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Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood. Here, the authors identify six genetic variants associated with adult weight-change, by leveraging 24.5 million weight records from the UK, USA, and Estonia. These variants influence weight change independently of baseline weight. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Idiopathic toe-walking (ITW) is a diagnosis of exclusion. A relationship between ITW and decreased range of motion (ROM) is postulated. Treatments focus on increasing ankle dorsiflexion including serial casting. There is no consensus for duration of serial casting. This study aimed to determine ROM changes with cast change intervals of one vs. two weeks, and the rate of ITW recurrence. METHODS: This was a retrospective study of 86 patients, ages 0–9 years with ITW undergoing weekly casting (N = 29) and two-week casting (N = 57) at a children's hospital from 2014–2020. ROM at baseline, two weeks, four weeks, and final cast removal were collected. Statistical analyses included chi-squared tests, two-sample t-tests, and linear mixed regression. Outcome distributions were assessed for normality. P-values < 0.05 were considered statistically significant. RESULTS: After adjusting for baseline ROM, the mean change in ROM from baseline to two weeks was 10.6∘ vs 7.5∘ in the one-week vs. two-week casting interval, respectively (p < 0.001). The baseline to final measurement was 13.4∘ vs 9.8∘ in the one-week vs. two-week casting interval, respectively (p < 0.001). The rate of recurrence of ITW was similar between the two groups. CONCLUSION: This study suggests greater improvement in ROM in the one-week vs. two-week casting interval group. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. RESEARCH DESIGN AND METHODS: Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program participants with adult-onset diabetes, 2011–2021, who had T1D genetic risk scores (GRS) defined as low (0 to <45%), medium (45 to <90%), high (90 to <95%), or highest (≥95%). RESULTS: T1D characteristics increased progressively with higher genetic risk (P < 0.001 for trend). A GRS ≥90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and their characteristics resembled those of individuals with T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared with the low-risk group, the highest-risk group was more likely to have diabetic ketoacidosis (low GRS 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C-peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase antibodies (6.9% vs. 45.2%), all P < 0.001. CONCLUSIONS: Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble those of people with T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates. [ABSTRACT FROM AUTHOR]

Previous studies found lipid levels, especially triglycerides (TG), are associated with acute pancreatitis, but their causalities and bi-directions were not fully examined. We determined whether abnormal levels of TG, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) are precursors and/or consequences of acute pancreatitis using bidirectional two-sample Mendelian randomization (MR) with two non-overlapping genome-wide association study (GWAS) summary statistics for lipid levels and acute pancreatitis. We found phenotypic associations that both higher TG levels and lower HDL-C levels contributed to increased risk of acute pancreatitis. Our GWAS meta-analysis of acute pancreatitis identified seven independent signals. Genetically predicted TG was positively associated with acute pancreatitis when using the variants specifically associated with TG using univariable MR [Odds ratio (OR), 95% CI 2.02, 1.22–3.31], but the reversed direction from acute pancreatitis to TG was not observed (mean difference = 0.003, SE = 0.002, P-value = 0.138). However, a bidirectional relationship of HDL-C and acute pancreatitis was observed: A 1-SD increment of genetically predicted HDL-C was associated with lower risk of acute pancreatitis (OR, 95% CI 0.84, 0.76–0.92) and genetically predisposed individuals with acute pancreatitis have, on average, 0.005 SD lower HDL-C (mean difference = − 0.005, SE = 0.002, P-value = 0.004). Our MR analysis confirms the evidence of TG as a risk factor of acute pancreatitis but not a consequence. A potential bidirectional relationship of HDL-C and acute pancreatitis occurs and raises the prospect of HDL-C modulation in the acute pancreatitis prevention and treatment. [ABSTRACT FROM AUTHOR]

Background: Statins are part of long‐term medical regimens for many older adults. Whether frailty modifies the protective relationship between statins, mortality, and major adverse cardiovascular events (MACE) is unknown. Methods: This was a retrospective study of US Veterans ≥65, without CVD or prior statin use seen in 2002–2012, followed through 2017. A 31‐item frailty index was used. The co‐primary endpoint was all‐cause mortality or MACE (MI, stroke/TIA, revascularization, or cardiovascular death). Cox proportional hazards models were developed to evaluate the association of statin use with outcomes; propensity score overlap weighting accounted for confounding by indication. Results: We identified 710,313 Veterans (mean age (SD) 75.3(6.5), 98% male, 89% white); 86,327 (12.1%) were frail. Over mean follow‐up of 8 (5) years, there were 48.6 and 72.6 deaths per 1000 person‐years (PY) among non‐frail statin‐users vs nonusers (weighted Incidence Rate Difference (wIRD)/1000 person years (PY), −24.0[95% CI, −24.5 to −23.6]), and 90.4 and 130.4 deaths per 1000PY among frail statin‐users vs nonusers (wIRD/1000PY, −40.0[95% CI, −41.8 to −38.2]). There were 51.7 and 60.8 MACE per 1000PY among non‐frail statin‐users vs nonusers (wIRD/1000PY, −9.1[95% CI, −9.7 to −8.5]), and 88.2 and 102.0 MACE per 1000PY among frail statin‐users vs nonusers (wIRD/1000PY, −13.8[95% CI, −16.2 to −11.4]). There were no significant interactions by frailty for statin users vs non‐users by either mortality or MACE outcomes, p‐interaction 0.770 and 0.319, respectively. Statin use was associated with lower risk of all‐cause mortality (HR, 0.61 (0.60–0.61)) and MACE (HR 0.86 (0.85–0.87)). Conclusions: New statin use is associated with a lower risk of mortality and MACE, independent of frailty. These findings should be confirmed in a randomized clinical trial. See related editorial by Aliberti et al. in this issue. [ABSTRACT FROM AUTHOR]

