Genome-wide association identifies a novel locus for delirium risk

Large-scale whole-exome sequencing of neuropsychiatric diseases and traits in 350,770 adults

While numerous genomic loci have been identified for neuropsychiatric conditions, the contribution of protein-coding variants has yet to be determined. Here we conducted a large-scale whole-exome-sequencing study to interrogate the impact of protein-coding variants on 46 neuropsychiatric diseases and 23 traits in 350,770 adults from the UK Biobank. Twenty new genes were associated with neuropsychiatric diseases through coding variants, among which 16 genes had impacts on the longitudinal risks of diseases. Thirty new genes were associated with neuropsychiatric traits, with SYNGAP1 showing pleiotropic effects across cognitive function domains. Pairwise estimation of genetic correlations at the coding-variant level highlighted shared genetic associations among pairs of neurodegenerative diseases and mental disorders. Lastly, a comprehensive multi-omics analysis suggested that alterations in brain structures, blood proteins and inflammation potentially contribute to the gene-phenotype linkages. Overall, our findings characterized a compendium of protein-coding variants for future research on the biology and therapeutics of neuropsychiatric phenotypes.

Exploration of key drug target proteins highlighting their related regulatory molecules, functional pathways and drug candidates associated with delirium: evidence from meta-data analyses

BACKGROUND

Delirium is a prevalent neuropsychiatric medical phenomenon that causes serious emergency outcomes, including mortality and morbidity. It also increases the suffering and the economic burden for families and carers. Unfortunately, the pathophysiology of delirium is still unknown, which is a major obstacle to therapeutic development. The modern network-based system biology and multi-omics analysis approach has been widely used to recover the key drug target biomolecules and signaling pathways associated with disease pathophysiology. This study aimed to identify the major drug target hub-proteins associated with delirium, their regulatory molecules with functional pathways, and repurposable drug candidates for delirium treatment.

METHODS

We used a comprehensive proteomic seed dataset derived from a systematic literature review and the Comparative Toxicogenomics Database (CTD). An integrated multi-omics network-based bioinformatics approach was utilized in this study. The STRING database was used to construct the protein-protein interaction (PPI) network. The gene set enrichment and signaling pathways analysis, the regulatory transcription factors and microRNAs were conducted using delirium-associated genes. Finally, hub-proteins associated repurposable drugs were retrieved from CMap database.

RESULTS

We have distinguished 11 drug targeted hub-proteins (MAPK1, MAPK3, TP53, JUN, STAT3, SRC, RELA, AKT1, MAPK14, HSP90AA1 and DLG4), 5 transcription factors (FOXC1, GATA2, YY1, TFAP2A and SREBF1) and 6 microRNA (miR-375, miR-17-5, miR-17-5p, miR-106a-5p, miR-125b-5p, and miR-125a-5p) associated with delirium. The functional enrichment and pathway analysis revealed the cytokines, inflammation, postoperative pain, oxidative stress-associated pathways, developmental biology, shigellosis and cellular senescence which are closely connected with delirium development and the hallmarks of aging. The hub-proteins associated computationally identified repurposable drugs were retrieved from database. The predicted drug molecules including aspirin, irbesartan, ephedrine-(racemic), nedocromil, and guanidine were characterized as anti-inflammatory, stimulating the central nervous system, neuroprotective medication based on the existing literatures. The drug molecules may play an important role for therapeutic development against delirium if they are investigated more extensively through clinical trials and various wet lab experiments.

CONCLUSION

This study could possibly help future research on investigating the delirium-associated therapeutic target biomarker hub-proteins and repurposed drug compounds. These results will also aid understanding of the molecular mechanisms that underlie the pathophysiology of delirium onset and molecular function.

Application of Multiple Omics to Understand Postoperative Delirium Pathophysiology in Humans

Delirium, an acute change in cognition, is common, morbid, and costly, particularly among hospitalized older adults. Despite growing knowledge of its epidemiology, far less is known about delirium pathophysiology. Initial work understanding delirium pathogenesis has focused on assaying single or a limited subset of molecules or genetic loci. Recent technological advances at the forefront of biomarker and drug target discovery have facilitated application of multiple "omics" approaches aimed to provide a more complete understanding of complex disease processes such as delirium. At its basic level, "omics" involves comparison of genes (genomics, epigenomics), transcripts (transcriptomics), proteins (proteomics), metabolites (metabolomics), or lipids (lipidomics) in biological fluids or tissues obtained from patients who have a certain condition (i.e., delirium) and those who do not. Multi-omics analyses of these various types of molecules combined with machine learning and systems biology enable the discovery of biomarkers, biological pathways, and predictors of delirium, thus elucidating its pathophysiology. This review provides an overview of the most recent omics techniques, their current impact on identifying delirium biomarkers, and future potential in enhancing our understanding of delirium pathogenesis. We summarize challenges in identification of specific biomarkers of delirium and, more importantly, in discovering the mechanisms underlying delirium pathophysiology. Based on mounting evidence, we highlight a heightened inflammatory response as one common pathway in delirium risk and progression, and we suggest other promising biological mechanisms that have recently emerged. Advanced multiple omics approaches coupled with bioinformatics methodologies have great promise to yield important discoveries that will advance delirium research.

