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.

Researching COVID to enhance recovery (RECOVER) tissue pathology study protocol: Rationale, objectives, and design

IMPORTANCE

SARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or organ dysfunction after the acute phase of infection, termed Post-Acute Sequelae of SARS-CoV-2 (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are poorly understood. The objectives of the Researching COVID to Enhance Recovery (RECOVER) tissue pathology study (RECOVER-Pathology) are to: (1) characterize prevalence and types of organ injury/disease and pathology occurring with PASC; (2) characterize the association of pathologic findings with clinical and other characteristics; (3) define the pathophysiology and mechanisms of PASC, and possible mediation via viral persistence; and (4) establish a post-mortem tissue biobank and post-mortem brain imaging biorepository.

METHODS

RECOVER-Pathology is a cross-sectional study of decedents dying at least 15 days following initial SARS-CoV-2 infection. Eligible decedents must meet WHO criteria for suspected, probable, or confirmed infection and must be aged 18 years or more at the time of death. Enrollment occurs at 7 sites in four U.S. states and Washington, DC. Comprehensive autopsies are conducted according to a standardized protocol within 24 hours of death; tissue samples are sent to the PASC Biorepository for later analyses. Data on clinical history are collected from the medical records and/or next of kin. The primary study outcomes include an array of pathologic features organized by organ system. Causal inference methods will be employed to investigate associations between risk factors and pathologic outcomes.

DISCUSSION

RECOVER-Pathology is the largest autopsy study addressing PASC among US adults. Results of this study are intended to elucidate mechanisms of organ injury and disease and enhance our understanding of the pathophysiology of PASC.

Researching COVID to enhance recovery (RECOVER) pregnancy study: Rationale, objectives and design

IMPORTANCE

Pregnancy induces unique physiologic changes to the immune response and hormonal changes leading to plausible differences in the risk of developing post-acute sequelae of SARS-CoV-2 (PASC), or Long COVID. Exposure to SARS-CoV-2 during pregnancy may also have long-term ramifications for exposed offspring, and it is critical to evaluate the health outcomes of exposed children. The National Institutes of Health (NIH) Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC aims to evaluate the long-term sequelae of SARS-CoV-2 infection in various populations. RECOVER-Pregnancy was designed specifically to address long-term outcomes in maternal-child dyads.

METHODS

RECOVER-Pregnancy cohort is a combined prospective and retrospective cohort that proposes to enroll 2,300 individuals with a pregnancy during the COVID-19 pandemic and their offspring exposed and unexposed in utero, including single and multiple gestations. Enrollment will occur both in person at 27 sites through the Eunice Kennedy Shriver National Institutes of Health Maternal-Fetal Medicine Units Network and remotely through national recruitment by the study team at the University of California San Francisco (UCSF). Adults with and without SARS-CoV-2 infection during pregnancy are eligible for enrollment in the pregnancy cohort and will follow the protocol for RECOVER-Adult including validated screening tools, laboratory analyses and symptom questionnaires followed by more in-depth phenotyping of PASC on a subset of the overall cohort. Offspring exposed and unexposed in utero to SARS-CoV-2 maternal infection will undergo screening tests for neurodevelopment and other health outcomes at 12, 18, 24, 36 and 48 months of age. Blood specimens will be collected at 24 months of age for SARS-CoV-2 antibody testing, storage and anticipated later analyses proposed by RECOVER and other investigators.

DISCUSSION

RECOVER-Pregnancy will address whether having SARS-CoV-2 during pregnancy modifies the risk factors, prevalence, and phenotype of PASC. The pregnancy cohort will also establish whether there are increased risks of adverse long-term outcomes among children exposed in utero.

CLINICAL TRIALS.GOV IDENTIFIER

Clinical Trial Registration: http://www.clinicaltrials.gov. Unique identifier: NCT05172011.

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.

Researching COVID to Enhance Recovery (RECOVER) adult study protocol: Rationale, objectives, and design

IMPORTANCE

SARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or other health effects after the acute phase of infection; termed post-acute sequelae of SARS-CoV-2 infection (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are ill-defined. The objectives of the Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC in Adults (RECOVER-Adult) are to: (1) characterize PASC prevalence; (2) characterize the symptoms, organ dysfunction, natural history, and distinct phenotypes of PASC; (3) identify demographic, social and clinical risk factors for PASC onset and recovery; and (4) define the biological mechanisms underlying PASC pathogenesis.

METHODS

RECOVER-Adult is a combined prospective/retrospective cohort currently planned to enroll 14,880 adults aged ≥18 years. Eligible participants either must meet WHO criteria for suspected, probable, or confirmed infection; or must have evidence of no prior infection. Recruitment occurs at 86 sites in 33 U.S. states, Washington, DC and Puerto Rico, via facility- and community-based outreach. Participants complete quarterly questionnaires about symptoms, social determinants, vaccination status, and interim SARS-CoV-2 infections. In addition, participants contribute biospecimens and undergo physical and laboratory examinations at approximately 0, 90 and 180 days from infection or negative test date, and yearly thereafter. Some participants undergo additional testing based on specific criteria or random sampling. Patient representatives provide input on all study processes. The primary study outcome is onset of PASC, measured by signs and symptoms. A paradigm for identifying PASC cases will be defined and updated using supervised and unsupervised learning approaches with cross-validation. Logistic regression and proportional hazards regression will be conducted to investigate associations between risk factors, onset, and resolution of PASC symptoms.

DISCUSSION

RECOVER-Adult is the first national, prospective, longitudinal cohort of PASC among US adults. Results of this study are intended to inform public health, spur clinical trials, and expand treatment options.

REGISTRATION

NCT05172024.

Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection

Importance

SARS-CoV-2 infection is associated with persistent, relapsing, or new symptoms or other health effects occurring after acute infection, termed postacute sequelae of SARS-CoV-2 infection (PASC), also known as long COVID. Characterizing PASC requires analysis of prospectively and uniformly collected data from diverse uninfected and infected individuals.

Objective

To develop a definition of PASC using self-reported symptoms and describe PASC frequencies across cohorts, vaccination status, and number of infections.

