Polygenic overlap with body-mass index improves prediction of treatment-resistant schizophrenia

Polygenic overlap with granulocyte counts identifies novel loci for clozapine metabolism and clozapine-induced agranulocytosis

While clozapine is the most effective antipsychotic drug, its use is limited due to hematological adverse effects involving the reduction of granulocyte counts with potential life-threatening agranulocytosis. It is not yet possible to predict or prevent the risk of agranulocytosis, and the mechanisms are unknown but likely related to clozapine metabolism. Genome-wide association studies (GWASs) of clozapine metabolism and clozapine-induced agranulocytosis have identified few genetic loci. We used the largest available GWAS summary statistics of clozapine metabolism (clozapine-to-norclozapine ratio) and clozapine-induced agranulocytosis, applying the conditional false discovery rate (condFDR) method to increase power for genetic discovery by conditioning on granulocyte counts variants. To investigate potential causal effects of shared loci, we performed Mendelian Randomization analyses. After conditioning on granulocyte counts, we identified two novel loci associated with clozapine-to-norclozapine ratio. These loci were significantly associated with clozapine metabolism in a validation sample of 392 clozapine-treated individuals. For clozapine-induced agranulocytosis, five loci were identified after conditioning on granulocyte counts. These five loci were significantly associated with reduced granulocyte counts in a small independent sample of clozapine-treated individuals. Genetic liability to slow clozapine metabolism (high clozapine-to-norclozapine ratio) showed evidence of a causal effect on reduced neutrophil counts, and genetic liability to low neutrophil counts exhibited weak evidence of a causal effect on clozapine-induced agranulocytosis. Our findings of shared genetic variants associated with clozapine metabolism and granulocyte counts may form the basis for developing prediction models for clozapine-induced agranulocytosis.

Pathway-Specific Polygenic Scores for Lithium Response for Predicting Clinical Lithium Treatment Response in Patients with Bipolar Disorder

Background

Polygenic scores (PGSs) hold the potential to identify patients who respond favourably to specific psychiatric treatments. However, their biological interpretations remain unclear. In this study, we developed pathway-specific PGSs (PS PGS ) for lithium response and assessed their association with clinical lithium response in patients with bipolar disorder (BD).

Methods

Using sets of genes involved in pathways affected by lithium, we developed nine PS PGSs and evaluated their associations with lithium response in the International Consortium on Lithium Genetics cohort (ConLi + Gen: N = 2367), validated in the combined PsyCourse (N = 105) and BipoLife (N = 102) cohorts. Lithium responsiveness was assessed using the Retrospective Assessment of the Lithium Response Phenotype Scale (ALDA scale), for categorical outcome (good vs poor response) and continuous ALDA total score. Logistic and linear regressions, adjusting for age, sex, chip type, and the first four genetic principal components, were used to test associations, after multiple testing corrections ( p <0.05).

Results

Response to lithium was associated with PS PGS for acetylcholine, GABA, calcium channel signalling, mitochondria, circadian rhythm, and GSK pathways, R² ranging from 0.29% to 1.91%, with R² of 3.71% for the combined PS PGS. Associations for GABA PGS and CIR PGS were replicated. In decile-based stratified analysis, patients with the highest genetic loading (10 th decile) for acetylcholine pathway genetic variants were 3.03 times (95%CI: 1.95 - 4.69) more likely to have a good lithium response than the lowest decile (1 st decile).

Conclusion

PS PGSs achieved predictive performance comparable with conventional genome-wide PGSs, with more biological interpretability and using a smaller list of genetic variants, facilitating further investigation into the interaction of variants and biological pathways underlying lithium response.

A stratified treatment algorithm in psychiatry: a program on stratified pharmacogenomics in severe mental illness (Psych-STRATA): concept, objectives and methodologies of a multidisciplinary project funded by Horizon Europe

