Alessandro Serretti’s research while affiliated with Università degli Studi di Enna Kore and other places

The relationship between major depressive disorder (MDD) and mild motor signs (MMS) remains to be elucidated. The present study aims to assess the association between neurological symptoms and medications and treatment response. Neurological signs in 790 patients with MDD were correlated with treatment outcome. Three hundred ten (39.2%) were responders and 480 (60.8%) were non-responders. 342 (43.3%) presented neurological signs. In the whole sample negative associations between dystonia and rigidity and various medications was observed. Non-response was associated with dystonia, rigidity, and hypokinesia independent from age and medications. This study highlighted an association between MMS and specific medications. Moreover, MMS were associated with non-response to treatment, regardless of medication use. This may suggest that a subgroup of patients with MDD may respond less to therapy because of an underlying still undetected neurological disorder.

Precision psychiatry aims to improve routine clinical practice by integrating biological, clinical, and environmental data. Many studies have been performed in different areas of research on major depressive disorder, bipolar disorder, and schizophrenia. Neuroimaging and EEG findings have identified potential circuit-level abnormalities predictive of treatment response. Protein biomarkers, including IL-2, S100B, and NfL, and the kynurenine pathway illustrate the role of immune and metabolic dysregulation. Circadian rhythm disturbances and the gut microbiome have also emerged as critical transdiagnostic contributors to psychiatric symptomatology and outcomes. Moreover, advances in genomic research and polygenic scores support the perspective of personalized risk stratification and medication selection. While challenges remain, such as data replication issues, prediction model accuracy and scalability, the progress so far achieved underscores the potential of precision psychiatry in improving diagnostic accuracy and treatment effectiveness.

Suicidality phenotypes, consisting of suicidal ideation (SI), suicide attempt (SA), and suicide death (SD), are all heritable but present unique challenges in genome-wide association studies (GWAS) due to their individual complexity, overlap with each other and with related self-harm phenotypes, and varying associations with psychiatric disorders. GWAS have uncovered several loci associated with suicidality phenotypes by meta-analyzing data from multiple cohorts. However, combining datasets from many research groups, where each group may use different study designs, phenotyping instruments, and definitions of suicidality phenotypes, presents challenges. Heterogeneity resulting from these differences can limit genetic discovery; harmonizing phenotype definitions to ensure consistency will greatly improve results. Here, we describe a standardized phenotyping protocol that draws on the expertise of a subgroup of clinicians, researchers, and experts from the Psychiatric Genomics Consortium Suicide Working Group to propose consensus definitions for SI, SA, and SD for genetic studies.

The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a major public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N = 9,725–933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg| = 0.21–1, pFDR < 0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer’s disease, bipolar disorder, and Tourette’s syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as SLC39A8 and HLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings highlight the complex genetic architecture of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.

Anhedonia is a transdiagnostic domain that leads to poor disorder outcome and low remission rates. This narrative review describes a broad range of interventions targeting anhedonia, including pharmacological, neuromodulatory, behavioral, and lifestyle‐based approaches. Drugs such as vortioxetine, agomelatine, bupropion, ketamine, and brexpiprazole show promising anti‐anhedonic effects, while traditional antidepressants, such as serotonin‐norepinephrine reuptake inhibitors (SNRIs) and, even more so, selective serotonin reuptake inhibitors (SSRIs), are less effective. Neuromodulation techniques, including repetitive transcranial magnetic stimulation, transcranial direct current stimulation, and transcutaneous auricular vagus nerve stimulation, proved effective at improving anhedonia, particularly when used in targeted areas. Psychotherapeutic interventions, including behavioral activation, mindfulness‐based strategies, and savoring techniques, also help re‐engage patients with pleasurable activities and enhance positive affect. Innovative treatments, such as aticaprant and psilocybin, showed promising results. Substantial evidence suggests that improving anhedonia leads to better psychosocial functioning, quality of life, and sustained remission. Although most data come from short‐term studies, several long‐term analyses suggest that maintaining hedonic improvements is feasible and beneficial. The reviewed evidence underscores the importance of routine assessment of anhedonia and the integration of symptom‐specific strategies. Tailoring interventions to address individual patterns of reward disruption may optimize outcomes for patients with anhedonia.