We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide attempt, and overdose diagnoses with varying choices of study design and prediction methodology. Each model used twenty cross-sectional and 190 longitudinal variables observed in eight time intervals covering 7.5 years prior to the time of prediction. Ensembles of seven base models were created and fine-tuned with ten variables expected to change with study design and outcome definition in order to predict suicide and combined outcome in a prospective cohort. The ensemble models achieved c-statistics of 0.73 on 2-year suicide risk and 0.83 on the combined outcome when predicting on a prospective cohort of ∼ 4.2 M veterans. The ensembles rely on nonlinear base models trained using a matched retrospective nested case-control (Rcc) study cohort and show good calibration across a diversity of subgroups, including risk strata, age, sex, race, and level of healthcare utilization. In addition, a linear Rcc base model provided a rich set of biological predictors, including indicators of suicide, substance use disorder, mental health diagnoses and treatments, hypoxia and vascular damage, and demographics. [ABSTRACT FROM AUTHOR]

Most prior studies on the prognostic significance of newly-diagnosed atrial fibrillation (AF) in COVID-19 did not differentiate newly-diagnosed AF from pre-existing AF. To determine the association between newly-diagnosed AF and in-hospital and 30-day mortality among regular users of Veterans Health Administration using data linked to Medicare. We identified Veterans aged ≥ 65 years who were hospitalized for ≥ 24 h with COVID-19 from 06/01/2020 to 1/31/2022 and had ≥ 2 primary care visits within 24 months prior to the index hospitalization. We performed multivariable logistic regression analyses to estimate adjusted risks, risk differences (RD), and odds ratios (OR) for the association between newly-diagnosed AF and the mortality outcomes adjusting for patient demographics, baseline comorbidities, and presence of acute organ dysfunction on admission. Of 23,299 patients in the study cohort, 5.3% had newly-diagnosed AF, and 29.2% had pre-existing AF. In newly-diagnosed AF adjusted in-hospital and 30-day mortality were 16.5% and 22.7%, respectively. Newly-diagnosed AF was associated with increased mortality compared to pre-existing AF (in-hospital: OR 2.02, 95% confidence interval [CI] 1.72–2.37; RD 7.58%, 95% CI 5.54–9.62) (30-day: OR 1.86; 95% CI 1.60–2.16; RD 9.04%, 95% CI 6.61–11.5) or no AF (in-hospital: OR 2.24, 95% CI 1.93–2.60; RD 8.40%, 95% CI 6.44–10.4) (30-day: 2.07, 95% CI 1.80–2.37; RD 10.2%, 95% CI 7.89–12.6). There was a smaller association between pre-existing AF and the mortality outcomes. Newly-diagnosed AF is an important prognostic marker for patients hospitalized with COVID-19. Whether prevention or treatment of AF improves clinical outcomes in these patients remains unknown. [ABSTRACT FROM AUTHOR]

Background: There is inconsistent evidence on the association of moderate alcohol consumption and stroke risk in the general population and is not well studied among U.S. Veterans. Furthermore, it is unclear whether primarily drinking beer, wine, or liquor is associated with a difference in stroke risk. Methods: The study included 185,323 Million Veteran Program participants who self-reported alcohol consumption on the Lifestyle Survey. Moderate consumption was defined as 1–2 drinks/day and beverage preference of beer, wine or liquor was defined if ≥ 50% of total drinks consumed were from a single type of beverage. Strokes were defined using ICD-9 and ICD-10 codes from the participants' electronic health record. Results: The mean (sd) age of the sample was 64 (13) years and 11% were women. We observed 4,339 (94% ischemic; 6% hemorrhagic) strokes over a median follow-up of 5.2 years. In Cox models adjusted for age, sex, race, education, income, body mass index, smoking, exercise, diet, cholesterol, prevalent diabetes, prevalent hypertension, lipid-lowering medication, antihypertensive medication, and diabetes medication, moderate alcohol consumption (1–2 drinks/day) was associated with a 22% lower risk of total stroke compared with never drinking [Hazards ratio (HR) 95% confidence interval (CI): 0.78 (0.67, 0.92)]. When stratifying by stroke type, we observed a similar protective association with moderate consumption and ischemic stroke [HR (95% CI): 0.76 (0.65, 0.90)], but a non-statistically significant higher risk of hemorrhagic stroke [HR (95% CI): 1.29 (0.64, 2.61)]. We did not observe a difference in ischemic or hemorrhagic stroke risk among those who preferred beer, liquor or wine vs. no beverage preference. When stratifying by prior number of hospital visits (≤ 15, 16–33, 34–64, ≥ 65) as a proxy for health status, we observed attenuation of the protective association with greater number of visits [HR (95% CI): 0.87 (0.63, 1.19) for ≥ 65 visits vs. 0.80 (0.59, 1.08) for ≤ 15 visits]. Conclusions: We observed a lower risk of ischemic stroke, but not hemorrhagic stroke with moderate alcohol consumption and did not observe substantial differences in risk by beverage preference among a sample of U.S. Veterans. Healthy user bias of moderate alcohol consumption may be driving some of the observed protective association. [ABSTRACT FROM AUTHOR]

