Jennifer R. Dungan

Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events

Background—Molecular tools may provide insight into cardiovascular risk. We assessed whether metabolites discriminate coronary artery disease (CAD) and predict risk of cardiovascular events. Methods and Results—We performed mass–spectrometry–based profiling of 69 metabolites in subjects from the CATHGEN biorepository. To evaluate discriminative capabilities of metabolites for CAD, 2 groups were profiled: 174 CAD cases and 174 sex/race-matched controls (“initial”), and 140 CAD cases and 140 controls (“replication”). To evaluate the capability of metabolites to predict cardiovascular events, cases were combined (“event” group); of these, 74 experienced death/myocardial infarction during follow-up. A third independent group was profiled (“event-replication” group; n=63 cases with cardiovascular events, 66 controls). Analysis included principal-components analysis, linear regression, and Cox proportional hazards. Two principal components analysis–derived factors were associated with CAD: 1 comprising branched-chain amino acid metabolites (factor 4, initial P=0.002, replication P=0.01), and 1 comprising urea cycle metabolites (factor 9, initial P=0.0004, replication P=0.01). In multivariable regression, these factors were independently associated with CAD in initial (factor 4, odds ratio [OR], 1.36; 95% CI, 1.06 to 1.74; P=0.02; factor 9, OR, 0.67; 95% CI, 0.52 to 0.87; P=0.003) and replication (factor 4, OR, 1.43; 95% CI, 1.07 to 1.91; P=0.02; factor 9, OR, 0.66; 95% CI, 0.48 to 0.91; P=0.01) groups. A factor composed of dicarboxylacylcarnitines predicted death/myocardial infarction (event group hazard ratio 2.17; 95% CI, 1.23 to 3.84; P=0.007) and was associated with cardiovascular events in the event-replication group (OR, 1.52; 95% CI, 1.08 to 2.14; P=0.01). Conclusions—Metabolite profiles are associated with CAD and subsequent cardiovascular events.

Nursing Implications of Personalized and Precision Medicine

OBJECTIVES Identify and discuss the nursing implications of personalized and precision oncology care. DATA SOURCES PubMed, CINAHL. CONCLUSION The implications in personalized and precision cancer nursing care include interpretation and clinical use of novel and personalized information including genetic testing; patient advocacy and support throughout testing, anticipation of results and treatment; ongoing chronic monitoring; and support for patient decision-making. Attention must also be given to the family and ethical implications of a personalized approach to care. IMPLICATIONS FOR NURSING PRACTICE Nurses face increasing challenges and opportunities in communication, support, and advocacy for patients given the availability of advanced testing, care and treatment in personalized and precision medicine. Nursing education and continuing education, clinical decision support, and health systems changes will be necessary to provide personalized multidisciplinary care to patients, in which nurses play a key role.

Pharmacogenetics of Anesthesia: An Integrative Review

Background Monitoring a patient’s response to drug therapy and early identification of an adverse reaction are important responsibilities of nurses. Despite the relative safety of anesthesia practice, 1 in 20 perioperative medication administrations includes a medication error and/or adverse drug reaction. Although several factors contribute to an individual’s response to medications, genetic predisposition accounts for over 50% of that response. Objective The purpose of this review is to explore the evidence of genetic variability associated with response to volatile and intravenous anesthetics. Methods A comprehensive search of published literature in PubMed, CINAHL, and Cochrane databases from 1960 to May 30, 2015, was performed. Iterative reading of the primary articles was performed to ensure congruence between the extracted data and the primary article and reduce the data to draw conclusions. Results The analysis revealed that most anesthetics are metabolized by enzymes in the CYP2 and UGT1 family. CYP2B6 catalyzes propofol and ketamine metabolism. CYP2B6*6 allele is associated with decreased propofol and ketamine metabolism and increased adverse effects. Genetic variants in the UGT1A9 enzyme are associated with the need for higher induction dose and increased clearance of propofol. Discussion Despite the significant gaps in the literature, current evidence suggests that close monitoring is required when administering anesthetics to individuals with the CYP2B6*6 allele. Future research to address identified gaps in this review may have the potential to identify underlying genetic contribution to anesthetic response and prevent significant adverse events during anesthesia delivery and perioperative nursing care.

Case-Only Survival Analysis Reveals Unique Effects of Genotype, Sex, and Coronary Disease Severity on Survivorship

