.08]. Among those with a lifetime MDD, participants with high baseline GSE scores were less likely to have had a MDD over 2 years (OR 0.93, 95% CI 0.88-0.99) compared to others. CONCLUSIONS: A high level of GSE may be protective of the risk of persistent or recurrent MDD. More longitudinal studies in university students are needed to further investigate the impact of GSE on the first onset of MDD.