The routine in-depth characterization of patients with methods of clinical and scale-based examination, neuropsychology, based on biomaterials, and sensor-based information opens up transformative possibilities on the way to personalized diagnostics, treatment and prevention in psychiatry, psychotherapy, and psychosomatics. Effective integration of the additional temporal and logistical effort into everyday care as well as the acceptance by patients are critical to the success of such an approach but there is little evidence on this to date. We report here on the establishment of the Diagnosis and Admission Center (DAZ) at the Central Institute of Mental Health (ZI) in Mannheim. The DAZ is an outpatient unit upstream of other care structures for clinical and scientific phenotyping across diagnoses as a starting point for data-driven, individualized pathways to further treatment, diagnostics or research. We describe the functions, goals, and implementation of the newly created clinical scientific translational structure, provide an overview of the patient populations it has reached, and provide data on its acceptance. In this context, the close integration with downstream clinical processes enables a better coordinated and demand-oriented allocation. In addition, DAZ enables a faster start of disorder-specific diagnostics and treatment. Since its launch in April 2021 up to the end of 2022, 1021 patients underwent psychiatric evaluation at DAZ during a pilot phase. The patient sample corresponded to a representative sample from standard care and the newly established processes were regarded as helpful by patients. In summary, the DAZ uniquely combines the interests and needs of patient with the collection of scientifically relevant data.
UNASSIGNED: Die routinemäßige, tiefgreifende Charakterisierung von Patienten mit Methoden der klinischen und skalenbasierten Untersuchung, der Neuropsychologie, anhand von Biomaterialien und sensorbasierten Informationen verspricht transformative Möglichkeiten auf dem Weg zu einer personalisierten Diagnostik, Therapie und Prävention in der Psychiatrie, Psychotherapie und Psychosomatik. Die effektive Integration des zusätzlichen zeitlichen und logistischen Aufwands in den Versorgungsalltag sowie die Akzeptanz bei Patienten sind entscheidend für den Erfolg eines solchen Ansatzes, hierzu liegen jedoch bisher kaum Daten vor. Wir berichten hier über die Etablierung eines Diagnose- und Aufnahmezentrums (DAZ) am Zentralinstitut für Seelische Gesundheit (ZI) in Mannheim. Beim DAZ handelt es sich um eine den anderen Versorgungstrukturen vorgeschaltete ambulante Einheit zur klinischen und wissenschaftlichen diagnoseübergreifenden Phänotypisierung als Ausgangsbasis für eine datenunterstützte, individuelle Bahnung der weiteren Behandlungs‑, Diagnostik- oder Studienpfade. Wir beschreiben die Funktionen, Ziele und Implementierung der neu geschaffenen klinisch-wissenschaftlich translationalen Struktur, geben einen Überblick über die damit erreichten Patientenpopulationen und liefern Daten zur Akzeptanz. Die enge Verzahnung mit den nachgelagerten klinischen Prozessen ermöglicht dabei eine besser abgestimmte und bedarfsorientierte Zuweisung und einen schnelleren Beginn der störungsspezifischen Diagnostik und Therapie. Seit dem Start im April 2021 bis Ende 2022 wurden in einer Pilotphase 1021 Patienten im DAZ psychiatrisch untersucht. Die Patientenklientel entsprach dabei einer repräsentativen Stichprobe aus der Regelversorgung und die neu etablierten Prozesse wurden von Patienten als hilfreich erlebt. Zusammenfassend verknüpft das DAZ somit in hohem Maße Interessen und Bedürfnisse der Patienten mit der Erhebung wissenschaftlich relevanter Daten.

Research into neuroimaging biomarkers for Late Life Depression (LLD) has identified neural correlates of LLD including increased white matter hyperintensities and reduced hippocampal volume. However, studies into neuroimaging biomarkers for LLD largely fail to converge. This lack of replicability is potentially due to challenges linked to construct variability, etiological heterogeneity, and experimental rigor. We discuss suggestions to help address these challenges, including improved construct standardization, increased sample sizes, multimodal approaches to parse heterogeneity, and the use of individualized analytical models.

