Identification of Patients With High Mortality Risk and Prediction of Outcomes in Delirium by Bispectral EEG.

BACKGROUND: Delirium is common and dangerous, yet underdetected and undertreated. Current screening questionnaires are subjective and ineffectively implemented in busy hospital workflows. Electroencephalography (EEG) can objectively detect the diffuse slowing characteristic of delirium, but it is not suitable for high-throughput screening due to size, cost, and the expertise required for lead placement and interpretation. This study hypothesized that an efficient and reliable point-of-care EEG device for high-throughput screening could be developed. METHODS: This prospective study, which measured bispectral EEG (BSEEG) from elderly inpatients to assess their outcomes, was conducted at the University of Iowa Hospitals and Clinics from January 2016 to October 2017. A BSEEG score was defined based on the distribution of 2,938 EEG recordings from the 428 subjects who were assessed for delirium; primary outcomes measured were hospital length of stay, discharge disposition, and mortality. RESULTS: A total of 274 patients had BSEEG score data available for analysis. Delirium and BSEEG score had a significant association (P < .001). Higher BSEEG scores were significantly correlated with length of stay (P < .001 unadjusted, P = .001 adjusted for age, sex, and Charlson Comorbidity Index [CCI] score) as well as with discharge not to home (P < .01). Hazard ratio for survival controlling for age, sex, CCI score, and delirium status was 1.35 (95% CI,1.04 to 1.76; P = .025). CONCLUSIONS: In BSEEG, an efficient and reliable device that provides an objective measurement of delirium status was developed. The BSEEG score is significantly associated with pertinent clinical outcomes of mortality, hospital length of stay, and discharge disposition. The BSEEG score better predicts mortality than does clinical delirium status. This study identified a previously unrecognized subpopulation of patients without clinical features of delirium who are at increased mortality risk.

Genome-wide DNA methylation investigation of glucocorticoid exposure within buccal samples.

AIM: Glucocorticoids play a major role in regulating the stress response, and an imbalance of glucocorticoids has been implicated in stress-related disorders. Within mouse models, CpGs across the genome have been shown to be differentially methylated in response to glucocorticoid treatment, and using the Infinium 27K array, it was shown that humans given synthetic glucocorticoids had DNA methylation (DNAm) changes in blood. However, further investigation of the extent to which glucocorticoids affect DNAm across a larger proportion of the genome is needed. METHODS: Buccal samples were collected before and after synthetic glucocorticoid treatment in the context of a dental procedure. This included 30 tooth extraction surgery patients who received 10 mg of dexamethasone. Genome-wide DNAm was assessed with the Infinium HumanMethylationEPIC array. RESULTS: Five CpGs showed genome-wide significant DNAm changes that were >10%. These differentially methylated CpGs were in or nearest the following genes: ZNF438, KLHDC10, miR-544 or CRABP1, DPH5, and WDFY2. Using previously published datasets of human blood gene expression changes following dexamethasone exposure, a significant proportion of genes with false-discovery-rate-adjusted significant CpGs were also differentially expressed. A pathway analysis of the genes with false-discovery-rate-adjusted significant CpGs revealed significant enrichment of olfactory transduction, pentose and glucuronate interconversions, ascorbate and aldarate metabolism, and steroid hormone biosynthesis pathways. CONCLUSION: High-dose synthetic glucocorticoid administration in the setting of a dental procedure was significantly associated with DNAm changes within buccal samples. These findings are consistent with prior findings of an influence of glucocorticoids on DNAm in humans.

Genome-wide DNA methylation comparison between live human brain and peripheral tissues within individuals.

Differential DNA methylation in the brain is associated with many psychiatric diseases, but access to brain tissues is essentially limited to postmortem samples. The use of surrogate tissues has become common in identifying methylation changes associated with psychiatric disease. In this study, we determined the extent to which peripheral tissues can be used as surrogates for DNA methylation in the brain. Blood, saliva, buccal, and live brain tissue samples from 27 patients with medically intractable epilepsy undergoing brain resection were collected (age range 5-61 years). Genome-wide methylation was assessed with the Infinium HumanMethylation450 (n = 12) and HumanMethylationEPIC BeadChip arrays (n = 21). For the EPIC methylation data averaged for each CpG across subjects, the saliva-brain correlation (r = 0.90) was higher than that for blood-brain (r = 0.86) and buccal-brain (r = 0.85) comparisons. However, within individual CpGs, blood had the highest proportion of CpGs correlated to brain at nominally significant levels (20.8%), as compared to buccal tissue (17.4%) and saliva (15.1%). For each CpG and each gene, levels of brain-peripheral tissue correlation varied widely. This indicates that to determine the most useful surrogate tissue for representing brain DNA methylation, the patterns specific to the genomic region of interest must be considered. To assist in that objective, we have developed a website, IMAGE-CpG, that allows researchers to interrogate DNA methylation levels and degree of cross-tissue correlation in user-defined locations across the genome.

