Associations between genetic loci, environment factors and mental disorders: a genome-wide survival analysis using the UK Biobank data

It is well-accepted that both environment and genetic factors contribute to the development of mental disorders (MD). However, few genetic studies used time-to-event data analysis to identify the susceptibility genetic variants associated with MD and explore the role of environment factors in these associations. In order to detect novel genetic loci associated with MD based on the time-to-event data and identify the role of environmental factors in them, this study recruited 376,806 participants from the UK Biobank cohort. The MD outcomes (including overall MD status, anxiety, depression and substance use disorders (SUD)) were defined based on in-patient hospital, self-reported and death registry data collected in the UK Biobank. SPACOX approach was used to identify the susceptibility loci for MD using the time-to-event data of the UK Biobank cohort. And then we estimated the associations between identified candidate loci, fourteen environment factors and MD through a phenome-wide association study and mediation analysis. SPACOX identified multiple candidate loci for overall MD status, depression and SUD, such as rs139813674 (P value = 8.39 × 10–9, ZNF684) for overall MD status, rs7231178 (DCC, P value = 2.11 × 10–9) for depression, and rs10228494 (FOXP2, P value = 6.58 × 10–10) for SUD. Multiple environment factors could influence the associations between identified loci and MD, such as confide in others and felt hated. Our study identified novel candidate loci for MD, highlighting the strength of time-to-event data based genetic association studies. We also observed that multiple environment factors could influence the association between susceptibility loci and MD.

Infectious outcomes of fibrin sheath disruption in tunneled dialysis catheters

Background and objectives: Fibrin sheath (FS) formation around tunneled central venous catheters (CVC) increases the risk of catheter-related bloodstream infections due to bacterial adherence to a biofilm. We sought to investigate whether FS disruption (FSD) at the time of CVC removal or exchange affects infectious outcomes in patients with CVC-related infections. Design, setting, participants, and measurements: Retrospective cohort study of 307 adult maintenance hemodialysis patients aged 18 years or older at a single center academic-based hemodialysis program (UHN, Toronto) who developed CVC-related infections requiring CVC removal or exchange between January 2000 and January 2019. Exposure was FSD at the time of CVC removal or exchange. Outcomes were infectious metastatic complications, recurrent infection with the same organism within 1 year, or death due to infection. We created a Markov Multi-State Model (MMSM) to assess patients’ trajectories through time as they transitioned between states. A time-to-event analysis was performed, adjusted for clinically relevant factors. Results: There was no significant relationship between FSD status at the time of CVC removal, the development of infectious complications in the multivariable model (adjusted HR = 0.71, 95% CI 0.09−5.80, p = 0.76), or mortality from infection (HR = 0.84, 95% CI 0.34−2.11, p = 0.73). Conclusions: FSD at the time of CVC removal was not associated with increased risk of infectious complications or death due to infection. Further prospective study is needed to determine whether FSD contributes to reducing CVC infectious related complications.

Vehicle Interaction Behavior Prediction with Self-Attention

The structured road is a scene with high interaction between vehicles, but due to the high uncertainty of behavior, the prediction of vehicle interaction behavior is still a challenge. This prediction is significant for controlling the ego-vehicle. We propose an interaction behavior prediction model based on vehicle cluster (VC) by self-attention (VC-Attention) to improve the prediction performance. Firstly, a five-vehicle based cluster structure is designed to extract the interactive features between ego-vehicle and target vehicle, such as Deceleration Rate to Avoid a Crash (DRAC) and the lane gap. In addition, the proposed model utilizes the sliding window algorithm to extract VC behavior information. Then the temporal characteristics of the three interactive features mentioned above will be caught by two layers of self-attention encoder with six heads respectively. Finally, target vehicle’s future behavior will be predicted by a sub-network consists of a fully connected layer and SoftMax module. The experimental results show that this method has achieved accuracy, precision, recall, and F1 score of more than 92% and time to event of 2.9 s on a Next Generation Simulation (NGSIM) dataset. It accurately predicts the interactive behaviors in class-imbalance prediction and adapts to various driving scenarios.