Schizophrenia polygenic risk scores (SCZ PRS) have emerged as important tools for modulating factors not only in schizophrenia but also in major psychiatric disorders, such as major depression (MDD) and bipolar disorder (BD). Initially developed to capture the common variant risk for SCZ, accumulating evidence highlights the transdiagnostic impact of SCZ PRS on clinical severity, treatment response, and functional outcomes. This review synthesizes recent findings on the relationship between SCZ PRS and treatment outcomes across SCZ, BD, and MDD. A higher SCZ PRS is associated with poorer treatment outcomes, including treatment resistance or non-remission to antidepressants in MDD, reduced antipsychotic response in SCZ, and diminished lithium efficacy in BD. SCZ PRS is also linked to persistent negative symptoms, cognitive impairments, and long-term illness severity in SCZ. While the effect sizes are generally modest, integration of SCZ PRS with environmental factors, multiomics, and neuroimaging may enhance predictive accuracy. Despite variability in reported associations, the overarching evidence supports a transdiagnostic influence of SCZ PRS on disease trajectories and treatment responses. As a promising component of precision psychiatry, SCZ PRS holds potential for guiding more targeted and effective interventions. Future research should focus on combining SCZ PRS with multimodal approaches to fully realize its clinical utility.

Depression is a common comorbidity in Parkinson’s disease (PD), significantly reducing patients’ quality of life. This mini-review examines pharmacological and nonpharmacological therapies for managing depression in PD, analyzing their benefits, and limitations. Pharmacological options include tricyclic antidepressants, selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), levodopa, dopaminergic agonists, and monoamine oxidase B inhibitors. Nonpharmacological strategies involve brief psychodynamic therapy, cognitive-behavioral therapy (CBT), physical exercise, phytomedicine, massage therapy, music therapy, phototherapy, yoga, repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation, electroconvulsive therapy (ECT), and deep brain stimulation. SSRIs, SNRIs, and some dopamine agonists have shown effectiveness and good tolerability, especially when combined with CBT or rTMS. For severe or refractory cases, ECT remains a viable option. Although many of these therapies show promise, the limited number and scale of studies for each treatment restrict the strength of current evidence. Further large-scale, multicenter randomized-controlled trials are essential to validate these preliminary findings and establish evidence-based guidelines. In addition, the potential benefits of social support and brief psychodynamic therapy in the context of PD-related depression require further exploration to provide holistic care strategies for this population.

Capgras syndrome (CS) is a delusional misidentification phenomenon increasingly reported in patients with Parkinson’s disease (PD) and Lewy Body Dementia (LBD). Aim of the present scoping review was to provide an overview on current evidence on the pharmacological treatment of CS in PD and LBD, identifying knowledge gaps in the literature. The following databases were consulted: PubMed, Google Scholar, the Cochrane Database and Web of Science. The search query covered a time period from 1976 until 2022. Fourteen studies on PD (11 single case reports, 3 case series) and five on LBD (all single case reports) met the inclusion criteria. Most PD patients with CS had cognitive decline and visual hallucinations, and were managed by reducing dopaminergic therapy and prescribing neuroleptics (quetiapine, clozapine or pimavanserin), which often resulted in improvement. Neuroleptics have also been used in LBD, but with variable efficacy. Although neuroleptics and dopaminergic dose adjustments appear to be beneficial for CS in PD and LBD, robust evidence is lacking. Future prospective studies are essential to establish evidence-based guidelines for this challenging syndrome.

Background Major depressive disorder (MDD) is among the leading causes of disability worldwide and treatment efficacy is variable across patients. Polymorphisms in cytochrome P450 2C19 (CYP2C19) play a role in response and side effects to medications; however, they interact with other factors. We aimed to predict treatment outcome in MDD using a machine learning model combining CYP2C19 activity and nongenetic predictors. Methods A total of 1410 patients with MDD were recruited in a cross-sectional study. We extracted the subgroup treated with psychotropic drugs metabolized by CYP2C19. CYP2C19 metabolic activity was determined by the combination of *1, *2, *3, and *17 alleles. We tested if treatment response, treatment-resistant depression, and side effects could be inferred from CYP2C19 activity in combination with clinical-demographic and environmental features. The model used for the analysis was based on a decision tree algorithm using five-fold cross-validation. Results A total of 820 patients were treated with CYP2C19 metabolized drugs. The predictive performance of the model showed at best.70 accuracy for the classification of treatment response (average accuracy = 0.65, error = ±0.047) and an average accuracy of approximately 0.57 across all the tested outcomes. Age, BMI, and baseline depression severity were the main features influencing prediction across all the tested outcomes. CYP2C19 metabolizing status influenced both response and side effects but to a lower extent than the previously indicated features. Conclusion Predictive modeling could contribute to precision psychiatry. However, our study underlines the difficulty in selecting variables with sufficient impact on complex outcomes.