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Background: To assess the effect of insulin resistance (IR) on treatment response to the antidepressant, vortioxetine, in patients with Major Depressive Disorder (MDD)., Methods: This is a secondary analysis of an 8-week, open-label clinical trial. Ninety-five adults in a primary care setting experiencing a major depressive episode were included. Response to vortioxetine was measured using the THINC-integrated tool, Montgomery Åsberg Depression Rating Scale (MADRS), the Snaith-Hamilton Pleasure Scale (SHAPS), the Perceived Deficits Questionnaire (PDQ-5), and the Sheehan Disability Scale (SDS). Generalized estimating equation models were utilized for data analysis., Results: When adjusted for age, gender, dose, and BMI, there was a significant baseline IR by time interaction for SHAPS (p = 0.022), PDQ-5 (p = 0.037), and SDS (p = 0.013). Higher baseline IR predicted decreased early improvements in anhedonia. It also predicted poorer subjective assessments of cognition and increased functional impairment at the endpoint of treatment. For functional capacity (i.e. SDS) other covariates including severity of symptoms, illness course, other metabolic factors (e.g. cholesterol), and physical activity were included with no changes to the moderating effect of baseline IR., Limitations: This was a post-hoc analysis of a primarily non-diabetic sample. Also, only one agent was assessed., Conclusions: IR was a predictor of response to vortioxetine. This persisted after controlling for other factors including, but not limited to, BMI. These findings strengthen the link between depression and IR and may point to another novel metabolic predictor of response. These findings should be replicated using other antidepressants., (Copyright © 2020. Published by Elsevier B.V.)

Antidepressant pharmacotherapy dominates current treatment in psychiatry, including treatment for major depressive disorder (MDD). However, the current trial-and-error process of medication selection contributes to treatment failure and unnecessarily exposes patients to lengthy and insufficient treatment trials. Notably, improvements in measures of cognition have been demonstrated to occur early during treatment and prior to improvements in clinical state. Cognitions have been categorized based on emotional valence (i.e., cold versus hot cognitions). Cold cognitions describe cognitive operations that are relevant to the processing of non-emotional information. The current analysis investigates whether early changes in cold cognition can predict response after 8 weeks of vortioxetine treatment in adults with MDD. This was secondary analysis of an 8-week, open-label study. Cognition was assessed at week 0 and week 2 to measure early cognitive change. Depressive symptom severity was assessed at week 0 and week 8 to measure treatment response. Eighty-one subjects were analyzed using binomial logistic regression models. Early change in cognition was a non-significant predictor of response (p = 0.845, SE = 0.599, OR = 1.124), which may have resulted from high data variability. The overall predictive accuracy of the model was low (sensitivity = 37.5%, specificity = 89.8%, PPV = 70.6%, NPV = 68.8%). Future studies should include larger samples and stratify patients based on potentially moderating variables, such as baseline cognitive impairment and occupation. Stratification would likely produce more homogenous samples, reducing the amount of variability observed for early cognitive change., Competing Interests: Declaration of Competing Interest Roger S. McIntyre is a consultant to speak on behalf for, and/or has received research support from Lundbeck, Janssen, Shire, Purdue, Pfizer, Otsuka, Allergan, Takeda, Neurocrine, Sunovion, Stanley Medical Research Institute, and CIHR/GACD/Chinese National Natural Research Foundation. All other authors have no conflicts of interest to disclose., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Anhedonia and abnormalities in reward behavior are core features of major depressive disorder (MDD). Convergent evidence indicates that overweight/obesity (OW), a highly prevalent condition in MDD, is independently associated with reward disturbances. We therefore aimed to investigate the moderating effect of OW on the willingness to expend efforts for reward in individuals with MDD and healthy controls (HC)., Methods: Forty-one adults (HC n = 20, MDD n = 21) completed the Effort Expenditure for Rewards Task (EEfRT), clinical and cognitive measures. Anthropometric parameters were assessed in all participants, and an additional evaluation of laboratorial parameters were conducted solely on those with MDD. Individuals with MDD were all on vortioxetine monotherapy (10-20 mg/day)., Results: Interactions between reward magnitude, group and OW were observed (χ 2  = 9.192, p = 0.010); the OW-MDD group chose the hard task significantly less than normal weight (NW)-HC (p = 0.033) and OW-HC (p = 0.034), whereas there were no differences between NW-MDD and HCs. Within individuals with MDD, the proportion of hard task choices was more strongly correlated with body mass index (BMI) (r = -0.456, p = 0.043) and insulin resistance (HOMA2-IR) (r = -0.467, p = 0.038), than with depressive symptoms (r = 0.290, p = 0.214)., Conclusions: OW significantly moderated the association between MDD and willingness to make efforts for rewards. These findings offer novel evidence on the potential role of metabolic factors on the basis of anhedonia, and for the heuristic models proposing a pathophysiological connection between mood and metabolic disorders., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: No previous review has comprehensively assessed long-term changes in anxiety and depressive symptoms in bariatric surgery patients. This systematic review assessed the effects of bariatric surgery on long-term reductions (≥ 24 months) in anxiety and depressive symptom severity in morbidly obese (≥ 35 BMI kg/m 2 ) participants. Short term effects (< 24 months) are briefly reviewed for context., Methods: PsychINFO, Google Scholar and PubMed databases were systematically searched for prospective cohort studies published from inception to 14 June 2018 that evaluated long-term (≥ 24 months) changes in anxiety and depressive symptom severity in bariatric surgery patients with a BMI ≥ 35 kg/m 2 using a combination of the following search terms: bariatric surgery (and surgical approaches included under this term), obesity, depression, depressive disorder, anxiety, anxious, psychiatric disorders, mood disorders., Results: We reviewed 2058 articles for eligibility; 14 prospective studies were included in the systematic review. 13 studies (93%) reported significant reductions in depressive symptom severity 2-3 years after bariatric surgery. However, all studies recorded statistically significant reductions in depressive symptoms at the conclusion of the study. Similarly, there were reductions in overall anxiety symptom severity at ≥ 24 months follow-up (k = 8 studies, n = 1590 pooled). Pre-operative anxiety or depression scores did not predict outcomes of post-operative BMI. Similarly, post-surgery weight loss did not predict changes in anxiety symptoms., Limitations: Very few studies assessed anxiety or depression as a primary outcome. Therefore, we cannot suggest bariatric surgery as a stand-alone therapeutic tool for anxiety and depression based on our findings., Conclusion: Currently available evidence suggests that bariatric surgery is associated with long-term reductions in anxiety and depressive symptoms. This supports existing literature showing that metabolic treatments may be a viable therapeutic intervention for mood disorders., (Copyright © 2018. Published by Elsevier B.V.)

Background: No previous study has comprehensively reviewed the application of machine learning algorithms in mood disorders populations. Herein, we qualitatively and quantitatively evaluate previous studies of machine learning-devised models that predict therapeutic outcomes in mood disorders populations., Methods: We searched Ovid MEDLINE/PubMed from inception to February 8, 2018 for relevant studies that included adults with bipolar or unipolar depression; assessed therapeutic outcomes with a pharmacological, neuromodulatory, or manual-based psychotherapeutic intervention for depression; applied a machine learning algorithm; and reported predictors of therapeutic response. A random-effects meta-analysis of proportions and meta-regression analyses were conducted., Results: We identified 639 records: 75 full-text publications were assessed for eligibility; 26 studies (n=17,499) and 20 studies (n=6325) were included in qualitative and quantitative review, respectively. Classification algorithms were able to predict therapeutic outcomes with an overall accuracy of 0.82 (95% confidence interval [CI] of [0.77, 0.87]). Pooled estimates of classification accuracy were significantly greater (p < 0.01) in models informed by multiple data types (e.g., composite of phenomenological patient features and neuroimaging or peripheral gene expression data; pooled proportion [95% CI] = 0.93[0.86, 0.97]) when compared to models with lower-dimension data types (pooledproportion=0.68[0.62,0.74]to0.85[0.81,0.88])., Limitations: Most studies were retrospective; differences in machine learning algorithms and their implementation (e.g., cross-validation, hyperparameter tuning); cannot infer importance of individual variables fed into learning algorithm., Conclusions: Machine learning algorithms provide a powerful conceptual and analytic framework capable of integrating multiple data types and sources. An integrative approach may more effectively model neurobiological components as functional modules of pathophysiology embedded within the complex, social dynamics that influence the phenomenology of mental disorders., (Copyright © 2018. Published by Elsevier B.V.)

