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Understanding psychosis complexity through a syndemic framework: A systematic review.
Psychotic conditions pose significant challenges due to their complex aetiology and impact on individuals and communities. Syndemic theory offers a promising framework to understand the interconnectedness of various health and social problems in the context of psychosis. This systematic review aims to examine existing literature on testing whether psychosis is better understood as a component of a syndemic. We conducted a systematic search of 7 databases, resulting in the inclusion of five original articles. Findings from these studies indicate a syndemic characterized by the coexistence of various health and social conditions, are associated with a greater risk of psychosis, adverse health outcomes, and disparities, especially among ethnic minorities and deprived populations. This review underscores the compelling need for a new paradigm and datasets that can investigate how psychosis emerges in the context of a syndemic, ultimately guiding more effective preventive and care interventions as well as policies to improve the health of marginalised communities living in precarity.
Brain texture as a marker of transdiagnostic clinical profiles in patients with recent-onset psychosis and depression
The inter-relationships of voxels can be captured by the radiomics texture features across multiple spatial scales. Prediction models of brain texture changes captured by the contrast texture feature in recent-onset psychosis (ROP) and recent-onset depression (ROD) have recently been proposed, although the validation of these models transdiagnostically at the individual level and the investigation of the variability in clinical profiles are lacking. Established prevention and treatment approaches focus on specific diagnoses and do not address the heterogeneity and manifold potential outcomes of patients. Here we aimed to investigate the cross-sectional utility of brain texture changes for (1) identification of the psychopathological state (ROP and ROD) and (2) the association of individualized brain texture maps with clinical symptom severity and outcome profiles. We developed transdiagnostic models based on structural magnetic resonance imaging data for 116 patients with ROD, 122 patients with ROP and 197 healthy control participants from the PRONIA (Personalized pROgNostic tools for early psychosIs mAnagement) study by applying a set of tools and frameworks to explain the classification decisions of the deep-learning algorithm (named explainable artificial intelligence) and clustering analysis. We investigated the contrast texture feature as the key feature for the identification of a general psychopathological state. The discrimination power of the trained prediction model was >72% and was validated in a second independent age- and sex-matched sample of 137 ROP, 94 ROD and 159 healthy control participants. Clustering analysis was implemented to map the changes in texture brain produced from an explainable artificial intelligence algorithm, in a group fashion. The explained individualized brain contrast map grouped into eight homogeneous clusters. In the clinical group, we investigated the association between the explained brain contrast texture map and clinical symptom severity as well as outcome profiles. Different patterns in the explained brain contrast texture map showed unique associations of brain alterations with clinical symptom severity and clinical outcomes, that is, age, positive, negative and depressive symptoms, and functionality. In some clusters, the mean explained brain contrast texture map values and/or brain contrast texture voxels that contributed significantly to the classification decision predicted accurately the PANSS (positive and negative symptom scale) scores, functionality and change in functionality over time. In conclusion, we created homogeneous clusters which predict the clinical severity and outcome profile in ROP and ROD patients.
Predicting treatment resistance from first-episode psychosis using routinely collected clinical information
Around a quarter of people who experience a first episode of psychosis (FEP) will develop treatment-resistant schizophrenia, but there are currently no established clinically useful methods to predict this from baseline. We aimed to explore the predictive potential for clozapine use as a proxy for treatment-resistant schizophrenia of routinely collected, objective biomedical predictors at FEP onset, and to validate the model externally in a separate clinical sample of people with FEP. We developed and externally validated a forced-entry logistic regression risk prediction model for clozapine treatment, or MOZART, to predict up to 8-year risk of clozapine use from FEP using routinely recorded information including age, sex, ethnicity, triglycerides, alkaline phosphatase levels and lymphocyte counts. We also produced a least-absolute shrinkage and selection operator (LASSO) based model, additionally including neutrophil count, smoking status, body mass index and random glucose levels. The models were developed using data from two United Kingdom (UK) psychosis early intervention services and externally validated in another UK early intervention service. Model performance was assessed by discrimination and calibration. We developed the models in 785 patients and validated them externally in 1,110 patients. Both models predicted clozapine use well during internal validation (MOZART: C statistic, 0.70 (95% confidence interval, 0.63–0.76); LASSO: 0.69 (0.63–0.77)). At external validation, discrimination performance reduced (MOZART: 0.63 (0.58–0.69); LASSO: 0.64 (0.58–0.69)) but recovered after re-estimation of the lymphocyte predictor (0.67 (0.62–0.73)). Calibration plots showed good agreement between observed and predicted risk in the forced-entry model. We also present a decision-curve analysis and an online data visualization tool. The use of routinely collected clinical information including blood-based biomarkers taken at FEP onset can help to predict the individual risk of clozapine use, and should be considered equally alongside other potentially useful information such as symptom scores in large-scale efforts to predict psychiatric outcomes.
