Geisinger Neuroscience Institute

Abstract Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes 1 . Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel 2 ) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

PURPOSE: To provide guidance to clinicians regarding therapy for patients with brain metastases from solid tumors. METHODS: ASCO convened an Expert Panel and conducted a systematic review of the literature. RESULTS: Thirty-two randomized trials published in 2008 or later met eligibility criteria and form the primary evidentiary base. RECOMMENDATIONS: Surgery is a reasonable option for patients with brain metastases. Patients with large tumors with mass effect are more likely to benefit than those with multiple brain metastases and/or uncontrolled systemic disease. Patients with symptomatic brain metastases should receive local therapy regardless of the systemic therapy used. For patients with asymptomatic brain metastases, local therapy should not be deferred unless deferral is specifically recommended in this guideline. The decision to defer local therapy should be based on a multidisciplinary discussion of the potential benefits and harms that the patient may experience. Several regimens were recommended for non-small-cell lung cancer, breast cancer, and melanoma. For patients with asymptomatic brain metastases and no systemic therapy options, stereotactic radiosurgery (SRS) alone should be offered to patients with one to four unresected brain metastases, excluding small-cell lung carcinoma. SRS alone to the surgical cavity should be offered to patients with one to two resected brain metastases. SRS, whole brain radiation therapy, or their combination are reasonable options for other patients. Memantine and hippocampal avoidance should be offered to patients who receive whole brain radiation therapy and have no hippocampal lesions and 4 months or more expected survival. Patients with asymptomatic brain metastases with either Karnofsky Performance Status ≤ 50 or Karnofsky Performance Status < 70 with no systemic therapy options do not derive benefit from radiation therapy.Additional information is available at www.asco.org/neurooncology-guidelines.

Cancer treatments are often more successful when the disease is detected early. We evaluated the feasibility and safety of multicancer blood testing coupled with positron emission tomography-computed tomography (PET-CT) imaging to detect cancer in a prospective, interventional study of 10,006 women not previously known to have cancer. Positive blood tests were independently confirmed by a diagnostic PET-CT, which also localized the cancer. Twenty-six cancers were detected by blood testing. Of these, 15 underwent PET-CT imaging and nine (60%) were surgically excised. Twenty-four additional cancers were detected by standard-of-care screening and 46 by neither approach. One percent of participants underwent PET-CT imaging based on false-positive blood tests, and 0.22% underwent a futile invasive diagnostic procedure. These data demonstrate that multicancer blood testing combined with PET-CT can be safely incorporated into routine clinical care, in some cases leading to surgery with intent to cure.

Abstract Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry 1,2 . Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated ( P &lt; 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis 3 , and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN ) and variants (such as at GRK5 and NOS3 ). Using a three-pronged approach 4 , we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry 5 . Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

Primary open-angle glaucoma (POAG), is a heritable common cause of blindness world-wide. To identify risk loci, we conduct a large multi-ethnic meta-analysis of genome-wide association studies on a total of 34,179 cases and 349,321 controls, identifying 44 previously unreported risk loci and confirming 83 loci that were previously known. The majority of loci have broadly consistent effects across European, Asian and African ancestries. Cross-ancestry data improve fine-mapping of causal variants for several loci. Integration of multiple lines of genetic evidence support the functional relevance of the identified POAG risk loci and highlight potential contributions of several genes to POAG pathogenesis, including SVEP1, RERE, VCAM1, ZNF638, CLIC5, SLC2A12, YAP1, MXRA5, and SMAD6. Several drug compounds targeting POAG risk genes may be potential glaucoma therapeutic candidates.

Background: Long QT syndrome (LQTS) is the first described and most common inherited arrhythmia. Over the last 25 years, multiple genes have been reported to cause this condition and are routinely tested in patients. Because of dramatic changes in our understanding of human genetic variation, reappraisal of reported genetic causes for LQTS is required. Methods: Utilizing an evidence-based framework, 3 gene curation teams blinded to each other’s work scored the level of evidence for 17 genes reported to cause LQTS. A Clinical Domain Channelopathy Working Group provided a final classification of these genes for causation of LQTS after assessment of the evidence scored by the independent curation teams. Results: Of 17 genes reported as being causative for LQTS, 9 ( AKAP9, ANK2, CAV3, KCNE1, KCNE2, KCNJ2, KCNJ5, SCN4B, SNTA1 ) were classified as having limited or disputed evidence as LQTS-causative genes. Only 3 genes ( KCNQ1, KCNH2, SCN5A ) were curated as definitive genes for typical LQTS. Another 4 genes ( CALM1, CALM2, CALM3, TRDN ) were found to have strong or definitive evidence for causality in LQTS with atypical features, including neonatal atrioventricular block. The remaining gene ( CACNA1C ) had moderate level evidence for causing LQTS. Conclusions: More than half of the genes reported as causing LQTS have limited or disputed evidence to support their disease causation. Genetic variants in these genes should not be used for clinical decision-making, unless accompanied by new and sufficient genetic evidence. The findings of insufficient evidence to support gene-disease associations may extend to other disciplines of medicine and warrants a contemporary evidence-based evaluation for previously reported disease-causing genes to ensure their appropriate use in precision medicine.

Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.

Importance: Glioblastoma is the most lethal primary brain cancer. Clinical outcomes for glioblastoma remain poor, and new treatments are needed. Objective: To investigate whether adding autologous tumor lysate-loaded dendritic cell vaccine (DCVax-L) to standard of care (SOC) extends survival among patients with glioblastoma. Design, Setting, and Participants: This phase 3, prospective, externally controlled nonrandomized trial compared overall survival (OS) in patients with newly diagnosed glioblastoma (nGBM) and recurrent glioblastoma (rGBM) treated with DCVax-L plus SOC vs contemporaneous matched external control patients treated with SOC. This international, multicenter trial was conducted at 94 sites in 4 countries from August 2007 to November 2015. Data analysis was conducted from October 2020 to September 2021. Interventions: The active treatment was DCVax-L plus SOC temozolomide. The nGBM external control patients received SOC temozolomide and placebo; the rGBM external controls received approved rGBM therapies. Main Outcomes and Measures: The primary and secondary end points compared overall survival (OS) in nGBM and rGBM, respectively, with contemporaneous matched external control populations from the control groups of other formal randomized clinical trials. Results: A total of 331 patients were enrolled in the trial, with 232 randomized to the DCVax-L group and 99 to the placebo group. Median OS (mOS) for the 232 patients with nGBM receiving DCVax-L was 19.3 (95% CI, 17.5-21.3) months from randomization (22.4 months from surgery) vs 16.5 (95% CI, 16.0-17.5) months from randomization in control patients (HR = 0.80; 98% CI, 0.00-0.94; P = .002). Survival at 48 months from randomization was 15.7% vs 9.9%, and at 60 months, it was 13.0% vs 5.7%. For 64 patients with rGBM receiving DCVax-L, mOS was 13.2 (95% CI, 9.7-16.8) months from relapse vs 7.8 (95% CI, 7.2-8.2) months among control patients (HR, 0.58; 98% CI, 0.00-0.76; P < .001). Survival at 24 and 30 months after recurrence was 20.7% vs 9.6% and 11.1% vs 5.1%, respectively. Survival was improved in patients with nGBM with methylated MGMT receiving DCVax-L compared with external control patients (HR, 0.74; 98% CI, 0.55-1.00; P = .03). Conclusions and Relevance: In this study, adding DCVax-L to SOC resulted in clinically meaningful and statistically significant extension of survival for patients with both nGBM and rGBM compared with contemporaneous, matched external controls who received SOC alone. Trial Registration: ClinicalTrials.gov Identifier: NCT00045968.

BACKGROUND: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke. METHODS: We used 1.6 M resting 12-lead digital ECG traces from 430 000 patients collected from 1984 to 2019. Deep neural networks were trained to predict new-onset AF (within 1 year) in patients without a history of AF. Performance was evaluated using areas under the receiver operating characteristic curve and precision-recall curve. We performed an incidence-free survival analysis for a period of 30 years following the ECG stratified by model predictions. To simulate real-world deployment, we trained a separate model using all ECGs before 2010 and evaluated model performance on a test set of ECGs from 2010 through 2014 that were linked to our stroke registry. We identified the patients at risk for AF-related stroke among those predicted to be high risk for AF by the model at different prediction thresholds. RESULTS: The area under the receiver operating characteristic curve and area under the precision-recall curve were 0.85 and 0.22, respectively, for predicting new-onset AF within 1 year of an ECG. The hazard ratio for the predicted high- versus low-risk groups over a 30-year span was 7.2 (95% CI, 6.9-7.6). In a simulated deployment scenario, the model predicted new-onset AF at 1 year with a sensitivity of 69% and specificity of 81%. The number needed to screen to find 1 new case of AF was 9. This model predicted patients at high risk for new-onset AF in 62% of all patients who experienced an AF-related stroke within 3 years of the index ECG. CONCLUSIONS: Deep learning can predict new-onset AF from the 12-lead ECG in patients with no previous history of AF. This prediction may help identify patients at risk for AF-related strokes.

