A Systematic review of the validity of screening depression through Facebook, Twitter, Instagram, and Snapchat.

BACKGROUND: The aim of this study was to determine the validity of using social media for depression screening. METHOD: Article searches on PubMed and PsycINFO from database inception to August 20, 2019 were completed with a search string and filters. RESULTS: 15 articles made the inclusion criteria. Facebook, Twitter, and Instagram profiles of depressed people were distinguishable from nondepressed people shown by social media markers. Facebook studies showed that having fewer Facebook friends and mutual friends, posting frequently, and using fewer location tags positively correlated with depressive symptoms. Also, Facebook posts with explicit expression of depressive symptoms, use of personal pronouns, and words related to pain, depressive symptoms, aggressive emotions, and rumination predicted depression. Twitter studies showed that the use of "past focus" words, negative emotions and anger words, and fewer words per Tweet positively correlated with depression. Finally, Instagram studies showed that differences in follower patterns, photo posting and editing, and linguistic features between depressed people and nondepressed people could serve as a marker. LIMITATIONS: The primary articles analyzed had different methods, which constricts the amount of comparisons that can be made. Further, only four social media platforms were explored. CONCLUSION: Social media markers like number and content of Facebook messages, linguistic variability in tweets and tweet word count on Twitter, and number of followers, frequency of Instagram use and the content of messages on Instagram differed between depressed people and nondepressed people. Therefore, screening social media profiles on these platforms could be a valid way to detect depression.

Metabolic profiling for water-soluble metabolites in patients with schizophrenia and healthy controls in a Chinese population: A case-control study.

Objectives: Objective measures integrated with clinical symptoms may improve early prevention and detection of schizophrenia. Herein we aim to evaluate potential water-soluble metabolic biomarkers in schizophrenia.Methods: We recruited adults with schizophrenia (n = 113) who had not received pharmacological treatment for at least 1 month prior to enrollment and 111 age- and sex-matched healthy subjects from Weifang, Shandong province, China. All serum samples were analysed using liquid chromatography-tandem mass spectrometry coupled with a hydrophilic interaction liquid chromatography column.Results: Eleven metabolites, namely carnitines (oleoylcarnitine, l-palmitoylcarnitine, 9-decenoylcarnitine and 2-trans,4-cis-decadienoylcarnitine), polar lipids (lysophosphatidylcholine (LPC)(P-16:0), LPC (16:0), LPC (15:0) and LPC(14:0)), amino acids (taurine and l-arginine), and organic acid (2,5-dichloro-4-oxohex-2-enedioate), separated the patients and healthy controls. Compared with healthy controls, taurine, l-palmitoylcarnitine and oleoylcarnitine levels were higher, whereas the remaining eight metabolites were lower in patients with schizophrenia. A combination of four metabolites, i.e., oleoylcarnitine, 9-decenoylcarnitine, LPC (15:0) and LPC (14:0), provided the most robust between-group separation.Conclusions: This study appears to distinguish between groups of patients and controls, which should be considered as a contribution to putative potential biomarkers. The water-soluble metabolites were determined to be significantly different between the groups in the current study, and were primarily related to cellular bioenergetics, notably oxidative stress.

Correction.

This corrects the article DOI: 10.4088/JCP.18r12475.

Efficacy of omega-3 PUFAs in depression: A meta-analysis.

We conducted this meta-analysis of double-blind randomized placebo-controlled trials to estimate the efficacy of omega-3 polyunsaturated fatty acids (PUFAs), especially docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), in the improvement of depression. We applied a systematic bibliographic search in PubMed and EMBASE for articles published prior to 20 December 2017. This meta-analysis was performed using RevMan 5.3 and R 3.4.3, and means and standard deviations were calculated in fixed- or random-effects models based on the results of the Q-test. A sensitivity analysis was also conducted to evaluate the stability of the results, and publication bias was evaluated by a funnel plot and Egger's linear regression analysis. Our search resulted in 180 articles; we analyzed 26 studies, which included 2160 participants. The meta-analysis showed an overall beneficial effect of omega-3 polyunsaturated fatty acids on depression symptoms (SMD = -0.28, P = 0.004). Compared with placebo, EPA-pure (=100% EPA) and EPA-major formulations (≥60% EPA) demonstrated clinical benefits with an EPA dosage ≤1 g/d (SMD = -0.50, P = 0.003, and SMD = -1.03, P = 0.03, respectively), whereas DHA-pure and DHA-major formulations did not exhibit such benefits.Current evidence supports the finding that omega-3 PUFAs with EPA ≥ 60% at a dosage of ≤1 g/d would have beneficial effects on depression. Further studies are warranted to examine supplementation with omega-3 PUFAs for specific subgroups of subjects with inflammation, severity of depression, and the dose response for both EPA and DHA supplementation.