Background Over 870 000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have occurred among Veterans Health Administration users, and 24 000 have resulted in death. We examined early outcomes of SARS-CoV-2 infection in hospitalized veterans. Methods In an ongoing, prospective cohort study, we enrolled veterans age ≥18 tested for SARS-CoV-2 and hospitalized at 15 Department of Veterans Affairs medical centers between February 2021 and June 2022. We estimated adjusted odds ratios (aORs), adjusted incidence rate ratios (aIRRs), and adjusted hazard ratios (aHRs) for maximum illness severity within 30 days of study entry (defined using the 4-category VA Severity Index for coronavirus disease 2019 [COVID-19]), as well as length of hospitalization and rehospitalization within 60 days, in relationship with demographic characteristics, Charlson comorbidity index (CCI), COVID-19 vaccination, and calendar period of enrollment. Results The 542 participants included 329 (61%) who completed a primary vaccine series (with or without booster; "vaccinated"), 292 (54%) enrolled as SARS-CoV-2-positive, and 503 (93%) men, with a mean age of 64.4 years. High CCI scores (≥5) occurred in 61 (44%) vaccinated and 29 (19%) unvaccinated SARS-CoV-2-positive participants. Severe illness or death occurred in 29 (21%; 6% died) vaccinated and 31 (20%; 2% died) unvaccinated SARS-CoV-2-positive participants. SARS-CoV-2-positive inpatients per unit increase in CCI had greater multivariable-adjusted odds of severe illness (aOR, 1.21; 95% CI, 1.01–1.45), more hospitalization days (aIRR, 1.06; 95% CI, 1.03–1.10), and rehospitalization (aHR, 1.07; 95% CI, 1.01–1.12). Conclusions In a cohort of hospitalized US veterans with SARS-CoV-2 infection, those with a higher CCI had more severe COVID-19 illness, more hospital days, and rehospitalization, after adjusting for vaccination status, age, sex, and calendar period. [ABSTRACT FROM AUTHOR]

The development of phenotypes using electronic health records is a resource-intensive process. Therefore, the cataloging of phenotype algorithm metadata for reuse is critical to accelerate clinical research. The Department of Veterans Affairs (VA) has developed a standard for phenotype metadata collection which is currently used in the VA phenomics knowledgebase library, CIPHER (Centralized Interactive Phenomics Resource), to capture over 5000 phenotypes. The CIPHER standard improves upon existing phenotype library metadata collection by capturing the context of algorithm development, phenotyping method used, and approach to validation. While the standard was iteratively developed with VA phenomics experts, it is applicable to the capture of phenotypes across healthcare systems. We describe the framework of the CIPHER standard for phenotype metadata collection, the rationale for its development, and its current application to the largest healthcare system in the United States. [ABSTRACT FROM AUTHOR]

Objective: To examine the association between adherence to plant-based diets and mortality. Design: Prospective study. We calculated a plant-based diet index (PDI) by assigning positive scores to plant foods and reverse scores to animal foods. We also created a healthful PDI (hPDI) and an unhealthful PDI (uPDI) by further separating the healthy plant foods from less-healthy plant foods. Setting: The VA Million Veteran Program. Participants: 315 919 men and women aged 19–104 years who completed a FFQ at the baseline. Results: We documented 31 136 deaths during the follow-up. A higher PDI was significantly associated with lower total mortality (hazard ratio (HR) comparing extreme deciles = 0·75, 95 % CI: 0·71, 0·79, P trend < 0·001]. We observed an inverse association between hPDI and total mortality (HR comparing extreme deciles = 0·64, 95 % CI: 0·61, 0·68, P trend < 0·001), whereas uPDI was positively associated with total mortality (HR comparing extreme deciles = 1·41, 95 % CI: 1·33, 1·49, P trend < 0·001). Similar significant associations of PDI, hPDI and uPDI were also observed for CVD and cancer mortality. The associations between the PDI and total mortality were consistent among African and European American participants, and participants free from CVD and cancer and those who were diagnosed with major chronic disease at baseline. Conclusions: A greater adherence to a plant-based diet was associated with substantially lower total mortality in this large population of veterans. These findings support recommending plant-rich dietary patterns for the prevention of major chronic diseases. [ABSTRACT FROM AUTHOR]

