Quantifying lead-attributable cardiovascular disease burden in the United States.

Lead exposure has been linked to a myriad of cardiovascular diseases. Utilizing data from the 2019 Global Burden of Disease Study, we quantified age-standardized lead exposure-related mortality and disability-adjusted life years (DALYs) in the United States between 1990 and 2019. Our analysis revealed a substantial reduction in age-standardized cardiovascular disease (CVD) mortality attributable to lead exposure by 60 % (from 7.4 to 2.9 per 100,000), along with a concurrent decrease in age-standardized CVD DALYs by 66 % (from 143.2 to 48.7 per 100,000).

Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images.

Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.

Historical Neighborhood Redlining and Contemporary Environmental Racism.

To stabilize the housing market during the great depression, the government-sanctioned Home Owners' Loan Corporation (HOLC) created color coded maps of nearly 200 United States cities according to lending risk. These maps were largely driven by racial segregation, with the worst graded neighborhoods colored in red, later termed redlined neighborhoods. We sought to investigate the association between historical redlining, and trends in environmental disparities across the US over the past few decades. We characterized environmental exposures including air pollutants (e.g., NO2 and fine particulate matter), vegetation, noise, and light at night, proximity hazardous emission sources (e.g., hazardous water facilities, wastewater discharge indicator) and other environmental and social indicators harnessed from various sources across HOLC graded neighborhoods and extrapolated census tracts (A [lowest risk neighborhoods] to D [highest risk neighborhoods]). Lower graded areas (C and D) had consistently higher exposures to worse environmental factors. Additionally, there were consistent relative disparities in the exposures to PM2.5 (1981-2018) and NO2 (2005-2019), without significant improvement in the gap compared with HOLC grade A neighborhoods. Our findings illustrate that historical redlining, a form of residential segregation largely based on racial discrimination is associated with environmental injustice over the past 2-4 decades.

The Exposome and Cardiovascular Health.

The study of the interplay between social factors, environmental hazards, and health has garnered much attention in recent years. The term "exposome" was coined to describe the total impact of environmental exposures on an individual's health and well-being, serving as a complementary concept to the genome. Studies have shown a strong correlation between the exposome and cardiovascular health, with various components of the exposome having been implicated in the development and progression of cardiovascular disease. These components include the natural and built environment, air pollution, diet, physical activity, and psychosocial stress, among others. This review provides an overview of the relationship between the exposome and cardiovascular health, highlighting the epidemiologic and mechanistic evidence of environmental exposures on cardiovascular disease. The interplay between various environmental components is discussed, and potential avenues for mitigation are identified.

Pericoronary Adipose Tissue Radiomics from Coronary Computed Tomography Angiography Identifies Vulnerable Plaques.

Pericoronary adipose tissue (PCAT) features on Computed Tomography (CT) have been shown to reflect local inflammation and increased cardiovascular risk. Our goal was to determine whether PCAT radiomics extracted from coronary CT angiography (CCTA) images are associated with intravascular optical coherence tomography (IVOCT)-identified vulnerable-plaque characteristics (e.g., microchannels (MC) and thin-cap fibroatheroma (TCFA)). The CCTA and IVOCT images of 30 lesions from 25 patients were registered. The vessels with vulnerable plaques were identified from the registered IVOCT images. The PCAT-radiomics features were extracted from the CCTA images for the lesion region of interest (PCAT-LOI) and the entire vessel (PCAT-Vessel). We extracted 1356 radiomic features, including intensity (first-order), shape, and texture features. The features were reduced using standard approaches (e.g., high feature correlation). Using stratified three-fold cross-validation with 1000 repeats, we determined the ability of PCAT-radiomics features from CCTA to predict IVOCT vulnerable-plaque characteristics. In the identification of TCFA lesions, the PCAT-LOI and PCAT-Vessel radiomics models performed comparably (Area Under the Curve (AUC) ± standard deviation 0.78 ± 0.13, 0.77 ± 0.14). For the identification of MC lesions, the PCAT-Vessel radiomics model (0.89 ± 0.09) was moderately better associated than the PCAT-LOI model (0.83 ± 0.12). In addition, both the PCAT-LOI and the PCAT-Vessel radiomics model identified coronary vessels thought to be highly vulnerable to a similar standard (i.e., both TCFA and MC; 0.88 ± 0.10, 0.91 ± 0.09). The most favorable radiomic features tended to be those describing the texture and size of the PCAT. The application of PCAT radiomics can identify coronary vessels with TCFA or MC, consistent with IVOCT. Furthermore, the use of CCTA radiomics may improve risk stratification by noninvasively detecting vulnerable-plaque characteristics that are only visible with IVOCT.