AIMS: We investigated the relationship between clinically assessed left ventricular ejection fraction (LVEF) and survival in a large, heterogeneous clinical cohort. METHODS AND RESULTS: Physician-reported LVEF on 403 977 echocardiograms from 203 135 patients were linked to all-cause mortality using electronic health records (1998-2018) from US regional healthcare system. Cox proportional hazards regression was used for analyses while adjusting for many patient characteristics including age, sex, and relevant comorbidities. A dataset including 45 531 echocardiograms and 35 976 patients from New Zealand was used to provide independent validation of analyses. During follow-up of the US cohort, 46 258 (23%) patients who had undergone 108 578 (27%) echocardiograms died. Overall, adjusted hazard ratios (HR) for mortality showed a u-shaped relationship for LVEF with a nadir of risk at an LVEF of 60-65%, a HR of 1.71 [95% confidence interval (CI) 1.64-1.77] when ≥70% and a HR of 1.73 (95% CI 1.66-1.80) at LVEF of 35-40%. Similar relationships with a nadir at 60-65% were observed in the validation dataset as well as for each age group and both sexes. The results were similar after further adjustments for conditions associated with an elevated LVEF, including mitral regurgitation, increased wall thickness, and anaemia and when restricted to patients reported to have heart failure at the time of the echocardiogram. CONCLUSION: Deviation of LVEF from 60% to 65% is associated with poorer survival regardless of age, sex, or other relevant comorbidities such as heart failure. These results may herald the recognition of a new phenotype characterized by supra-normal LVEF.

Increased levels of the urinary albumin-to-creatinine ratio (UACR) are associated with higher risk of kidney disease progression and cardiovascular events, but underlying mechanisms are incompletely understood. Here, we conduct trans-ethnic (n = 564,257) and European-ancestry specific meta-analyses of genome-wide association studies of UACR, including ancestry- and diabetes-specific analyses, and identify 68 UACR-associated loci. Genetic correlation analyses and risk score associations in an independent electronic medical records database (n = 192,868) reveal connections with proteinuria, hyperlipidemia, gout, and hypertension. Fine-mapping and trans-Omics analyses with gene expression in 47 tissues and plasma protein levels implicate genes potentially operating through differential expression in kidney (including TGFB1, MUC1, PRKCI, and OAF), and allow coupling of UACR associations to altered plasma OAF concentrations. Knockdown of OAF and PRKCI orthologs in Drosophila nephrocytes reduces albumin endocytosis. Silencing fly PRKCI further impairs slit diaphragm formation. These results generate a priority list of genes and pathways for translational research to reduce albuminuria.

Importance: VEXAS (vacuoles, E1-ubiquitin-activating enzyme, X-linked, autoinflammatory, somatic) syndrome is a disease with rheumatologic and hematologic features caused by somatic variants in UBA1. Pathogenic variants are associated with a broad spectrum of clinical manifestations. Knowledge of prevalence, penetrance, and clinical characteristics of this disease have been limited by ascertainment biases based on known phenotypes. Objective: To determine the prevalence of pathogenic variants in UBA1 and associated clinical manifestations in an unselected population using a genomic ascertainment approach. Design, Setting, and Participants: This retrospective observational study evaluated UBA1 variants in exome data from 163 096 participants within the Geisinger MyCode Community Health Initiative. Clinical phenotypes were determined from Geisinger electronic health record data from January 1, 1996, to January 1, 2022. Exposures: Exome sequencing was performed. Main Outcomes and Measures: Outcome measures included prevalence of somatic UBA1 variation; presence of rheumatologic, hematologic, pulmonary, dermatologic, and other findings in individuals with somatic UBA1 variation on review of the electronic health record; review of laboratory data; bone marrow biopsy pathology analysis; and in vitro enzymatic assays. Results: In 163 096 participants (mean age, 52.8 years; 94% White; 61% women), 11 individuals harbored likely somatic variants at known pathogenic UBA1 positions, with 11 of 11 (100%) having clinical manifestations consistent with VEXAS syndrome (9 male, 2 female). A total of 5 of 11 individuals (45%) did not meet criteria for rheumatologic and/or hematologic diagnoses previously associated with VEXAS syndrome; however, all individuals had anemia (hemoglobin: mean, 7.8 g/dL; median, 7.5 g/dL), which was mostly macrocytic (10/11 [91%]) with concomitant thrombocytopenia (10/11 [91%]). Among the 11 patients identified, there was a pathogenic variant in 1 male participant prior to onset of VEXAS-related signs or symptoms and 2 female participants had disease with heterozygous variants. A previously unreported UBA1 variant (c.1861A>T; p.Ser621Cys) was found in a symptomatic patient, with in vitro data supporting a catalytic defect and pathogenicity. Together, disease-causing UBA1 variants were found in 1 in 13 591 unrelated individuals (95% CI, 1:7775-1:23 758), 1 in 4269 men older than 50 years (95% CI, 1:2319-1:7859), and 1 in 26 238 women older than 50 years (95% CI, 1:7196-1:147 669). Conclusions and Relevance: This study provides an estimate of the prevalence and a description of the clinical manifestations of UBA1 variants associated with VEXAS syndrome within a single regional health system in the US. Additional studies are needed in unselected and genetically diverse populations to better define general population prevalence and phenotypic spectrum.

