Objective: The aim of this study was to present the reliability and validity of the Children's Depression Rating Scale–Revised (CDRS-R) in the adolescent age group. Method: Adolescents with symptoms of depression were assessed using the CDRS-R and global severity and functioning scales at screening, baseline, and after 12 weeks of fluoxetine treatment. Global improvement was also assessed at week 12 (or exit). Reliability and validity were analyzed using Classical Test Theory (item-total correlations and internal consistency) and correlations between the CDRS-R and other outcomes. Results: Adolescents (n = 145) were evaluated at screening; 113 (77.9%) met criteria for major depressive disorder, 8 (5.5%) had subthreshold depressive symptoms, and 24 (16.6%) had minimal depressive symptoms. Ninety-four adolescents had a baseline visit after 1 week, and 88 were treated with fluoxetine. Internal consistency for the CDRS-R was good at all three visits (screening: 0.79; baseline: 0.74; exit: 0.92), and total score was highly correlated with global severity (r = 0.87, 0.80, and 0.93; p < 0.01). Only exit CDRS-R score was significantly correlated with global functioning (Children's Global Assessment Scale; r = −0.77; p < 0.01). Reductions on the CDRS-R total score were highly correlated with improvement scores at exit (Clinical Global Impressions–Improvement; r = −0.83; p < 0.01). Conclusions: The results demonstrate good reliability and validity in adolescents with depression.

Adolescents (N=2,272) from Hong Kong and the United States provided information regarding their depressive symptoms, cognitions (self-efficacy, negative cognitive errors, and hopelessness), and stressful events between 2 surveys 6 months apart. Depressive symptoms and hopelessness were higher, and self-efficacy and negative cognitive errors were lower in Hong Kong than in the United States. Cognitions were associated with concurrent depressive symptoms and predicted depressive symptoms 6 months later in both cultures. The "reverse" model was also supported with more variance predicted by depressive symptoms to later cognitions than from cognitions to depressive symptoms. There was some support for the hypothesis that self-efficacy is less salient in collective compared with individualistic cultures. These findings extend cognitive theories of depression to a non-Western culture.

Combination antidepressant pharmacotherapies are frequently used to treat major depressive disorder (MDD). However, there is no evidence that machine learning approaches combining multi-omics measures (e.g., genomics and plasma metabolomics) can achieve clinically meaningful predictions of outcomes to combination pharmacotherapy. This study examined data from 264 MDD outpatients treated with citalopram or escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) and 111 MDD outpatients treated with combination pharmacotherapies in the Combined Medications to Enhance Outcomes of Antidepressant Therapy (CO-MED) study to predict response to combination antidepressant therapies. To assess whether metabolomics with functionally validated single-nucleotide polymorphisms (SNPs) improves predictability over metabolomics alone, models were trained/tested with and without SNPs. Models trained with PGRN-AMPS’ and CO-MED’s escitalopram/citalopram patients predicted response in CO-MED’s combination pharmacotherapy patients with accuracies of 76.6% (p < 0.01; AUC: 0.85) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs. Then, models trained solely with PGRN-AMPS’ escitalopram/citalopram patients predicted response in CO-MED’s combination pharmacotherapy patients with accuracies of 75.3% (p < 0.05; AUC: 0.84) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs, demonstrating cross-trial replication of predictions. Plasma hydroxylated sphingomyelins were prominent predictors of treatment outcomes. To explore the relationship between SNPs and hydroxylated sphingomyelins, we conducted multi-omics integration network analysis. Sphingomyelins clustered with SNPs and metabolites related to monoamine neurotransmission, suggesting a potential functional relationship. These results suggest that integrating specific metabolites and SNPs achieves accurate predictions of treatment response across classes of antidepressants. Finally, these results motivate functional investigation into how sphingomyelins might influence MDD pathophysiology, antidepressant response, or both.

Alterations in the gut microbiome have been linked to a variety of mental illnesses including anxiety and depression. This study utilized advanced bioinformatics tools that integrated both the compositional and community nature of gut microbiota to investigate how gut microbiota influence clinical symptoms in a sample of participants with depression. Gut microbiota of 179 participants with major depressive disorder (MDD) in the Texas Resilience Against Depression (T-RAD) study were analyzed by 16S rRNA gene sequencing of stool samples. Severity of anxiety, depression, and anhedonia symptoms were assessed with General Anxiety Disorder – 7 item scale, Patient Health 9-item Questionnaire, and Dimensional Anhedonia Rating Scale, respectively. Using weighted correlation network analysis, a data-driven approach, three co-occurrence networks of bacterial taxa were identified. One of these co-occurrence networks was significantly associated with clinical features including depression and anxiety. The hub taxa associated with this co-occurrence module –one Ruminococcaceae family taxon, one Clostridiales vadinBB60 group family taxon, and one Christencenellaceae family taxon– were connected to several additional butyrate-producing bacteria suggesting that deficits in butyrate production may contribute to clinical symptoms. Therefore, by considering the community nature of the gut microbiome in a real world clinical sample, this study identified a gut microbial co-occurrence network that was significantly associated with clinical anxiety in a cohort of depressed individuals.

Objective: Recent acute efficacy trials of antidepressants in youth have suggested that high placebo-response rates in children (<12 years of age) indicate that children may be more responsive to non-specific treatment interventions. Yet, these studies generally have not presented age-specific outcome data. The objective of this study was to compare the efficacy outcomes for children (<12 years of age) and adolescents (≥12 years of age) using the combined data from two previously published double-blind, placebo-controlled trials of fluoxetine.Methods: Children (<12 years of age) and adolescents (≥12 years of age) with major depressive disorder were randomized to fluoxetine or placebo for 8–9 weeks of treatment. Outcome was assessed using the Children's Depression Rating Scale-Revised (CDRS-R) and Clinical Global Impressions scale.Results: Random regression of the CDRS-R showed a treatment group by age group interaction (F_1,338=4.10, P=.044), indicating that the treatment effect was significantly more pronounced in children than adolescents. Within children, response at exit to fluoxetine was significantly better than placebo (56.9% vs 33.3%; P=.009). Adolescent response rates at exit were not significantly different between the groups (51.1% vs 38.6%; P=.128). Remission rates were low for both groups.Conclusion: In the combined fluoxetine trials, drug-placebo difference was greater in children compared with adolescents. Contrary to expectations, the placebo-response rate was lower in the children than the adolescents.