Impact of repeated blast exposure on active-duty United States Special Operations Forces.

United States (US) Special Operations Forces (SOF) are frequently exposed to explosive blasts in training and combat, but the effects of repeated blast exposure (RBE) on SOF brain health are incompletely understood. Furthermore, there is no diagnostic test to detect brain injury from RBE. As a result, SOF personnel may experience cognitive, physical, and psychological symptoms for which the cause is never identified, and they may return to training or combat during a period of brain vulnerability. In 30 active-duty US SOF, we assessed the relationship between cumulative blast exposure and cognitive performance, psychological health, physical symptoms, blood proteomics, and neuroimaging measures (Connectome structural and diffusion MRI, 7 Tesla functional MRI, [11C]PBR28 translocator protein [TSPO] positron emission tomography [PET]-MRI, and [18F]MK6240 tau PET-MRI), adjusting for age, combat exposure, and blunt head trauma. Higher blast exposure was associated with increased cortical thickness in the left rostral anterior cingulate cortex (rACC), a finding that remained significant after multiple comparison correction. In uncorrected analyses, higher blast exposure was associated with worse health-related quality of life, decreased functional connectivity in the executive control network, decreased TSPO signal in the right rACC, and increased cortical thickness in the right rACC, right insula, and right medial orbitofrontal cortex-nodes of the executive control, salience, and default mode networks. These observations suggest that the rACC may be susceptible to blast overpressure and that a multimodal, network-based diagnostic approach has the potential to detect brain injury associated with RBE in active-duty SOF.

Human Macrophage Long Intergenic Noncoding RNA, SIMALR, Suppresses Inflammatory Macrophage Apoptosis via NTN1 (Netrin-1).

BACKGROUND: Long noncoding RNAs (lncRNAs) have emerged as novel regulators of macrophage biology and inflammatory cardiovascular diseases. However, studies focused on lncRNAs in human macrophage subtypes, particularly human lncRNAs that are not conserved in rodents, are limited. METHODS: Through RNA-sequencing of human monocyte-derived macrophages, we identified suppressor of inflammatory macrophage apoptosis lncRNA (SIMALR). Lipopolysaccharide/IFNγ (interferon γ) stimulated human macrophages were treated with SIMALR antisense oligonucleotides and subjected to RNA-sequencing to investigate the function of SIMALR. Western blots, luciferase assay, and RNA immunoprecipitation were performed to validate function and potential mechanism of SIMALR. RNAscope was performed to identify SIMALR expression in human carotid atherosclerotic plaques. RESULTS: RNA-sequencing of human monocyte-derived macrophages identified SIMALR, a human macrophage-specific long intergenic noncoding RNA that is highly induced in lipopolysaccharide/IFNγ-stimulated macrophages. SIMALR knockdown in lipopolysaccharide/IFNγ stimulated THP1 human macrophages induced apoptosis of inflammatory macrophages, as shown by increased protein expression of cleaved PARP (poly[ADP-ribose] polymerase), caspase 9, caspase 3, and Annexin V+. RNA-sequencing of control versus SIMALR knockdown in lipopolysaccharide/IFNγ-stimulated macrophages showed Netrin-1 (NTN1) to be significantly decreased upon SIMALR knockdown. We confirmed that NTN1 knockdown in lipopolysaccharide/IFNγ-stimulated macrophages induced apoptosis. The SIMALR knockdown-induced apoptotic phenotype was rescued by adding recombinant NTN1. NTN1 promoter-luciferase reporter activity was increased in HEK293T (human embryonic kidney 293) cells treated with lentiviral overexpression of SIMALR. NTN1 promoter activity is known to require HIF1α (hypoxia-inducible factor 1 subunit alpha), and our studies suggest that SIMALR may interact with HIF1α to regulate NTN1 transcription, thereby regulating macrophages apoptosis. SIMALR was found to be expressed in macrophages in human carotid atherosclerotic plaques of symptomatic patients. CONCLUSIONS: SIMALR is a nonconserved, human macrophage lncRNA expressed in atherosclerosis that suppresses macrophage apoptosis. SIMALR partners with HIF1α (hypoxia-inducible factor 1 subunit alpha) to regulate NTN1, which is a known macrophage survival factor. This work illustrates the importance of interrogating the functions of human lncRNAs and exploring their translational and therapeutic potential in human atherosclerosis.

