Large differences in collateral blood vessel abundance among individuals arise from multiple genetic variants.

Collateral blood flow varies greatly among humans for reasons that remain unclear, resulting in significant differences in ischemic tissue damage. A similarly large variation has also been found in mice that is caused by genetic background-dependent differences in the extent of collateral formation, termed collaterogenesis-a unique angiogenic process that occurs during development and determines collateral number and diameter in the adult. Previous studies have identified several quantitative trait loci (QTL) linked to this variation. However, understanding has been hampered by the use of closely related inbred strains that do not model the wide genetic variation present in the "outbred" human population. The Collaborative Cross (CC) multiparent mouse genetic reference panel was developed to address this limitation. Herein we measured the number and average diameter of cerebral collaterals in 60 CC strains, their 8 founder strains, 8 F1 crosses of CC strains selected for abundant versus sparse collaterals, and 2 intercross populations created from the latter. Collateral number evidenced 47-fold variation among the 60 CC strains, with 14% having poor, 25% poor-to-intermediate, 47% intermediate-to-good, and 13% good collateral abundance, that was associated with large differences in post-stroke infarct volume. Collateral number in skeletal muscle and intestine of selected high- and low-collateral strains evidenced the same relative abundance as in brain. Genome-wide mapping demonstrated that collateral abundance is a highly polymorphic trait. Subsequent analysis identified: 6 novel QTL circumscribing 28 high-priority candidate genes harboring putative loss-of-function polymorphisms (SNPs) associated with low collateral number; 335 predicted-deleterious SNPs present in their human orthologs; and 32 genes associated with vascular development but lacking protein coding variants. Six additional suggestive QTL (LOD > 4.5) were also identified in CC-wide QTL mapping. This study provides a comprehensive set of candidate genes for future investigations aimed at identifying signaling proteins within the collaterogenesis pathway whose variants potentially underlie genetic-dependent collateral insufficiency in brain and other tissues.

Large differences in collateral blood vessel abundance among individuals arise from multiple genetic variants.

Collateral blood flow varies greatly among humans for reasons that remain unclear, resulting in significant differences in ischemic tissue damage. A similarly large variation has also been found in mice that is caused by genetic background-dependent differences in the extent of collateral formation, termed collaterogenesis-a unique angiogenic process that occurs during development and determines collateral number and diameter in the adult. Previous studies have identified several quantitative trait loci (QTL) linked to this variation. However, understanding has been hampered by the use of closely related inbred strains that do not model the wide genetic variation present in the "outbred" human population. The Collaborative Cross (CC) multiparent mouse genetic reference panel was developed to address this limitation. Herein we measured the number and average diameter of cerebral collaterals in 60 CC strains, their 8 founder strains, 8 F1 crosses of CC strains selected for abundant versus sparse collaterals, and 2 intercross populations created from the latter. Collateral number evidenced 47-fold variation among the 60 CC strains, with 14% having poor, 25% poor-to-intermediate, 47% intermediate-to-good, and 13% good collateral abundance, that was associated with large differences in post-stroke infarct volume. Genome-wide mapping demonstrated that collateral abundance is a highly polymorphic trait. Subsequent analysis identified: 6 novel QTL circumscribing 28 high-priority candidate genes harboring putative loss-of-function polymorphisms (SNPs) associated with low collateral number; 335 predicted-deleterious SNPs present in their human orthologs; and 32 genes associated with vascular development but lacking protein coding variants. This study provides a comprehensive set of candidate genes for future investigations aimed at identifying signaling proteins within the collaterogenesis pathway whose variants potentially underlie genetic-dependent collateral insufficiency in brain and other tissues.

Forward genetic screen of homeostatic antibody levels in the Collaborative Cross identifies MBD1 as a novel regulator of B cell homeostasis.