Design, Setting, and Participants

Prospective observational cohort study of adults with and without SARS-CoV-2 infection at 85 enrolling sites (hospitals, health centers, community organizations) located in 33 states plus Washington, DC, and Puerto Rico. Participants who were enrolled in the RECOVER adult cohort before April 10, 2023, completed a symptom survey 6 months or more after acute symptom onset or test date. Selection included population-based, volunteer, and convenience sampling.

Exposure

SARS-CoV-2 infection.

Main Outcomes and Measures

PASC and 44 participant-reported symptoms (with severity thresholds).

Results

A total of 9764 participants (89% SARS-CoV-2 infected; 71% female; 16% Hispanic/Latino; 15% non-Hispanic Black; median age, 47 years [IQR, 35-60]) met selection criteria. Adjusted odds ratios were 1.5 or greater (infected vs uninfected participants) for 37 symptoms. Symptoms contributing to PASC score included postexertional malaise, fatigue, brain fog, dizziness, gastrointestinal symptoms, palpitations, changes in sexual desire or capacity, loss of or change in smell or taste, thirst, chronic cough, chest pain, and abnormal movements. Among 2231 participants first infected on or after December 1, 2021, and enrolled within 30 days of infection, 224 (10% [95% CI, 8.8%-11%]) were PASC positive at 6 months.

Conclusions and Relevance

A definition of PASC was developed based on symptoms in a prospective cohort study. As a first step to providing a framework for other investigations, iterative refinement that further incorporates other clinical features is needed to support actionable definitions of PASC.

Researching COVID to enhance recovery (RECOVER) pediatric study protocol: Rationale, objectives and design

Importance

The prevalence, pathophysiology, and long-term outcomes of COVID-19 (post-acute sequelae of SARS-CoV-2 [PASC] or "Long COVID") in children and young adults remain unknown. Studies must address the urgent need to define PASC, its mechanisms, and potential treatment targets in children and young adults.

Observations

We describe the protocol for the Pediatric Observational Cohort Study of the NIH's RE searching COV ID to E nhance R ecovery (RECOVER) Initiative. RECOVER-Pediatrics is an observational meta-cohort study of caregiver-child pairs (birth through 17 years) and young adults (18 through 25 years), recruited from more than 100 sites across the US. This report focuses on two of five cohorts that comprise RECOVER-Pediatrics: 1) a de novo RECOVER prospective cohort of children and young adults with and without previous or current infection; and 2) an extant cohort derived from the Adolescent Brain Cognitive Development (ABCD) study ( n =10,000). The de novo cohort incorporates three tiers of data collection: 1) remote baseline assessments (Tier 1, n=6000); 2) longitudinal follow-up for up to 4 years (Tier 2, n=6000); and 3) a subset of participants, primarily the most severely affected by PASC, who will undergo deep phenotyping to explore PASC pathophysiology (Tier 3, n=600). Youth enrolled in the ABCD study participate in Tier 1. The pediatric protocol was developed as a collaborative partnership of investigators, patients, researchers, clinicians, community partners, and federal partners, intentionally promoting inclusivity and diversity. The protocol is adaptive to facilitate responses to emerging science.

Conclusions and Relevance

RECOVER-Pediatrics seeks to characterize the clinical course, underlying mechanisms, and long-term effects of PASC from birth through 25 years old. RECOVER-Pediatrics is designed to elucidate the epidemiology, four-year clinical course, and sociodemographic correlates of pediatric PASC. The data and biosamples will allow examination of mechanistic hypotheses and biomarkers, thus providing insights into potential therapeutic interventions.

Clinical Trialsgov Identifier

Clinical Trial Registration: http://www.clinicaltrials.gov . Unique identifier: NCT05172011.

Researching COVID to enhance recovery (RECOVER) pregnancy study: Rationale, objectives and design

Importance

Pregnancy induces unique physiologic changes to the immune response and hormonal changes leading to plausible differences in the risk of developing post-acute sequelae of SARS-CoV-2 (PASC), or Long COVID. Exposure to SARS-CoV-2 during pregnancy may also have long-term ramifications for exposed offspring, and it is critical to evaluate the health outcomes of exposed children. The National Institutes of Health (NIH) Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC aims to evaluate the long-term sequelae of SARS-CoV-2 infection in various populations. RECOVER- Pregnancy was designed specifically to address long-term outcomes in maternal-child dyads.

Methods

RECOVER-Pregnancy cohort is a combined prospective and retrospective cohort that proposes to enroll 2,300 individuals with a pregnancy during the COVID-19 pandemic and their offspring exposed and unexposed in utero, including single and multiple gestations. Enrollment will occur both in person at 27 sites through the Eunice Kennedy Shriver National Institutes of Health Maternal-Fetal Medicine Units Network and remotely through national recruitment by the study team at the University of California San Francisco (UCSF). Adults with and without SARS-CoV-2 infection during pregnancy are eligible for enrollment in the pregnancy cohort and will follow the protocol for RECOVER-Adult including validated screening tools, laboratory analyses and symptom questionnaires followed by more in-depth phenotyping of PASC on a subset of the overall cohort. Offspring exposed and unexposed in utero to SARS-CoV-2 maternal infection will undergo screening tests for neurodevelopment and other health outcomes at 12, 18, 24, 36 and 48 months of age. Blood specimens will be collected at 24 months of age for SARS-CoV-2 antibody testing, storage and anticipated later analyses proposed by RECOVER and other investigators.

Discussion

RECOVER-Pregnancy will address whether having SARS-CoV-2 during pregnancy modifies the risk factors, prevalence, and phenotype of PASC. The pregnancy cohort will also establish whether there are increased risks of adverse long-term outcomes among children exposed in utero.

Registration

NCT05172024.