Acetaminophen for Prevention and Treatment of Organ Dysfunction in Critically Ill Patients With Sepsis: The ASTER Randomized Clinical Trial.

Importance: Acetaminophen (paracetamol) has many pharmacological effects that might be beneficial in sepsis, including inhibition of cell-free hemoglobin-induced oxidation of lipids and other substrates. Objective: To determine whether acetaminophen increases days alive and free of organ dysfunction in sepsis compared with placebo. Design, Setting, and Participants: Phase 2b randomized, double-blind, clinical trial conducted from October 2021 to April 2023 with 90-day follow-up. Adults with sepsis and respiratory or circulatory organ dysfunction were enrolled in the emergency department or intensive care unit of 40 US academic hospitals within 36 hours of presentation. Intervention: Patients were randomized to 1 g of acetaminophen intravenously every 6 hours or placebo for 5 days. Main Outcome and Measures: The primary end point was days alive and free of organ support (mechanical ventilation, vasopressors, and kidney replacement therapy) to day 28. Treatment effect modification was evaluated for acetaminophen by prerandomization plasma cell-free hemoglobin level higher than 10 mg/dL. Results: Of 447 patients enrolled (mean age, 64 [SD, 15] years, 51% female, mean Sequential Organ Failure Assessment [SOFA] score, 5.4 [SD, 2.5]), 227 were randomized to acetaminophen and 220 to placebo. Acetaminophen was safe with no difference in liver enzymes, hypotension, or fluid balance between treatment arms. Days alive and free of organ support to day 28 were not meaningfully different for acetaminophen (20.2 days; 95% CI, 18.8 to 21.6) vs placebo (19.6 days; 95% CI, 18.2 to 21.0; P = .56; difference, 0.6; 95% CI, -1.4 to 2.6). Among 15 secondary outcomes, total, respiratory, and coagulation SOFA scores were significantly lower on days 2 through 4 in the acetaminophen arm as was the rate of development of acute respiratory distress syndrome within 7 days (2.2% vs 8.5% acetaminophen vs placebo; P = .01; difference, -6.3; 95% CI, -10.8 to -1.8). There was no significant interaction between cell-free hemoglobin levels and acetaminophen. Conclusions and Relevance: Intravenous acetaminophen was safe but did not significantly improve days alive and free of organ support in critically ill sepsis patients. Trial Registration: ClinicalTrials.gov Identifier: NCT04291508.

Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study.

BACKGROUND: Morbidity from undiagnosed atrial fibrillation (AF) may be preventable with early detection. Many consumer wearables contain optical photoplethysmography (PPG) sensors to measure pulse rate. PPG-based software algorithms that detect irregular heart rhythms may identify undiagnosed AF in large populations using wearables, but minimizing false-positive detections is essential. METHODS: We performed a prospective remote clinical trial to examine a novel PPG-based algorithm for detecting undiagnosed AF from a range of wrist-worn devices. Adults aged ≥22 years in the United States without AF, using compatible wearable Fitbit devices and Android or iOS smartphones, were included. PPG data were analyzed using a novel algorithm that examines overlapping 5-minute pulse windows (tachograms). Eligible participants with an irregular heart rhythm detection (IHRD), defined as 11 consecutive irregular tachograms, were invited to schedule a telehealth visit and were mailed a 1-week ambulatory ECG patch monitor. The primary outcome was the positive predictive value of the first IHRD during ECG patch monitoring for concurrent AF. RESULTS: A total of 455 699 participants enrolled (median age 47 years, 71% female, 73% White) between May 6 and October 1, 2020. IHRDs occurred for 4728 (1%) participants, and 2070 (4%) participants aged ≥65 years during a median of 122 (interquartile range, 110-134) days at risk for an IHRD. Among 1057 participants with an IHRD notification and subsequent analyzable ECG patch monitor, AF was present in 340 (32.2%). Of the 225 participants with another IHRD during ECG patch monitoring, 221 had concurrent AF on the ECG and 4 did not, resulting in an IHRD positive predictive value of 98.2% (95% CI, 95.5%-99.5%). For participants aged ≥65 years, the IHRD positive predictive value was 97.0% (95% CI, 91.4%-99.4%). CONCLUSIONS: A novel PPG software algorithm for wearable Fitbit devices exhibited a high positive predictive value for concurrent AF and identified participants likely to have AF on subsequent ECG patch monitoring. Wearable devices may facilitate identifying individuals with undiagnosed AF. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04380415.

ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation.

BACKGROUND: Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF. METHODS: We trained a convolutional neural network (ECG-AI) to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit 3 Cox proportional hazards models, composed of ECG-AI 5-year AF probability, CHARGE-AF clinical risk score (Cohorts for Heart and Aging in Genomic Epidemiology-Atrial Fibrillation), and terms for both ECG-AI and CHARGE-AF (CH-AI), respectively. We assessed model performance by calculating discrimination (area under the receiver operating characteristic curve) and calibration in an internal test set and 2 external test sets (Brigham and Women's Hospital [BWH] and UK Biobank). Models were recalibrated to estimate 2-year AF risk in the UK Biobank given limited available follow-up. We used saliency mapping to identify ECG features most influential on ECG-AI risk predictions and assessed correlation between ECG-AI and CHARGE-AF linear predictors. RESULTS: The training set comprised 45 770 individuals (age 55±17 years, 53% women, 2171 AF events) and the test sets comprised 83 162 individuals (age 59±13 years, 56% women, 2424 AF events). Area under the receiver operating characteristic curve was comparable using CHARGE-AF (MGH, 0.802 [95% CI, 0.767-0.836]; BWH, 0.752 [95% CI, 0.741-0.763]; UK Biobank, 0.732 [95% CI, 0.704-0.759]) and ECG-AI (MGH, 0.823 [95% CI, 0.790-0.856]; BWH, 0.747 [95% CI, 0.736-0.759]; UK Biobank, 0.705 [95% CI, 0.673-0.737]). Area under the receiver operating characteristic curve was highest using CH-AI (MGH, 0.838 [95% CI, 0.807 to 0.869]; BWH, 0.777 [95% CI, 0.766 to 0.788]; UK Biobank, 0.746 [95% CI, 0.716 to 0.776]). Calibration error was low using ECG-AI (MGH, 0.0212; BWH, 0.0129; UK Biobank, 0.0035) and CH-AI (MGH, 0.012; BWH, 0.0108; UK Biobank, 0.0001). In saliency analyses, the ECG P-wave had the greatest influence on AI model predictions. ECG-AI and CHARGE-AF linear predictors were correlated (Pearson r: MGH, 0.61; BWH, 0.66; UK Biobank, 0.41). CONCLUSIONS: AI-based analysis of 12-lead ECGs has similar predictive usefulness to a clinical risk factor model for incident AF and the approaches are complementary. ECG-AI may enable efficient quantification of future AF risk.

Efficacy of Tocilizumab in Patients Hospitalized with Covid-19.

BACKGROUND: The efficacy of interleukin-6 receptor blockade in hospitalized patients with coronavirus disease 2019 (Covid-19) who are not receiving mechanical ventilation is unclear. METHODS: We performed a randomized, double-blind, placebo-controlled trial involving patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, hyperinflammatory states, and at least two of the following signs: fever (body temperature >38°C), pulmonary infiltrates, or the need for supplemental oxygen in order to maintain an oxygen saturation greater than 92%. Patients were randomly assigned in a 2:1 ratio to receive standard care plus a single dose of either tocilizumab (8 mg per kilogram of body weight) or placebo. The primary outcome was intubation or death, assessed in a time-to-event analysis. The secondary efficacy outcomes were clinical worsening and discontinuation of supplemental oxygen among patients who had been receiving it at baseline, both assessed in time-to-event analyses. RESULTS: We enrolled 243 patients; 141 (58%) were men, and 102 (42%) were women. The median age was 59.8 years (range, 21.7 to 85.4), and 45% of the patients were Hispanic or Latino. The hazard ratio for intubation or death in the tocilizumab group as compared with the placebo group was 0.83 (95% confidence interval [CI], 0.38 to 1.81; P = 0.64), and the hazard ratio for disease worsening was 1.11 (95% CI, 0.59 to 2.10; P = 0.73). At 14 days, 18.0% of the patients in the tocilizumab group and 14.9% of the patients in the placebo group had had worsening of disease. The median time to discontinuation of supplemental oxygen was 5.0 days (95% CI, 3.8 to 7.6) in the tocilizumab group and 4.9 days (95% CI, 3.8 to 7.8) in the placebo group (P = 0.69). At 14 days, 24.6% of the patients in the tocilizumab group and 21.2% of the patients in the placebo group were still receiving supplemental oxygen. Patients who received tocilizumab had fewer serious infections than patients who received placebo. CONCLUSIONS: Tocilizumab was not effective for preventing intubation or death in moderately ill hospitalized patients with Covid-19. Some benefit or harm cannot be ruled out, however, because the confidence intervals for efficacy comparisons were wide. (Funded by Genentech; ClinicalTrials.gov number, NCT04356937.).