Survival bias may unduly impact genetic association with complex diseases; gene-specific survival effects may further complicate such investigations. Coronary artery disease (CAD) is a complex phenotype for which little is understood about gene-specific survival effects; yet, such information can offer insight into refining genetic associations, improving replications, and can provide candidate genes for both mortality risk and improved survivorship in CAD. Building on our previous work, the purpose of this current study was to: evaluate LSAMP SNP-specific hazards for all-cause mortality post-catheterization in a larger cohort of our CAD cases; and, perform additional replication in an independent dataset. We examined two LSAMP SNPs—rs1462845 and rs6788787—using CAD case-only Cox proportional hazards regression for additive genetic effects, censored on time-to-all-cause mortality or last follow-up among Caucasian subjects from the Catheterization Genetics Study (CATHGEN; n = 2,224) and the Intermountain Heart Collaborative Study (IMHC; n = 3,008). Only after controlling for age, sex, body mass index, histories of smoking, type 2 diabetes, hyperlipidemia and hypertension (HR = 1.11, 95%CI = 1.01–1.22, p = 0.032), rs1462845 conferred significantly increased hazards of all-cause mortality among CAD cases. Even after controlling for multiple covariates, but in only the primary cohort, rs6788787 conferred significantly improved survival (HR = 0.80, 95% CI = 0.69–0.92, p = 0.002). Post-hoc analyses further stratifying by sex and disease severity revealed replicated effects for rs1462845: even after adjusting for aforementioned covariates and coronary interventional procedures, males with severe burden of CAD had significantly amplified hazards of death with the minor variant of rs1462845 in both cohorts (HR = 1.29, 95% CI = 1.08–1.55, p = 0.00456; replication HR = 1.25, 95% CI = 1.05–1.49, p = 0.013). Kaplan-Meier curves revealed unique cohort-specific genotype effects on survival. Additional analyses demonstrated that the homozygous risk genotype (‘A/A’) fully explained the increased hazard in both cohorts. None of the post-hoc analyses in control subjects were significant for any model. This suggests that genetic effects of rs1462845 on survival are unique to CAD presence. This represents formal, replicated evidence of genetic contribution of rs1462845 to increased risk for all-cause mortality; the contribution is unique to CAD case status and specific to males with severe burden of CAD.

The genetic basis for survivorship in coronary artery disease.

Survivorship is a trait characterized by endurance and virility in the face of hardship. It is largely considered a psychosocial attribute developed during fatal conditions, rather than a biological trait for robustness in the context of complex, age-dependent diseases like coronary artery disease (CAD). The purpose of this paper is to present the novel phenotype, survivorship in CAD as an observed survival advantage concurrent with clinically significant CAD. We present a model for characterizing survivorship in CAD and its relationships with overlapping time- and clinically-related phenotypes. We offer an optimal measurement interval for investigating survivorship in CAD. We hypothesize genetic contributions to this construct and review the literature for evidence of genetic contribution to overlapping phenotypes in support of our hypothesis. We also present preliminary evidence of genetic effects on survival in people with clinically significant CAD from a primary case-control study of symptomatic coronary disease. Identifying gene variants that confer improved survival in the context of clinically appreciable CAD may improve our understanding of cardioprotective mechanisms acting at the gene level and potentially impact patients clinically in the future. Further, characterizing other survival-variant genetic effects may improve signal-to-noise ratio in detecting gene associations for CAD.

Unraveling interrelationships among psychopathology symptoms, cognitive domains and insight dimensions in chronic schizophrenia

INTRODUCTION Insight in schizophrenia is long known to have a complex relationship with psychopathology symptoms and cognition. However, very few studies have examined models that explain these interrelationships. METHODS In a large sample derived from the NIMH Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial (N=1391), we interrogated these interrelationships for potential causal pathways using structural equation modeling. Using the NIMH consensus model, latent variables were constructed for psychopathology symptom dimensions, including positive, negative, disorganized, excited and depressed from the Positive and Negative Syndrome Scale (PANSS) items. Neurocognitive variables were created from five predefined domains of working memory, verbal memory, reasoning, vigilance and processing speed. Illness insight and treatment insight were tested using latent variables constructed from the Illness and Treatment Attitude Questionnaire (ITAQ). RESULTS Disorganized symptoms had the strongest effect on insight. Illness insight mediated the relationship of positive, depressed, and disorganized symptoms with treatment insight. Neurocognition mediated the relationship between disorganized and treatment insight and depressed symptoms and treatment insight. There was no effect of negative symptoms on either illness insight or treatment insight. Taken together, our results indicate overlapping and unique relational paths for illness and treatment insight dimensions, which could suggest differences in causal mechanisms and potential interventions to improve insight.

Genetic correlates of insight in schizophrenia

Insight in schizophrenia is clinically important as it is associated with several adverse outcomes. Genetic contributions to insight are unknown. We examined genetic contributions to insight by investigating if polygenic risk scores (PRS) and candidate regions were associated with insight. METHOD Schizophrenia case-only analysis of the Clinical Antipsychotics Trials of Intervention Effectiveness trial. Schizophrenia PRS was constructed using Psychiatric Genomics Consortium (PGC) leave-one out GWAS as discovery data set. For candidate regions, we selected 105 schizophrenia-associated autosomal loci and 11 schizophrenia-related oligodendrocyte genes. We used regressions to examine PRS associations and set-based testing for candidate analysis. RESULTS We examined data from 730 subjects. Best-fit PRS at p-threshold of 1e-07 was associated with total insight (R2=0.005, P=0.05, empirical P=0.054) and treatment insight (R2=0.005, P=0.048, empirical P=0.048). For models that controlled for neurocognition, PRS significantly predicted treatment insight but at higher p-thresholds (0.1 to 0.5) but did not survive correction. Patients with highest polygenic burden had 5.9 times increased risk for poor insight compared to patients with lowest burden. PRS explained 3.2% (P=0.002, empirical P=0.011) of variance in poor insight. Set-based analyses identified two variants associated with poor insight- rs320703, an intergenic variant (within-set P=6e-04, FDR P=0.046) and rs1479165 in SOX2-OT (within-set P=9e-04, FDR P=0.046). CONCLUSION To the best of our knowledge, this is the first study examining genetic basis of insight. We provide evidence for genetic contributions to impaired insight. Relevance of findings and necessity for replication are discussed.