Automated detection of axonal damage along white matter tracts in acute severe traumatic brain injury

New techniques for individualized assessment of white matter integrity are needed to detect traumatic axonal injury (TAI) and predict outcomes in critically ill patients with acute severe traumatic brain injury (TBI). Diffusion MRI tractography has the potential to quantify white matter microstructure in vivo and has been used to characterize tract-specific changes following TBI. However, tractography is not routinely used in the clinical setting to assess the extent of TAI, in part because focal lesions reduce the robustness of automated methods. Here, we propose a pipeline that combines automated tractography reconstructions of 40 white matter tracts with multivariate analysis of along-tract diffusion metrics to assess the presence of TAI in individual patients with acute severe TBI. We used the Mahalanobis distance to identify abnormal white matter tracts in each of 18 patients with acute severe TBI as compared to 33 healthy subjects. In all patients for which a FreeSurfer anatomical segmentation could be obtained (17 of 18 patients), including 13 with focal lesions, the automated pipeline successfully reconstructed a mean of 37.5 ± 2.1 white matter tracts without the need for manual intervention. A mean of 2.5 ± 2.1 tracts resulted in partial or failed reconstructions and needed to be reinitialized upon visual inspection. The pipeline detected at least one abnormal tract in all patients (mean: 9.1 ± 7.9) and accurately discriminated between patients and controls (AUC: 0.91). The number and neuroanatomic location of abnormal tracts varied across patients and levels of consciousness. The premotor, temporal, and parietal sections of the corpus callosum were the most commonly damaged tracts (in 10, 9, and 8 patients, respectively), consistent with prior histopathological studies of TAI. TAI measures were not associated with concurrent behavioral measures of consciousness. In summary, we provide proof-of-principle evidence that an automated tractography pipeline has translational potential to detect and quantify TAI in individual patients with acute severe TBI.

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.

Electrophysiological correlates of thalamocortical function in acute severe traumatic brain injury

Tools assaying the neural networks that modulate consciousness may facilitate tracking of recovery after acute severe brain injury. The ABCD framework classifies resting-state EEG into categories reflecting levels of thalamocortical network function that correlate with outcome in post-cardiac arrest coma. In this longitudinal cohort study, we applied the ABCD framework to 20 patients with acute severe traumatic brain injury requiring intensive care (12 of whom were also studied at ≥6-months post-injury) and 16 healthy controls. We tested four hypotheses: 1) EEG ABCD classifications are spatially heterogeneous and temporally variable; 2) ABCD classifications improve longitudinally, commensurate with the degree of behavioral recovery; 3) ABCD classifications correlate with behavioral level of consciousness; and 4) the Coma Recovery Scale-Revised arousal facilitation protocol yields improved ABCD classifications. Channel-level EEG power spectra were classified based on spectral peaks within pre-defined frequency bands: 'A' = no peaks above delta (<4 Hz) range (complete thalamocortical disruption); 'B' = theta (4-8 Hz) peak (severe thalamocortical disruption); 'C' = theta and beta (13-24 Hz) peaks (moderate thalamocortical disruption); or 'D' = alpha (8-13 Hz) and beta peaks (normal thalamocortical function). Acutely, 95% of patients demonstrated 'D' signals in at least one channel but exhibited within-session temporal variability and spatial heterogeneity in the proportion of different channel-level ABCD classifications. By contrast, healthy participants and patients at follow-up consistently demonstrated signals corresponding to intact thalamocortical network function. Patients demonstrated longitudinal improvement in ABCD classifications (p < .05) and ABCD classification distinguished patients with and without command-following in the subacute-to-chronic phase of recovery (p < .01). In patients studied acutely, ABCD classifications improved after the Coma Recovery Scale-Revised arousal facilitation protocol (p < .05) but did not correspond with behavioral level of consciousness. These findings support the use of the ABCD framework to characterize channel-level EEG dynamics and track fluctuations in functional thalamocortical network integrity in spatial detail.

Long-Term Effects of Repeated Blast Exposure in United States Special Operations Forces Personnel: A Pilot Study Protocol

Emerging evidence suggests that repeated blast exposure (RBE) is associated with brain injury in military personnel. United States (U.S.) Special Operations Forces (SOF) personnel experience high rates of blast exposure during training and combat, but the effects of low-level RBE on brain structure and function in SOF have not been comprehensively characterized. Further, the pathophysiological link between RBE-related brain injuries and cognitive, behavioral, and physical symptoms has not been fully elucidated. We present a protocol for an observational pilot study, Long-Term Effects of Repeated Blast Exposure in U.S. SOF Personnel (ReBlast). In this exploratory study, 30 active-duty SOF personnel with RBE will participate in a comprehensive evaluation of: 1) brain network structure and function using Connectome magnetic resonance imaging (MRI) and 7 Tesla MRI; 2) neuroinflammation and tau deposition using positron emission tomography; 3) blood proteomics and metabolomics; 4) behavioral and physical symptoms using self-report measures; and 5) cognition using a battery of conventional and digitized assessments designed to detect subtle deficits in otherwise high-performing individuals. We will identify clinical, neuroimaging, and blood-based phenotypes that are associated with level of RBE, as measured by the Generalized Blast Exposure Value. Candidate biomarkers of RBE-related brain injury will inform the design of a subsequent study that will test a diagnostic assessment battery for detecting RBE-related brain injury. Ultimately, we anticipate that the ReBlast study will facilitate the development of interventions to optimize the brain health, quality of life, and battle readiness of U.S. SOF personnel.

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.

Biomarkers of Cardiac Injury, Renal Injury, and Inflammation Are Strong Mediators of Sex-Associated Death in COVID-19

Background

Studies examining outcomes among individuals with COronaVIrus Disease 2019 (COVID-19) have consistently demonstrated that men have worse outcomes than women, with a higher incidence of myocardial injury, respiratory failure, and death. However, mechanisms of higher morbidity and mortality among men remain poorly understood. We aimed to identify mediators of the relationship between sex and COVID-19-associated mortality.

Methods

Patients hospitalized at two quaternary care facilities, New York Presbyterian Hospital (CUIMC/NYPH) and Massachusetts General Hospital (MGH), for SARS-CoV-2 infection between February and May 2020 were included. Five independent biomarkers were identified as mediators of sex effects, including high-sensitivity cardiac troponin T (hs-cTNT), high sensitivity C-reactive protein (hs-CRP), ferritin, D-dimer, and creatinine.

Results

In the CUIMC/NYPH cohort (n = 2,626, 43% female), male sex was associated with significantly greater mortality (26 vs. 21%, p = 0.0146) and higher peak hs-cTNT, hs-CRP, ferritin, D-dimer, and creatinine (p &lt; 0.001). The effect of male sex on the primary outcome of death was partially mediated by peak values of all five biomarkers, suggesting that each pathophysiological pathway may contribute to increased risk of death in men. Hs-cTnT, creatinine, and hs-CRP were the strongest mediators. Findings were highly consistent in the MGH cohort with the exception of D-dimer.