Additionally, as MVP expands eligibility to Veterans who may not use the VA for health care services, the importance of self-report data becomes imperative as MVP builds the infrastructure to access external health records. For greater applicability, it is envisioned that the MVP Baseline and MVP Lifestyle Surveys will be validated in future MVP scientific projects to enhance the understanding of Veteran health and wellbeing. Data resource access As described in detail in Gaziano's MVP introduction paper[3] and MVP website [https://www.research.va.gov/mvp/], access to MVP data and/or samples is governed by the scope of MVP informed consent and VA policies and requires scientific review by appropriate VA review committees. Keywords: Health surveys; Veteran health; cohort studies; survey development; Veteran lifestyle EN Health surveys Veteran health cohort studies survey development Veteran lifestyle e1 e17 17 02/16/23 20230201 NES 230201 Key Features The Department of Veterans Affairs (VA) Million Veteran Program (MVP) is a national voluntary research programme designed to examine genetic influences on health with the goal of improving health for Veterans. [Extracted from the article]

Motivation Predicting molecule–disease indications and side effects is important for drug development and pharmacovigilance. Comprehensively mining molecule–molecule, molecule–disease and disease–disease semantic dependencies can potentially improve prediction performance. Methods We introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular-disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients. Specifically, M2REMAP first learns a multimodal molecule representation that synthesizes chemical property and clinical semantic information by mapping molecule chemicals via a deep neural network onto the clinical semantic embedding space shared by drugs, diseases and other common clinical concepts. To infer molecule–disease relations, M2REMAP combines multimodal molecule representation and disease semantic embedding to jointly infer indications and side effects. Results We extensively evaluate M2REMAP on molecule indications, side effects and interactions. Results show that incorporating EHR embeddings improves performance significantly, for example, attaining an improvement over the baseline models by 23.6% in PRC-AUC on indications and 23.9% on side effects. Further, M2REMAP overcomes the limitation of existing methods and effectively predicts drugs for novel diseases and emerging pathogens. Availability and implementation The code is available at https://github.com/celehs/M2REMAP , and prediction results are provided at https://shiny.parse-health.org/drugs-diseases-dev/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Purpose: In young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population. Methods: A retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS. Results: Among the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%). Conclusion: Trough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor. [ABSTRACT FROM AUTHOR]

Pharmacologic clinical trials for heart failure with preserved ejection fraction have been largely unsuccessful as compared to those for heart failure with reduced ejection fraction. Whether differences in the genetic underpinnings of these major heart failure subtypes may provide insights into the disparate outcomes of clinical trials remains unknown. We utilize a large, uniformly phenotyped, single cohort of heart failure sub-classified into heart failure with reduced and with preserved ejection fractions based on current clinical definitions, to conduct detailed genetic analyses of the two heart failure sub-types. We find different genetic architectures and distinct genetic association profiles between heart failure with reduced and with preserved ejection fraction suggesting differences in underlying pathobiology. The modest genetic discovery for heart failure with preserved ejection fraction (one locus) compared to heart failure with reduced ejection fraction (13 loci) despite comparable sample sizes indicates that clinically defined heart failure with preserved ejection fraction likely represents the amalgamation of several, distinct pathobiological entities. Development of consensus sub-phenotyping of heart failure with preserved ejection fraction is paramount to better dissect the underlying genetic signals and contributors to this highly prevalent condition. While the genetic basis of heart failure has been explored by genetic studies, the differences between subtypes are not well understood. Here, the authors performed genetic analyses on the two major subtypes of heart failure in a large biobank with genetic and health record data, finding unique genetic architecture for each subtype. [ABSTRACT FROM AUTHOR]

Rationale: A common MUC5B gene polymorphism, rs35705950-T, is associated with idiopathic pulmonary fibrosis (IPF), but its role in severe acute respiratory syndrome coronavirus 2 infection and disease severity is unclear. Objectives: To assess whether rs35705950-T confers differential risk for clinical outcomes associated with coronavirus disease (COVID-19) infection among participants in the Million Veteran Program (MVP). Methods: The MUC5B rs35705950-T allele was directly genotyped among MVP participants; clinical events and comorbidities were extracted from the electronic health records. Associations between the incidence or severity of COVID-19 and rs35705950-T were analyzed within each ancestry group in the MVP followed by transancestry meta-analysis. Replication and joint meta-analysis were conducted using summary statistics from the COVID-19 Host Genetics Initiative (HGI). Sensitivity analyses with adjustment for additional covariates (body mass index, Charlson comorbidity index, smoking, asbestosis, rheumatoid arthritis with interstitial lung disease, and IPF) and associations with post-COVID-19 pneumonia were performed in MVP subjects. Measurements and Main Results: The rs35705950-T allele was associated with fewer COVID-19 hospitalizations in transancestry meta-analyses within the MVP (Ncases = 4,325; Ncontrols = 507,640; OR = 0.89 [0.82-0.97]; P = 6.86 × 10-3) and joint meta-analyses with the HGI (Ncases = 13,320; Ncontrols = 1,508,841; OR, 0.90 [0.86-0.95]; P = 8.99 × 10-5). The rs35705950-T allele was not associated with reduced COVID-19 positivity in transancestry meta-analysis within the MVP (Ncases = 19,168/Ncontrols = 492,854; OR, 0.98 [0.95-1.01]; P = 0.06) but was nominally significant (P < 0.05) in the joint meta-analysis with the HGI (Ncases = 44,820; Ncontrols = 1,775,827; OR, 0.97 [0.95-1.00]; P = 0.03). Associations were not observed with severe outcomes or mortality. Among individuals of European ancestry in the MVP, rs35705950-T was associated with fewer post-COVID-19 pneumonia events (OR, 0.82 [0.72-0.93]; P = 0.001). Conclusions: The MUC5B variant rs35705950-T may confer protection in COVID-19 hospitalizations. [ABSTRACT FROM AUTHOR]

