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

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.