Placebo effects in randomized trials of pharmacological and neurostimulation interventions for mental disorders: An umbrella review.

There is a growing literature exploring the placebo response within specific mental disorders, but no overarching quantitative synthesis of this research has analyzed evidence across mental disorders. We carried out an umbrella review of meta-analyses of randomized controlled trials (RCTs) of biological treatments (pharmacotherapy or neurostimulation) for mental disorders. We explored whether placebo effect size differs across distinct disorders, and the correlates of increased placebo effects. Based on a pre-registered protocol, we searched Medline, PsycInfo, EMBASE, and Web of Knowledge up to 23.10.2022 for systematic reviews and/or meta-analyses reporting placebo effect sizes in psychopharmacological or neurostimulation RCTs. Twenty meta-analyses, summarising 1,691 RCTs involving 261,730 patients, were included. Placebo effect size varied, and was large in alcohol use disorder (g = 0.90, 95% CI [0.70, 1.09]), depression (g = 1.10, 95% CI [1.06, 1.15]), restless legs syndrome (g = 1.41, 95% CI [1.25, 1.56]), and generalized anxiety disorder (d = 1.85, 95% CI [1.61, 2.09]). Placebo effect size was small-to-medium in obsessive-compulsive disorder (d = 0.32, 95% CI [0.22, 0.41]), primary insomnia (g = 0.35, 95% CI [0.28, 0.42]), and schizophrenia spectrum disorders (standardized mean change = 0.33, 95% CI [0.22, 0.44]). Correlates of larger placebo response in multiple mental disorders included later publication year (opposite finding for ADHD), younger age, more trial sites, larger sample size, increased baseline severity, and larger active treatment effect size. Most (18 of 20) meta-analyses were judged 'low' quality as per AMSTAR-2. Placebo effect sizes varied substantially across mental disorders. Future research should explore the sources of this variation. We identified important gaps in the literature, with no eligible systematic reviews/meta-analyses of placebo response in stress-related disorders, eating disorders, behavioural addictions, or bipolar mania.

A framework for a brain-derived nosology of psychiatric disorders.

Current psychiatric diagnoses are not defined by neurobiological measures which hinders the development of therapies targeting mechanisms underlying mental illness 1,2 . Research confined to diagnostic boundaries yields heterogeneous biological results, whereas transdiagnostic studies often investigate individual symptoms in isolation. There is currently no paradigm available to comprehensively investigate the relationship between different clinical symptoms, individual disorders, and the underlying neurobiological mechanisms. Here, we propose a framework that groups clinical symptoms derived from ICD-10/DSM-V according to shared brain mechanisms defined by brain structure, function, and connectivity. The reassembly of existing ICD-10/DSM-5 symptoms reveal six cross-diagnostic psychopathology scores related to mania symptoms, depressive symptoms, anxiety symptoms, stress symptoms, eating pathology, and fear symptoms. They were consistently associated with multimodal neuroimaging components in the training sample of young adults aged 23, the independent test sample aged 23, participants aged 14 and 19 years, and in psychiatric patients. The identification of symptom groups of mental illness robustly defined by precisely characterized brain mechanisms enables the development of a psychiatric nosology based upon quantifiable neurobiological measures. As the identified symptom groups align well with existing diagnostic categories, our framework is directly applicable to clinical research and patient care.

Distinct personality profiles associated with disease risk and diagnostic status in eating disorders.

BACKGROUND: Personality traits have been associated with eating disorders (EDs) and comorbidities. However, it is unclear which personality profiles are premorbid risk rather than diagnostic markers. METHODS: We explored associations between personality and ED-related mental health symptoms using canonical correlation analyses. We investigated personality risk profiles in a longitudinal sample, associating personality at age 14 with onset of mental health symptoms at ages 16 or 19. Diagnostic markers were identified in a sample of young adults with anorexia nervosa (AN, n = 58) or bulimia nervosa (BN, n = 63) and healthy controls (n = 47). RESULTS: Two significant premorbid risk profiles were identified, successively explaining 7.93 % and 5.60 % of shared variance (Rc2). The first combined neuroticism (canonical loading, rs = 0.68), openness (rs = 0.32), impulsivity (rs = 0.29), and conscientiousness (rs = 0.27), with future onset of anxiety symptoms (rs = 0.87) and dieting (rs = 0.58). The other, combined lower agreeableness (rs = -0.60) and lower anxiety sensitivity (rs = -0.47), with future deliberate self-harm (rs = 0.76) and purging (rs = 0.55). Personality profiles associated with "core psychopathology" in both AN (Rc2 = 80.56 %) and BN diagnoses (Rc2 = 64.38 %) comprised hopelessness (rs = 0.95, 0.87) and neuroticism (rs = 0.93, 0.94). For BN, this profile also included impulsivity (rs = 0.60). Additionally, extraversion (rs = 0.41) was associated with lower depressive risk in BN. LIMITATIONS: The samples were not ethnically diverse. The clinical cohort included only females. There was non-random attrition in the longitudinal sample. CONCLUSIONS: The results suggest neuroticism and impulsivity as risk and diagnostic markers for EDs, with neuroticism and hopelessness as shared diagnostic markers. They may inform the design of more personalised prevention and intervention strategies.