Use of a violence risk prediction tool (Oxford Mental Illness and Violence) in early intervention in psychosis services: mixed methods study of acceptability, feasibility and clinical role.
BACKGROUND: Scalable assessment tools for precision psychiatry are of increasing clinical interest. One clinical risk assessment that might be improved by such approaches is assessment of violence perpetration risk. This is an important adverse outcome to reduce for some people presenting to services for first-episode psychosis. A prediction tool (Oxford Mental Illness and Violence (OxMIV)) has been externally validated in these services, but clinical acceptability and role need to be examined and developed. AIMS: This study aimed to understand clinical use of the OxMIV tool to support violence risk management in early intervention in psychosis services in terms of acceptability to clinicians, patients and carers, practical feasibility, perceived utility, impact and role. METHOD: A mixed methods approach integrated quantitative data on utility and patterns of use of the OxMIV tool over 12 months in two services with qualitative data from interviews of 20 clinicians and 12 patients and carers. RESULTS: The OxMIV tool was used 141 times, mostly in new assessments. Required information was available, with only family history items scored unknown to any notable degree. The OxMIV tool was deemed helpful by clinicians in most cases, especially if there were previous risk concerns. It was acceptable practically, and broadly for the service, for which its concordance with clinical judgement was important. Patients and carers thought it could improve openness. There was some limited impact on plans for clinical support. CONCLUSIONS: The OxMIV tool met an identified clinical need to support clinical assessment for violence risk. Linkage to intervention pathways is a research priority.
A systematic review of in vivo brain insulin resistance biomarkers in humans
Type 2 diabetes mellitus (T2DM) is associated with an elevated risk of dementia, prompting interest into the concept of brain-specific insulin resistance. However, the brain's reliance on insulin-independent glucose transporters complicates attempts to measure in vivo brain insulin resistance using the definition of system-wide insulin resistance, which is based on glucose-insulin interactions. In this review, we explore three available biomarkers for evaluating in vivo brain-specific insulin resistance in humans: (1) correlating systemic insulin resistance with brain function, (2) examining functional brain changes after the administration of intranasal insulin, and (3) quantifying insulin signalling proteins in neuronally enriched blood-derived extracellular vesicles. Integrating evidence from these three approaches tentatively suggests for the first time that a comprehensive assessment of the brain's default mode network (DMN), combining these methodologies within a single study, may offer a useful biomarker to quantify in vivo brain-specific insulin resistance in humans. Correlating DMN responses to concentrations of pY-IRS-1 in blood-derived extracellular vesicles would corroborate evidence for a brain-specific biomarker and provide a scalable approach to detecting brain-specific insulin resistance in humans. This advancement would enable in vivo evaluations of insulin resistance in the central nervous system, akin to the precise measurements of systemic insulin resistance seen in T2DM. An established and clearly defined biomarker of in vivo brain insulin resistance in humans would permit further investigation into the links between diabetes and dementia, ultimately bolstering support for secondary dementia prevention by identifying those at higher risk for cognitive decline.