Functional principal component analysis and sparse-group LASSO to identify associations between biomarker trajectories and mortality among hospitalized SARS-CoV-2 infected individuals.

BACKGROUND: A substantial body of clinical research involving individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evaluated the association between in-hospital biomarkers and severe SARS-CoV-2 outcomes, including intubation and death. However, most existing studies considered each of multiple biomarkers independently and focused analysis on baseline or peak values. METHODS: We propose a two-stage analytic strategy combining functional principal component analysis (FPCA) and sparse-group LASSO (SGL) to characterize associations between biomarkers and 30-day mortality rates. Unlike prior reports, our proposed approach leverages: 1) time-varying biomarker trajectories, 2) multiple biomarkers simultaneously, and 3) the pathophysiological grouping of these biomarkers. We apply this method to a retrospective cohort of 12, 941 patients hospitalized at Massachusetts General Hospital or Brigham and Women's Hospital and conduct simulation studies to assess performance. RESULTS: Renal, inflammatory, and cardio-thrombotic biomarkers were associated with 30-day mortality rates among hospitalized SARS-CoV-2 patients. Sex-stratified analysis revealed that hematogolical biomarkers were associated with higher mortality in men while this association was not identified in women. In simulation studies, our proposed method maintained high true positive rates and outperformed alternative approaches using baseline or peak values only with respect to false positive rates. CONCLUSIONS: The proposed two-stage approach is a robust strategy for identifying biomarkers that associate with disease severity among SARS-CoV-2-infected individuals. By leveraging information on multiple, grouped biomarkers' longitudinal trajectories, our method offers an important first step in unraveling disease etiology and defining meaningful risk strata.

Association of Statins and 28-Day Mortality Rates in Patients Hospitalized With Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

BACKGROUND: Statins may be protective in severe acute respiratory syndrome coronavirus 2 SARS-CoV-2 infection. The aim of the current study was to evaluate the effect of in-hospital statin use on 28-day mortality rates and intensive care unit (ICU) admission among patients with SARS-CoV-2, stratified into 4 groups: those who used statins before hospitalization (treatment continued or discontinued in the hospital) and those who did not (treatment newly initiated in the hospital or never initiated). METHODS: In a cohort study of 1179 patients with SARS-CoV-2, record review was used to assess demographics, laboratory measurements, comorbid conditions, and time from admission to death, ICU admission, or discharge. Using marginal structural Cox models, we estimated hazard ratios (HRs) for death and ICU admission. RESULTS: Among 1179 patients, 676 (57%) were male, 443 (37%) were >65 years old, and 493 (46%) had a body mass index ≥30 (calculated as weight in kilograms divided by height in meters squared). Inpatient statin use reduced the hazard of death (HR, 0.566; P=.008). This association held among patients who did and those who did not use statins before hospitalization (HR, 0.270 [P=.003] and 0.493 [P=.04], respectively). Statin use was associated with improved time to death for patients aged >65 years but not for those ≤65 years old. CONCLUSION: Statin use during hospitalization for SARS-CoV-2 infection was associated with reduced 28-day mortality rates. Well-designed randomized control trials are needed to better define this relationship.

Cis-regulated expression of non-conserved lincRNAs associates with cardiometabolic related traits.