Towards experimental models of delirium utilizing zebrafish

Delirium is an acute neuropsychiatric condition characterized by impaired behavior and cognition. Although the syndrome has been known for millennia, its CNS mechanisms and risk factors remain poorly understood. Experimental animal models, especially rodent-based, are commonly used to probe various pathogenetic aspects of delirium. Complementing rodents, the zebrafish (Danio rerio) emerges as a promising novel model organism to study delirium. Zebrafish demonstrate high genetic and physiological homology to mammals, easy maintenance, robust behaviors in various sensitive behavioral tests, and the potential to screen for pharmacological agents relevant to delirium. Here, we critically discuss recent developments in the field, and emphasize the developing utility of zebrafish models for translational studies of delirium and deliriant drugs. Overall, the zebrafish represents a valuable and promising aquatic model species whose use may help understand delirium etiology, as well as develop novel therapies for this severely debilitating disorder.

A Review of Biomarkers in Delirium Superimposed on Dementia (DSD) and Their Clinical Application to Personalized Treatment and Management

Delirium superimposed on dementia (DSD) occurs when patients with pre-existing dementia develop delirium. This complication causes patients to become impaired, posing safety concerns for both hospital staff and patients. Furthermore, there is an increased risk of worsening functional disability and death. Despite medical advances, DSD provides both diagnostic and therapeutic challenges to providers. Identifying at-risk patients and providing personalized medicine and patient care can decrease disease burden in a time-efficient manner. This review delves into bioinformatics-based studies of DSD in order to design and implement a personalized medicine-based approach. Our findings suggest alternative medical treatment methods based on gene-gene interactions, gene-microRNA (miRNA) interactions, gene-drug interactions, and pharmacogenetic variants involved in dementia and psychiatric disorders. We identify 17 genes commonly associated with both dementia and delirium including apolipoprotein E (ApoE), brain-derived neurotrophic factor (BDNF), catechol-O-methyltransferase (COMT), butyrylcholinesterase (BChE), acetylcholinesterase (AChE), DNA methyltransferase 1 (DNMT1), prion protein (PrP), tumor necrosis factor (TNF), serine palmitoyltransferase long chain base subunit 1 (SPTLC1), microtubule-associated protein tau (MAPT), alpha-synuclein (αS), superoxide dismutase 1 (SOD1), amyloid beta precursor protein (APP), neurofilament light (NFL), neurofilament heavy, 5-hydroxytryptamine receptor 2A (HTR2A), and serpin family A member 3 (ERAP3). In addition, we identify six main genes that form an inner concentric model, as well as their associated miRNA. The FDA-approved medications that were found to be effective against the six main genes were identified. Furthermore, the PharmGKB database was used to identify variants of these six genes in order to suggest future treatment options. We also looked at previous research and evidence on biomarkers that could be used to detect DSD. According to research, there are three types of biomarkers that can be used depending on the stage of delirium. The pathological mechanisms underlying delirium are also discussed. This review will identify treatment and diagnostic options for personalized DSD management.

Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients

OBJECTIVE

To validate a previously published machine learning model of delirium risk in hospitalized patients with coronavirus disease 2019 (COVID-19).

METHOD

Using data from six hospitals across two academic medical networks covering care occurring after initial model development, we calculated the predicted risk of delirium using a previously developed risk model applied to diagnostic, medication, laboratory, and other clinical features available in the electronic health record (EHR) at time of hospital admission. We evaluated the accuracy of these predictions against subsequent delirium diagnoses during that admission.

RESULTS

Of the 5102 patients in this cohort, 716 (14%) developed delirium. The model's risk predictions produced a c-index of 0.75 (95% CI, 0.73-0.77) with 27.7% of cases occurring in the top decile of predicted risk scores. Model calibration was diminished compared to the initial COVID-19 wave.

CONCLUSION

This EHR delirium risk prediction model, developed during the initial surge of COVID-19 patients, produced consistent discrimination over subsequent larger waves; however, with changing cohort composition and delirium occurrence rates, model calibration decreased. These results underscore the importance of calibration, and the challenge of developing risk models for clinical contexts where standard of care and clinical populations may shift.

