Acute Trauma Factor Associations With Suicidality Across the First 5 Years After Traumatic Brain Injury

AbstractObjectiveTo determine whether severity of head and extracranial injuries (ECI) is associated with suicidal ideation (SI) or suicide attempt (SA) after traumatic brain injury (TBI).DesignFactors associated with SI and SA were assessed in this inception cohort study using data collected 1, 2, and 5 years post-TBI from the National Trauma Data Bank and Traumatic Brain Injury Model Systems (TBIMS) databases.SettingLevel I trauma centers, inpatient rehabilitation centers, and the community.ParticipantsParticipants with TBI from 15 TBIMS Centers with linked National Trauma Data Bank trauma data (N=3575).InterventionsNot applicable.Main Outcome MeasuresSI was measured via the Patient Health Questionnaire 9 (question 9). SA in the last year was assessed via interview. ECI was measured by the Injury Severity Scale (nonhead) and categorized as none, mild, moderate, or severe.ResultsThere were 293 (8.2%) participants who had SI without SA and 109 (3.0%) who had SA at least once in the first 5 years postinjury. Random effects logit modeling showed a higher likelihood of SI when ECI was severe (odds ratio=2.73; 95% confidence interval, 1.55–4.82; P=.001). Drug use at time of injury was also associated with SI (odds ratio=1.69; 95% confidence interval, 1.11–2.86; P=.015). Severity of ECI was not associated with SA.ConclusionsSevere ECI carried a nearly 3-fold increase in the odds of SI after TBI, but it was not related to SA. Head injury severity and less severe ECI were not associated with SI or SA. These findings warrant additional work to identify factors associated with severe ECI that make individuals more susceptible to SI after TBI

Risk Prediction of Prostate Cancer with Single Nucleotide Polymorphisms and Prostate Specific Antigen

Purpose:Combined information on single nucleotide polymorphisms and prostate specific antigen offers opportunities to improve the performance of screening by risk stratification. We aimed to predict the risk of prostate cancer based on prostate specific antigen together with single nucleotide polymorphism information.Materials and Methods:We performed a prospective study of 20,575 men with prostate specific antigen testing and 4,967 with a polygenic risk score for prostate cancer based on 66 single nucleotide polymorphisms from the Finnish population based screening trial of prostate cancer and 5,269 samples of 7 single nucleotide polymorphisms from the Finnish prostate cancer DNA study. A Bayesian predictive model was built to estimate the risk of prostate cancer by sequentially combining genetic information with prostate specific antigen compared with prostate specific antigen alone in study subjects limited to those with prostate specific antigen 4 ng/ml or above.Results:The posterior odds of prostate cancer based on 7 single nucleotide polymorphisms together with the prostate specific antigen level ranged from 3.7 at 4 ng/ml, 14.2 at 6 and 40.7 at 8 to 98.2 at 10 ng/ml. The ROC AUC was elevated to 88.8% (95% CI 88.6-89.1) for prostate specific antigen combined with the risk score based on 7 single nucleotide polymorphisms compared with 70.1% (95% CI 69.6-70.7) for prostate specific antigen alone. It was further escalated to 96.7% (95% CI 96.5-96.9) when all prostate cancer susceptibility polygenes were combined.Conclusions:Expedient use of multiple genetic variants together with information on prostate specific antigen levels better predicts the risk of prostate cancer than prostate specific antigen alone and allows for higher prostate specific antigen cutoffs. Combined information also provides a basis for risk stratification which can be used to optimize the performance of prostate cancer screening.Peer reviewe

Assessing interactions of two loci (rs4242382 and rs10486567) in familial prostate cancer : statistical evaluation of epistasis

Understanding the impact of multiple genetic variants and their interactions on the disease penetrance of familial multiple prostate cancer is very relevant to the overall understanding of carcinogenesis. We assessed the joint effect of two loci on rs4242382 at 8q24 and rs10486567 at 7p15.2 to this end. We analyzed the data from a Finnish family-based genetic study, which was composed of 947 men including 228 cases in 75 families, to evaluate the respective effects of the two loci on the disease penetrance; in particular, the occurrence and number of prostate cancer cases within a family were utilized to evaluate the interactions between the two loci under the additive and multiplicative Poisson regression models. The risk alleles A at rs4242382 (OR = 1.14, 95% CI 1.08–1.19, P<0.0001) and a risk allele A at rs10486567 (OR = 1.06, 96%CI 1.01–1.11, P = 0.0208) were found to be associated with an increased risk of familial PrCa, especially with four or more cases within a family. A multiplicative model fitted the joint effect better than an additive model (likelihood ratio test X2 = 13.89, P<0.0001). The influence of the risk allele A at rs10486567 was higher in the presence of the risk allele A at rs4242382 (OR = 1.09 (1.01–1.18) vs. 1.01 (0.95–1.07)). Similar findings were observed in non-aggressive PrCa, but not in aggressive PrCa. We demonstrated that two loci (rs4242382 and rs10486567) are highly associated with familial multiple PrCa, and the gene-gene interaction or statistical epistasis was consistent with the Fisher's multiplicative model. These loci's association and epistasis were observed for non-aggressive but not for aggressive tumors. The proposed statistical model can be further developed to accommodate multi-loci interactions to provide further insights into epistasis.Public Library of Science open acces