Many complex disease risk loci map to intergenic regions containing long intergenic noncoding RNAs (lincRNAs). The majority of these is not conserved outside humans, raising the question whether genetically regulated expression of non-conserved and conserved lincRNAs has similar rates of association with complex traits. Here we leveraged data from the Genotype-Tissue Expression (GTEx) project and multiple public genome-wide association study (GWAS) resources. Using an established transcriptome-wide association study (TWAS) tool, FUSION, we interrogated the associations between cis-regulated expression of lincRNAs and multiple cardiometabolic traits. We found that cis-regulated expression of non-conserved lincRNAs had a strikingly similar trend of association with complex cardiometabolic traits as conserved lincRNAs. This finding challenges the conventional notion of conservation that has led to prioritization of conserved loci for functional studies and calls attention to the need to develop comprehensive strategies to study the large number of non-conserved human lincRNAs that may contribute to human disease.

Nonconserved Long Intergenic Noncoding RNAs Associate With Complex Cardiometabolic Disease Traits.

OBJECTIVE: Transcriptome profiling of human tissues has revealed thousands of long intergenic noncoding RNAs (lincRNAs) at loci identified through large-scale genome-wide studies for complex cardiometabolic traits. This raises the question of whether genetic variation at nonconserved lincRNAs has any systematic association with complex disease, and if so, how different this pattern is from conserved lincRNAs. We evaluated whether the associations between nonconserved lincRNAs and 8 complex cardiometabolic traits resemble or differ from the pattern of association for conserved lincRNAs. Approach and Results: Our investigation of over 7000 lincRNA annotations from GENCODE Release 33-GRCh38.p13 for complex trait genetic associations leveraged several large, established meta-analyses genome-wide association study summary data resources, including GIANT (Genetic Investigation of Anthropometric Traits), UK Biobank, GLGC (Global Lipids Genetics Consortium), Cardiogram (Coronary Artery Disease Genome Wide Replication and Meta-Analysis), and DIAGRAM (Diabetes Genetics Replication and Meta-Analysis)/DIAMANTE (Diabetes Meta-Analysis of Trans-Ethnic Association Studies). These analyses revealed that (1) nonconserved lincRNAs associate with a range of cardiometabolic traits at a rate that is generally consistent with conserved lincRNAs; (2) these findings persist across different definitions of conservation; and (3) overall across all cardiometabolic traits, approximately one-third of genome-wide association study-associated lincRNAs are nonconserved, and this increases to about two-thirds using a more stringent definition of conservation. CONCLUSIONS: These findings suggest that the traditional notion of conservation driving prioritization for functional and translational follow-up of complex cardiometabolic genomic discoveries may need to be revised in the context of the abundance of nonconserved long noncoding RNAs in the human genome and their apparent predilection to associate with complex cardiometabolic traits.

Rationale and design of a large population study to validate software for the assessment of atrial fibrillation from data acquired by a consumer tracker or smartwatch: The Fitbit heart study.

BACKGROUND: Early detection of atrial fibrillation or flutter (AF) may enable prevention of downstream morbidity. Consumer wrist-worn wearable technology is capable of detecting AF by identifying irregular pulse waveforms using photoplethysmography (PPG). The validity of PPG-based software algorithms for AF detection requires prospective assessment. METHODS: The Fitbit Heart Study (NCT04380415) is a single-arm remote clinical trial examining the validity of a novel PPG-based software algorithm for detecting AF. The proprietary Fitbit algorithm examines pulse waveform intervals during analyzable periods in which participants are sufficiently stationary. Fitbit consumers with compatible wrist-worn trackers or smartwatches were invited to participate. Enrollment began May 6, 2020 and as of October 1, 2020, 455,699 participants enrolled. Participants in whom an irregular heart rhythm was detected were invited to attend a telehealth visit and eligible participants were then mailed a one-week single lead electrocardiographic (ECG) patch monitor. The primary study objective is to assess the positive predictive value of an irregular heart rhythm detection for AF during the ECG patch monitor period. Additional objectives will examine the validity of irregular pulse tachograms during subsequent heart rhythm detections, self-reported AF diagnoses and treatments, and relations between irregular heart rhythm detections and AF episode duration and time spent in AF. CONCLUSIONS: The Fitbit Heart Study is a large-scale remote clinical trial comprising a unique software algorithm for detection of AF. The study results will provide critical insights into the use of consumer wearable technology for AF detection, and for characterizing the nature of AF episodes detected using consumer-based PPG technology.