Pericoronary adipose tissue radiomics from coronary CT angiography identifies vulnerable plaques characteristics in intravascular OCT.

Pericoronary adipose tissue (PCAT) features on CT have been shown to reflect local inflammation, and signals increased cardiovascular risk. Our goal was to determine if PCAT radiomics extracted from coronary CT angiography (CCTA) images are associated with intravascular optical coherence tomography (IVOCT)-identified vulnerable plaque characteristics (e.g., microchannels [MC] and thin-cap fibroatheroma [TCFA]). CCTA and IVOCT images of 30 lesions from 25 patients were registered. Vessels with vulnerable plaques were identified from the registered IVOCT images. PCAT radiomics features were extracted from CCTA images for the lesion region of interest (PCAT-LOI) and the entire vessel (PCAT-Vessel). We extracted 1356 radiomics features, including intensity (first-order), shape, and texture features. Features were reduced using standard approaches (e.g., high feature correlation). Using stratified three-fold cross-validation with 1000 repeats, we determined the ability of PCAT radiomics features from CCTA to predict IVOCT vulnerable plaque characteristics. In identification of TCFA lesions, PCAT-LOI and PCAT-Vessel radiomics models performed comparably (AUC±standard deviation 0.78±0.13, 0.77±0.14). For identification of MC lesions, PCAT-Vessel radiomics model (0.89±0.09) was moderately better associated than that of PCAT-LOI model (0.83±0.12). Both PCAT-LOI and PCAT-Vessel radiomics models also similarly identified coronary vessels thought to be highly vulnerable (i.e., both TCFA and MC) (0.88±0.10, 0.91±0.09). Favorable radiomics features tended to be those describing texture and size of PCAT. PCAT radiomics can identify coronary vessels with TCFA or MC, consistent with IVOCT. CCTA radiomics may improve risk stratification by noninvasively detecting vulnerable plaque characteristics that are only visible with IVOCT.

Neighborhood Walkability and Cardiovascular Risk in the United States.

Neighborhood walkability may be associated with increased physical activity and thus may confer protection against cardiovascular disease and associated risk factors. We sought to characterize the association between neighborhood-level cardiovascular diseases and risk factors with neighborhood walkability across US census tracts.We linked the Centers for Disease Control and Prevention (CDC) PLACES dataset which provided census-tract level prevalence of coronary artery disease (CAD) and cardiovascular risk factors (hypertension, high cholesterol, obesity, and diabetes), with census tract population-weighted national walkability index (NWI) from the US Environmental Protection Agency (EPA). We calculated the mean prevalence of each cardiovascular health indicator per quartile of the walkability score. We also fit a multivariable linear regression model to estimate the association between walkability index and the prevalence of CAD adjusting for age, sex, race, and the CDC'S social vulnerability index, an integrated metric of socioeconomic position. We additionally performed mediation analyses to understand the mediating effects of CAD risk factors on the relationship between NWI and CAD prevalence. A total of 70,123 census tracts were analyzed nationwide. Across walkability quartiles Q1 (least walkable) through Q4 (most walkable), we found statistically significant decrease in the prevalence of CAD (7.0% to 5.4%), and risk factors including hypertension (35.5% to 29.7%), high cholesterol (34.5% to 29.2%), obesity (35.0% to 30.2%), and diabetes (11.6% to 10.6%). After multivariable adjustment, continuous walkability index was negatively and significantly associated with the prevalence of CAD (ß = -0.09, P<0.0001). The relationship between NWI and CAD is partially mediated by the risk factors. High cholesterol accounted for 45%, high blood pressure 41% and diabetes 10% of the total effect of walkability on CAD. While direct relationship between walkability and CAD accounted for 9% of the total effect. This nationwide analysis demonstrates that neighborhood walkability is associated with a lower prevalence of cardiovascular risk factors and CAD. The association between NWI and CAD seems to be partly mediated by prevalence of traditional risk factors.