暂无摘要。

The field of psychiatry is facing an important paradigm shift in the provision of clinical care and mental health service organization toward personalization and integration of multimodal data science. This approach, termed precision psychiatry, aims at identifying subgroups of patients more prone to the development of a certain phenotype, such as symptoms or severe mental disorders (risk detection), and/or to guide treatment selection. Pharmacogenomics and computational psychiatry are two fundamental tools of precision psychiatry, which have seen increasing levels of integration in clinical settings. Here we present a brief overview of these two applications of precision psychiatry in clinical settings.

Antipsychotic (AP)-induced adverse drug reactions (ADRs) are a current problem of biological and clinical psychiatry. Despite the development of new generations of APs, the problem of AP-induced ADRs has not been solved and continues to be actively studied. One of the important mechanisms for the development of AP-induced ADRs is a genetically-determined impairment of AP efflux across the blood-brain barrier (BBB). We present a narrative review of publications in databases (PubMed, Springer, Scopus, Web of Science E-Library) and online resources: The Human Protein Atlas; GeneCards: The Human Gene Database; US National Library of Medicine; SNPedia; OMIM Online Mendelian Inheritance in Man; The PharmGKB. The role of 15 transport proteins involved in the efflux of drugs and other xenobiotics across cell membranes (P-gp, TAP1, TAP2, MDR3, BSEP, MRP1, MRP2, MRP3, MRP4, MRP5, MRP6, MRP7, MRP8, MRP9, BCRP) was analyzed. The important role of three transporter proteins (P-gp, BCRP, MRP1) in the efflux of APs through the BBB was shown, as well as the association of the functional activity and expression of these transport proteins with low-functional and non-functional single nucleotide variants (SNVs)/polymorphisms of the ABCB1, ABCG2, ABCC1 genes, encoding these transport proteins, respectively, in patients with schizophrenia spectrum disorders (SSDs). The authors propose a new pharmacogenetic panel \"Transporter protein (PT)-Antipsychotic (AP) Pharmacogenetic test (PGx)\" (PTAP-PGx), which allows the evaluation of the cumulative contribution of the studied genetic biomarkers of the impairment of AP efflux through the BBB. The authors also propose a riskometer for PTAP-PGx and a decision-making algorithm for psychiatrists. Conclusions: Understanding the role of the transportation of impaired APs across the BBB and the use of genetic biomarkers for its disruption may make it possible to reduce the frequency and severity of AP-induced ADRs, since this risk can be partially modified by the personalized selection of APs and their dosing rates, taking into account the genetic predisposition of the patient with SSD.

暂无摘要。

The inadequate efficacy and adverse effects of antipsychotics severely affect the recovery of patients with schizophrenia spectrum disorders (SSD). We report the evidence for associations between pharmacogenetic (PGx) variants and antipsychotics outcomes, including antipsychotic response, antipsychotic-induced weight/BMI gain, metabolic syndrome, antipsychotic-related prolactin levels, antipsychotic-induced tardive dyskinesia (TD), clozapine-induced agranulocytosis (CLA), and drug concentration level (pharmacokinetics) in SSD patients. Through an in-depth systematic search in 2010-2022, we identified 501 records. We included 29 meta-analyses constituting pooled data from 298 original studies over 69 PGx variants across 39 genes, 4 metabolizing phenotypes of CYP2D9, and 3 of CYP2C19. We observed weak unadjusted nominal significant (p < 0.05) additive effects of PGx variants of DRD1, DRD2, DRD3, HTR1A, HTR2A, HTR3A, and COMT (10 variants) on antipsychotic response; DRD2, HTR2C, BDNF, ADRA2A, ADRB3, GNB3, INSIG2, LEP, MC4R, and SNAP25 (14 variants) on weight gain; HTR2C (one variant) on metabolic syndrome; DRD2 (one variant) on prolactin levels; COMT and BDNF (two variants) on TD; HLA-DRB1 (one variant) on CLA; CYP2D6 (four phenotypes) and CYP2C19 (two phenotypes) on antipsychotics plasma levels. In the future, well-designed longitudinal naturalistic multi-center PGx studies are needed to validate the effectiveness of PGx variants in antipsychotic outcomes before establishing any reproducible PGx passport in clinical practice.