Body composition predictors of outcome in patients with COVID-19.

BACKGROUND/OBJECTIVE: Obesity is a strong risk factor for adverse outcomes in patients hospitalized with COVID-19, however, the distribution of fat and the amount of muscle mass are more accurate risk factors than BMI. The objective of this study was to assess body composition measures obtained on opportunistic abdominal CTs as predictors of outcome in patients hospitalized with COVID-19. We hypothesized that elevated visceral and intermuscular adipose tissue would be associated with adverse outcome. SUBJECTS/METHODS: Our retrospective study was IRB-approved and HIPAA-compliant. The study group comprised 124 patients (median age: 68 years, IQR: 56, 77; 59 weeks, 65 months) who were admitted with COVID-19 to a single hospital and who had undergone abdominal CT for clinical purposes. Visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intermuscular adipose tissue (IMAT), and paraspinal and abdominal muscle cross-sectional areas (CSA) were assessed. Clinical information including prognostic factors, time of admission to the intensive care unit (ICU) and time of death within 28 days were obtained. Multivariate time-to-event competing risk models were fitted to estimate the hazard ratio (HR) for a composite outcome of ICU admission/mortality associated with a one standard deviation increase in each body compositional measure. Each model was adjusted for age, sex, race, BMI, and cardiometabolic comorbidities. RESULTS: There were 50 patients who were admitted to the ICU or deceased over a median time of 1 day [IQR 1, 6] from hospital admission. Higher VAT/SAT ratio (HR of 1.30; 95% CI 1.04-1.62, p = 0.022) and higher IMAT CSA (HR of 1.44; 95% CI 1.10-1.89, p = 0.008) were associated with a reduced time to ICU admission or death in adjusted models. CONCLUSION: VAT/SAT and IMAT are predictors of adverse outcome in patients hospitalized with COVID-19, independent of other established prognostic factors. This suggests that body composition measures may serve as novel biomarkers of outcome in patients with COVID-19.

Reemergence of the language network during recovery from severe traumatic brain injury: A pilot functional MRI study.

PRIMARY OBJECTIVE: We hypothesized that, in patients with acute severe traumatic brain injury (TBI) who recover basic language function, speech-evoked blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) responses within the canonical language network increase over the first 6 months post-injury. RESEARCH DESIGN: We conducted a prospective, longitudinal fMRI pilot study of adults with acute severe TBI admitted to the intensive care unit. We also enrolled age- and sex-matched healthy subjects. METHODS AND PROCEDURES: We evaluated BOLD signal in bilateral superior temporal gyrus (STG) and inferior frontal gyrus (IFG) regions of interest acutely and approximately 6 months post-injury. Given evidence that regions outside the canonical language network contribute to language processing, we also performed exploratory whole-brain analyses. MAIN OUTCOMES AND RESULTS: Of the 16 patients enrolled, eight returned for follow-up fMRI, all of whom recovered basic language function. We observed speech-evoked longitudinal BOLD increases in the left STG, but not in the right STG, right IFG, or left IFG. Whole-brain analysis revealed increases in the right supramarginal and middle temporal gyri but no differences between patients and healthy subjects (n = 16). CONCLUSION: This pilot study suggests that, in patients with severe TBI who recover llanguage function, speech-evoked responses in bihemispheric language-processing cortex reemerge by 6 months post-injury.

Personalized Connectome Mapping to Guide Targeted Therapy and Promote Recovery of Consciousness in the Intensive Care Unit.