BACKGROUND: There is preliminary evidence of racial and social-economic disparities in the population infected by and dying from COVID-19. The goal of this study is to report the associations of COVID-19 with respect to race, health and economic inequality in the United States. METHODS: We performed a cross-sectional study of the associations between infection and mortality rate of COVID-19 and demographic, socioeconomic and mobility variables from 369 counties (total population: 102,178,117 [median: 73,447, IQR: 30,761-256,098]) from the seven most affected states (Michigan, New York, New Jersey, Pennsylvania, California, Louisiana, Massachusetts). FINDINGS: The risk factors for infection and mortality are different. Our analysis shows that counties with more diverse demographics, higher population, education, income levels, and lower disability rates were at a higher risk of COVID-19 infection. However, counties with higher disability and poverty rates had a higher death rate. African Americans were more vulnerable to COVID-19 than other ethnic groups (1,981 African American infected cases versus 658 Whites per million). Data on mobility changes corroborate the impact of social distancing. INTERPRETATION: The observed inequality might be due to the workforce of essential services, poverty, and access to care. Counties in more urban areas are probably better equipped at providing care. The lower rate of infection, but a higher death rate in counties with higher poverty and disability could be due to lower levels of mobility, but a higher rate of comorbidities and health care access.

PURPOSE To provide guidance to clinicians regarding therapy for diffuse astrocytic and oligodendroglial tumors in adults. METHODS ASCO and the Society for Neuro-Oncology convened an Expert Panel and conducted a systematic review of the literature. RESULTS Fifty-nine randomized trials focusing on therapeutic management were identified. RECOMMENDATIONS Adults with newly diagnosed oligodendroglioma, isocitrate dehydrogenase (IDH)–mutant, 1p19q codeleted CNS WHO grade 2 and 3 should be offered radiation therapy (RT) and procarbazine, lomustine, and vincristine (PCV). Temozolomide (TMZ) is a reasonable alternative for patients who may not tolerate PCV, but no high-level evidence supports upfront TMZ in this setting. People with newly diagnosed astrocytoma, IDH-mutant, 1p19q non-codeleted CNS WHO grade 2 should be offered RT with adjuvant chemotherapy (TMZ or PCV). People with astrocytoma, IDH-mutant, 1p19q non-codeleted CNS WHO grade 3 should be offered RT and adjuvant TMZ. People with astrocytoma, IDH-mutant, CNS WHO grade 4 may follow recommendations for either astrocytoma, IDH-mutant, 1p19q non-codeleted CNS WHO grade 3 or glioblastoma, IDH-wildtype, CNS WHO grade 4. Concurrent TMZ and RT should be offered to patients with newly diagnosed glioblastoma, IDH-wildtype, CNS WHO grade 4 followed by 6 months of adjuvant TMZ. Alternating electric field therapy, approved by the US Food and Drug Administration, should be considered for these patients. Bevacizumab is not recommended. In situations in which the benefits of 6-week RT plus TMZ may not outweigh the harms, hypofractionated RT plus TMZ is reasonable. In patients age ≥ 60 to ≥ 70 years, with poor performance status or for whom toxicity or prognosis are concerns, best supportive care alone, RT alone (for MGMT promoter unmethylated tumors), or TMZ alone (for MGMT promoter methylated tumors) are reasonable treatment options. Additional information is available at www.asco.org/neurooncology-guidelines .