GWAS and systems biology analysis of depressive symptoms among smokers from the COPDGene cohort.

BACKGROUND: Large sample GWAS is needed to identify genetic factors associated with depression. This study used genome-wide genotypic and phenotypic data from the COPDGene study to identify genetic risk factors for depression. METHODS: Data were from 9716 COPDGene subjects with ≥10 pack-year history. Depression was defined as antidepressant use and/or a HADS depression subscale score ≥8. Non-Hispanic White (6576) and African-American (3140) subsets were analyzed. A GWAS pipeline identified SNPs associated with depression in each group. Network analysis software analyzed gene interactions through common biological pathways, genetic interactions, and tissue-specific gene expression. RESULTS: The mean age was 59.4 years (SD 9.0) with 46.5% female subjects. Depression was in 24.7% of the NHW group (1622) and 12.5% of the AA group (391). No SNPs had genome-wide significance. One of the top SNPs, rs12036147 (p = 1.28 × 10-6), is near CHRM3. Another SNP was near MDGA2 (rs17118176, p = 3.52 × 10-6). Top genes formed networks for synaptic transmission with a statistically significant level of more co-expression in brain than other tissues, particularly in the basal ganglia (p = 1.00 × 10-4). LIMITATIONS: Limitations included a depression definition based on antidepressant use and a limited HADS score subgroup, which could increase false negatives in depressed patients not on antidepressants. Antidepressants used for smoking cessation in non-depressed patients could lead to false positives. CONCLUSIONS: Systems biology analysis identified statistically significant pathways whereby multiple genes influence depression. The gene set pathway analysis and COPDGene data can help investigate depression in future studies.

Epigenetics of Delirium and Aging: Potential Role of DNA Methylation Change on Cytokine Genes in Glia and Blood Along With Aging.

Background: Delirium in elderly patients is common and dangerous. Major risk factors include aging and exogenous insults, such as infection or surgery. In animal models, aging enhances pro-inflammatory cytokine release from microglia in response to exogenous insults. The epigenetic mechanism DNA methylation (DNAm) regulates gene expression and changes with age. Older individuals may have methylation changes that influence the increased cytokine upon insult, but the degree to which aging affects DNAm of cytokine genes is not fully understood. Methods: The relationship between DNAm and aging of pro-inflammatory cytokine genes (TNF-alpha, IL1-beta, IL-6) was investigated using methylation array data in two cohorts. Brain and blood samples were collected from a neurosurgery cohort (NSG) of 21 subjects who underwent brain resection. A second cohort, the Grady Trauma Project (GTP), included blood samples from 265 subjects. Results: In the NSG cohort, a significant negative correlation between age and DNAm in brain was found at a CpG in IL-6. With the GTP dataset, significant negative correlations between age and DNAm were seen at most of the CpGs in TNF-alpha. Also, TNF-Alpha expression increases with age. These GTP DNAm correlations were also nominally significant in NSG blood samples. In neuronal negative NSG brain tissue, a similar negative trend was observed. Conclusions: With aging, a decrease in DNAm of cytokines gene CpGs in glia and blood was seen. As this can affect their expression, additional research is needed to fully elucidate the role of DNAm in aging and how it may influence the pathogenesis of delirium.

Delirium detection by a novel bispectral electroencephalography device in general hospital.

AIM: Delirium is common and dangerous among elderly inpatients; yet, it is underdiagnosed and thus undertreated. This study aimed to test the diagnostic characteristics of a noninvasive point-of-care device with two-channel (bispectral) electroencephalography (EEG) for the screening of delirium in the hospital. METHODS: Patients admitted to the University of Iowa Hospitals and Clinics were assessed for the presence of delirium with a clinical assessment, the Confusion Assessment Method for Intensive Care Unit and Delirium Rating Scale. Subsequently, we obtained a 10-min bispectral EEG (BSEEG) recording from a hand-held electroencephalogram device during hospitalization. We performed power spectral density analysis to differentiate between those patients with and without delirium. RESULTS: Initially 45 subjects were used as a test dataset to establish a cut-off. The BSEEG index was determined to be a significant indicator of delirium, with sensitivity 80% and specificity 87.7%. An additional independent validation dataset with 24 patients confirmed the validity of the approach, with a sensitivity of 83.3% and specificity of 83.3%. CONCLUSION: In this pilot study, the BSEEG method was able to distinguish delirious patients from non-delirious patients. Our data showed the feasibility of this technology for mass screening of delirium in the hospital.