There are currently no therapies proven to promote early recovery of consciousness in patients with severe brain injuries in the intensive care unit (ICU). For patients whose families face time-sensitive, life-or-death decisions, treatments that promote recovery of consciousness are needed to reduce the likelihood of premature withdrawal of life-sustaining therapy, facilitate autonomous self-expression, and increase access to rehabilitative care. Here, we present the Connectome-based Clinical Trial Platform (CCTP), a new paradigm for developing and testing targeted therapies that promote early recovery of consciousness in the ICU. We report the protocol for STIMPACT (Stimulant Therapy Targeted to Individualized Connectivity Maps to Promote ReACTivation of Consciousness), a CCTP-based trial in which intravenous methylphenidate will be used for targeted stimulation of dopaminergic circuits within the subcortical ascending arousal network (ClinicalTrials.gov NCT03814356). The scientific premise of the CCTP and the STIMPACT trial is that personalized brain network mapping in the ICU can identify patients whose connectomes are amenable to neuromodulation. Phase 1 of the STIMPACT trial is an open-label, safety and dose-finding study in 22 patients with disorders of consciousness caused by acute severe traumatic brain injury. Patients in Phase 1 will receive escalating daily doses (0.5-2.0 mg/kg) of intravenous methylphenidate over a 4-day period and will undergo resting-state functional magnetic resonance imaging and electroencephalography to evaluate the drug's pharmacodynamic properties. The primary outcome measure for Phase 1 relates to safety: the number of drug-related adverse events at each dose. Secondary outcome measures pertain to pharmacokinetics and pharmacodynamics: (1) time to maximal serum concentration; (2) serum half-life; (3) effect of the highest tolerated dose on resting-state functional MRI biomarkers of connectivity; and (4) effect of each dose on EEG biomarkers of cerebral cortical function. Predetermined safety and pharmacodynamic criteria must be fulfilled in Phase 1 to proceed to Phase 2A. Pharmacokinetic data from Phase 1 will also inform the study design of Phase 2A, where we will test the hypothesis that personalized connectome maps predict therapeutic responses to intravenous methylphenidate. Likewise, findings from Phase 2A will inform the design of Phase 2B, where we plan to enroll patients based on their personalized connectome maps. By selecting patients for clinical trials based on a principled, mechanistic assessment of their neuroanatomic potential for a therapeutic response, the CCTP paradigm and the STIMPACT trial have the potential to transform the therapeutic landscape in the ICU and improve outcomes for patients with severe brain injuries.

Bayesian variable selection for high dimensional predictors and self-reported outcomes.

BACKGROUND: The onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts. Self-reported outcomes are cost-effective; however, they are subject to error. Diagnosis of silent events may also occur through the use of imperfect laboratory-based diagnostic tests. In this paper, we describe an approach for variable selection in high dimensional datasets for settings in which the outcome is observed with error. METHODS: We adapt the spike and slab Bayesian Variable Selection approach in the context of error-prone, self-reported outcomes. The performance of the proposed approach is studied through simulation studies. An illustrative application is included using data from the Women's Health Initiative SNP Health Association Resource, which includes extensive genotypic (>900,000 SNPs) and phenotypic data on 9,873 African American and Hispanic American women. RESULTS: Simulation studies show improved sensitivity of our proposed method when compared to a naive approach that ignores error in the self-reported outcomes. Application of the proposed method resulted in discovery of several single nucleotide polymorphisms (SNPs) that are associated with risk of type 2 diabetes in a dataset of 9,873 African American and Hispanic participants in the Women's Health Initiative. There was little overlap among the top ranking SNPs associated with type 2 diabetes risk between the racial groups, adding support to previous observations in the literature of disease associated genetic loci that are often not generalizable across race/ethnicity populations. The adapted Bayesian variable selection algorithm is implemented in R. The source code for the simulations are available in the Supplement. CONCLUSIONS: Variable selection accuracy is reduced when the outcome is ascertained by error-prone self-reports. For this setting, our proposed algorithm has improved variable selection performance when compared to approaches that neglect to account for the error-prone nature of self-reports.

A likelihood-based approach to transcriptome association analysis.

Elucidating the mechanistic underpinnings of genetic associations with complex traits requires formally characterizing and testing associated cell and tissue-specific expression profiles. New opportunities exist to bolster this investigation with the growing numbers of large publicly available omics level data resources. Herein, we describe a fully likelihood-based strategy to leveraging external resources in the setting that expression profiles are partially or fully unobserved in a genetic association study. A general framework is presented to accommodate multiple data types, and strategies for implementation using existing software packages are described. The method is applied to an investigation of the genetics of evoked inflammatory response in cardiovascular disease research. Simulation studies suggest appropriate type-1 error control and power gains compared to single regression imputation, the most commonly applied practice in this setting.

Genome-wide interrogation reveals hundreds of long intergenic noncoding RNAs that associate with cardiometabolic traits.