Embodied Cognition and MR-Based Interactive Narrative Design: The Case of 'Encountering Sanmao' at the Former Residence of Zhang Leping
This research examines interactive narratives within museum contexts utilizing Mixed Reality (MR) technology and introduces two design strategies inspired by the theory of embodied cognition: multisensory experiences and embodied interaction models, with an emphasis on both verbal and non-verbal interactions with virtual characters. Utilizing these strategies, the MR interactive narrative application Encountering Sanmao was developed for the Former Residence of Zhang Leping in Shanghai. To assess the effectiveness of these design strategies, a controlled experiment with a within-group design was performed on-site, involving 32 participants. Analysis of the collected interview data confirmed the efficacy of the strategies, providing valuable guidance for the implementation of MR interactive narrative experiences in small to medium-sized museums.
A network approach exploring the effects of cognitive remediation on cognition, symptoms, and functioning in early psychosis
BACKGROUND: Although cognitive remediation (CR) improves cognition and functioning, the key features that promote or inhibit its effectiveness, especially between cognitive domains, remain unknown. Discovering these key features will help to develop CR for more impact. AIM: To identify interrelations between cognition, symptoms, and functioning, using a novel network analysis approach and how CR affects these recovery outcomes. METHODS: A secondary analysis of randomized controlled trial data (N = 165) of CR in early psychosis. Regularized partial correlation networks were estimated, including symptoms, cognition, and functioning, for pre-, post-treatment, and change over time. Pre- and post-CR networks were compared on global strength, structure, edge invariance, and centrality invariance. RESULTS: Cognition, negative, and positive symptoms were separable constructs, with symptoms showing independent relationships with cognition. Negative symptoms were central to the CR networks and most strongly associated with change in functioning. Verbal and visual learning improvement showed independent relationships to improved social functioning and negative symptoms. Only visual learning improvement was positively associated with personal goal achievement. Pre- and post-CR networks did not differ in structure (M = 0.20, p = 0.45) but differed in global strength, reflecting greater overall connectivity in the post-CR network (S = 0.91, p = 0.03). CONCLUSIONS: Negative symptoms influenced network changes following therapy, and their reduction was linked to improvement in verbal and visual learning following CR. Independent relationships between visual and verbal learning and functioning suggest that they may be key intervention targets to enhance social and occupational functioning.
The Psychosis MRI Shared Data Resource (Psy-ShareD).
Neuroimaging research in the field of schizophrenia and other psychotic disorders has sought to investigate neuroanatomical markers, relative to healthy control groups. In recent decades, a large number of structural magnetic resonance imaging (MRI) studies have been funded and undertaken, but their small sample sizes and heterogeneous methods have led to inconsistencies across findings. To tackle this, efforts have been made to combine datasets across studies and sites. While notable recent multicentre initiatives and the resulting meta- and mega-analytical outputs have progressed the field, efforts have generally been restricted to MRI scans in one or two illness stages, often overlook patient heterogeneity, and study populations have rarely been globally representative of the diversity of patients who experience psychosis. Furthermore, access to these datasets is often restricted to consortia members who can contribute data, likely from research institutions located in high-income countries. The Psychosis MRI Shared Data Resource (Psy-ShareD) is a new open access structural MRI data sharing partnership that will host pre-existing structural T1-weighted MRI data collected across multiple sites worldwide, including the Global South. MRI T1 data included in Psy-ShareD will be available in image and feature-level formats, having been harmonised using state-of-the-art approaches. All T1 data will be linked to demographic and illness-related (diagnosis, symptoms, medication status) measures, and in a number of datasets, IQ and cognitive data, and medication history will also be available, allowing subgroup and dimensional analyses. Psy-ShareD will be free-to-access for all researchers. Importantly, comprehensive data catalogues, scientific support and training resources will be available to facilitate use by early career researchers and build capacity in the field. We are actively seeking new collaborators to contribute further T1 data. Collaborators will benefit in terms of authorships, as all publications arising from Psy-ShareD will include data contributors as authors.
Acute angiotensin receptor blockade and mnemonic discrimination in healthy participants.