Oral Ketamine for Depression: A Systematic Review.

OBJECTIVE: Intravenous (IV) ketamine has rapid and robust antidepressant effects; however, poor accessibility of the IV route often limits its use. Numerous alternative routes of administration are being investigated. Oral ketamine is particularly appealing for its ease of use with the potential for high accessibility. The objective of the current systematic review, in accordance with PRISMA, is to determine the efficacy, safety, tolerability, and dose range of oral ketamine for bipolar and unipolar depression. DATA SOURCES: The MEDLINE/PubMed, EMBASE, and Google Scholar databases were systematically searched for relevant articles, written in English, published prior to July 2018 using relevant keywords for all variants of ketamine, oral, and depression. STUDY SELECTION: All clinical studies assessing oral ketamine for bipolar or unipolar depression were included. A total of 13 published articles were identified, of which 2 were proof-of-concept, randomized controlled trials (RCTs); 1 was a prospective open-label trial; 5 were retrospective chart reviews; and 5 were case reports. DATA EXTRACTION: Included articles were qualitatively analyzed to determine antidepressant efficacy, tolerability, safety, dose range, antisuicide effects, time to effect, and efficacy in treatment-resistant depression and study bias. RESULTS: Both RCTs demonstrated antidepressant efficacy with good tolerability; however, significant changes in depressive symptom severity were observed only after 2-6 weeks of treatment (P < .05). Both RCTs had high risk for bias, due to inadequate intent-to-treat analysis and adverse effect monitoring. Rapid antidepressant effects (ie, within 24 hours), antisuicide effects, and efficacy in treatment-resistant depression were reported only in retrospective studies. Dosages and frequency of administration were variable (ie, 0.5-7.0 mg/kg 3 times daily to once monthly), with most studies providing dosages of 1-2 mg/kg every 1-3 days. No clinically significant adverse effects were reported. CONCLUSIONS: A small number of clinical studies assessed the antidepressant effects of oral ketamine. Initial results suggest that oral ketamine has significant antidepressant effects with good overall tolerability; however, antidepressant effects are not as rapid as those associated with IV ketamine. Antisuicide effects and efficacy in treatment-resistant depression have yet to be demonstrated. Additional well-designed RCTs are warranted.

Pharmacological interventions targeting anhedonia in patients with major depressive disorder: A systematic review.

Anhedonia is defined as a diminished ability to experience interest or pleasure, and is a critical psychopathological dimension of major depressive disorder (MDD). The purpose of the current systematic review is to evaluate the therapeutic efficacy of pharmacological treatments on measures of anhedonia in adults with MDD. Electronic databases Cochrane Library (CENTRAL), Ovid MEDLINE, PubMed, PsycINFO, and Google Scholar were searched from inception to June 1, 2018 for longitudinal studies utilizing pharmacotherapy for the treatment of anhedonia in patients with MDD. A total of 17 eligible studies were identified (i.e., evaluated the effects of pharmacotherapy on a measure of anhedonia). Among the identified studies, the efficacy of 14 different pharmacotherapies on measures of anhedonia were evaluated, including melatonergic agents (i.e. agomelatine), monoaminergic agents (i.e. moclobemide, clomipramine, bupropion, venlafaxine, fluoxetine, amitifadine and levomilnacipran, escitalopram, and sertraline), glutamatergic agents (i.e., ketamine and riluzole), stimulants (i.e., methylphenidate), and psychedelics (i.e., psilocybin). Based on the available evidence, most antidepressants demonstrated beneficial effects on measures of anhedonia as well as the other depressive symptoms. Only escitalopram/riluzole combination treatment was ineffective in treating symptoms of anhedonia in MDD. Continued research is warranted to further support the efficacy of mechanistically-distinct antidepressants in treating symptoms of anhedonia in MDD. Future research should also aim to parse out the heterogeneous effects of different pharmacotherapies on anhedonic symptoms.

Characterizing acyl-carnitine biosignatures for schizophrenia: a longitudinal pre- and post-treatment study.