Conclusions

This study suggests that the effect of sex on COVID-19 outcomes is mediated by cardiac and kidney injury, as well as underlying differences in inflammation and iron metabolism. Exploration of these specific pathways may facilitate sex-directed diagnostic and therapeutic strategies for patients with COVID-19 and provides a framework for the study of sex differences in other complex diseases.

Understanding the Link Between Obesity and Severe COVID-19 Outcomes: Causal Mediation by Systemic Inflammatory Response

BACKGROUND

Obesity is an established risk factor for severe COVID-19 outcomes. The mechanistic underpinnings of this association are not well-understood.

OBJECTIVE

To evaluate the mediating role of systemic inflammation in obesity-associated COVID-19 outcomes.

METHODS

This hospital-based, observational study included 3828 SARS-CoV-2-infected patients who were hospitalized February to May 2020 at Massachusetts General Hospital (MGH) or Columbia University Irving Medical Center/New York Presbyterian Hospital (CUIMC/NYP). We use mediation analysis to evaluate whether peak inflammatory biomarkers (C-reactive protein [CRP], erythrocyte sedimentation rate [ESR], D-dimer, ferritin, white blood cell count and interleukin-6) are in the causal pathway between obesity (BMI ≥ 30) and mechanical ventilation or death within 28 days of presentation to care.

RESULTS

In the MGH cohort (n = 1202), obesity was associated with greater likelihood of ventilation or death (OR = 1.73; 95% CI = [1.25, 2.41]; P = 0.001) and higher peak CRP (P < 0.001) compared with nonobese patients. The estimated proportion of the association between obesity and ventilation or death mediated by CRP was 0.49 (P < 0.001). Evidence of mediation was more pronounced in patients < 65 years (proportion mediated = 0.52 [P < 0.001] vs 0.44 [P = 0.180]). Findings were more moderate but consistent for peak ESR. Mediation by other inflammatory markers was not supported. Results were replicated in CUIMC/NYP cohort (n = 2626).

CONCLUSION

Findings support systemic inflammatory pathways in obesity-associated severe COVID-19 disease, particularly in patients < 65 years, captured by CRP and ESR. Contextualized in clinical trial findings, these results reveal therapeutic opportunity to target systemic inflammatory pathways and monitor interventions in high-risk subgroups and particularly obese patients.

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.

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.

Disorders of Consciousness Associated With COVID-19: A Prospective, Multimodal Study of Recovery and Brain Connectivity

BACKGROUND AND OBJECTIVES

In patients with severe coronavirus disease 2019 (COVID-19), disorders of consciousness (COVID-DoC) have emerged as a serious complication. The prognosis and pathophysiology of COVID-DoC remain unclear, complicating decisions about continuing life-sustaining treatment. We describe the natural history of COVID-DoC and investigate its associated brain connectivity profile.

METHODS

In a prospective, longitudinal study, we screened consecutive patients with COVID-19 at our institution. We enrolled critically ill adult patients with a DoC unexplained by sedation or structural brain injury, and who were planned to undergo a brain MRI. We performed resting state functional MRI and diffusion MRI to evaluate functional and structural connectivity, as compared to healthy controls and patients with DoC resulting from severe traumatic brain injury (TBI). We assessed the recovery of consciousness (command-following) and functional outcomes (Glasgow Outcome Scale Extended [GOSE] and the Disability Rating Scale [DRS]) at hospital discharge, three months post-discharge, and six months post-discharge. We also explored whether clinical variables were associated with recovery from COVID-DoC.

RESULTS

After screening 1,105 patients with COVID-19, we enrolled twelve with COVID-DoC. The median age was 63.5 years [interquartile range 55-76.3]. Excluding one who died shortly after enrollment, all of the remaining eleven patients recovered consciousness, after 0-25 days (median 7 [5-14.5]) following the cessation of continuous intravenous sedation. At discharge, all surviving patients remained dependent - median GOSE 3 [1-3], median DRS 23 [16-30]. However ultimately, except for two patients with severe polyneuropathy, all returned home with normal cognition and minimal disability - at three months, median GOSE 3 [3-3], median DRS 7 [5-13]; at six months, median GOSE 4 [4-5], median DRS 3 [3-5]. Ten patients with COVID-DoC underwent advanced neuroimaging; functional and structural brain connectivity in COVID-DoC was diminished compared to healthy controls, and structural connectivity was comparable to patients with severe TBI.

DISCUSSION

Patients who survived invariably recovered consciousness after COVID-DoC. Though disability was common following hospitalization, functional status improved over the ensuing months. While future research is necessary, these prospective findings inform the prognosis and pathophysiology of COVID-DoC.

TRIAL REGISTRATION INFORMATION

Clinicaltrials.gov, NCT04476589, submitted 7/2020, first enrolled 7/20/2020, https://clinicaltrials.gov/ct2/show/NCT04476589.

Effect modification by age of the association between obstructive lung diseases, smoking, and COVID-19 severity

Introduction

Obstructive lung diseases (asthma and chronic obstructive pulmonary disease (COPD)) and smoking are associated with greater risk of respiratory infections and hospitalisations, but conflicting data exist regarding their association with severity of COVID-19, and few studies have evaluated whether these associations differ by age.

Objectives

To examine the associations between asthma, COPD and smoking on the severity of COVID-19 among a cohort of hospitalised patients, and to test for effect modification by age.

Methods

We performed a retrospective analysis of electronic health record data of patients admitted to Massachusetts General Hospital, assigning the maximal WHO Clinical Progression Scale score for each patient during the first 28 days following hospital admission. Using ordered logistic regression, we measured the association between maximal severity score and asthma, COPD and smoking and their interaction with age.

Measurements and main results

Among 1391 patients hospitalised with COVID-19, we found an increased risk of severe disease among patients with COPD and prior smoking, independent of age. We also found evidence of effect modification by age with asthma and current smoking; in particular, asthma was associated with decreased COVID-19 severity among older adults, and current smoking was associated with decreased severity among younger patients.