BACKGROUND: The renin angiotensin system (RAS) is implicated in various cognitive processes relevant to anxiety. However, the role of the RAS in pattern separation, a hippocampal memory mechanism that enables discrete encoding of similar stimuli, is unclear. Given the proposed role of this mechanism in overgeneralization and the maintenance of anxiety, we explored the influence of the RAS on mnemonic discrimination, i.e., the behavioral ability arising from pattern separation. DESIGN: In a double-blind experimental medicine trial, we examined the effect of losartan, an angiotensin receptor blocker, on mnemonic discrimination in N = 60 healthy volunteers aged 18-50. Participants were randomly allocated to a 50 mg losartan or placebo condition, and then completed the Mnemonic Similarity Task (MST), an established measure of mnemonic discrimination. Main outcome measures were the lure discrimination index (LDI), calculated as the rate of 'similar' responses to lures minus 'similar' responses to foils, and recognition (REC) memory, calculated as the difference between the rate of 'old' responses to targets minus 'old' responses to foils. RESULTS: Data were available for N = 56 participants (N = 40 females, N = 16 males). Participants in the losartan group (N = 29) achieved significantly higher LDI scores (t(54) = 2.30, p = 0.025) compared to the placebo group (N = 27), indicating better mnemonic discrimination. No significant group differences were found in REC scores (U = 324, z = -1.10, d = 0.08; p = 0.271). CONCLUSIONS: We demonstrate for the first time that losartan improves mnemonic discrimination in healthy individuals, suggesting that the RAS may influence pattern separation and anxiety.
A cross-sectional survey of psychiatrists' experiences of using telepsychiatry during the COVID-19 pandemic and the relationship with burnout in selected high, middle, and low-income countries.
BackgroundThe COVID-19 pandemic necessitated substantial modifications in the delivery of patient care on a global scale. Telemedicine-based care services were implemented worldwide to maximize access to healthcare systems.AimsThis study aimed to investigate the use of and satisfaction with telepsychiatry services implemented during the COVID-19 pandemic by psychiatrists across low, middle, and high income countries, and to assess levels of burnout among psychiatrists providing telepsychiatry services in different settings and countries. We hypothesized that use of telepsychiatry will have increased during the pandemic and is associated with increased risk of burnout.MethodsA cross-sectional survey was conducted from October 2020 to June 2021 on psychiatrists practicing in Egypt, United Kingdom, Croatia, Belgium, Indonesia, Italy, and El Salvador. Participants were asked to provide sociodemographic data and to complete a questionnaire developed ad-hoc by the authors on telepsychiatry use, the Modified Arizona Telemedicine Program Satisfaction (MATPS) survey and the Oldenburg Burnout Inventory questionnaire.ResultsA total of 347 participants completed the survey. Sixty three percent indicated that they had not utilized teleservices for clinical consultation or academic purposes prior to the COVID-19 pandemic. A substantial shift was observed during the pandemic, with this percentage increasing to 98%. Over two-thirds of psychiatrists expressed satisfaction with the visual (76%) and audio (77%) aspects of teleconsultation. No significant correlation was found between burnout and satisfaction. While older age was negatively correlated with burnout levels, years of experience showed a positive correlation with levels of burnout.ConclusionWhile there was an increased acceptance and satisfaction with teleconsultation, a persistent preference for in-person consultations remained. The study did not find a correlation between satisfaction in telepsychiatry and levels of burnout. Moreover, increasing age was correlated with lower burn out rate, whereas a correlation between years of experience and heightened levels of burnout was evident.
Using Electronic Health Records to Facilitate Precision Psychiatry.
The use of clinical prediction models to produce individualized risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implement them in routine clinical care. The current review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number needed to test). We review 4 externally validated clinical prediction models designed to predict psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models and the potential added value of integrating data from evidence syntheses, standardized psychometric assessments, and biological data into EHRs. Clinical prediction models can utilize routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g., meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve the performance of clinical prediction models.
Proactive integrated consultation-liaison psychiatry and time spent in hospital by older medical inpatients in England (The HOME Study): a multicentre, parallel-group, randomised controlled trial.
