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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 [Formula: see text] 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.

The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups.

OBJECTIVE: Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. METHODS: This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. RESULTS: Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values

Suicide is a major public health problem affecting US Veterans and the US in general. Many variables (e.g., demographic, clinical, biological, geographic) have been associated with risk for suicide and suicidal behavior, including altitude; however, the exact nature of the relationship between altitude and suicide remains unclear in part due to the fact that previous studies have used either geospatial data or individual-level data, but not both. Prior research has also failed to consider the full range of suicidal thoughts and behaviors, ranging from suicidal ideation to suicide deaths. Accordingly, the objective of the present research was to use both geospatial data (county and zip codes) and individual-level data to comprehensively assess the association between altitude and suicide mortality, suicide attempts, and suicidal ideation among US Veterans between 2000 and 2018. Taken together, our results demonstrate that there is a strong correlation between altitude and suicide rates at all the levels investigated and using different statistical analyses and even after controlling for significant covariates such as percent of age >50yr, percent male, percent white, percent non-Hispanic, median household income, and population density. We show that there is a positive correlation between altitude and suicide attempts especially when controlling by the covariates and a weak correlation between altitude and suicide ideation and the combination of suicide, suicide attempts and suicide ideation.

BackgroundEmerging pathogens such as Zika, chikungunya, Ebola, and dengue viruses are serious threats to national and global health security. Accurate forecasts of emerging epidemics and their severity are critical to minimizing subsequent mortality, morbidity, and economic loss. The recent introduction of chikungunya and Zika virus to the Americas underscores the need for better methods for disease surveillance and forecasting.MethodsTo explore the suitability of current approaches to forecasting emerging diseases, the Defense Advanced Research Projects Agency (DARPA) launched the 2014-2015 DARPA Chikungunya Challenge to forecast the number of cases and spread of chikungunya disease in the Americas. Challenge participants (n=38 during final evaluation) provided predictions of chikungunya epidemics across the Americas for a six-month period, from September 1, 2014 to February 16, 2015, to be evaluated by comparison with incidence data reported to the Pan American Health Organization (PAHO). This manuscript presents an overview of the challenge and a summary of the approaches used by the winners.ResultsParticipant submissions were evaluated by a team of non-competing government subject matter experts based on numerical accuracy and methodology. Although this manuscript does not include in-depth analyses of the results, cursory analyses suggest that simpler models appear to outperform more complex approaches that included, for example, demographic information and transportation dynamics, due to the reporting biases, which can be implicitly captured in statistical models. Mosquito-dynamics, population specific information, and dengue-specific information correlated best with prediction accuracy.ConclusionWe conclude that with careful consideration and understanding of the relative advantages and disadvantages of particular methods, implementation of an effective prediction system is feasible. However, there is a need to improve the quality of the data in order to more accurately predict the course of epidemics.

A common property of Cu/Zn superoxide dismutase 1 (SOD1), harboring mutations associated with amyotrophic lateral sclerosis, is a high propensity to misfold and form abnormal aggregates. The aggregation of mutant SOD1 has been demonstrated in vitro, with purified proteins, in mouse models, in human tissues, and in cultured cell models. In vitro translation studies have determined that SOD1 with amyotrophic lateral sclerosis mutations is slower to mature, and thus perhaps vulnerable to off-pathway folding that could generate aggregates. The aggregation of mutant SOD1 in living cells can be monitored by tagging the protein with fluorescent fluorophores. In this study, we have taken advantage of the Dendra2 fluorophore technology in which excitation can be used to switch the output color from green to red, thereby clearly creating a time stamp that distinguishes pre-existing and newly made proteins. In cells that transiently over-express the Ala 4 to Val variant of SOD1-Dendra2, we observed that newly made mutant SOD1 was rapidly captured by pathologic intracellular inclusions. In cell models of mutant SOD1 aggregation over-expressing untagged A4V-SOD1, we observed that immature forms of the protein, lacking a Cu co-factor and a normal intramolecular disulfide, persist for extended periods. Our findings fit with a model in which immature forms of mutant A4V-SOD1, including newly made protein, are prone to misfolding and aggregation.