Conclusions

This cohort study identifies age as a modifying factor for the association between asthma and smoking on severity of COVID-19. Our findings highlight the complexities of determining risk factors for COVID-19 severity, and suggest that the effect of risk factors may vary across the age spectrum.

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.

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.

Sex differences in inflammatory markers in patients hospitalized with COVID-19 infection: Insights from the MGH COVID-19 patient registry

Background

Men are at higher risk for serious complications related to COVID-19 infection than women. More robust immune activation in women has been proposed to contribute to decreased disease severity, although systemic inflammation has been associated with worse outcomes in COVID-19 infection. Whether systemic inflammation contributes to sex differences in COVID-19 infection is not known.

Study design and methods

We examined sex differences in inflammatory markers among 453 men (mean age 61) and 328 women (mean age 62) hospitalized with COVID-19 infection at the Massachusetts General Hospital from March 8 to April 27, 2020. Multivariable linear regression models were used to examine the association of sex with initial and peak inflammatory markers. Exploratory analyses examined the association of sex and inflammatory markers with 28-day clinical outcomes using multivariable logistic regression.

Results

Initial and peak CRP were higher in men compared with women after adjustment for baseline differences (initial CRP: ß 0.29, SE 0.07, p = 0.0001; peak CRP: ß 0.31, SE 0.07, p<0.0001) with similar findings for IL-6, PCT, and ferritin (p<0.05 for all). Men had greater than 1.5-greater odds of dying compared with women (OR 1.71, 95% CI 1.04–2.80, p = 0.03). Sex modified the association of peak CRP with both death and ICU admission, with stronger associations observed in men compared with women (death: OR 9.19, 95% CI 4.29–19.7, p <0.0001 in men vs OR 2.81, 95% CI 1.52–5.18, p = 0.009 in women, P_interaction = 0.02).

Conclusions

In a sample of 781 men and women hospitalized with COVID-19 infection, men exhibited more robust inflammatory activation as evidenced by higher initial and peak inflammatory markers, as well as worse clinical outcomes. Better understanding of sex differences in immune responses to COVID-19 infection may shed light on the pathophysiology of COVID-19 infection.

Statins Are Associated with Improved 28-day Mortality in Patients Hospitalized with SARS-CoV-2 Infection

Background:

Statins may be protective in viral infection and have been proposed as treatment in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection.

Objective:

We evaluated the effect of statins on mortality in four groups hospitalized with (SARS-CoV-2) infection (continued statin, newly initiated statin, discontinued statin, never on statin).

Design:

In a single center cohort study of 1179 patients hospitalized with SARS-CoV-2 infection, the outcome of death, Intensive Care Unit (ICU) admission or hospital discharge was evaluated. Patients’ statin use, laboratory data, and co-morbidities were determined via chart review and electronic health records. Using marginal structural models to account for timing of statin initiation and competing risks, we compared the likelihood of severe outcomes in the four statin exposure groups.

Setting:

Academic medical center in the United States

Participants:

Patients hospitalized with SARS-CoV-2 infection

Measurements:

28-day mortality, ICU admission, or discharge

Results:

Among 1179 patients, 360 were never on a statin, 311 were newly initiated on a statin, 466 were continued on a statin, and 42 had a statin discontinued. In this cohort, 154 (13.1%) patients died by 28-days. With marginal structural model analysis, statin use reduced the hazard of 28-day mortality (HR 0.566 [CI 0.372, 0.862], p = 0.008). Both new initiation of statins (HR 0.493 [CI 0.253, 0.963], p=0.038) and continuing statin therapy reduced the hazard of 28-day mortality (HR 0.270 [CI 0.114, 0.637], p=0.003). Sensitivity analysis found that statin use was associated with improved mortality for patients > 65 years, but not for patients 65 years or younger.

Limitation:

Observational design

Conclusion:

Statin therapy during hospitalization for SARS-CoV-2 infection, including new initiation and continuation of therapy, was associated with reduced short-term mortality.

Massachusetts general hospital Covid-19 registry reveals two distinct populations of hospitalized patients by race and ethnicity

OBJECTIVE

To evaluate differences by race/ethnicity in clinical characteristics and outcomes among hospitalized patients with Covid-19 at Massachusetts General Hospital (MGH).

METHODS

The MGH Covid-19 Registry includes confirmed SARS-CoV-2-infected patients hospitalized at MGH and is based on manual chart reviews and data extraction from electronic health records (EHRs). We evaluated differences between White/Non-Hispanic and Hispanic patients in demographics, complications and 14-day outcomes among the N = 866 patients hospitalized with Covid-19 from March 11, 2020-May 4, 2020.

RESULTS

Overall, 43% of patients hospitalized with Covid-19 were women, median age was 60.4 [IQR = (48.2, 75)], 11.3% were Black/non-Hispanic and 35.2% were Hispanic. Hispanic patients, representing 35.2% of patients, were younger than White/non-Hispanic patients [median age 51y; IQR = (40.6, 61.6) versus 72y; (58.0, 81.7) (p<0.001)]. Hispanic patients were symptomatic longer before presenting to care (median 5 vs 3d, p = 0.039) but were more likely to be sent home with self-quarantine than be admitted to hospital (29% vs 16%, p<0.001). Hispanic patients had fewer comorbidities yet comparable rates of ICU or death (34% vs 36%). Nonetheless, a greater proportion of Hispanic patients recovered by 14 days after presentation (62% vs 45%, p<0.001; OR = 1.99, p = 0.011 in multivariable adjusted model) and fewer died (2% versus 18%, p<0.001).

CONCLUSIONS

Hospitalized Hispanic patients were younger and had fewer comorbidities compared to White/non-Hispanic patients; despite comparable rates of ICU care or death, a greater proportion recovered. These results have implications for public health policy and the design and conduct of clinical trials.

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.).

Diabetes as a Risk Factor for Poor Early Outcomes in Patients Hospitalized With COVID-19

OBJECTIVE

Diabetes and obesity are highly prevalent among hospitalized patients with coronavirus disease 2019 (COVID-19), but little is known about their contributions to early COVID-19 outcomes. We tested the hypothesis that diabetes is a risk factor for poor early outcomes, after adjustment for obesity, among a cohort of patients hospitalized with COVID-19.