Cardiac pacing is the only effective therapy for patients with symptomatic bradyarrhythmia. Traditional right ventricular apical pacing causes electrical and mechanical dyssynchrony resulting in left ventricular dysfunction, recurrent heart failure, and atrial arrhythmias. Physiological pacing activates the normal cardiac conduction, thereby providing synchronized contraction of ventricles. Though His bundle pacing (HBP) acts as an ideal physiological pacing modality, it is technically challenging and associated with troubleshooting issues during follow-up. Left bundle branch pacing (LBBP) has been suggested as an effective alternative to overcome the limitations of HBP as it provides low and stable pacing threshold, lead stability, and correction of distal conduction system disease. This paper will focus on the implantation technique, troubleshooting, clinical implications, and a review of published literature of LBBP.

BACKGROUND: Missing data is a challenge for all studies; however, this is especially true for electronic health record (EHR)-based analyses. Failure to appropriately consider missing data can lead to biased results. While there has been extensive theoretical work on imputation, and many sophisticated methods are now available, it remains quite challenging for researchers to implement these methods appropriately. Here, we provide detailed procedures for when and how to conduct imputation of EHR laboratory results. OBJECTIVE: The objective of this study was to demonstrate how the mechanism of missingness can be assessed, evaluate the performance of a variety of imputation methods, and describe some of the most frequent problems that can be encountered. METHODS: We analyzed clinical laboratory measures from 602,366 patients in the EHR of Geisinger Health System in Pennsylvania, USA. Using these data, we constructed a representative set of complete cases and assessed the performance of 12 different imputation methods for missing data that was simulated based on 4 mechanisms of missingness (missing completely at random, missing not at random, missing at random, and real data modelling). RESULTS: Our results showed that several methods, including variations of Multivariate Imputation by Chained Equations (MICE) and softImpute, consistently imputed missing values with low error; however, only a subset of the MICE methods was suitable for multiple imputation. CONCLUSIONS: The analyses we describe provide an outline of considerations for dealing with missing EHR data, steps that researchers can perform to characterize missingness within their own data, and an evaluation of methods that can be applied to impute clinical data. While the performance of methods may vary between datasets, the process we describe can be generalized to the majority of structured data types that exist in EHRs, and all of our methods and code are publicly available.

Abstract A barrier to incorporating genomics more broadly is limited access to providers with genomics expertise. Chatbots are a technology‐based simulated conversation used in scaling communications. Geisinger and Clear Genetics, Inc. have developed chatbots to facilitate communication with participants receiving clinically actionable genetic variants from the MyCode ® Community Health Initiative (MyCode ® ). The consent chatbot walks patients through the consent allowing them to opt to receive more or less detail on key topics (goals, benefits, risks, etc.). The follow‐up chatbot reminds participants of suggested actions following result receipt and the cascade chatbot can be sent to at‐risk relatives by participants to share their genetic test results and facilitate cascade testing. To explore the acceptability, usability, and understanding of the study consent, post‐result follow‐up and cascade testing chatbots, we conducted six focus groups with MyCode ® participants. Sixty‐two individuals participated in a focus group ( n = 33 consent chatbot, n = 29 follow‐up and cascade chatbot). Participants were mostly female ( n = 42, 68%), Caucasian ( n = 58, 94%), college‐educated ( n = 33,53%), retirees ( n = 38, 61%), and of age 56 years or older ( n = 52, 84%). Few participants reported that they knew what a chatbot was ( n = 10, 16%), and a small number reported that they had used a chatbot ( n = 5, 8%). Qualitative analysis of transcripts and notes from focus groups revealed four main themes: (a) overall impressions, (b) suggested improvements, (c) concerns and limitations, and (d) implementation. Participants supported using chatbots to consent for genomics research and to interact with healthcare providers for care coordination following receipt of genomic results. Most expressed willingness to use a chatbot to share genetic information with relatives. The consent chatbot presents an engaging alternative to deliver content challenging to comprehend in traditional paper or in‐person consent . The cascade and follow‐up chatbots may be acceptable, user‐friendly, scalable approaches to manage ancillary genetic counseling tasks.