Subjects with schizophrenia have high risks of metabolic abnormalities and bioenergetic dysfunction. Acyl-carnitines involved in bioenergetic pathways provide potential biomarker targets for identifying early changes and onset characteristics in subjects with schizophrenia. We measured 29 acyl-carnitine levels within well-characterized plasma samples of adults with schizophrenia and healthy controls using liquid chromatography-mass spectrometry (LC-MS). Subjects with schizophrenia were measured at baseline and after 8 weeks of treatment. A total of 225 subjects with schizophrenia and 175 age- and gender-matched healthy controls were enrolled and 156 subjects completed the 8-week follow-up. With respect to plasma acyl-carnitines, the individuals with schizophrenia at baseline showed significantly higher levels of C4-OH (C3-DC) and C16:1, but lower concentrations of C3, C8, C10, C10:1, C10:2, C12, C14:1-OH, C14:2, and C14:2-OH when compared with healthy controls after controlling for age, sex, body mass index (BMI), smoking, and drinking. For the comparison between pretreatment and posttreatment subjects, all detected acyl-carnitines were significantly different between the two groups. Only the concentration of C3 and C4 were increased after selection by variable importance in projection (VIP) value >1.0 and false discovery rate (FDR) q value <0.05. A panel of acyl-carnitines were selected for the ability to differentiate subjects of schizophrenia at baseline from controls, pre- from post-treatment, and posttreatment from controls. Our data implicated acyl-carnitines with abnormalities in cellular bioenergetics of schizophrenia. Therefore, acyl-carnitines can be potential targets for future investigations into their roles in the pathoetiology of schizophrenia.

Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry.

INTRODUCTION: Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learning/Artificial Intelligence (AI) may instantiate such capabilities, as well as provide rationale for its application to psychiatry in both research and clinical ecosystems. METHODS: Databases PubMed and PsycINFO were searched from 1966 to June 2016 for keywords:Big Data, Machine Learning, Precision Medicine, Artificial Intelligence, Mental Health, Mental Disease, Psychiatry, Data Mining, RDoC, and Research Domain Criteria. Articles selected for review were those that were determined to be aligned with the objective of this particular paper. RESULTS: Results indicate that AI is a viable option to build useful predictors of outcome while offering objective and comparable accuracy metrics, a unique opportunity, particularly in mental health research. The approach has also consistently brought notable insight into disease models through processing the vast amount of already available multi-domain, semi-structured medical data. The opportunity for AI in psychiatry, in addition to disease-model refinement, is in characterizing those at risk, and it is likely also relevant to personalizing and discovering therapeutics. CONCLUSIONS: Machine learning currently provides an opportunity to parse disease models in complex, multi-factorial disease states (e.g. mental disorders) and could possibly inform treatment selection with existing therapies and provide bases for domain-based therapeutic discovery.

Revisiting the International Physical Activity Questionnaire (IPAQ): Assessing physical activity among individuals with schizophrenia.

BACKGROUND: Individuals with schizophrenia tend to have low levels of physical activity (PA) which contributes to high rates of physical comorbidities. Valid and reliable methods of assessing PA are essential for advancing health research. Ten years after initial validation of the Short-Form International Physical Activity Questionnaire (IPAQ), this study expands on the initial validation study by examining retest reliability over a 4-week period, assessing validity with a larger sample, and comparing validity of the IPAQ to a 24-hour recall alternative. METHODS: Participants completed the IPAQ at baseline and 4weeks later, along with a 24-hour PA recall at week 4. At week 3 participants wore waist accelerometers for 7days. Spearman's correlation coefficients and Bland-Altman plots were calculated based on weekly minutes of moderate to vigorous PA (MVPA). RESULTS: Test-retest reliability for the self-administered IPAQ was ρ=0.47, p<0.001 for MVPA. Correlation between IPAQ assessment and accelerometer-determined MVPA was ρ=0.30, p=0.003. The 24-hour recall correlated significantly with MVPA on the previous day ρ=0.27, p=0.012. A Bland-Altman plot indicated the IPAQ-SF underreported by -119.2min (-72%) on average compared to accelerometry (95% limits of agreement -1017.1 to 778.7min, -292% to 147%). CONCLUSION: Compared to previous IPAQ validation work in this population, criterion validity was similar, but reliability was lower over a 4-week period. MVPA criterion validity of the 24-hour recall was comparable to the 7-day self-report IPAQ. Findings further support that the IPAQ is a suitable assessment tool for epidemiological studies. Objective measures of physical activity are recommended for intervention assessment.