Variation in immune homeostasis, the state in which the immune system is maintained in the absence of stimulation, is highly variable across populations. This variation is attributed to both genetic and environmental factors. However, the identity and function of specific regulators have been difficult to identify in humans. We evaluated homeostatic antibody levels in the serum of the Collaborative Cross (CC) mouse genetic reference population. We found heritable variation in all antibody isotypes and subtypes measured. We identified 4 quantitative trait loci (QTL) associated with 3 IgG subtypes: IgG1, IgG2b, and IgG2c. While 3 of these QTL map to genome regions of known immunological significance (major histocompatibility and immunoglobulin heavy chain locus), Qih1 (associated with variation in IgG1) mapped to a novel locus on Chromosome 18. We further associated this locus with B cell proportions in the spleen and identify Methyl-CpG binding domain protein 1 under this locus as a novel regulator of homeostatic IgG1 levels in the serum and marginal zone B cells (MZB) in the spleen, consistent with a role in MZB differentiation to antibody secreting cells.

An interaction of inorganic arsenic exposure with body weight and composition on type 2 diabetes indicators in Diversity Outbred mice.

Type 2 diabetes (T2D) is a complex metabolic disorder with no cure and high morbidity. Exposure to inorganic arsenic (iAs), a ubiquitous environmental contaminant, is associated with increased T2D risk. Despite growing evidence linking iAs exposure to T2D, the factors underlying inter-individual differences in susceptibility remain unclear. This study examined the interaction between chronic iAs exposure and body composition in a cohort of 75 Diversity Outbred mice. The study design mimics that of an exposed human population where the genetic diversity of the mice provides the variation in response, in contrast to a design that includes untreated mice. Male mice were exposed to iAs in drinking water (100 ppb) for 26 weeks. Metabolic indicators used as diabetes surrogates included fasting blood glucose and plasma insulin (FBG, FPI), blood glucose and plasma insulin 15 min after glucose challenge (BG15, PI15), homeostatic model assessment for [Formula: see text]-cell function and insulin resistance (HOMA-B, HOMA-IR), and insulinogenic index. Body composition was determined using magnetic resonance imaging, and the concentrations of iAs and its methylated metabolites were measured in liver and urine. Associations between cumulative iAs consumption and FPI, PI15, HOMA-B, and HOMA-IR manifested as significant interactions between iAs and body weight/composition. Arsenic speciation analyses in liver and urine suggest little variation in the mice's ability to metabolize iAs. The observed interactions accord with current research aiming to disentangle the effects of multiple complex factors on T2D risk, highlighting the need for further research on iAs metabolism and its consequences in genetically diverse mouse strains.

A mixed-effects two-part model for twin-data and an application on identifying important factors associated with extremely preterm children's health disorders.

Our recent studies identifying factors significantly associated with the positive child health index (PCHI) in a mixed cohort of preterm-born singletons, twins, and triplets posed some analytic and modeling challenges. The PCHI transforms the total number of health disorders experienced (of the eleven ascertained) to a scale from 0 to 100%. While some of the children had none of the eleven health disorders (i.e., PCHI = 1), others experienced a subset or all (i.e., 0 ≤PCHI< 1). This indicates the existence of two distinct data processes-one for the healthy children, and another for those with at least one health disorder, necessitating a two-part model to accommodate both. Further, the scores for twins and triplets are potentially correlated since these children share similar genetics and early environments. The existing approach for analyzing PCHI data dichotomizes the data (i.e., number of health disorders) and uses a mixed-effects logistic or multiple logistic regression to model the binary feature of the PCHI (1 vs. < 1). To provide an alternate analytic framework, in this study we jointly model the two data processes under a mixed-effects two-part model framework that accounts for the sample correlations between and within the two data processes. The proposed method increases power to detect factors associated with disorders. Extensive numerical studies demonstrate that the proposed joint-test procedure consistently outperforms the existing method when the type I error is controlled at the same level. Our numerical studies also show that the proposed method is robust to model misspecifications and it is applicable to a set of correlated semi-continuous data.

Methodological Challenges and Statistical Approaches in the COMprehensive Post-Acute Stroke Services Study.