Psychosocial and medication interventions to stop or reduce alcohol consumption during pregnancy.

BACKGROUND: Despite the known harms, alcohol consumption is common in pregnancy. Rates vary between countries, and are estimated to be 10% globally, with up to 25% in Europe. OBJECTIVES: To assess the efficacy of psychosocial interventions and medications to reduce or stop alcohol consumption during pregnancy. SEARCH METHODS: We searched the Cochrane Drugs and Alcohol Group Specialised Register (via CRSLive), Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, CINAHL, Web of Science, and PsycINFO, from inception to 8 January 2024. We also searched for ongoing and unpublished studies via ClinicalTrials.gov and the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP). All searches included non-English language literature. We handsearched references of topic-related systematic reviews and included studies. SELECTION CRITERIA: We included randomised controlled trials that compared medications or psychosocial interventions, or both, to placebo, no intervention, usual care, or other medications or psychosocial interventions used to reduce or stop alcohol use during pregnancy. Our primary outcomes of interest were abstinence from alcohol, reduction in alcohol consumption, retention in treatment, and women with any adverse event. DATA COLLECTION AND ANALYSIS: We used standard Cochrane methodological procedures. MAIN RESULTS: We included eight studies (1369 participants) in which pregnant women received an intervention to stop or reduce alcohol use during pregnancy. In one study, almost half of participants had a current diagnosis of alcohol use disorder (AUD); in another study, 40% of participants had a lifetime diagnosis of AUD. Six studies took place in the USA, one in Spain, and one in the Netherlands. All included studies evaluated the efficacy of psychosocial interventions; we did not find any study that evaluated the efficacy of medications for the treatment of AUD during pregnancy. Psychosocial interventions were mainly brief interventions ranging from a single session of 10 to 60 minutes to five sessions of 10 minutes each. Pregnant women received the psychosocial intervention approximately at the end of the first trimester of pregnancy, and the outcome of alcohol use was reassessed 8 to 24 weeks after the psychosocial intervention. Women in the control group received treatment as usual (TAU) or similar treatments such as comprehensive assessment of alcohol use and advice to stop drinking during pregnancy. Globally, we found that, compared to TAU, psychosocial interventions may increase the rate of continuously abstinent participants (risk ratio (RR) 1.34, 95% confidence interval (CI) 1.14 to 1.57; I2 =0%; 3 studies; 378 women; low certainty evidence). Psychosocial interventions may have little to no effect on the number of drinks per day, but the evidence is very uncertain (mean difference -0.42, 95% CI -1.13 to 0.28; I2 = 86%; 2 studies; 157 women; very low certainty evidence). Psychosocial interventions probably have little to no effect on the number of women who completed treatment (RR 0.98, 95% CI 0.94 to 1.02; I2 = 0%; 7 studies; 1283 women; moderate certainty evidence). None of the included studies assessed adverse events of treatments. We downgraded the certainty of the evidence due to risk of bias and imprecision of the estimates. AUTHORS' CONCLUSIONS: Brief psychosocial interventions may increase the rate of continuous abstinence among pregnant women who report alcohol use during pregnancy. Further studies should be conducted to investigate the efficacy and safety of psychosocial interventions and other treatments (e.g. medications) for women with AUD. These studies should provide detailed information on alcohol use before and during pregnancy using consistent measures such as the number of drinks per drinking day. When heterogeneous populations are recruited, more detailed information on alcohol use during pregnancy should be provided to allow future systematic reviews to be conducted. Other important information that would enhance the usefulness of these studies would be the presence of other comorbid conditions such as anxiety, mood disorders, and the use of other psychoactive substances.

Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder.

This study uses machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder (MDD), and alcohol use disorder (AUD). Utilizing case-control samples (ages 18-25 years) and a longitudinal population-based sample (n=1,851), the models, incorporating diverse data domains, achieved high accuracy in classifying EDs, MDD, and AUD from healthy controls. The area under the receiver operating characteristic curves (AUC-ROC [95% CI]) reached 0.92 [0.86-0.97] for AN and 0.91 [0.85-0.96] for BN, without relying on body mass index as a predictor. The classification accuracies for MDD (0.91 [0.88-0.94]) and AUD (0.80 [0.74-0.85]) were also high. Each data domain emerged as accurate classifiers individually, with personality distinguishing AN, BN, and their controls with AUC-ROCs ranging from 0.77 to 0.89. The models demonstrated high transdiagnostic potential, as those trained for EDs were also accurate in classifying AUD and MDD from healthy controls, and vice versa (AUC-ROCs, 0.75-0.93). Shared predictors, such as neuroticism, hopelessness, and symptoms of attention-deficit/hyperactivity disorder, were identified as reliable classifiers. For risk prediction in the longitudinal population sample, the models exhibited moderate performance (AUC-ROCs, 0.64-0.71), highlighting the potential of combining multi-domain data for precise diagnostic and risk prediction applications in psychiatry.