Understanding the genetic relationships between human disorders could lead to better treatment and prevention strategies, especially for individuals with multiple comorbidities. A common resource for studying genetic-disease relationships is the GWAS Catalog, a large and well curated repository of SNP-trait associations from various studies and populations. Some of these populations are contained within mega-biobanks such as the Million Veteran Program (MVP), which has enabled the genetic classification of several diseases in a large well-characterized and heterogeneous population. Here we aim to provide a network of the genetic relationships among diseases and to demonstrate the utility of quantifying the extent to which a given resource such as MVP has contributed to the discovery of such relations. We use a network-based approach to evaluate shared variants among thousands of traits in the GWAS Catalog repository. Our results indicate many more novel disease relationships that did not exist in early studies and demonstrate that the network can reveal clusters of diseases mechanistically related. Finally, we show novel disease connections that emerge when MVP data is included, highlighting methodology that can be used to indicate the contributions of a given biobank. [ABSTRACT FROM AUTHOR]

Background: COVID‐19 and influenza are important sources of morbidity and mortality among older adults. Understanding how outcomes differ for older adults hospitalized with either infection is important for improving care. We compared outcomes from infection with COVID‐19 and influenza among hospitalized older adults. Methods: We conducted a retrospective study of 30‐day mortality among veterans aged 65+ hospitalized with COVID‐19 from March 1, 2020–December 31, 2020 or with influenza A/B from September 1, 2017 to August 31, 2019 in Veterans Affairs Health Care System (VAHCS). COVID‐19 infection was determined by a positive PCR test and influenza by tests used in the VA system. Frailty was defined by the claims‐based Veterans Affairs Frailty Index (VA‐FI). Logistic regressions of mortality on frailty, age, and infection were adjusted for multiple confounders. Results: A total of 15,474 veterans were admitted with COVID‐19 and 7867 with influenza. Mean (SD) ages were 76.1 (7.8) and 75.8 (8.3) years, 97.7% and 97.4% were male, and 66.9% and 76.4% were white in the COVID‐19 and influenza cohorts respectively. Crude 30‐day mortality (95% CI) was 18.9% (18.3%–19.5%) for COVID‐19 and 4.3% (3.8%–4.7%) for influenza. Combining cohorts, the odds ratio for 30‐day mortality from COVID‐19 (versus influenza) was 6.61 (5.74–7.65). There was a statistically significant interaction between infection with COVID‐19 and frailty, but there was no significant interaction between COVID‐19 and age. Separating cohorts, greater 30‐day mortality was significantly associated with older age (p: COVID‐19: <0.001, Influenza: <0.001) and for frail compared with robust individuals (p for trend: COVID‐19: <0.001, Influenza: <0.001). Conclusion: Mortality from COVID‐19 exceeded that from influenza among hospitalized older adults. However, odds of mortality were higher at every level of frailty among those admitted with influenza compared to COVID‐19. Prevention will remain key to reducing mortality from viral illnesses among older adults. [ABSTRACT FROM AUTHOR]

A better understanding of the sequential and temporal aspects in which diseases occur in patient's lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems. [ABSTRACT FROM AUTHOR]

Background: Genome-wide Association Studies (GWAS) aims to uncover the link between genomic variation and phenotype. They have been actively applied in cancer biology to investigate associations between variations and cancer phenotypes, such as susceptibility to certain types of cancer and predisposed responsiveness to specific treatments. Since GWAS primarily focuses on finding associations between individual genomic variations and cancer phenotypes, there are limitations in understanding the mechanisms by which cancer phenotypes are cooperatively affected by more than one genomic variation. Results: This paper proposes a network representation learning approach to learn associations among genomic variations using a prostate cancer cohort. The learned associations are encoded into representations that can be used to identify functional modules of genomic variations within genes associated with early- and late-onset prostate cancer. The proposed method was applied to a prostate cancer cohort provided by the Veterans Administration's Million Veteran Program to identify candidates for functional modules associated with early-onset prostate cancer. The cohort included 33,159 prostate cancer patients, 3181 early-onset patients, and 29,978 late-onset patients. The reproducibility of the proposed approach clearly showed that the proposed approach can improve the model performance in terms of robustness. Conclusions: To our knowledge, this is the first attempt to use a network representation learning approach to learn associations among genomic variations within genes. Associations learned in this way can lead to an understanding of the underlying mechanisms of how genomic variations cooperatively affect each cancer phenotype. This method can reveal unknown knowledge in the field of cancer biology and can be utilized to design more advanced cancer-targeted therapies. [ABSTRACT FROM AUTHOR]