BACKGROUND: The COMprehensive Post-Acute Stroke Services study was a cluster-randomized pragmatic trial designed to evaluate a comprehensive care transitions model versus usual care. The data collected during this trial were complex and analysis methodology was required that could simultaneously account for the cluster-randomized design, missing patient-level covariates, outcome nonresponse, and substantial nonadherence to the intervention. OBJECTIVE: The objective of this study was to discuss an array of complementary statistical methods to evaluate treatment effectiveness that appropriately addressed the challenges presented by the complex data arising from this pragmatic trial. METHODS: We utilized multiple imputation combined with inverse probability weighting to account for missing covariate and outcome data in the estimation of intention-to-treat effects (ITT). The ITT estimand reflects the effectiveness of assignment to the COMprehensive Post-Acute Stroke Services intervention compared with usual care (ie, it does not take into account intervention adherence). Per-protocol analyses provide complementary information about the effect of treatment, and therefore are relevant for patients to inform their decision-making. We describe estimation of the complier average causal effect using an instrumental variables approach through 2-stage least squares estimation. For all preplanned analyses, we also discuss additional sensitivity analyses. DISCUSSION: Pragmatic trials are well suited to inform clinical practice. Care should be taken to proactively identify the appropriate balance between control and pragmatism in trial design. Valid estimation of ITT and per-protocol effects in the presence of complex data requires application of appropriate statistical methods and concerted efforts to ensure high-quality data are collected.

Gabra2 is a genetic modifier of Scn8a encephalopathy in the mouse.

OBJECTIVE: SCN8A encephalopathy is a developmental epileptic encephalopathy typically caused by de novo gain-of-function mutations in Nav 1.6. Severely affected individuals exhibit refractory seizures, developmental delay, cognitive disabilities, movement disorders, and elevated risk of sudden death. Patients with the identical SCN8A variant can differ in clinical course, suggesting a role for modifier genes in determining disease severity. The identification of genetic modifiers contributes to understanding disease pathogenesis and suggesting therapeutic interventions. METHODS: We generated F1 and F2 crosses between inbred mouse strains and mice carrying the human pathogenic variants SCN8A-R1872W and SCN8A-N1768D. Quantitative trait locus (QTL) analysis of seizure-related phenotypes was used for chromosomal mapping of modifier loci. RESULTS: In an F2 cross between strain SJL/J and C57BL/6J mice carrying the patient mutation R1872W, we identified a major QTL on chromosome 5 containing the Gabra2 gene. Strain C57BL/6J carries a splice site mutation that reduces expression of Gabra2, encoding the α2 subunit of the aminobutyric acid type A receptor. The protective wild-type allele of Gabra2 from strain SJL/J delays the age at seizure onset and extends life span of the Scn8a mutant mice. Additional Scn8a modifiers were observed in the F2 cross and in an F1 cross with strain C3HeB/FeJ. SIGNIFICANCE: These studies demonstrate that the SJL/J strain carries multiple modifiers with protective effects against seizures induced by gain-of-function mutations in Scn8a. Homozygosity for the hypomorphic variant of Gabra2 in strain C57BL/6J is associated with early seizure onset and short life span. GABRA2 is a potential therapeutic target for SCN8A encephalopathy.

Antipsychotic Behavioral Phenotypes in the Mouse Collaborative Cross Recombinant Inbred Inter-Crosses (RIX).

Schizophrenia is an idiopathic disorder that affects approximately 1% of the human population, and presents with persistent delusions, hallucinations, and disorganized behaviors. Antipsychotics are the standard pharmacological treatment for schizophrenia, but are frequently discontinued by patients due to inefficacy and/or side effects. Chronic treatment with the typical antipsychotic haloperidol causes tardive dyskinesia (TD), which manifests as involuntary and often irreversible orofacial movements in around 30% of patients. Mice treated with haloperidol develop many of the features of TD, including jaw tremors, tongue protrusions, and vacuous chewing movements (VCMs). In this study, we used genetically diverse Collaborative Cross (CC) recombinant inbred inter-cross (RIX) mice to elucidate the genetic basis of antipsychotic-induced adverse drug reactions (ADRs). We performed a battery of behavioral tests in 840 mice from 73 RIX lines (derived from 62 CC strains) treated with haloperidol or placebo in order to monitor the development of ADRs. We used linear mixed models to test for strain and treatment effects. We observed highly significant strain effects for almost all behavioral measurements investigated (P < 0.001). Further, we observed strong strain-by-treatment interactions for most phenotypes, particularly for changes in distance traveled, vertical activity, and extrapyramidal symptoms (EPS). Estimates of overall heritability ranged from 0.21 (change in body weight) to 0.4 (VCMs and change in distance traveled) while the portion attributable to the interactions of treatment and strain ranged from 0.01 (for change in body weight) to 0.15 (for change in EPS). Interestingly, close to 30% of RIX mice exhibited VCMs, a sensitivity to haloperidol exposure, approximately similar to the rate of TD in humans chronically exposed to haloperidol. Understanding the genetic basis for the susceptibility to antipsychotic ADRs may be possible in mouse, and extrapolation to humans could lead to safer therapeutic approaches for schizophrenia.