Schizophrenia (SCZ), bipolar (BD) and major depression disorder (MDD) are severe psychiatric disorders that are challenging to treat, often leading to treatment resistance (TR). It is crucial to develop effective methods to identify and treat patients at risk of TR at an early stage in a personalized manner, considering their biological basis, their clinical and psychosocial characteristics. Effective translation of theoretical knowledge into clinical practice is essential for achieving this goal. The Psych-STRATA consortium addresses this research gap through a seven-step approach. First, transdiagnostic biosignatures of SCZ, BD and MDD are identified by GWAS and multi-modal omics signatures associated with treatment outcome and TR (steps 1 and 2). In a next step (step 3), a randomized controlled intervention study is conducted to test the efficacy and safety of an early intensified pharmacological treatment. Following this RCT, a combined clinical and omics-based algorithm will be developed to estimate the risk for TR. This algorithm-based tool will be designed for early detection and management of TR (step 4). This algorithm will then be implemented into a framework of shared treatment decision-making with a novel mental health board (step 5). The final focus of the project is based on patient empowerment, dissemination and education (step 6) as well as the development of a software for fast, effective and individualized treatment decisions (step 7). The project has the potential to change the current trial and error treatment approach towards an evidence-based individualized treatment setting that takes TR risk into account at an early stage.

How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.

Pharmacogenomic scores in psychiatry: systematic review of current evidence

In the past two decades, significant progress has been made in the development of polygenic scores (PGSs). One specific application of PGSs is the development and potential use of pharmacogenomic- scores (PGx-scores) to identify patients who can benefit from a specific medication or are likely to experience side effects. This systematic review comprehensively evaluates published PGx-score studies in psychiatry and provides insights into their potential clinical use and avenues for future development. A systematic literature search was conducted across PubMed, EMBASE, and Web of Science databases until 22 August 2023. This review included fifty-three primary studies, of which the majority (69.8%) were conducted using samples of European ancestry. We found that over 90% of PGx-scores in psychiatry have been developed based on psychiatric and medical diagnoses or trait variants, rather than pharmacogenomic variants. Among these PGx-scores, the polygenic score for schizophrenia (PGSSCZ) has been most extensively studied in relation to its impact on treatment outcomes (32 publications). Twenty (62.5%) of these studies suggest that individuals with higher PGSSCZ have negative outcomes from psychotropic treatment - poorer treatment response, higher rates of treatment resistance, more antipsychotic-induced side effects, or more psychiatric hospitalizations, while the remaining studies did not find significant associations. Although PGx-scores alone accounted for at best 5.6% of the variance in treatment outcomes (in schizophrenia treatment resistance), together with clinical variables they explained up to 13.7% (in bipolar lithium response), suggesting that clinical translation might be achieved by including PGx-scores in multivariable models. In conclusion, our literature review found that there are still very few studies developing PGx-scores using pharmacogenomic variants. Research with larger and diverse populations is required to develop clinically relevant PGx-scores, using biology-informed and multi-phenotypic polygenic scoring approaches, as well as by integrating clinical variables with these scores to facilitate their translation to psychiatric practice.

Macrod1 suppresses diabetic cardiomyopathy via regulating PARP1-NAD+-SIRT3 pathway

Diabetic cardiomyopathy (DCM), one of the most serious long-term consequences of diabetes, is closely associated with oxidative stress, inflammation and apoptosis in the heart. MACRO domain containing 1 (Macrod1) is an ADP-ribosylhydrolase 1 that is highly enriched in mitochondria, participating in the pathogenesis of cardiovascular diseases. In this study, we investigated the role of Macrod1 in DCM. A mice model was established by feeding a high-fat diet (HFD) and intraperitoneal injection of streptozotocin (STZ). We showed that Macrod1 expression levels were significantly downregulated in cardiac tissue of DCM mice. Reduced expression of Macrod1 was also observed in neonatal rat cardiomyocytes (NRCMs) treated with palmitic acid (PA, 400 μM) in vitro. Knockout of Macrod1 in DCM mice not only worsened glycemic control, but also aggravated cardiac remodeling, mitochondrial dysfunction, NAD+ consumption and oxidative stress, whereas cardiac-specific overexpression of Macrod1 partially reversed these pathological processes. In PA-treated NRCMs, overexpression of Macrod1 significantly inhibited PARP1 expression and restored NAD+ levels, activating SIRT3 to resist oxidative stress. Supplementation with the NAD+ precursor Niacin (50 μM) alleviated oxidative stress in PA-stimulated cardiomyocytes. We revealed that Macrod1 reduced NAD+ consumption by inhibiting PARP1 expression, thereby activating SIRT3 and anti-oxidative stress signaling. This study identifies Macrod1 as a novel target for DCM treatment. Targeting the PARP1-NAD+-SIRT3 axis may open a novel avenue to development of new intervention strategies in DCM. Schematic illustration of macrod1 ameliorating diabetic cardiomyopathy oxidative stress via PARP1-NAD+-SIRT3 axis.