A Composite Ranking of Risk Factors for COVID-19 Time-To-Event Data from a Turkish Cohort

Having a complete and reliable list of risk factors from routine laboratory blood test for COVID-19 disease severity and mortality is important for patient care and hospital management. It is common to use meta-analysis to combine analysis results from different studies to make it more reproducible. In this paper, we propose to run multiple analyses on the same set of data to produce a more robust list of risk factors. With our time-to-event survival data, the standard survival analysis were extended in three directions. The first is to extend from tests and corresponding p-values to machine learning and their prediction performance. The second is to extend from single-variable to multiple-variable analysis. The third is to expand from analyzing time-to-decease data with death as the event of interest to analyzing time-to-hospital-release data to treat early recover as a meaningful event as well. Our extension of the type of analyses leads to ten ranking lists. We conclude that 20 out of 30 factors are deemed to be reliably associated to faster-death or faster-recovery. Considering correlation among factors and evidenced by stepwise variable selection in random survival forest, 10~15 factors seem to be able to achieve the optimal prognosis performance. Our final list of risk factors contains calcium, white blood cell and neutrophils count, urea and creatine, d-dimer, red cell distribution widths, age, ferritin, glucose, lactate dehydrogenase, lymphocyte, basophils, anemia related factors (hemoglobin, hematocrit, mean corpuscular hemoglobin concentration), sodium, potassium, eosinophils, and aspartate aminotransferase.

Comparison of infectious SARS-CoV-2 from the nasopharynx of vaccinated and unvaccinated individuals

The frequency of SARS-CoV-2 breakthrough infections in fully vaccinated individuals increased with the emergence of the Delta variant, particularly with longer time from vaccine completion. However, whether breakthrough infections lead to onward transmission remains unclear. Here, we conducted a study involving 125 patients comprised of 72 vaccinated and 53 unvaccinated individuals, to assess the levels of infectious virus in in vaccinated and unvaccinated individuals. Quantitative plaque assays showed no significant differences in the titers of virus between these cohorts. However, the proportion of nasopharyngeal samples with culturable virus was lower in the vaccinated patients relative to unvaccinated patients (21% vs. 40%). Finally, time-to-event analysis with Kaplan-Myer curves revealed that protection from culturable infectious virus waned significantly starting at 5 months after completing a 2-dose regimen of mRNA vaccines. These results have important implications in timing of booster dose to prevent onward transmission from breakthrough cases.

Latent class analysis of 216 patients with adult-onset Still’s disease

Background Adult-onset Still’s disease (AOSD) is a rare systemic autoinflammatory disease which encompasses patients with heterogenous presentation and a wide range of clinical courses. In this study, we aimed to identify potential subgroups of AOSD and reveal risk factors for relapse. Methods We included a total of 216 AOSD patients who received treatment in nine hospitals between 2000 and 2019. All patients fulfilled the Yamaguchi classification criteria. We retrospectively collected information about baseline characteristics, laboratory tests, treatment, relapse, and death. We performed latent class analysis and time-to-event analysis for relapse using the Cox proportional hazard model. Results The median age at disease onset was 51.6 years. The median follow-up period was 36.8 months. At disease onset, 22.3% of the patients had macrophage activation syndrome. The median white blood cell count was 12,600/μL, and the median serum ferritin level was 7230 ng/mL. Systemic corticosteroids were administered in all but three patients (98.6%) and the median initial dosage of prednisolone was 40mg/day. Ninety-six patients (44.4%) were treated with concomitant immunosuppressants, and 22 (10.2%) were treated with biologics. Latent class analysis revealed that AOSD patients were divided into two subgroups: the typical group (Class 1: 71.8%) and the elderly-onset group (Class 2: 28.2%). During the follow-up period, 13 of 216 patients (6.0%) died (12 infections and one senility), and 76 of 216 patients (35.1%) experienced relapses. Overall and relapse-free survival rates at 5 years were 94.9% and 57.3%, respectively, and those rates were not significantly different between Class 1 and 2 (p=0.30 and p=0.19). Time-to-event analysis suggested higher neutrophil count, lower hemoglobin, and age ≥65 years at disease onset as risk factors for death and age ≥65 years at disease onset as a risk factor for relapse. Conclusions AOSD patients were divided into two subgroups: the typical group and the elderly-onset group. Although the survival of patients with AOSD was generally good, the patients often experienced relapses. Age ≥65 years at disease onset was the risk factor for relapse.