RESEARCH DESIGN AND METHODS

We used data from the Massachusetts General Hospital (MGH) COVID-19 Data Registry of patients hospitalized with COVID-19 between 11 March 2020 and 30 April 2020. Primary outcomes were admission to the intensive care unit (ICU), need for mechanical ventilation, and death within 14 days of presentation to care. Logistic regression models were adjusted for demographic characteristics, obesity, and relevant comorbidities.

RESULTS

Among 450 patients, 178 (39.6%) had diabetes-mostly type 2 diabetes. Among patients with diabetes versus patients without diabetes, a higher proportion was admitted to the ICU (42.1% vs. 29.8%, respectively, P = 0.007), required mechanical ventilation (37.1% vs. 23.2%, P = 0.001), and died (15.9% vs. 7.9%, P = 0.009). In multivariable logistic regression models, diabetes was associated with greater odds of ICU admission (odds ratio 1.59 [95% CI 1.01-2.52]), mechanical ventilation (1.97 [1.21-3.20]), and death (2.02 [1.01-4.03]) at 14 days. Obesity was associated with greater odds of ICU admission (2.16 [1.20-3.88]) and mechanical ventilation (2.13 [1.14-4.00]) but not with death.

CONCLUSIONS

Among hospitalized patients with COVID-19, diabetes was associated with poor early outcomes, after adjustment for obesity. These findings can help inform patient-centered care decision making for people with diabetes at risk for COVID-19.

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.

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.

Pre-selected class-level testing of longitudinal biomarkers reduces required multiple testing corrections to yield novel insights in longitudinal small sample human studies

Objectives

Exploratory studies that aim to evaluate novel therapeutic strategies in human cohorts often involve the collection of hundreds of variables measured over time on a small sample of individuals. Stringent error control for testing hypotheses in this setting renders it difficult to identify statistically signification associations. The objective of this study is to demonstrate how leveraging prior information about the biological relationships among variables can increase power for novel discovery.

Methods

We apply the class level association score statistic for longitudinal data (CLASS-LD) as an analysis strategy that complements single variable tests. An example is presented that aims to evaluate the relationships among 14 T-cell and monocyte activation variables measured with CD4 T-cell count over three time points after antiretroviral therapy (n=62).

Results

CLASS-LD using three classes with emphasis on T-cell activation with either classical vs. intermediate/inflammatory monocyte subsets detected associations in two of three classes, while single variable testing detected only one out of the 14 variables considered.

Conclusions

Application of a class-level testing strategy provides an alternative to single immune variables by defining hypotheses based on a collection of variables that share a known underlying biological relationship. Broader use of class-level analysis is expected to increase the available information that can be derived from limited sample clinical studies.

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.

Non-random sampling leads to biased estimates of transcriptome association

Integration of independent data resources across -omics platforms offers transformative opportunity for novel clinical and biological discoveries. However, application of emerging analytic methods in the context of selection bias represents a noteworthy and pervasive challenge. We hypothesize that combining differentially selected samples for integrated transcriptome analysis will lead to bias in the estimated association between predicted expression and the trait. Our results are based on in silico investigations and a case example focused on body mass index across four well-described cohorts apparently derived from markedly different populations. Our findings suggest that integrative analysis can lead to substantial relative bias in the estimate of association between predicted expression and the trait. The average estimate of association ranged from 51.3% less than to 96.7% greater than the true value for the biased sampling scenarios considered, while the average error was - 2.7% for the unbiased scenario. The corresponding 95% confidence interval coverage rate ranged from 46.4% to 69.5% under biased sampling, and was equal to 75% for the unbiased scenario. Inverse probability weighting with observed and estimated weights is applied as one corrective measure and appears to reduce the bias and improve coverage. These results highlight a critical need to address selection bias in integrative analysis and to use caution in interpreting findings in the presence of different sampling mechanisms between groups.

Stochastic imputation for integrated transcriptome association analysis of a longitudinally measured trait

The mechanistic pathways linking genetic polymorphisms and complex disease traits remain largely uncharacterized. At the same time, expansive new transcriptome data resources offer unprecedented opportunity to unravel the mechanistic underpinnings of complex disease associations. Two-stage strategies involving conditioning on a single, penalized regression imputation for transcriptome association analysis have been described for cross-sectional traits. In this manuscript, we propose an alternative two-stage approach based on stochastic regression imputation that additionally incorporates error in the predictive model. Application of a bootstrap procedure offers flexibility when a closed form predictive distribution is not available. The two-stage strategy is also generalized to longitudinally measured traits, using a linear mixed effects modeling framework and a composite test statistic to evaluate whether the genetic component of gene-level expression modifies the biomarker trajectory over time. Simulations studies are performed to evaluate relative performance with respect to type-1 error rates, coverage, estimation error, and power under a range of conditions. A case study is presented to investigate the association between whole blood expression for each of five inflammasome genes with inflammatory response over time after endotoxin challenge.

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.

Deep RNA Sequencing Uncovers a Repertoire of Human Macrophage Long Intergenic Noncoding RNAs Modulated by Macrophage Activation and Associated With Cardiometabolic Diseases

Background

Sustained and dysfunctional macrophage activation promotes inflammatory cardiometabolic disorders, but the role of long intergenic noncoding RNA (lincRNA) in human macrophage activation and cardiometabolic disorders is poorly defined. Through transcriptomics, bioinformatics, and selective functional studies, we sought to elucidate the lincRNA landscape of human macrophages.

