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).

Cardiovascular disease in Arab Americans: A literature review of prevalence, risk factors, and directions for future research

Cardiovascular disease (CVD) is the leading cause of mortality worldwide. Recent evidence suggests Arab Americans, individuals with ancestry from Arabic-speaking countries, have an elevated risk for CVD compared to other ethnicities in the US. However, research focusing specifically on CVD in this population is limited. This literature review synthesizes studies investigating CVD prevalence, risk factors, and outcomes in Arab Americans. Multiple studies found higher rates of coronary heart disease, cerebrovascular disease, and hypertension compared to non-Hispanic White participants. The prevalence of type 2 diabetes, a major CVD risk factor, was also markedly higher, ranging from 16 % to 41 % in Arab Americans based on objective measures. Possible explanations include high rates of vitamin D deficiency, genetic factors, and poor diabetes control. Other metabolic factors like dyslipidemia and obesity did not consistently differ from general population estimates. Psychosocial factors may further increase CVD risk, including acculturative stress, discrimination, low health literacy, and barriers to healthcare access. Smoking, especially waterpipe use, was more prevalent in Arab American men. Though heterogenous, Arab Americans overall appear to have elevated CVD risk, warranting tailored screening and management. Culturally appropriate educational initiatives on CVD prevention are greatly needed. Future directions include better characterizing CVD prevalence across Arab American subgroups, delineating genetic and environmental factors underlying increased diabetes susceptibility, and testing culturally tailored interventions to mitigate CVD risks. In summary, this review highlights concerning CVD disparities in Arab Americans and underscores the need for group-specific research and preventive strategies.

Houston Methodist cardiovascular learning health system (CVD-LHS) registry: Methods for development and implementation of an automated electronic medical record-based registry using an informatics framework approach

Objectives

To investigate the potential value and feasibility of creating a listing system-wide registry of patients with at-risk and established Atherosclerotic Cardiovascular Disease (ASCVD) within a large healthcare system using automated data extraction methods to systematically identify burden, determinants, and the spectrum of at-risk patients to inform population health management. Additionally, the Houston Methodist Cardiovascular Disease Learning Health System (HM CVD-LHS) registry intends to create high-quality data-driven analytical insights to assess, track, and promote cardiovascular research and care.

Methods

We conducted a retrospective multi-center, cohort analysis of adult patients who were seen in the outpatient settings of a large healthcare system between June 2016 - December 2022 to create an EMR-based registry. A common framework was developed to automatically extract clinical data from the EMR and then integrate it with the social determinants of health information retrieved from external sources. Microsoft's SQL Server Management Studio was used for creating multiple Extract-Transform-Load scripts and stored procedures for collecting, cleaning, storing, monitoring, reviewing, auto-updating, validating, and reporting the data based on the registry goals.

Results

A real-time, programmatically deidentified, auto-updated EMR-based HM CVD-LHS registry was developed with ∼450 variables stored in multiple tables each containing information related to patient's demographics, encounters, diagnoses, vitals, labs, medication use, and comorbidities. Out of 1,171,768 adult individuals in the registry, 113,022 (9.6%) ASCVD patients were identified between June 2016 and December 2022 (mean age was 69.2 ± 12.2 years, with 55% Men and 15% Black individuals). Further, multi-level groupings of patients with laboratory test results and medication use have been analyzed for evaluating the outcomes of interest.

Conclusions

HM CVD-LHS registry database was developed successfully providing the listing registry of patients with established ASCVD and those at risk. This approach empowers knowledge inference and provides support for efforts to move away from manual patient chart abstraction by suggesting that a common registry framework with a concurrent design of data collection tools and reporting rapidly extracting useful structured clinical data from EMRs for creating patient or specialty population registries.

Enhancing cardiovascular risk prediction through AI-enabled calcium-omics

Whole-heart coronary calcium Agatston score is a well-established predictor of major adverse cardiovascular events (MACE), but it does not account for individual calcification features related to the pathophysiology of the disease (e.g., multiple-vessel disease, spread of the disease along the vessel, stable calcifications, numbers of lesions, and density). We used novel, hand-crafted calcification features (calcium-omics); Cox time-to-event modeling; elastic net; and up and down synthetic sampling methods for imbalanced data, to assess MACE risk. We used 2457 CT calcium score (CTCS) images enriched for MACE events from our large no-cost CLARIFY program (ClinicalTrials.gov Identifier: NCT04075162). Among calcium-omics features, numbers of calcifications, LAD mass, and diffusivity (a measure of spatial distribution) were especially important determinants of increased risk, with dense calcification (> 1000HU, stable calcifications) associated with reduced risk Our calcium-omics model with (training/testing, 80/20) gave C-index (80.5%/71.6%) and 2-year AUC (82.4%/74.8%). Although the C-index is notoriously impervious to model improvements, calcium-omics compared favorably to Agatston and gave a significant difference (P < 0.001). The calcium-omics model identified 73.5% of MACE cases in the high-risk group, a 13.2% improvement as compared to Agatston, suggesting that calcium-omics could be used to better identity candidates for intensive follow-up and therapies. The categorical net-reclassification index was NRI = 0.153. Our findings from this exploratory study suggest the utility of calcium-omics in improved risk prediction. These promising results will pave the way for more extensive, multi-institutional studies of calcium-omics.

Artificial intelligence-based assessment of built environment from Google Street View and coronary artery disease prevalence

BACKGROUND AND AIMS

Built environment plays an important role in the development of cardiovascular disease. Tools to evaluate the built environment using machine vision and informatic approaches have been limited. This study aimed to investigate the association between machine vision-based built environment and prevalence of cardiometabolic disease in US cities.