Cluster Headache Genomewide Association Study and Meta-Analysis Identifies Eight Loci and Implicates Smoking as Causal Risk Factor

Objective: The objective of this study was to aggregate data for the first genomewide association study meta-analysis of cluster headache, to identify genetic risk variants, and gain biological insights. Methods: A total of 4,777 cases (3,348 men and 1,429 women) with clinically diagnosed cluster headache were recruited from 10 European and 1 East Asian cohorts. We first performed an inverse-variance genomewide association meta-analysis of 4,043 cases and 21,729 controls of European ancestry. In a secondary trans-ancestry meta-analysis, we included 734 cases and 9,846 controls of East Asian ancestry. Candidate causal genes were prioritized by 5 complementary methods: expression quantitative trait loci, transcriptome-wide association, fine-mapping of causal gene sets, genetically driven DNA methylation, and effects on protein structure. Gene set and tissue enrichment analyses, genetic correlation, genetic risk score analysis, and Mendelian randomization were part of the downstream analyses. Results: The estimated single nucleotide polymorphism (SNP)-based heritability of cluster headache was 14.5%. We identified 9 independent signals in 7 genomewide significant loci in the primary meta-analysis, and one additional locus in the trans-ethnic meta-analysis. Five of the loci were previously known. The 20 genes prioritized as potentially causal for cluster headache showed enrichment to artery and brain tissue. Cluster headache was genetically correlated with cigarette smoking, risk-taking behavior, attention deficit hyperactivity disorder (ADHD), depression, and musculoskeletal pain. Mendelian randomization analysis indicated a causal effect of cigarette smoking intensity on cluster headache. Three of the identified loci were shared with migraine. Interpretation: This first genomewide association study meta-analysis gives clues to the biological basis of cluster headache and indicates that smoking is a causal risk factor

Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function.

Reduced glomerular filtration rate defines chronic kidney disease and is associated with cardiovascular and all-cause mortality. We conducted a meta-analysis of genome-wide association studies for estimated glomerular filtration rate (eGFR), combining data across 133,413 individuals with replication in up to 42,166 individuals. We identify 24 new and confirm 29 previously identified loci. Of these 53 loci, 19 associate with eGFR among individuals with diabetes. Using bioinformatics, we show that identified genes at eGFR loci are enriched for expression in kidney tissues and in pathways relevant for kidney development and transmembrane transporter activity, kidney structure, and regulation of glucose metabolism. Chromatin state mapping and DNase I hypersensitivity analyses across adult tissues demonstrate preferential mapping of associated variants to regulatory regions in kidney but not extra-renal tissues. These findings suggest that genetic determinants of eGFR are mediated largely through direct effects within the kidney and highlight important cell types and biological pathways

Engaging women in computer science and engineering: Insights from a national study of undergraduate research experiences

At UCLA, the Center for Embedded Network Sensing (CENS) in the School of Engineering received NSF funding for a unique project titled: Women @ CENS, created to explore issues of gender equity in engineering and computer science (ECS) undergraduate research internship programs. The Women @ CENS project includes two studies: 1) an evaluation of our own CENS REU program and 2) a national study of REUs in ECS. The goals of these studies were to learn about promising practices in addressing gender equity in the REU setting from our own summer internship program, and to learn about what other REUs were doing in regards to promoting gender equity such that more women will choose to pursue advanced degrees and faculty careers in ECS. Study One utilizes the evaluation of the CENS REU over four program years to understand what has and has not worked for our female students in particular. For Study Two, we surveyed program directors of NSF funded Computer Science and Engineering REUs nationwide about espoused program goals, practices, participant demographics, and in particular, specific efforts designed to address gender inequity in these fields

Engaging women in computer science and engineering: Insights from a national study of undergraduate research experiences

At UCLA, the Center for Embedded Network Sensing (CENS) in the School of Engineering received NSF funding for a unique project titled: Women @ CENS, created to explore issues of gender equity in engineering and computer science (ECS) undergraduate research internship programs. The Women @ CENS project includes two studies: 1) an evaluation of our own CENS REU program and 2) a national study of REUs in ECS. The goals of these studies were to learn about promising practices in addressing gender equity in the REU setting from our own summer internship program, and to learn about what other REUs were doing in regards to promoting gender equity such that more women will choose to pursue advanced degrees and faculty careers in ECS. Study One utilizes the evaluation of the CENS REU over four program years to understand what has and has not worked for our female students in particular. For Study Two, we surveyed program directors of NSF funded Computer Science and Engineering REUs nationwide about espoused program goals, practices, participant demographics, and in particular, specific efforts designed to address gender inequity in these fields

EDU 2: Diversity at CENS

At CENS, our diversity initiatives continue to gain momentum. We have developed several programs designed to increase the number of diverse students pursuing advanced degrees in CENS related fields, particularly women, underrepresented minorities, and US citizens. To that end, we have developed a number of key projects to help establish a pipeline to graduate school and CENS research. Our Women@CENS program is designed to promote gender equity by focusing on our summer internship program and research promising practices of undergraduate research programs nationally. We have also established collaboration with other STCs, partnering institutions and local colleges and organizations that focus on serving underrepresented students. These programs are designed not only to help us provide services and opportunities that support gender equity and underrepresented minorities but also to identify promising diversity practices that can be broadly shared