Background: Despite the widespread use of antidepressants in clinical practice, the current trial-and-error approach to medication selection contributes to treatment failure and underscores the need to identify reliable predictors of antidepressant response. Since changes in measures of cognition have been reported to occur early in treatment and prior to improvements in overall mood symptoms, the present review aims to determine whether early changes in measures of cognition can predict response in individuals with MDD., Methods: A systematic review of studies evaluating early cognitive change as a predictor of later treatment response in MDD was conducted using PubMed/Medline, Embase and PsychINFO., Results: A total of seven articles were identified. The available evidence suggests the early changes in cognition may predict treatment response in individuals with MDD. This was shown across antidepressant classes (i.e., SSRIs, SNRIs, NRIs, melatonergic antidepressants) and forms of therapy (i.e., pharmacotherapy, rTMS). The results depict an emerging trend towards early changes in facial emotion recognition (i.e., a hot cognitive process) as a predictor of treatment outcome., Limitations: Our qualitative analysis reflects a very limited number of studies. Moreover, there was significant heterogeneity in the evaluation of cognition across studies. Future research should aim to parse out this heterogeneity by evaluating the relative predictive value of different measures of cognition., Conclusion: The identification of reliable early treatment predictors of antidepressant response would be clinically significant, enabling clinicians to more accurately evaluate the efficacy of selected treatment avenues., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objectives: To explore differences in adipokine levels (i.e., leptin and adiponectin levels) between adults with Major Depressive Disorder (MDD) and healthy controls (HC), and to discuss the possible role of adipokine regulation in the development and progression of MDD., Methods: A systematic review and meta-analysis were conducted based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. A systematic search was conducted for all English and Chinese peer-reviewed articles from inception to November 2017. A random effects model was used to calculate the standardized mean difference (SMD) of leptin and/or adiponectin levels in subjects diagnosed with MDD versus HC within a 95% confidence interval (CI)., Results: Thirty-three studies were included in this meta-analysis. In total, 4,372 (52.3%) subjects with MDD and 3,984 (47.7%) HC were compared. We identified significant lower adiponectin levels in MDD compared to HC with a small effect size (ES) (SMD = -0.25; 95% CI: -0.48, -0.02; P < 0.001). However, no significant difference was observed in leptin levels between MDD subjects and HC (SMD = 0.13; 95% CI: -0.06, 0.31; P = 0.170). The heterogeneity in the results of our meta-analysis could not be completely explained by dividing subjects into subgroups. Results from subgroup analyses suggested that studies involving samples with BMI ≥ 25 had lower adiponectin levels in subjects with MDD compared to HC, and older age samples (i.e., age ≥ 40) with BMI ≥ 25 had both higher leptin levels and lower adiponectin levels in MDD subjects as compared to HC., Limitations: The heterogeneity of included studies, small sample sizes, and potential publication bias were significant limitations., Conclusions: The current systematic review and meta-analysis indicated that lower adiponectin levels may be associated with MDD. Moreover, the results suggest that males expressing lower adiponectin and leptin levels have an increased likelihood of developing MDD. Future studies should aim to investigate the manifestation of depressive phenotypes in older, obese populations with altered metabolic profiles resulting from adipokine dysregulation. The review has been registered with PROSPERO (registration number CRD42018082733)., (Copyright © 2018 Elsevier B.V. All rights reserved.)