Randomized Pragmatic Trial of Stroke Transitional Care: The COMPASS Study.

Background The objectives of this study were to develop and test in real-world clinical practice the effectiveness of a comprehensive postacute stroke transitional care (TC) management program. Methods and Results The COMPASS study (Comprehensive Post-Acute Stroke Services) was a pragmatic cluster-randomized trial where the hospital was the unit of randomization. The intervention (COMPASS-TC) was initiated at 20 hospitals, and 20 hospitals provided their usual care. Hospital staff enrolled 6024 adult stroke and transient ischemic attack patients discharged home between 2016 and 2018. COMPASS-TC was patient-centered and assessed social and functional determinates of health to inform individualized care plans. Ninety-day outcomes were evaluated by blinded telephone interviewers. The primary outcome was functional status (Stroke Impact Scale-16); secondary outcomes were mortality, disability, medication adherence, depression, cognition, self-rated health, fatigue, care satisfaction, home blood pressure monitoring, and falls. The primary analysis was intention to treat. Of intervention hospitals, 58% had uninterrupted intervention delivery. Thirty-five percent of patients at intervention hospitals attended a COMPASS clinic visit. The primary outcome was measured for 59% of patients and was not significantly influenced by the intervention. Mean Stroke Impact Scale-16 (±SD) was 80.6±21.1 in TC versus 79.9±21.4 in usual care. Home blood pressure monitoring was self-reported by 72% of intervention patients versus 64% of usual care patients (adjusted odds ratio, 1.43 [95% CI, 1.21-1.70]). No other secondary outcomes differed. Conclusions Although designed according to the best available evidence with input from various stakeholders and consistent with Centers for Medicare and Medicaid Services TC policies, the COMPASS model of TC was not consistently incorporated into real-world health care. We found no significant effect of the intervention on functional status at 90 days post-discharge. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02588664.

Implementation of a billable transitional care model for stroke patients: the COMPASS study.

BACKGROUND: The COMprehensive Post-Acute Stroke Services (COMPASS) pragmatic trial compared the effectiveness of comprehensive transitional care (COMPASS-TC) versus usual care among stroke and transient ischemic attack (TIA) patients discharged home from North Carolina hospitals. We evaluated implementation of COMPASS-TC in 20 hospitals randomized to the intervention using the RE-AIM framework. METHODS: We evaluated hospital-level Adoption of COMPASS-TC; patient Reach (meeting transitional care management requirements of timely telephone and face-to-face follow-up); Implementation using hospital quality measures (concurrent enrollment, two-day telephone follow-up, 14-day clinic visit scheduling); and hospital-level sustainability (Maintenance). Effectiveness compared 90-day physical function (Stroke Impact Scale-16), between patients receiving COMPASS-TC versus not. Associations between hospital and patient characteristics with Implementation and Reach measures were estimated with mixed logistic regression models. RESULTS: Adoption: Of 95 eligible hospitals, 41 (43%) participated in the trial. Of the 20 hospitals randomized to the intervention, 19 (95%) initiated COMPASS-TC. Reach: A total of 24% (656/2751) of patients enrolled received a billable TC intervention, ranging from 6 to 66% across hospitals. IMPLEMENTATION: Of eligible patients enrolled, 75.9% received two-day calls (or two attempts) and 77.5% were scheduled/offered clinic visits. Most completed visits (78% of 975) occurred within 14 days. Effectiveness: Physical function was better among patients who attended a 14-day visit versus those who did not (adjusted mean difference: 3.84, 95% CI 1.42-6.27, p = 0.002). Maintenance: Of the 19 adopting hospitals, 14 (74%) sustained COMPASS-TC. CONCLUSIONS: COMPASS-TC implementation varied widely. The greatest challenge was reaching patients because of system difficulties maintaining consistent delivery of follow-up visits and patient preferences to pursue alternate post-acute care. Receiving COMPASS-TC was associated with better functional status. TRIAL REGISTRATION: ClinicalTrials.gov number: NCT02588664. Registered 28 October 2015.