Association between genetic variants of the cholinergic system and postoperative delirium and cognitive dysfunction in elderly patients

Background

Postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) are frequent and serious complications after surgery. We aim to investigate the association between genetic variants in cholinergic candidate genes according to the Kyoto encyclopedia of genes and genomes - pathway: cholinergic neurotransmission with the development of POD or POCD in elderly patients.

Methods

This analysis is part of the European BioCog project (www.biocog.eu), a prospective multicenter observational study with elderly surgical patients. Patients with a Mini-Mental-State-Examination score ≤ 23 points were excluded. POD was assessed up to seven days after surgery using the Nursing Delirium Screening Scale, Confusion Assessment Method and a patient chart review. POCD was assessed three months after surgery with a neuropsychological test battery. Genotyping was performed on the Illumina Infinium Global Screening Array. Associations with POD and POCD were analyzed using logistic regression analysis, adjusted for age, comorbidities and duration of anesthesia (for POCD analysis additionally for education). Odds ratios (OR) refer to minor allele counts (0, 1, 2).

Results

745 patients could be included in the POD analysis, and 452 in the POCD analysis. The rate of POD within this group was 20.8% (155 patients), and the rate of POCD was 10.2% (46 patients). In a candidate gene approach three genetic variants of the cholinergic genes CHRM2 and CHRM4 were associated with POD (OR [95% confidence interval], rs8191992: 0.61[0.46; 0.80]; rs8191992: 1.60[1.22; 2.09]; rs2067482: 1.64[1.10; 2.44]). No associations were found for POCD.

Conclusions

We found an association between genetic variants of CHRM2 and CHRM4 and POD. Further studies are needed to investigate whether disturbances in acetylcholine release and synaptic plasticity are involved in the development of POD.

Trial registration: ClinicalTrials.gov: NCT02265263.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01071-1.

The complex interaction of genetics and delirium: a systematic review and meta-analysis

The objective is to understand genetic predisposition to delirium. Following PRISMA guidelines, we undertook a systematic review of studies involving delirium and genetics in the databases of Pubmed, Scopus, Cochrane Library and PsycINFO, and performed a meta-analysis when appropriate. We evaluated 111 articles, of which 25 were finally included in the analysis. The studies were assessed by two independent researchers for methodological quality using the Downs and Black Tool and for genetic analysis quality. We performed a meta-analysis of 10 studies of the Apolipoprotein E (APOE) gene, obtaining no association with the presence of delirium (LOR 0.18, 95% CI - 0.10-0.47, p = 0.21). Notably, only 5 out of 25 articles met established criteria for genetic studies (good quality) and 6 were of moderate quality. Seven studies found an association with APOE4, the dopamine transporter gene SCL6A3, dopamine receptor 2 gene, glucocorticoid receptor, melatonin receptor and mitochondrial DNA haplotypes. One genome-wide association study found two suggestive long intergenic non-coding RNA genes. Five studies found no association with catechol-o-methyltransferase, melatonin receptor or several interleukins genes. The studies were heterogenous in establishing the presence of delirium. Future studies with large samples should further specify the delirium phenotype and deepen our understanding of interactions between genes and other biological factors.

Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019

Background

The coronavirus disease 2019 pandemic has placed unprecedented stress on health systems and has been associated with elevated risk for delirium. The convergence of pandemic resource limitation and clinical demand associated with delirium requires careful risk stratification for targeted prevention efforts.

Objectives

To develop an incident delirium predictive model among coronavirus disease 2019 patients.

Methods

We applied supervised machine learning to electronic health record data for inpatients with coronavirus disease 2019 at three hospitals to build an incident delirium diagnosis prediction model. We validated this model in three different hospitals. Both hospital cohorts included academic and community settings.

Results

Among 2907 patients across 6 hospitals, 488 (16.8%) developed delirium. Applying the predictive model in the external validation cohort of 755 patients, the c-index was 0.75 (0.71–0.79) and the lift in the top quintile was 2.1. At a sensitivity of 80%, the specificity was 56%, negative predictive value 92%, and positive predictive value 30%. Equivalent model performance was observed in subsamples stratified by age, sex, race, need for critical care and care at community vs. academic hospitals.

Conclusion

Machine learning applied to electronic health records available at the time of inpatient admission can be used to risk-stratify patients with coronavirus disease 2019 for incident delirium. Delirium is common among patients with coronavirus disease 2019, and resource constraints during a pandemic demand careful attention to the optimal application of predictive models.