Methods and Results

We used deep RNA sequencing to assemble the lincRNA transcriptome of human monocyte‐derived macrophages at rest and following stimulation with lipopolysaccharide and IFN‐γ (interferon γ) for M1 activation and IL‐4 (interleukin 4) for M2 activation. Through de novo assembly, we identified 2766 macrophage lincRNAs, including 861 that were previously unannotated. The majority (≈85%) was nonsyntenic or was syntenic but not annotated as expressed in mouse. Many macrophage lincRNAs demonstrated tissue‐enriched transcription patterns (21.5%) and enhancer‐like chromatin signatures (60.9%). Macrophage activation, particularly to the M1 phenotype, markedly altered the lincRNA expression profiles, revealing 96 lincRNAs differentially expressed, suggesting potential roles in regulating macrophage inflammatory functions. A subset of lincRNAs overlapped genomewide association study loci for cardiometabolic disorders. MacORIS (macrophage‐enriched obesity‐associated lincRNA serving as a repressor of IFN‐γ signaling), a macrophage‐enriched lincRNA not expressed in mouse macrophages, harbors variants associated with central obesity. Knockdown of MacORIS, which is located in the cytoplasm, enhanced IFN‐γ–induced JAK2 (Janus kinase 2) and STAT1 (signal transducer and activator of transcription 1) phosphorylation in THP‐1 macrophages, suggesting a potential role as a repressor of IFN‐γ signaling. Induced pluripotent stem cell–derived macrophages recapitulated the lincRNA transcriptome of human monocyte‐derived macrophages and provided a high‐fidelity model with which to study lincRNAs in human macrophage biology, particularly those not conserved in mouse.

Conclusions

High‐resolution transcriptomics identified lincRNAs that form part of the coordinated response during macrophage activation, including specific macrophage lincRNAs associated with human cardiometabolic disorders that modulate macrophage inflammatory functions.

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.

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.

HIV-1-negative female sex workers sustain high cervical IFNɛ, low immune activation, and low expression of HIV-1-required host genes

Sex workers practicing in high HIV endemic areas have been extensively targeted to test anti-HIV prophylactic strategies. We hypothesize that in women with high levels of genital exposure to semen changes in cervico-vaginal mucosal and/or systemic immune activation will contribute to a decreased susceptibility to HIV-1 infection. To address this question, we assessed sexual activity and immune activation status (in peripheral blood), as well as cellular infiltrates and gene expression in ectocervical mucosa biopsies in female sex workers (FSWs; n=50), as compared with control women (CG; n=32). FSWs had low-to-absent HIV-1-specific immune responses with significantly lower CD38 expression on circulating CD4(+) or CD8(+) T-cells (both: P<0.001) together with lower cervical gene expression of genes associated with leukocyte homing and chemotaxis. FSWs also had increased levels of interferon-ɛ (IFNɛ) gene and protein expression in the cervical epithelium together with reduced expression of genes associated with HIV-1 integration and replication. A correlative relationship between semen exposure and elevated type-1 IFN expression in FSWs was also established. Overall, our data suggest that long-term condomless sex work can result in multiple changes within the cervico-vaginal compartment that would contribute to sustaining a lower susceptibility for HIV-1 infection in the absence of HIV-specific responses.

Transcriptome-Wide Analysis Reveals Modulation of Human Macrophage Inflammatory Phenotype Through Alternative Splicing

OBJECTIVE

Human macrophages can shift phenotype across the inflammatory M1 and reparative M2 spectrum in response to environmental challenges, but the mechanisms promoting inflammatory and cardiometabolic disease-associated M1 phenotypes remain incompletely understood. Alternative splicing (AS) is emerging as an important regulator of cellular function, yet its role in macrophage activation is largely unknown. We investigated the extent to which AS occurs in M1 activation within the cardiometabolic disease context and validated a functional genomic cell model for studying human macrophage-related AS events.

APPROACH AND RESULTS

From deep RNA-sequencing of resting, M1, and M2 primary human monocyte-derived macrophages, we found 3860 differentially expressed genes in M1 activation and detected 233 M1-induced AS events; the majority of AS events were cell- and M1-specific with enrichment for pathways relevant to macrophage inflammation. Using genetic variant data for 10 cardiometabolic traits, we identified 28 trait-associated variants within the genomic loci of 21 alternatively spliced genes and 15 variants within 7 differentially expressed regulatory splicing factors in M1 activation. Knockdown of 1 such splicing factor, CELF1, in primary human macrophages led to increased inflammatory response to M1 stimulation, demonstrating CELF1's potential modulation of the M1 phenotype. Finally, we demonstrated that an induced pluripotent stem cell-derived macrophage system recapitulates M1-associated AS events and provides a high-fidelity macrophage AS model.

CONCLUSIONS

AS plays a role in defining macrophage phenotype in a cell- and stimulus-specific fashion. Alternatively spliced genes and splicing factors with trait-associated variants may reveal novel pathways and targets in cardiometabolic diseases.

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.

A guide to genome-wide association analysis and post-analytic interrogation

This tutorial is a learning resource that outlines the basic process and provides specific software tools for implementing a complete genome‐wide association analysis. Approaches to post‐analytic visualization and interrogation of potentially novel findings are also presented. Applications are illustrated using the free and open‐source R statistical computing and graphics software environment, Bioconductor software for bioinformatics and the UCSC Genome Browser. Complete genome‐wide association data on 1401 individuals across 861,473 typed single nucleotide polymorphisms from the PennCATH study of coronary artery disease are used for illustration. All data and code, as well as additional instructional resources, are publicly available through the Open Resources in Statistical Genomics project: http://www.stat-gen.org. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Serum Fractalkine (CX3CL1) and Cardiovascular Outcomes and Diabetes: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study

BACKGROUND

Cardiometabolic disease is a major cause of morbidity and mortality in persons with chronic kidney disease (CKD). Fractalkine (CX3CL1) is a potential mediator of both atherosclerosis and metabolic disease. Studies of the relationship of CX3CL1 with risk of cardiovascular disease (CVD) events and metabolic traits are lacking, particularly in the high-risk setting of CKD.

STUDY DESIGN

Cross-sectional and longitudinal observational analysis.

SETTING & PARTICIPANTS

Adults with CKD from 7 US sites participating in the Chronic Renal Insufficiency Cohort (CRIC) Study.

PREDICTOR

Quartiles of plasma CX3CL1 levels at baseline.

OUTCOMES

Baseline estimated glomerular filtration rate from a creatinine and cystatin C-based equation, prevalent and incident CVD, diabetes, metabolic syndrome and its criteria, homeostatic model assessment of insulin resistance, hemoglobin A1c level, myocardial infarction, all-cause mortality, and the composite outcome of myocardial infarction/all-cause mortality.