Large clinical datasets derived from insurance claims and electronic health record (EHR) systems are valuable sources for precision medicine research. These datasets can be used to develop models for personalized prediction of risk or treatment response. Efficiently deriving prediction models using real world data, however, faces practical and methodological challenges. Precise information on important clinical outcomes such as time to cancer progression are not readily available in these databases. The true clinical event times typically cannot be approximated well based on simple extracts of billing or procedure codes. Whereas, annotating event times manually is time and resource prohibitive. In this paper, we propose a two-step semi-supervised multi-modal automated time annotation (MATA) method leveraging multi-dimensional longitudinal EHR encounter records. In step I, we employ a functional principal component analysis approach to estimate the underlying intensity functions based on observed point processes from the unlabeled patients. In step II, we fit a penalized proportional odds model to the event time outcomes with features derived in step I in the labeled data where the non-parametric baseline function is approximated using B-splines. Under regularity conditions, the resulting estimator of the feature effect vector is shown as root-n consistent. We demonstrate the superiority of our approach relative to existing approaches through simulations and a real data example on annotating lung cancer recurrence in an EHR cohort of lung cancer patients from Veteran Health Administration. [ABSTRACT FROM AUTHOR]

Background: Genetic studies may help identify causal pathways; therefore, we sought to identify genetic determinants of ideal CVH and their association with CVD outcomes in the multi-population Veteran Administration Million Veteran Program. Methods: An ideal health score (IHS) was calculated from 3 clinical factors (blood pressure, total cholesterol, and blood glucose levels) and 3 behavioral factors (smoking status, physical activity, and BMI), ascertained at baseline. Multi-population genome-wide association study (GWAS) was performed on IHS and binary ideal health using linear and logistic regression, respectively. Using the genome-wide significant SNPs from the IHS GWAS, we created a weighted IHS polygenic risk score (PRSIHS) which was used (i) to conduct a phenome-wide association study (PheWAS) of associations between PRSIHS and ICD-9 phenotypes and (ii) to further test for associations with mortality and selected CVD outcomes using logistic and Cox regression and, as an instrumental variable, in Mendelian Randomization. Results: The discovery and replication cohorts consisted of 142,404 (119,129 European American (EUR); 16,495 African American (AFR)), and 45,766 (37,646 EUR; 5,366 AFR) participants, respectively. The mean age was 65.8 years (SD = 11.2) and 92.7% were male. Overall, 4.2% exhibited ideal CVH based on the clinical and behavioral factors. In the multi-population meta-analysis, variants at 17 loci were associated with IHS and each had known GWAS associations with multiple components of the IHS. PheWAS analysis in 456,026 participants showed that increased PRSIHS was associated with a lower odds ratio for many CVD outcomes and risk factors. Both IHS and PRSIHS measures of ideal CVH were associated with significantly less CVD outcomes and CVD mortality. Conclusion: A set of high interest genetic variants contribute to the presence of ideal CVH in a multi-ethnic cohort of US Veterans. Genetically influenced ideal CVH is associated with lower odds of CVD outcomes and mortality. [ABSTRACT FROM AUTHOR]

Background: In response to the novel Coronavirus Disease 2019 (COVID-19) pandemic, the Department of Veterans Affairs (VA) Million Veteran Program (MVP) organized efforts to better understand the impact of COVID-19 on Veterans by developing and deploying a self-reported survey. Methods: The MVP COVID-19 Survey was developed to collect COVID-19 specific elements including symptoms, diagnosis, hospitalization, behavioral and psychosocial factors and to augment existing MVP data with longitudinal collection of key domains in physical and mental health. Due to the rapidly evolving nature of the pandemic, a multipronged strategy was implemented to widely disseminate the COVID-19 Survey and capture data using both the online platform and mailings. Results: We limited the findings of this paper to the initial phase of survey dissemination which began in May 2020. A total of 729,625 eligible MVP Veterans were invited to complete version 1 of the COVID-19 Survey. As of October 31, 2020, 58,159 surveys have been returned. The mean and standard deviation (SD) age of responders was 71 (11) years, 8.6% were female, 8.2% were Black, 5.6% were Hispanic, and 446 (0.8%) self-reported a COVID-19 diagnosis. Over 90% of responders reported wearing masks, practicing social distancing, and frequent hand washing. Conclusion: The MVP COVID-19 Survey provides a systematic collection of data regarding COVID-19 behaviors among Veterans and represents one of the first large-scale, national surveillance efforts of COVID-19 in the Veteran population. Continued work will examine the overall response to the survey with comparison to available VA health record data. [ABSTRACT FROM AUTHOR]