Smoking cessation treatment and outcomes patterns simulation: a new framework for evaluating the potential health and economic impact of smoking cessation interventions.

BACKGROUND: Most existing models of smoking cessation treatments have considered a single quit attempt when modelling long-term outcomes. OBJECTIVE: To develop a model to simulate smokers over their lifetimes accounting for multiple quit attempts and relapses which will allow for prediction of the long-term health and economic impact of smoking cessation strategies. METHODS: A discrete event simulation (DES) that models individuals' life course of smoking behaviours, attempts to quit, and the cumulative impact on health and economic outcomes was developed. Each individual is assigned one of the available strategies used to support each quit attempt; the outcome of each attempt, time to relapses if abstinence is achieved, and time between quit attempts is tracked. Based on each individual's smoking or abstinence patterns, the risk of developing diseases associated with smoking (chronic obstructive pulmonary disease, lung cancer, myocardial infarction and stroke) is determined and the corresponding costs, changes to mortality, and quality of life assigned. Direct costs are assessed from the perspective of a comprehensive US healthcare payer ($US, 2012 values). Quit attempt strategies that can be evaluated in the current simulation include unassisted quit attempts, brief counselling, behavioural modification therapy, nicotine replacement therapy, bupropion, and varenicline, with the selection of strategies and time between quit attempts based on equations derived from survey data. Equations predicting the success of quit attempts as well as the short-term probability of relapse were derived from five varenicline clinical trials. RESULTS: Concordance between the five trials and predictions from the simulation on abstinence at 12 months was high, indicating that the equations predicting success and relapse in the first year following a quit attempt were reliable. Predictions allowing for only a single quit attempt versus unrestricted attempts demonstrate important differences, with the single quit attempt simulation predicting 19 % more smoking-related diseases and 10 % higher costs associated with smoking-related diseases. Differences are most prominent in predictions of the time that individuals abstain from smoking: 13.2 years on average over a lifetime allowing for multiple quit attempts, versus only 1.2 years with single quit attempts. Differences in abstinence time estimates become substantial only 5 years into the simulation. In the multiple quit attempt simulations, younger individuals survived longer, yet had lower lifetime smoking-related disease and total costs, while the opposite was true for those with high levels of nicotine dependence. CONCLUSION: By allowing for multiple quit attempts over the course of individuals' lives, the simulation can provide more reliable estimates on the health and economic impact of interventions designed to increase abstinence from smoking. Furthermore, the individual nature of the simulation allows for evaluation of outcomes in populations with different baseline profiles. DES provides a framework for comprehensive and appropriate predictions when applied to smoking cessation over smoker lifetimes.

A discrete-event simulation of smoking-cessation strategies based on varenicline pivotal trial data.