BACKGROUND: Older people admitted to hospital in an emergency often have prolonged inpatient stays that worsen their outcomes, increase health-care costs, and reduce bed availability. Growing evidence suggests that the biopsychosocial complexity of their problems, which include cognitive impairment, depression, anxiety, multiple medical illnesses, and care needs resulting from functional dependency, prolongs hospital stays by making medical treatment less efficient and the planning of post-discharge care more difficult. We aimed to assess the effects of enhancing older inpatients' care with Proactive Integrated Consultation-Liaison Psychiatry (PICLP) in The HOME Study. We have previously described the benefits of PICLP reported by patients and clinicians. In this Article, we report the effectiveness and cost-effectiveness of PICLP-enhanced care, compared with usual care alone, in reducing time in hospital. METHODS: We did a parallel-group, multicentre, randomised controlled trial in 24 medical wards of three English acute general hospitals. Patients were eligible to take part if they were 65 years or older, had been admitted in an emergency, and were expected to remain in hospital for at least 2 days from the time of enrolment. Participants were randomly allocated to PICLP or usual care in a 1:1 ratio by a database software algorithm that used stratification by hospital, sex, and age, and randomly selected block sizes to ensure allocation concealment. PICLP clinicians (consultation-liaison psychiatrists supported by assisting clinicians) made proactive biopsychosocial assessments of patients' problems, then delivered discharge-focused care as integrated members of ward teams. The primary outcome was time spent as an inpatient (during the index admission and any emergency readmissions) in the 30 days post-randomisation. Secondary outcomes were the rate of discharge from hospital for the total length of the index admission; discharge destination; the length of the index admission after random allocation truncated at 30 days; the number of emergency readmissions to hospital, the number of days spent as an inpatient in an acute general hospital, and the rate of death in the year after random allocation; the patient's experience of the hospital stay; their view on the length of the hospital stay; anxiety (Generalized Anxiety Disorder-2); depression (Patient Health Questionnaire-2); cognitive function (Montreal Cognitive Assessment-Telephone version); independent functioning (Barthel Index of Activities of Daily Living); health-related quality of life (five-level EuroQol five-dimension questionnaire); and overall quality of life. Statisticians and data collectors were masked to treatment allocation; participants and ward staff could not be. Analyses were intention-to-treat. The trial had a patient and public involvement panel and was registered with ISRTCN (ISRCTN86120296). FINDINGS: 2744 participants (1399 [51·0%] male and 1345 [49·0%] female) were enrolled between May 2, 2018, and March 5, 2020; 1373 were allocated to PICLP and 1371 to usual care. Participants' mean age was 82·3 years (SD 8·2) and 2565 (93·5%) participants were White. The mean time spent in hospital in the 30 days post-randomisation (analysed for 2710 [98·8%] participants) was 11·37 days (SD 8·74) with PICLP and 11·85 days (SD 9·00) with usual care; adjusted mean difference -0·45 (95% CI -1·11 to 0·21; p=0·18). The only statistically and clinically significant difference in secondary outcomes was the rate of discharge, which was 8.5% higher (rate ratio 1·09 [95% CI 1·00 to 1·17]; p=0·042) with PICLP-a difference most apparent in patients who stayed for more than 2 weeks. Compared with usual care, PICLP was estimated to be modestly cost-saving and cost-effective over 1 and 3, but not 12, months. No intervention-related serious adverse events occurred. INTERPRETATION: This is the first randomised controlled trial of PICLP. PICLP is experienced by older medical inpatients and ward staff as enhancing medical care. It is also likely to be cost-saving in the short-term. Although the trial does not provide strong evidence that PICLP reduces time in hospital, it does support and inform its future development and evaluation. FUNDING: UK National Institute for Health and Care Research.