Background: Surgical treatment of superficial peritoneal endometriosis includes excision or ablation. Controversy exists about which method is better for providing pain relief. We performed a meta-analysis of randomized control trials (RCTs) comparing the efficacy of excision versus ablation for improving the most frequently encountered endometriosis pain symptoms: dysmenorrhea, dyspareunia, non-cyclic pelvic pain, and dyschezia. Methods: A search from inception to May 2020 was conducted in PubMed, MEDLINE, Clinicaltrials.gov, Cochrane Central Register of Controlled Trials, and Web of Science. RCTs comparing excision versus ablation for peritoneal endometriosis were included if they assessed dysmenorrhea, dyspareunia, dyschezia, and pelvic pain/non-menstrual pain. Exclusion criteria included extra-peritoneal endometriosis or endometriomas, and non-English publications. Publications were assessed for risk of bias and quality of evidence using Cochrane risk of bias tool (RoB 2) and GradePro. Results: Of the 2025 records identified initially, four met inclusion criteria. The sample sizes of the included studies ranged from 24 to 133 participants. The excision and ablation groups included 204 participants each. There was no difference after excision or ablation for dysmenorrhea when comparing the mean change in pain scores from baseline to 12 months after surgery (−0.31, 95% CI −1.66, 1.04, p = 0.65), dyspareunia (−0.24, 95% CI −1.78, 1.30, p = 0.76), dyschezia (−0.72, 95% CI −1.94, 0.50, p = 0.25), or non-cyclic pain (−0.78, 95% CI −2.47, 0.91, p = 0.37). Conclusions: We found low to moderate quality evidence suggesting that neither excision nor ablation is superior in reduction of endometriosis-related pain up to 12 months after surgery. [ABSTRACT FROM AUTHOR]

Aims Frailty is associated with an increased risk of all-cause mortality and cardiovascular (CV) events. Limited data exist from the modern era of CV prevention on the relationship between frailty and CV mortality. We hypothesized that frailty is associated with an increased risk of CV mortality. Methods and results All US Veterans aged ≥65 years who were regular users of Veteran Affairs care from 2002 to 2017 were included. Frailty was defined using a 31-item previously validated frailty index, ranging from 0 to 1. The primary outcome was CV mortality with secondary analyses examining the relationship between frailty and CV events (myocardial infarction, stroke, revascularization). Survival analysis models were adjusted for age, sex, ethnicity, geographic region, smoking, hyperlipidaemia, statin use, and blood pressure medication use. There were 3 068 439 US Veterans included in the analysis. Mean age was 74.1 ± 5.8 years in 2002, 76.0 ± 8.3 years in 2014, 98% male, and 87.5% White. In 2002, the median (interquartile range) frailty score was 0.16 (0.10–0.23). This increased and stabilized to 0.19 (0.10–0.32) for 2006–14. The presence of frailty was associated with an increased risk of CV mortality at every stage of frailty. Frailty was associated with an increased risk of myocardial infarction and stroke, but not revascularization. Conclusion In this population, both the presence and severity of frailty are tightly correlated with CV death, independent of underlying CV disease. This study is the largest and most contemporary evaluation of the relationship between frailty and CV mortality to date. Further work is needed to understand how this risk can be diminished. Key Question Can an electronic frailty index identify adults aged 65 and older who are at risk of CV mortality and major CV events? Key Finding Among 3 068 439 US Veterans aged 65 and older, frailty was associated with an increased risk of CV mortality at every level of frailty. Frailty was also associated with an increased risk of myocardial infarction and stroke, but not revascularization. Take Home Message Both the presence and severity of frailty are associated with CV mortality and major CV events, independent of underlying CV disease. [ABSTRACT FROM AUTHOR]

We studied male centenarian Veterans using VA health care to understand the impact of social characteristics on their annual mortality rate, adjusting for prevalent health conditions. This longitudinal study used VA Electronic Health Record data from 1997 to 2012 0 - 1,858). Covariates included age, race, marital status, and periods of military service. The mean age was 100,4 ± 1.4 years, 76% were white, and 49% were married. The average annual mortality rate was 32 per 100 person-years. The annual mortality rate was stable and not affected by race but did vary by marital status. Divorced or separated centenarians had a21% higher rate of death than married centenarians. A diagnosis of dementia or of congestive heart failure each increased the mortality risk by 37%. Providers should consider prevalent health conditions, as well as marital status, in managing care of centenarian Veterans. [ABSTRACT FROM AUTHOR]

Although affecting different arterial territories, the related atherosclerotic vascular diseases coronary artery disease (CAD) and peripheral artery disease (PAD) share similar risk factors and have shared pathobiology. To identify novel pleiotropic loci associated with atherosclerosis, we performed a joint analysis of their shared genetic architecture, along with that of common risk factors. Using summary statistics from genome-wide association studies of nine known atherosclerotic (CAD, PAD) and atherosclerosis risk factors (body mass index, smoking initiation, type 2 diabetes, low density lipoprotein, high density lipoprotein, total cholesterol, and triglycerides), we perform 15 separate multi-trait genetic association scans which resulted in 25 novel pleiotropic loci not yet reported as genome-wide significant for their respective traits. Colocalization with single-tissue eQTLs identified candidate causal genes at 14 of the detected signals. Notably, the signal between PAD and LDL-C at the PCSK6 locus affects PCSK6 splicing in human liver tissue and induced pluripotent derived hepatocyte-like cells. These results show that joint analysis of related atherosclerotic disease traits and their risk factors allowed identification of unified biology that may offer the opportunity for therapeutic manipulation. The signal at PCSK6 represent possible shared causal biology where existing inhibitors may be able to be leveraged for novel therapies. [ABSTRACT FROM AUTHOR]

