Modelling survival : exposure pattern, species sensitivity and uncertainty

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans

Cardiovascular Disease Risk Estimation for Transgender and Gender-Diverse Patients: Cross-Sectional Analysis of Baseline Data From the LITE Plus Cohort Study

Introduction: Approximately 2% of the U.S. population identifies as transgender, and transgender people experience disproportionate rates of cardiovascular disease mortality. However, widely used cardiovascular disease risk estimators have not been validated in this population. This study sought to determine the impact on statin therapy recommendations using 3 different approaches to operationalizing sex in the American Health Association/American College of Cardiology Pooled Cohort Equation Risk Estimator. Methods: This is a cross-sectional analysis of baseline clinical data from LITE Plus, a prospective cohort study of Black and/or Latina transgender women with HIV. Data were collected from October 2020 to June 2022 and used to calculate Pooled Cohort Equation scores. Results: The 102 participants had a mean age of 43 years. A total of 88% were Black, and 18% were Latina. A total of 79% were taking gender-affirming hormones. The average Pooled Cohort Equation risk score was 6% when sex assigned at birth was used and statins would be recommended for the 31% with Pooled Cohort Equation >7.5%. The average risk score was 4%, and 18% met the criteria for statin initiation when current gender was used; the mean risk score was 5%, and 22% met the criteria for statin initiation when current hormone therapy was used. Conclusions: Average Pooled Cohort Equation risk scores vary substantially depending on the approach to operationalizing the sex variable, suggesting that widely used cardiovascular risk estimators may be unreliable predictors of cardiovascular disease risk in transgender populations. Collection of sex, gender, and hormone use in longitudinal studies of cardiovascular health is needed to address this important limitation of current risk estimators