Systematic review of risk factors for violence in psychosis: 10-year update
BackgroundUnderstanding risk factors for violence in people with psychosis can inform risk management and violence prevention. However, much of the evidence comes from cross-sectional studies, and previous reviews require updating.AimsTo synthesise evidence from longitudinal studies on risk factors for violence in people with schizophrenia-spectrum disorders, bipolar disorder or other affective psychoses.MethodWe searched five bibliographic databases up to June 2022. We identified longitudinal studies reporting risk factors for violence in individuals diagnosed with schizophrenia or other psychoses using DSM or ICD criteria. If ≥3 independent samples reported a risk factor, we conducted random-effects meta-analyses to provide a pooled estimate. We also meta-analysed risk factors by major domains.ResultsWe identified 47 longitudinal studies on risk factors for violence in psychosis, representing 41 independent samples – 21 from the original and 20 from the updated review – and 203 297 individuals. A total of 30 risk factors were present in ≥3 independent samples. Criminal history factors were associated with the greatest risk of violent outcomes (pooled odds ratio 3.50, 95% CI = 2.37, 5.16), followed by substance misuse factors (odds ratio 2.36, 95% CI = 1.99, 2.80). Many treatment-related factors were protective (odds ratio 0.54, 95% CI = 0.34, 0.85). Effect estimates were attenuated in inpatient settings. We also identified novel risk factors, including cannabis use, in a secondary analysis (odds ratio 3.34, 95% CI = 2.32, 4.82).ConclusionsUsing longitudinal evidence, we have validated comorbid substance misuse and criminal history as major risk factors for violence in psychosis. Novel factors such as cannabis use need further replication. Several identified factors are possible intervention targets if associations are found to be causal.
Development and Validation of a Prediction Tool for Reoffending Risk in Domestic Violence.
IMPORTANCE: Current risk assessment tools for domestic violence against family members were developed with small and selected samples, have low accuracy with few external validations, and do not report key performance measures. OBJECTIVE: To develop new tools to assess risk of reoffending among individuals who have perpetrated domestic violence. DESIGN, SETTING, AND PARTICIPANTS: This prognostic study investigated a national cohort of all individuals arrested for domestic violence between 1998 and 2013 in Sweden using information from multiple national registers, including National Crime Register, National Patient Register, Longitudinal Integrated Database for Health Insurance and Labour Market Studies Register, and Multi-Generation Register. Data were analyzed from August 2022 to June 2023. EXPOSURE: Arrest for domestic violence. MAIN OUTCOMES AND MEASURES: Prediction models were developed for 3 reoffending outcomes after arrest for domestic violence: conviction of a new violent crime (including domestic violence), conviction of any new crime, and rearrest for domestic violence at 1 year, 3 years, and 5 years. The prediction models were created using sociodemographic factors, criminological factors, and mental health status-related factors, linking data from multiple population-based longitudinal registers. Cox proportional hazard multivariable regression was used to develop prediction models and validate them in external samples. Key performance measures, including discrimination at prespecified cutoffs and calibration statistics, were investigated. RESULTS: The cohort included 27 456 individuals (mean [SD] age, 39.4 [11.6] years; 24 804 men [90.3%]) arrested for domestic violence, of whom 4222 (15.4%) reoffended and were convicted for a new violent crime during a mean (SD) follow-up of 26.5 (27.0) months, 9010 (32.8%) reoffended and were convicted for a new crime (mean [SD] follow-up, 22.4 [25.1] months), and 2080 (7.6%) were rearrested for domestic violence (mean [SD] follow-up, 25.7 [30.6] months). Prediction models were developed with sociodemographic, criminological, and mental health factors and showed good measures of discrimination and calibration for violent reoffending and any reoffending. The area under the receiver operating characteristic curve (AUC) for risk of violent reoffending was 0.75 (95% CI, 0.74-0.76) at 1 year, 0.76 (95% CI, 0.75-0.77) at 3 years, and 0.76 (95% CI, 0.75-0.77) 5 years. The AUC for risk of any reoffending was 0.76 (95% CI, 0.75-0.77) at 1 year and at 3 years and 0.76 (95% CI, 0.75-0.76) at 5 years. The model for domestic violence reoffending showed modest discrimination (C index, 0.63; 95% CI, 0.61-0.65) and good calibration. The validation models showed discrimination and calibration performance similar to those of derivation models for all 3 reoffending outcomes. The prediction models have been translated into 3 simple online risk calculators that are freely available to use. CONCLUSIONS AND RELEVANCE: This prognostic study developed scalable, evidence-based prediction tools that could support decision-making in criminal justice systems, particularly at the arrest stage when identifying those at higher risk of reoffending and screening out individuals at low risk of reoffending. Furthermore, these tools can enhance treatment allocation by enabling criminal justice services to focus on modifiable risk factors identified in the tools for individuals at high risk of reoffending.