Aims: This study aims to develop the first race‐specific and sex‐specific risk prediction models for heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF). Methods and results: We created a cohort of 1.8 million individuals who had an outpatient clinic visit between 2002 and 2007 within the Veterans Affairs (VA) Healthcare System and obtained information on HFpEF, HFrEF, and several risk factors from electronic health records (EHR). Variables were selected for the risk prediction models in a 'derivation cohort' that consisted of individuals with baseline date in 2002, 2003, or 2004 using a forward stepwise selection based on a change in C‐index threshold. Discrimination and calibration were assessed in the remaining participants (internal 'validation cohort'). A total of 66 831 individuals developed HFpEF, and 92 233 developed HFrEF (52 679 and 71 463 in the derivation cohort) over a median of 11.1 years of follow‐up. The HFpEF risk prediction model included age, diabetes, BMI, COPD, previous MI, antihypertensive treatment, SBP, smoking status, atrial fibrillation, and estimated glomerular filtration rate (eGFR), while the HFrEF model additionally included previous CAD. For the HFpEF model, C‐indices were 0.74 (SE = 0.002) for white men, 0.76 (0.005) for black men, 0.79 (0.015) for white women, and 0.77 (0.026) for black women, compared with 0.72 (0.002), 0.72 (0.004), 0.77 (0.017), and 0.75 (0.028), respectively, for the HFrEF model. These risk prediction models were generally well calibrated in each race‐specific and sex‐specific stratum of the validation cohort. Conclusions: Our race‐specific and sex‐specific risk prediction models, which used easily obtainable clinical variables, can be a useful tool to implement preventive strategies or subtype‐specific prevention trials in the nine million users of the VA healthcare system and the general population after external validation. [ABSTRACT FROM AUTHOR]

Background: Electronic frailty indices (eFIs) are increasingly used to identify patients at risk for morbidity and mortality. Whether eFIs capture the spectrum of frailty change, including decline, stability, and improvement, is unknown.Methods: In a nationwide retrospective birth cohort of U.S. Veterans, a validated eFI, including 31 health deficits, was calculated annually using medical record and insurance claims data (2002-2012). K-means clustering was used to assign patients into frailty trajectories measured 5 years prior to death.Results: There were 214 250 veterans born between 1927 and 1934 (mean [SD] age at death = 79.4 [2.8] years, 99.2% male, 90.3% White) with an annual eFI in the 5 years before death. Nine frailty trajectories were identified. Those starting at nonfrail or prefrail had 2 stable trajectories (nonfrail to prefrail, n = 29 786 and stable prefrail, n = 28 499) and 2 rapidly increasing trajectories (prefrail to moderately frail, n = 28 244 and prefrail to severely frail, n = 22 596). Those who were mildly frail at baseline included 1 gradually increasing trajectory (mildly to moderately frail, n = 33 806) and 1 rapidly increasing trajectory (mildly to severely frail, n = 15 253). Trajectories that started at moderately or severely frail included 2 gradually increasing trajectories (moderately to severely frail, n = 27 662 and progressing severely frail, n = 14 478) and 1 recovering trajectory (moderately frail to mildly frail, n = 13 926).Conclusions: Nine frailty trajectories, including 1 recovering trajectory, were identified in this cohort of older U.S. Veterans. Future work is needed to understand whether prevention and treatment strategies can improve frailty trajectories and contribute to compression of morbidity toward the end of life. [ABSTRACT FROM AUTHOR]

Background: Early convalescent plasma transfusion may reduce mortality in patients with non-severe coronavirus disease 2019 (COVID-19).Methods: This study emulates a (hypothetical) target trial using observational data from a cohort of United States Veterans admitted to a Department of Veterans Affairs (VA) facility between May 1 and November 17, 2020 with non-severe COVID-19. The intervention was convalescent plasma initiated within 2 days of eligibility. Thirty-day mortality was compared using cumulative incidence curves, risk differences, and hazard ratios estimated from pooled logistic models with inverse probability weighting to adjust for confounding.Results: Of 11,269 eligible person-trials contributed by 4,755 patients, 402 trials were assigned to the convalescent plasma group. Forty and 671 deaths occurred within the plasma and non-plasma groups, respectively. The estimated 30-day mortality risk was 6.5% (95% CI: 4.0, 9.7) in the plasma group and 6.2% (95% CI: 5.6, 7.0) in the non-plasma group. The associated risk difference was 0.30% (95% CI -2.30, 3.60) and the hazard ratio was 1.04 (95% CI 0.64,1.62).Conclusion: Our target trial emulation estimated no meaningful differences in 30-day mortality between non-severe COVID-19 patients treated and untreated with convalescent plasma. [ABSTRACT FROM AUTHOR]