Abnormal phosphorylation of tau protein and neuroinflammation induced by laparotomy in an animal model of postoperative delirium

Postoperative delirium (POD) is an acute neuropsychological disturbance after surgery, whose prevalence is related with advancing age. Neuroinflammation and abnormal tau phosphorylation that commonly presenting in Alzheimer's disease (AD) may contribute to the progression and duration of POD. To study the acute influence of surgery on cognitive function, wild type male C57BL/6 N mice were randomly divided into three groups: Control (CON), Laparotomy at 4 h and 24 h (LAP-4 h, LAP-24 h), then subjected to laparotomy under sevoflurane anaesthesia. The cognitive performance, peripheral and central inflammatory responses and tau phosphorylation levels were evaluated at 4 h and 24 h postoperatively. When LAP4-hrs displayed anxiety behaviors with high mRNA levels of inflammatory cytokines, such as interleukin-1β (IL-1β), IL-6, IL-8, TNF-α and MCP-1 in the liver, and IL-8 in the hippocampus, results at 24 h were different. In the liver, only IL-10 protein was obviously elevated, but in the hippocampus, both pro- and anti-inflammatory cytokines were significantly decreased whilst the elimination of anxiety. The activity of major related kinases and phosphatases was remarkably changed which may contribute to the dephosphorylated tau protein. With tremendous neuropathological changes and significant numbers of activated microglias and astrocytes observed in the sub-regions of hippocampus, the memory impairment existed at both 4 h and 24 h. Since the association of dephosphorylated tau with POD, these findings may supply novel implications for the understanding of tauopathies and as a theoretical basis for preventions from the postoperative cognitive dysfunction (POCD).

Distribution of agitation and related symptoms among hospitalized patients using a scalable natural language processing method

BACKGROUND

Agitation is a common feature of many neuropsychiatric disorders.

OBJECTIVE

Understanding the prevalence, implications, and characteristics of agitation among hospitalized populations can facilitate more precise recognition of disability arising from neuropsychiatric diseases.

METHODS

We developed two agitation phenotypes using an expansion of expert curated term lists. These phenotypes were used to characterize five years of psychiatric admissions. The relationship of agitation symptoms and length of stay was examined.

RESULTS

Among 4548 psychiatric admissions, 1134 (24.9%) included documentation of agitation based on the primary agitation phenotype. These symptoms were greater among individuals with public insurance, and those with mania and psychosis compared to major depressive disorder. Greater symptoms were associated with longer hospital stay, with ~0.9 day increase in stay for every 10% increase in agitation phenotype.

CONCLUSION

Agitation was common at hospital admission and associated with diagnosis and longer length of stay. Characterizing agitation-related symptoms through natural language processing may provide new tools for understanding agitated behaviors and their relationship to delirium.

Delirium

Delirium, a syndrome characterized by an acute change in attention, awareness and cognition, is caused by a medical condition that cannot be better explained by a pre-existing neurocognitive disorder. Multiple predisposing factors (for example, pre-existing cognitive impairment) and precipitating factors (for example, urinary tract infection) for delirium have been described, with most patients having both types. Because multiple factors are implicated in the aetiology of delirium, there are likely several neurobiological processes that contribute to delirium pathogenesis, including neuroinflammation, brain vascular dysfunction, altered brain metabolism, neurotransmitter imbalance and impaired neuronal network connectivity. The Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) is the most commonly used diagnostic system upon which a reference standard diagnosis is made, although many other delirium screening tools have been developed given the impracticality of using the DSM-5 in many settings. Pharmacological treatments for delirium (such as antipsychotic drugs) are not effective, reflecting substantial gaps in our understanding of its pathophysiology. Currently, the best management strategies are multidomain interventions that focus on treating precipitating conditions, medication review, managing distress, mitigating complications and maintaining engagement to environmental issues. The effective implementation of delirium detection, treatment and prevention strategies remains a major challenge for health-care organizations globally.

Systems Level Analysis and Identification of Pathways and Key Genes Associated with Delirium

Delirium is a complex pathophysiological process, and multiple contributing mechanisms have been identified. However, it is largely unclear how the genes associated with delirium contribute and which of them play key roles. In this study, the genes associated with delirium were retrieved from the Comparative Toxicogenomics Database (CTD) and integrated through a protein-protein interaction (PPI) network. Delirium-associated genes formed a highly interconnected PPI subnetwork, indicating a high tendency to interact and agglomerate. Using the Molecular Complex Detection (MCODE) algorithm, we identified the top two delirium-relevant network modules, M1 and M5, that have the most significant enrichments for the delirium-related gene sets. Functional enrichment analysis showed that genes related to neurotransmitter receptor activity were enriched in both modules. Moreover, analyses with genes located in human accelerated regions (HARs) provided evidence that HAR-Brain genes were overrepresented in the delirium-relevant network modules. We found that four of the HAR-Brain genes, namely APP, PLCB1, NPY, and HTR2A, in the M1 module were highly connected and appeared to exhibit hub properties, which might play vital roles in delirium development. Further understanding of the function of the identified modules and member genes could help to identify therapeutic intervention targets and diagnostic biomarkers for delirium.