BACKGROUND AND OBJECTIVES: Rapid correction of severe hyponatremia can result in serious neurologic complications, including osmotic demyelination. Few data exist on incidence and risk factors of rapid correction or osmotic demyelination. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: In a retrospective cohort of 1490 patients admitted with serum sodium <120 mEq/L to seven hospitals in the Geisinger Health System from 2001 to 2017, we examined the incidence and risk factors of rapid correction and osmotic demyelination. Rapid correction was defined as serum sodium increase of >8 mEq/L at 24 hours. Osmotic demyelination was determined by manual chart review of all available brain magnetic resonance imaging reports. RESULTS: Mean age was 66 years old (SD=15), 55% were women, and 67% had prior hyponatremia (last outpatient sodium <135 mEq/L). Median change in serum sodium at 24 hours was 6.8 mEq/L (interquartile range, 3.4-10.2), and 606 patients (41%) had rapid correction at 24 hours. Younger age, being a woman, schizophrenia, lower Charlson comorbidity index, lower presentation serum sodium, and urine sodium <30 mEq/L were associated with greater risk of rapid correction. Prior hyponatremia, outpatient aldosterone antagonist use, and treatment at an academic center were associated with lower risk of rapid correction. A total of 295 (20%) patients underwent brain magnetic resonance imaging on or after admission, with nine (0.6%) patients showing radiologic evidence of osmotic demyelination. Eight (0.5%) patients had incident osmotic demyelination, of whom five (63%) had beer potomania, five (63%) had hypokalemia, and seven (88%) had sodium increase >8 mEq/L over a 24-hour period before magnetic resonance imaging. Five patients with osmotic demyelination had apparent neurologic recovery. CONCLUSIONS: Among patients presenting with severe hyponatremia, rapid correction occurred in 41%; nearly all patients with incident osmotic demyelination had a documented episode of rapid correction. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2018_06_05_CJASNPodcast_18_7_G.mp3.

PURPOSE: Three genetic conditions-hereditary breast and ovarian cancer syndrome, Lynch syndrome, and familial hypercholesterolemia-have tier 1 evidence for interventions that reduce morbidity and mortality, prompting proposals to screen unselected populations for these conditions. We examined the impact of genomic screening on risk management and early detection in an unselected population. METHODS: Observational study of electronic health records (EHR) among individuals in whom a pathogenic/likely pathogenic variant in a tier 1 gene was discovered through Geisinger's MyCode project. EHR of all eligible participants was evaluated for a prior genetic diagnosis and, among participants without such a diagnosis, relevant personal/family history, postdisclosure clinical diagnoses, and postdisclosure risk management. RESULTS: Eighty-seven percent of participants (305/351) did not have a prior genetic diagnosis of their tier 1 result. Of these, 65% had EHR evidence of relevant personal and/or family history of disease. Of 255 individuals eligible to have risk management, 70% (n = 179) had a recommended risk management procedure after results disclosure. Thirteen percent of participants (41/305) received a relevant clinical diagnosis after results disclosure. CONCLUSION: Genomic screening programs can identify previously unrecognized individuals at increased risk of cancer and heart disease and facilitate risk management and early cancer detection.

Heart failure is a leading cause of cardiovascular morbidity and mortality. However, the contribution of common genetic variation to heart failure risk has not been fully elucidated, particularly in comparison to other common cardiometabolic traits. We report a multi-ancestry genome-wide association study meta-analysis of all-cause heart failure including up to 115,150 cases and 1,550,331 controls of diverse genetic ancestry, identifying 47 risk loci. We also perform multivariate genome-wide association studies that integrate heart failure with related cardiac magnetic resonance imaging endophenotypes, identifying 61 risk loci. Gene-prioritization analyses including colocalization and transcriptome-wide association studies identify known and previously unreported candidate cardiomyopathy genes and cellular processes, which we validate in gene-expression profiling of failing and healthy human hearts. Colocalization, gene expression profiling, and Mendelian randomization provide convergent evidence for the roles of BCKDHA and circulating branch-chain amino acids in heart failure and cardiac structure. Finally, proteome-wide Mendelian randomization identifies 9 circulating proteins associated with heart failure or quantitative imaging traits. These analyses highlight similarities and differences among heart failure and associated cardiovascular imaging endophenotypes, implicate common genetic variation in the pathogenesis of heart failure, and identify circulating proteins that may represent cardiomyopathy treatment targets.