Reliable prediction models of treatment outcome in Major Depressive Disorder (MDD) are currently lacking in clinical practice. Data-driven outcome definitions, combining data from multiple modalities and incorporating clinician expertise might improve predictions.
We used unsupervised machine learning to identify treatment outcome classes in 1060 MDD inpatients. Subsequently, classification models were created on clinical and biological baseline information to predict treatment outcome classes and compared to the performance of two widely used classical outcome definitions. We also related the findings to results from an online survey that assessed which information clinicians use for outcome prognosis.
Three and four outcome classes were identified by unsupervised learning. However, data-driven outcome classes did not result in more accurate prediction models. The best prediction model was targeting treatment response in its standard definition and reached accuracies of 63.9 % in the test sample, and 59.5 % and 56.9 % in the validation samples. Top predictors included sociodemographic and clinical characteristics, while biological parameters did not improve prediction accuracies. Treatment history, personality factors, prior course of the disorder, and patient attitude towards treatment were ranked as most important indicators by clinicians.
Missing data limited the power to identify biological predictors of treatment outcome from certain modalities.
So far, the inclusion of available biological measures in addition to psychometric and clinical information did not improve predictive value of the models, which was overall low. Optimized biomarkers, stratified predictions and the inclusion of clinical expertise may improve future prediction models.

Extrapyramidal symptoms (EPS) are one of the most prominent side effects of haloperidol. Variability of EPS severity may be associated with the genetic factors, affecting both haloperidol pharmacokinetics (e.g., CYP2D6) and pharmacodynamics (e.g., DRD2, ANKK1). We conducted a 3-week prospective study to investigate the associations of ANKK1/DRD2 TaqIA (rs1800497), DRD2 -141C Ins/Del (rs1799732) polymorphisms and CYP2D6 metabolic phenotype on the efficacy of haloperidol treatment and severity of EPS in patients with schizophrenia spectrum disorders.
In total, 57 inpatients with schizophrenia spectrum disorders (24 (42.1%)) females; age -46.7 (11.8) years (M(SD)) of European ancestry were enrolled. BARS and SAS scales were used to assess EPS. PANSS and CGI scales - to assess the efficacy of haloperidol treatment. Genotyping was performed by real-time PCR. CYP2D6 metabolic phenotype was predicted by the CYP2D6 *3, *4, *5, *6, *9, *10, *41 and xN genotypes.
Minor C allele of TaqIA was associated with higher scores of BARS (p=0.029) and SAS (p=0.024) on day 21 and minor Del allele of -141C Ins/Del - with more prominent clinical improvement by CGI scale (p=0.007) but not by PANSS. These differences were observed only in extensive CYP2D6 metabolizers, although no associations with the metabolic type itself were found. General linear model showed that the combination of TaqIA genotype and metabolic type was significantly associated with BARS score on day 21 (p=0.013).
Our results highlight the importance of using both pharmacokinetic and pharmacodynamic genetic markers for predicting haloperidol treatment response to personalize schizophrenia spectrum disorders treatment.

OBJECTIVE: The aim of the current study is to provide insight into if, how, and when meaningful changes occur in individual patients who discontinue antidepressant medication. Agreement between macro-level quantitative symptom data, qualitative ratings, and micro-level Ecological Momentary Assessments is examined.
METHODS: During and shortly after antidepressant discontinuation, depressive symptoms and \'feeling down\' were measured in 56 participants, using the SCL-90 depression subscale weekly (macro-level) for 6 months, and 5 Ecological Momentary Assessments daily (micro-level) for 4 months (30.404 quantitative measurements in total). Qualitative information was also obtained, providing additional information to verify that changes were clinically meaningful.
RESULTS: At the macro-level, an increase in depressive symptoms was found in 58.9% of participants that (a) was statistically reliable, (b) persisted for 3 weeks and/or required intervention, and (c) was clinically meaningful to patients. Of these increases, 30.3% happened suddenly, 42.4% gradually, and for 27.3% criteria were inconclusive. Quantitative and qualitative criteria showed a very high agreement (Cohen\'s κ = 0.85) regarding if a participant experienced a recurrence of depression, but a moderate agreement (Cohen\'s κ = 0.49) regarding how that change occurred. At the micro-level, 41.1% of participants experienced only sudden increases in depressed mood, 12.5% only gradual, 30.4% experienced both types of increase, and 16.1% neither.
CONCLUSIONS: Meaningful change is common in patients discontinuing antidepressants, and there is substantial heterogeneity in how and when these changes occur. Depressive symptom change at the macro-level is not the same as depressive symptom change at the micro-level.