BACKGROUND: Smoking is the leading cause of preventable death in the US. While one in five individuals smoke, and 70% of these indicate a desire to quit, <5% of unaided quit attempts succeed. Cessation aids can double or triple the odds of successfully quitting. Models of smoking-cessation behaviour can elucidate the implications of individual abstinence patterns to allow better tailoring of quit attempts to an individual's characteristics. OBJECTIVE: The objectives of this study were to develop and validate a discrete-event simulation (DES) to evaluate the benefits of smoking abstinence using data from the pooled pivotal clinical trials of varenicline versus bupropion or placebo for smoking cessation and to provide a foundation for the development of a lifetime smoking-cessation model. METHODS: The DES model simulated the outcome of a single smoking-cessation attempt over 1 year, in accordance with the clinical trial timeframes. Pharmaceutical costs were assessed from the perspective of a healthcare payer. The model randomly sampled patient profiles from the pooled varenicline clinical trials. All patients were physically and mentally healthy adult smokers who were motivated to quit abruptly. The model allowed for comparisons of up to five distinct treatment approaches for smoking cessation. In the current analyses, three interventions corresponding to the clinical trials were evaluated, which included brief counselling plus varenicline 1.0 mg twice daily (bid) or bupropion SR 150 mg bid versus placebo (i.e. brief counselling only). The treatment periods in the clinical trials were 12 weeks (target quit date: day 8), with a 40-week non-treatment follow-up, and counselling continuing over the entire 52-week period in all treatment groups. The main outcome modelled was the continuous abstinence rate (CAR; defined as complete abstinence from smoking and confirmed by exhaled carbon monoxide ≤ 10 ppm) at end of treatment (weeks 9-12) and long-term follow-up (weeks 9-52), and total time abstinent from smoking over the course of 52 weeks. The model also evaluated costs and cost-effectiveness outcomes. RESULTS: For the varenicline, bupropion and placebo cohorts, respectively, the model predicted CARs for weeks 9-12 of 44.3%, 30.4% and 18.6% compared with observed rates of 44.0%, 29.7% and 17.7%; over weeks 9-52, predicted CARs in the model compared with observed rates in the pooled clinical studies were 22.9%, 16.4% and 9.4% versus 22.4%, 15.4% and 9.3%, respectively. Total mean abstinence times accrued in the model varenicline, bupropion and placebo groups, respectively, were 3.6, 2.6 and 1.5 months and total pharmaceutical treatment costs were $US261, $US442 and $US0 (year 2008 values) over the 1-year model period. Using cost per abstinent-month achieved as a measure of cost effectiveness, varenicline dominated bupropion and yielded an incremental cost-effectiveness ratio of $US124 compared with placebo. CONCLUSION: The model accurately replicated abstinence patterns observed in the clinical trial data using individualized predictions and indicated that varenicline was more effective and may be less costly than bupropion. This simulation incorporated individual predictions of abstinence and relapse, and provides a framework for lifetime modelling that considers multiple quit attempts over time in diverse patient populations using a variety of quit attempt strategies.

Budgetary impact of treating acute bipolar mania in hospitalized patients with quetiapine: an economic analysis of clinical trials.

OBJECTIVE: To present a tool that allows estimation of the budget impact of treatments for acute mania in bipolar I disorder from a US healthcare payer perspective. METHODS: Using discrete event simulation, the course of individuals is simulated beginning with hospitalization. Discharge depends on symptom level measured by the Young Mania Rating Scale (YMRS). The treatment effect is determined using time-dependent regression equations derived from trial data, and decision rules obtained from clinical experts. Outcomes include: time to response and symptom resolution; proportion of subjects reaching each outcome; number of adverse events. Costs were obtained from hospital discharge databases, the National Medicare Physician Fee Schedule and RedBook. Different scenarios are examined, each describing the proportion of subjects on the various treatments (lithium, divalproex sodium, olanzapine, risperidone, and quetiapine--monotherapy and in combination with lithium). Analyses are intention-to-treat over 100 days, corresponding to follow-up in mania trials. Despite its flexibility and structural adaptability, the model has some important limitations related to the characteristics of the clinical trials. These include focus on inpatient management of acute mania, use of the YMRS as the model driver, polypharmacy restricted to two-drug regimens, no explicit consideration of titration and dose changes, and relatively short time horizon. RESULTS: Scenarios with a greater proportion of quetiapine users (5% vs. 40% and 100%) result in a smaller impact on the healthcare budget (6912, 6277, and 5525 dollars per patient, respectively) and improvements in patient outcomes (e.g., 43%, 47%, and 54% responding at day 21; 74%, 77%, and 80% remitting by day 84). Sensitivity analyses showed that the budget impact is influenced by drug prices, discharge criteria and side-effect management. CONCLUSION: Results suggest that increased use of quetiapine for bipolar mania in the US is economically justified and improves health outcomes. In addition, this model illustrates that discrete event simulation is a useful and versatile tool for budget impact analyses.