RESULTS

Among 3,687 participants, baseline CX3CL1 levels were associated positively with several CVD risk factors and metabolic traits, lower estimated glomerular filtration rate, and higher levels of inflammatory cytokines, as well as prevalent CVD (OR, 1.09; 95% CI, 1.01-1.19; P=0.03). Higher CX3CL1 level also was associated with prevalent diabetes (OR, 1.26; 95% CI, 1.16-1.38; P<0.001) in adjusted models. During a mean follow-up of 6 years, there were 352 deaths, 176 myocardial infarctions, and 484 composite outcomes. In fully adjusted models, 1-SD higher CX3CL1 level increased the hazard for all-cause mortality (1.11; 95% CI, 1.00-1.22; P=0.02) and the composite outcome (1.09; 95% CI, 1.00-1.19; P=0.04).

LIMITATIONS

Study design did not allow evaluation of changes over time, correlation with progression of phenotypes, or determination of causality of effect.

CONCLUSIONS

Circulating CX3CL1 level may contribute to both atherosclerotic CVD and diabetes in a CKD cohort. Further studies are required to establish mechanisms through which CX3CL1 affects the pathogenesis of atherosclerosis and diabetes.

Higher plasma CXCL12 levels predict incident myocardial infarction and death in chronic kidney disease: findings from the Chronic Renal Insufficiency Cohort study

AIMS

Genome-wide association studies revealed an association between a locus at 10q11, downstream from CXCL12, and myocardial infarction (MI). However, the relationship among plasma CXCL12, cardiovascular disease (CVD) risk factors, incident MI, and death is unknown.

METHODS AND RESULTS

We analysed study-entry plasma CXCL12 levels in 3687 participants of the Chronic Renal Insufficiency Cohort (CRIC) Study, a prospective study of cardiovascular and kidney outcomes in chronic kidney disease (CKD) patients. Mean follow-up was 6 years for incident MI or death. Plasma CXCL12 levels were positively associated with several cardiovascular risk factors (age, hypertension, diabetes, hypercholesterolaemia), lower estimated glomerular filtration rate (eGFR), and higher inflammatory cytokine levels (P < 0.05). In fully adjusted models, higher study-entry CXCL12 was associated with increased odds of prevalent CVD (OR 1.23; 95% confidence interval 1.14, 1.33, P < 0.001) for one standard deviation (SD) increase in CXCL12. Similarly, one SD higher CXCL12 increased the hazard of incident MI (1.26; 1.09,1.45, P < 0.001), death (1.20; 1.09,1.33, P < 0.001), and combined MI/death (1.23; 1.13-1.34, P < 0.001) adjusting for demographic factors, known CVD risk factors, and inflammatory markers and remained significant for MI (1.19; 1.03,1.39, P = 0.01) and the combined MI/death (1.13; 1.03,1.24, P = 0.01) after further controlling for eGFR and urinary albumin:creatinine ratio.

CONCLUSIONS

In CKD, higher plasma CXCL12 was associated with CVD risk factors and prevalent CVD as well as the hazard of incident MI and death. Further studies are required to establish if plasma CXCL12 reflect causal actions at the vessel wall and is a tool for genomic and therapeutic trials.

Candidate gene association study of coronary artery calcification in chronic kidney disease: findings from the CRIC study (Chronic Renal Insufficiency Cohort)

OBJECTIVES

This study sought to identify loci for coronary artery calcification (CAC) in patients with chronic kidney disease (CKD).

BACKGROUND

CKD is associated with increased CAC and subsequent coronary heart disease (CHD), but the mechanisms remain poorly defined. Genetic studies of CAC in CKD may provide a useful strategy for identifying novel pathways in CHD.

METHODS

We performed a candidate gene study (∼2,100 genes; ∼50,000 single nucleotide polymorphisms [SNPs]) of CAC within the CRIC (Chronic Renal Insufficiency Cohort) study (N = 1,509; 57% European, 43% African ancestry). SNPs with preliminary evidence of association with CAC in CRIC were examined for association with CAC in the PennCAC (Penn Coronary Artery Calcification) (N = 2,560) and AFCS (Amish Family Calcification Study) (N = 784) samples. SNPs with suggestive replication were further analyzed for association with myocardial infarction (MI) in the PROMIS (Pakistan Risk of Myocardial Infarction Study) (N = 14,885).

RESULTS

Of 268 SNPs reaching p < 5 × 10(-4) for CAC in CRIC, 28 SNPs in 23 loci had nominal support (p < 0.05 and in same direction) for CAC in PennCAC or AFCS. Besides chr9p21 and COL4A1, known loci for CHD, these included SNPs having reported genome-wide association study association with hypertension (e.g., ATP2B1). In PROMIS, 4 of the 23 suggestive CAC loci (chr9p21, COL4A1, ATP2B1, and ABCA4) had significant associations with MI, consistent with their direction of effect on CAC.

CONCLUSIONS

We identified several loci associated with CAC in CKD that also relate to MI in a general population sample. CKD imparts a high risk of CHD and may provide a useful setting for discovery of novel CHD genes and pathways.

Mixed modeling of meta-analysis P-values (MixMAP) suggests multiple novel gene loci for low density lipoprotein cholesterol

Informing missing heritability for complex disease will likely require leveraging information across multiple SNPs within a gene region simultaneously to characterize gene and locus-level contributions to disease phenotypes. To this aim, we introduce a novel strategy, termed Mixed modeling of Meta-Analysis P-values (MixMAP), that draws on a principled statistical modeling framework and the vast array of summary data now available from genetic association studies, to test formally for locus level association. The primary inputs to this approach are: (a) single SNP level p-values for tests of association; and (b) the mapping of SNPs to genomic regions. The output of MixMAP is comprised of locus level estimates and tests of association. In application of MixMAP to summary data from the Global Lipids Gene Consortium, we suggest twelve new loci (PKN, FN1, UGT1A1, PPARG, DMDGH, PPARD, CDK6, VPS13B, GAD2, GAB2, APOH and NPC1) for low-density lipoprotein cholesterol (LDL-C), a causal risk factor for cardiovascular disease and we also demonstrate the potential utility of MixMAP in small data settings. Overall, MixMAP offers novel and complementary information as compared to SNP-based analysis approaches and is straightforward to implement with existing open-source statistical software tools.