Pharmacogenetics of Ketamine-induced Emergence Phenomena: A Pilot Study

Background Up to 55% of patients who are administered ketamine experience an emergence phenomena (EP) that closely mimics schizophrenia and increases their risk of injury; however, to date, no studies have investigated genetic association of ketamine-induced EP in healthy patients. Objectives The aim of the study was to investigate the feasibility and sample sizes required to explore the relationship between CYP2B6*6 and GRIN2B single-nucleotide polymorphisms and ketamine-induced EP. Methods This cross-sectional, pharmacogenetic candidate, gene pilot study recruited 75 patients having minor elective outpatient surgeries. EP was measured with the Clinician Administered Dissociative State Scale. Genetic association of CYP2B6*6 and GRIN2B (rs1019385 and rs1806191) single-nucleotide polymorphisms and ketamine-induced EP occurrence and severity were tested using logistic and linear regression. Results Forty-seven patients (63%) received ketamine and were genotyped, and 40% of them experienced EP. Occurrence and severity of EP were not associated with CYP2B6*6 or GRIN2B (p > .10). Exploratory analysis of nongenotype models containing age, ketamine dose, duration of anesthesia, and time from ketamine administration to assessment for EP significantly predicted EP occurrence (p = .001) and severity (p = .007). This pilot study demonstrates feasibility for implementing a pharmacogenetic study in a clinical setting, and we estimate that between 380 and 570 cases will be needed to adequately power future genetic association studies. Discussion Younger age, higher dose, and longer duration of anesthesia significantly predicted EP occurrence and severity among our pilot sample. Although the small sample size limited our ability to demonstrate significant genotype differences, we generated effect sizes, sample size estimates, and nongenetic covariates information in order to support future pharmacogenetic study design for evaluating this adverse event.

Polygenic signal for symptom dimensions and cognitive performance in patients with chronic schizophrenia

Genetic etiology of psychopathology symptoms and cognitive performance in schizophrenia is supported by candidate gene and polygenic risk score (PRS) association studies. Such associations are reported to be dependent on several factors - sample characteristics, illness phase, illness severity etc. We aimed to examine if schizophrenia PRS predicted psychopathology symptoms and cognitive performance in patients with chronic schizophrenia. We also examined if schizophrenia associated autosomal loci were associated with specific symptoms or cognitive domains. Case-only analysis using data from the Clinical Antipsychotics Trials of Intervention Effectiveness-Schizophrenia trials (n = 730). PRS was constructed using Psychiatric Genomics Consortium (PGC) leave one out genome wide association analysis as the discovery data set. For candidate region analysis, we selected 105-schizophrenia associated autosomal loci from the PGC study. We found a significant effect of PRS on positive symptoms at p-threshold (PT) of 0.5 (R2 = 0.007, p = 0.029, empirical p = 0.029) and negative symptoms at PT of 1e-07 (R2 = 0.005, p = 0.047, empirical p = 0.048). For models that additionally controlled for neurocognition, best fit PRS predicted positive (p-threshold 0.01, R2 = 0.007, p = 0.013, empirical p = 0.167) and negative symptoms (p-threshold 0.1, R2 = 0.012, p = 0.004, empirical p = 0.329). No associations were seen for overall neurocognitive and social cognitive performance tests. Post-hoc analyses revealed that PRS predicted working memory and vigilance performance but did not survive correction. No candidate regions that survived multiple testing corrections were associated with either symptoms or cognitive performance. Our findings point to potentially distinct pathogenic mechanisms for schizophrenia symptoms.

Biases in Genetic Association of Coronary Heart Disease Events May Be Less Likely Than Suspected: Here Is When to Check for Them

Investigating lethal diseases like coronary heart disease (CHD) and major adverse events like myocardial infarction (MI) and death can sometimes seem a bit macabre. We are interested in understanding the events with the hope of preventing them; yet, to demonstrate effects, it is essential for a high rate of such unfortunate events to occur and to be observed. Fortunately, the increasing availability of big [event] data supports an unprecedented ability and power to explore genetic influences on primary and subsequent CHD events. Tempering the enthusiasm around this opportunity is the concern for biases that threaten the internal and external validity of such investigations. See Article by Hu and Schmidt et al In particular, selection and survival biases are of concern to the context of CHD events. When subjects are nonrandomly selected or tend to be systematically included based on the presence of a related risk profile, diagnosis, or event, this is deemed selection bias (also known as index event bias). Survival bias occurs when sampling is dependent on an individual’s likelihood of surviving an event in the first place, or when attrition or study closure prevents complete observation of time to censoring. These types of bias can systematically distort variance and lead to spurious outcomes. Selection bias has been a well-acknowledged concern in genetic association.1 …