Long intergenic noncoding RNAs (lincRNAs) play important roles in disease, but the vast majority of these transcripts remain uncharacterized. We defined a set of 54 944 human lincRNAs by drawing on four publicly available lincRNA datasets, and annotated ∼2.5 million single nucleotide polymorphisms (SNPs) from each of 15 cardiometabolic genome-wide association study datasets into these lincRNAs. We identified hundreds of lincRNAs with at least one trait-associated SNP: 898 SNPs in 343 unique lincRNAs at 5% false discovery rate, and 469 SNPs in 146 unique lincRNAs meeting Bonferroni-corrected P < 0.05. An additional 64 trait-associated lincRNAs were identified using a class-level testing strategy at Bonferroni-corrected P < 0.05. To better understand the genomic context and prioritize trait-associated lincRNAs, we examined the pattern of linkage disequilibrium between SNPs in the lincRNAs and SNPs that met genome-wide-significance in the region (±500 kb of lincRNAs). A subset of the lincRNA-trait association findings was replicated in independent Genome-wide association studies data from the Pakistan Risk of Myocardial Infarction Study study. For trait-associated lincRNAs, we also investigated synteny and conservation relative to mouse, expression patterns in five cardiometabolic-relevant tissues, and allele-specific expression in RNA sequencing data for adipose tissue and leukocytes. Finally, we revealed a functional role in human adipocytes for linc-NFE2L3-1, which is expressed in adipose and is associated with waist-hip ratio adjusted for BMI. This comprehensive profile of trait-associated lincRNAs provides novel insights into disease mechanism and serves as a launching point for interrogation of the biology of specific lincRNAs in cardiometabolic disease.

Bayesian variable selection for post-analytic interrogation of susceptibility loci.

Understanding the complex interplay among protein coding genes and regulatory elements requires rigorous interrogation with analytic tools designed for discerning the relative contributions of overlapping genomic regions. To this aim, we offer a novel application of Bayesian variable selection (BVS) for classifying genomic class level associations using existing large meta-analysis summary level resources. This approach is applied using the expectation maximization variable selection (EMVS) algorithm to typed and imputed SNPs across 502 protein coding genes (PCGs) and 220 long intergenic non-coding RNAs (lncRNAs) that overlap 45 known loci for coronary artery disease (CAD) using publicly available Global Lipids Gentics Consortium (GLGC) (Teslovich et al., 2010; Willer et al., 2013) meta-analysis summary statistics for low-density lipoprotein cholesterol (LDL-C). The analysis reveals 33 PCGs and three lncRNAs across 11 loci with >50% posterior probabilities for inclusion in an additive model of association. The findings are consistent with previous reports, while providing some new insight into the architecture of LDL-cholesterol to be investigated further. As genomic taxonomies continue to evolve, additional classes such as enhancer elements and splicing regions, can easily be layered into the proposed analysis framework. Moreover, application of this approach to alternative publicly available meta-analysis resources, or more generally as a post-analytic strategy to further interrogate regions that are identified through single point analysis, is straightforward. All coding examples are implemented in R version 3.2.1 and provided as supplemental material.

A score test for genetic class-level association with nonlinear biomarker trajectories.

Emerging data suggest that the genetic regulation of the biological response to inflammatory stress may be fundamentally different to the genetic underpinning of the homeostatic control (resting state) of the same biological measures. In this paper, we interrogate this hypothesis using a single-SNP score test and a novel class-level testing strategy to characterize protein-coding gene and regulatory element-level associations with longitudinal biomarker trajectories in response to stimulus. Using the proposed class-level association score statistic for longitudinal data, which accounts for correlations induced by linkage disequilibrium, the genetic underpinnings of evoked dynamic changes in repeatedly measured biomarkers are investigated. The proposed method is applied to data on two biomarkers arising from the Genetics of Evoked Responses to Niacin and Endotoxemia study, a National Institutes of Health-sponsored investigation of the genomics of inflammatory and metabolic responses during low-grade endotoxemia. Our results suggest that the genetic basis of evoked inflammatory response is different than the genetic contributors to resting state, and several potentially novel loci are identified. A simulation study demonstrates appropriate control of type-1 error rates, relative computational efficiency, and power. Copyright © 2017 John Wiley & Sons, Ltd.