Ketogenic metabolic therapy in the remission of chronic major depressive disorder: a retrospective case study
BackgroundThere is limited evidence describing the use of ketogenic metabolic therapy (KMT), also known as a ketogenic diet (KD), to achieve full remission of treatment-resistant major depressive disorder (MDD) in real-world clinical settings. This case study examines a 47-year-old woman with lifelong treatment-resistant MDD who achieved complete remission of depressive symptoms and improved functioning through a ketogenic diet.MethodsThe patient engaged in KMT with a 1.5:1 macronutrient ratio under the supervision of a treatment team consisting of a medical professional, psychotherapist, and ketogenic-informed nutrition professional through an online program that provided both individual and group support. Interventions included dietary modifications, micronutrient supplementation, and participation in a group coaching program. Outcomes were assessed using validated tools for symptom severity, including PHQ-9 for depression and GAD-7 for anxiety, at baseline, 2 months, and 4 months post-intervention. Qualitative data on patient experiences and functional improvements were also collected.ResultsThe patient achieved remission of MDD within 8 weeks of initiating KMT, with PHQ-9 scores decreasing from 25 (severe depression) at baseline to 0 at 2- and 4-month assessments. GAD-7 scores decreased from 3 (minimal anxiety) to 0 over the same period. Qualitative findings revealed significant improvements in emotional regulation, energy levels, and cognitive function.ConclusionThis case study demonstrates the potential of KMT as a non-pharmacological intervention for achieving full remission in treatment-resistant MDD. These findings suggest further research to evaluate feasibility, efficacy, and broader applicability in diverse clinical settings.
Ketogenic metabolic therapy for schizoaffective disorder: a retrospective case series of psychotic symptom remission and mood recovery
BackgroundSchizoaffective disorder is a severe psychiatric condition characterized by mood disturbances and psychotic symptoms. Standard treatments, primarily pharmacological, often fail to control symptoms fully and can lead to significant metabolic side effects. Emerging evidence suggests that ketogenic metabolic therapy (KMT), also known as the ketogenic diet, may offer a powerful alternative to conventional treatments for mood components and resolve psychiatric symptoms in patients with schizoaffective disorder.MethodsThis case series investigates the effects of KMT on two individuals diagnosed with schizoaffective disorder who pursued this therapy due to the ineffectiveness of conventional treatments. Both case presentations followed a modified ketogenic diet with medical oversight. Symptom changes in mood were assessed using validated tools, including the Generalized Anxiety Disorder-7 (GAD-7), Depression Anxiety Stress Scales (DASS-42), PTSD Checklist for DSM-5 (PCL-5), and Patient Health Questionnaire-9 (PHQ-9).ResultsBoth case presentations experienced the complete cessation of psychotic symptoms and improvements in mood. Case 1, a 17-year-old female, achieved full remission of severe suicidal ideation, hallucinations, and anxiety within 6 weeks, with sustained improvements at a 24-week follow-up. Case 2, a 32-year-old female, achieved full remission of chronic psychotic and mood symptoms by 6 months. Patients either achieved full psychiatric deprescription or were in the process of deprescription at time of follow-up.ConclusionThis case series demonstrates that ketogenic metabolic therapy can resolve chronic psychotic and mood symptoms in patients with schizoaffective disorder, leading to full remission and significant functional recovery and reported improvements in quality of life that extend beyond symptom control with standard of care interventions.