Characterizing the mental health and functioning of Canadian respiratory therapists during the COVID-19 pandemic.

Introduction: Healthcare professionals (HCPs) appear to be at increased risk for negative psychological outcomes [e.g. depression, anxiety, post-traumatic stress disorder (PTSD), moral distress] and associated impacts on functioning throughout the COVID-19 pandemic. HCPs working on designated COVID-19 units may be further impacted than their colleagues not on these units given added demands of patient care and risk of contracting COVID-19. Little is known, however, about the mental health and functioning of specific professional groups beyond nurses and physicians, including respiratory therapists (RTs), over the course of the pandemic. Accordingly, the purpose of the present study was to characterize the mental health and functioning of Canadian RTs and compare profiles between RTs working on and off designated COVID-19 units.Methods: Canadian RTs completed an online survey between February and June 2021, including demographic information (e.g. age, sex, gender,) and measures of depression, anxiety, stress, PTSD, moral distress and functional impairment. Descriptive statistics, correlation analyses and between-groups comparisons were conducted to characterize RTs and compare profiles between those on and off COVID-19 units.Results: Two hundred and eighteen (N = 218) RTs participated in this study. The estimated response rate was relatively low (6.2%) Approximately half of the sample endorsed clinically relevant symptoms of depression (52%), anxiety (51%) and stress (54%) and one in three (33%) screened positively for potential PTSD. All symptoms correlated positively with functional impairment (p's < .05). RTs working on COVID-19 units reported significantly greater patient-related moral distress compared to those not on these units (p < .05).Conclusion: Moral distress and symptoms of depression, anxiety, stress and PTSD were prevalent among Canadian RTs and were associated with functional impacts. These results must be interpreted with caution given a low response rate, yet raise concern regarding the long-term impacts of pandemic service among RTs.

Research on RTs' mental health prior to and during the COVID-19 pandemic is scant, especially in comparison to other HCPs.RTs in the present study reported experiencing moral distress and clinically significant symptoms of depression, anxiety and PTSD, with associated functional impairment.One in three RTs screened positive for likely PTSD on the PCL-5.There is a need to provide RTs with adequate mental health supports and to understand the long-term impacts of pandemic service among RTs.

Identification of new susceptibility loci for type 2 diabetes and shared etiological pathways with coronary heart disease.

To evaluate the shared genetic etiology of type 2 diabetes (T2D) and coronary heart disease (CHD), we conducted a genome-wide, multi-ancestry study of genetic variation for both diseases in up to 265,678 subjects for T2D and 260,365 subjects for CHD. We identify 16 previously unreported loci for T2D and 1 locus for CHD, including a new T2D association at a missense variant in HLA-DRB5 (odds ratio (OR) = 1.29). We show that genetically mediated increase in T2D risk also confers higher CHD risk. Joint T2D-CHD analysis identified eight variants-two of which are coding-where T2D and CHD associations appear to colocalize, including a new joint T2D-CHD association at the CCDC92 locus that also replicated for T2D. The variants associated with both outcomes implicate new pathways as well as targets of existing drugs, including icosapent ethyl and adipocyte fatty-acid-binding protein.

Impact of a Genetic Risk Score on Myocardial Infarction Risk Across Different Ethnic Populations.

BACKGROUND: Myocardial infarction (MI) risk varies by ethnicity, although the influence of genetic factors remains unclear. Using a genetic risk score (GRS), we examined the association between 25 coronary artery disease (CAD)-related single nucleotide polymorphisms and MI across 6 ethnic groups. METHODS: We studied 8556 participants in the INTERHEART case-control study from 6 ethnic groups: Europeans, South Asians, Southeast Asians, Arabs, Latin Americans, and Africans. Associations between the GRS and MI were tested in each group by logistic regression and overall by meta-analysis. RESULTS: Overall, the GRS increased the odds of MI by 1.07 (95% confidence interval [CI], 1.04-1.09) per risk allele in the unadjusted model, with little change (odds ratio, 1.06; 95% CI, 1.04-1.09) after adjusting for demographic and modifiable factors. In Europeans, South Asians, Southeast Asians, and Arabs, the GRS was significantly associated with MI, with minimal heterogeneity observed. In these groups, a score > 23 risk alleles (highest 4 quintiles) was associated with only a 5% difference in population attributable risk (PAR) (36% to 41%) for MI. The GRS was not significant in Latin Americans or Africans. In the overall cohort, modest changes, beyond clinical factors, in PAR (88% to 91%), concordance statistic (0.73 to 0.74), and continuous net reclassification improvement (12%) were observed with the GRS. CONCLUSIONS: A CAD GRS is associated with MI across a multiethnic cohort, with significant and consistent effects across 4 distinct ethnicities. However, it only modestly improves MI risk prediction beyond clinical factors.

Contribution of large region joint associations to complex traits genetics.

A polygenic model of inheritance, whereby hundreds or thousands of weakly associated variants contribute to a trait's heritability, has been proposed to underlie the genetic architecture of complex traits. However, relatively few genetic variants have been positively identified so far and they collectively explain only a small fraction of the predicted heritability. We hypothesized that joint association of multiple weakly associated variants over large chromosomal regions contributes to complex traits variance. Confirmation of such regional associations can help identify new loci and lead to a better understanding of known ones. To test this hypothesis, we first characterized the ability of commonly used genetic association models to identify large region joint associations. Through theoretical derivation and simulation, we showed that multivariate linear models where multiple SNPs are included as independent predictors have the most favorable association profile. Based on these results, we tested for large region association with height in 3,740 European participants from the Health and Retirement Study (HRS) study. Adjusting for SNPs with known association with height, we demonstrated clustering of weak associations (p = 2x10-4) in regions extending up to 433.0 Kb from known height loci. The contribution of regional associations to phenotypic variance was estimated at 0.172 (95% CI 0.063-0.279; p < 0.001), which compared favorably to 0.129 explained by known height variants. Conversely, we showed that suggestively associated regions are enriched for known height loci. To extend our findings to other traits, we also tested BMI, HDLc and CRP for large region associations, with consistent results for CRP. Our results demonstrate the presence of large region joint associations and suggest these can be used to pinpoint weakly associated SNPs.

Meta-analysis of SNPs involved in variance heterogeneity using Levene's test for equal variances.

Meta-analysis is a commonly used approach to increase the sample size for genome-wide association searches when individual studies are otherwise underpowered. Here, we present a meta-analysis procedure to estimate the heterogeneity of the quantitative trait variance attributable to genetic variants using Levene's test without needing to exchange individual-level data. The meta-analysis of Levene's test offers the opportunity to combine the considerable sample size of a genome-wide meta-analysis to identify the genetic basis of phenotypic variability and to prioritize single-nucleotide polymorphisms (SNPs) for gene-gene and gene-environment interactions. The use of Levene's test has several advantages, including robustness to departure from the normality assumption, freedom from the influence of the main effects of SNPs, and no assumption of an additive genetic model. We conducted a meta-analysis of the log-transformed body mass index of 5892 individuals and identified a variant with a highly suggestive Levene's test P-value of 4.28E-06 near the NEGR1 locus known to be associated with extreme obesity.