Introduction: The study of Alzheimer's disease (AD) has revealed biological pathways with implications for disease neuropathology and pathophysiology. These pathway‐level effects may also be mediated by individual characteristics or covariates such as age or sex. Evaluation of AD biological pathways in the context of interactions with these covariates is critical to the understanding of AD as well as the development of model systems used to study the disease. Methods: Gene set enrichment methods are powerful tools used to interpret gene‐level statistics at the level of biological pathways. We introduce a method for quantifying gene set enrichment using likelihood ratio‐derived test statistics (gsLRT), which accounts for sample covariates like age and sex. We then use our method to test for age and sex interactions with protein expression levels in AD and to compare the pathway results between human and mouse species. Results: Our method, based on nested logistic regressions is competitive with the existing standard for gene set testing in the context of linear models and complex experimental design. The gene sets we identify as having a significant association with AD—both with and without additional covariate interactions—are validated by previous studies. Differences between gsLRT results on mouse and human datasets are observed. Discussion: Characterizing biological pathways involved in AD builds on the important work involving single gene drivers. Our gene set enrichment method finds pathways that are significantly related to AD while accounting for covariates that may be relevant to disease development. The method highlights commonalities and differences between human AD and mouse models, which may inform the development of higher fidelity models for the study of AD.

ObjectiveShort-term ambient fine particulate matter (PM2.5) is associated with adverse cardiovascular events including myocardial infarction (MI). However, few studies have examined associations between PM2.5 and subclinical cardiomyocyte damage outside of overt cardiovascular events. Here we evaluate the impact of daily PM2.5 on cardiac troponin I, a cardiomyocyte specific biomarker of cellular damage.MethodsWe conducted a retrospective cohort study of 2924 patients identified using electronic health records from the University of North Carolina Healthcare System who had a recorded MI between 2004 and 2016. Troponin I measurements were available from 2014 to 2016, and were required to be at least 1 week away from a clinically diagnosed MI. Daily ambient PM2.5 concentrations were estimated at 1 km resolution and assigned to patient residence. Associations between log-transformed troponin I and daily PM2.5 were evaluated using distributed lag linear mixed effects models adjusted for patient demographics, socioeconomic status and meteorology.ResultsA 10 µg/m3 elevation in PM2.5 3 days before troponin I measurement was associated with 0.06 ng/mL higher troponin I (95% CI=0.004 to 0.12). In stratified models, this association was strongest in patients that were men, white and living in less urban areas. Similar associations were observed when using 2-day rolling averages and were consistently strongest when using the average exposure over the 5 days prior to troponin I measurement.ConclusionsDaily elevations in PM2.5 were associated with damage to cardiomyocytes, outside of the occurrence of an MI. Poor air quality may cause persistent damage to the cardiovascular system leading to increased risk of cardiovascular disease and adverse cardiovascular events.

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