METHODS

This cross-sectional study used features extracted from Google Street View (GSV) images to measure the built environment and link them with prevalence of coronary heart disease (CHD). Convolutional neural networks, linear mixed-effects models, and activation maps were utilized to predict health outcomes and identify feature associations with CHD at the census tract level. The study obtained 0.53 million GSV images covering 789 census tracts in seven US cities (Cleveland, OH; Fremont, CA; Kansas City, MO; Detroit, MI; Bellevue, WA; Brownsville, TX; and Denver, CO).

RESULTS

Built environment features extracted from GSV using deep learning predicted 63% of the census tract variation in CHD prevalence. The addition of GSV features improved a model that only included census tract-level age, sex, race, income, and education or composite indices of social determinant of health. Activation maps from the features revealed a set of neighbourhood features represented by buildings and roads associated with CHD prevalence.

CONCLUSIONS

In this cross-sectional study, the prevalence of CHD was associated with built environment factors derived from GSV through deep learning analysis, independent of census tract demographics. Machine vision-enabled assessment of the built environment could potentially offer a more precise approach to identify at-risk neighbourhoods, thereby providing an efficient avenue to address and reduce cardiovascular health disparities in urban environments.

County-level socio-environmental factors and obesity prevalence in the United States

AIMS

To investigate high-risk sociodemographic and environmental determinants of health (SEDH) potentially associated with adult obesity in counties in the United States using machine-learning techniques.

MATERIALS AND METHODS

We performed a cross-sectional analysis of county-level adult obesity prevalence (body mass index ≥30 kg/m2) in the United States using data from the Diabetes Surveillance System 2017. We harvested 49 county-level SEDH factors that were used in a classification and regression trees (CART) model to identify county-level clusters. The CART model was validated using a 'hold-out' set of counties and variable importance was evaluated using Random Forest.

RESULTS

Overall, we analysed 2752 counties in the United States, identifying a national median (interquartile range) obesity prevalence of 34.1% (30.2%, 37.7%). The CART method identified 11 clusters with a 60.8% relative increase in prevalence across the spectrum. Additionally, seven key SEDH variables were identified by CART to guide the categorization of clusters, including Physically Inactive (%), Diabetes (%), Severe Housing Problems (%), Food Insecurity (%), Uninsured (%), Population over 65 years (%) and Non-Hispanic Black (%).

CONCLUSION

There is significant county-level geographical variation in obesity prevalence in the United States, which can in part be explained by complex SEDH factors. The use of machine-learning techniques to analyse these factors can provide valuable insights into the importance of these upstream determinants of obesity and, therefore, aid in the development of geo-specific strategic interventions and optimize resource allocation to help battle the obesity pandemic.

Mapping Proximity to Infectious Disease Physicians Across the United States

Enduring shortages of infectious disease physicians across the United States continue despite efforts to mitigate the problem. The recent fellowship match results underscore the difficulty in rectifying that shortage. Our report sheds light on the current geographic distribution of US infectious disease physicians and highlights the challenges faced by rural communities.

Deep Learning-Based Assessment of Built Environment From Satellite Images and Cardiometabolic Disease Prevalence

Importance

Built environment plays an important role in development of cardiovascular disease. Large scale, pragmatic evaluation of built environment has been limited owing to scarce data and inconsistent data quality.

Objective

To investigate the association between image-based built environment and the prevalence of cardiometabolic disease in urban cities.

Design, Setting, and Participants

This cross-sectional study used features extracted from Google satellite images (GSI) to measure the built environment and link them with prevalence of cardiometabolic disease. Convolutional neural networks, light gradient-boosting machines, and activation maps were used to assess the association with health outcomes and identify feature associations with coronary heart disease (CHD), stroke, and chronic kidney disease (CKD). The study obtained aerial images from GSI covering census tracts in 7 cities (Cleveland, Ohio; Fremont, California; Kansas City, Missouri; Detroit, Michigan; Bellevue, Washington; Brownsville, Texas; and Denver, Colorado). The study used census tract-level data from the US Centers for Disease Control and Prevention's 500 Cities project. The data were originally collected from the Behavioral Risk Factor Surveillance System that surveyed people 18 years and older across the country. Analyses were conducted from February to December 2022.

Exposures

GSI images of built environment and cardiometabolic disease prevalence.

Main Outcomes and Measures

Census tract-level estimated prevalence of CHD, stroke, and CKD based on image-based built environment features.

Results

The study obtained 31 786 aerial images from GSI covering 789 census tracts. Built environment features extracted from GSI using machine learning were associated with prevalence of CHD (R2 = 0.60), stroke (R2 = 0.65), and CKD (R2 = 0.64). The model performed better at distinguishing differences between cardiometabolic prevalence between cities than within cities (eg, highest within-city R2 = 0.39 vs between-city R2 = 0.64 for CKD). Addition of GSI features both outperformed and improved the model that only included age, sex, race, income, education, and composite indices for social determinants of health (R2 = 0.83 vs R2 = 0.76 for CHD; P <.001). Activation maps from the features revealed certain health-related built environment such as roads, highways, and railroads and recreational facilities such as amusement parks, arenas, and baseball parks.

Conclusions and Relevance

In this cross-sectional study, a significant portion of cardiometabolic disease prevalence was associated with GSI-based built environment using convolutional neural networks.

The Urban Environment and Cardiometabolic Health

Urban environments contribute substantially to the rising burden of cardiometabolic diseases worldwide. Cities are complex adaptive systems that continually exchange resources, shaping exposures relevant to human health such as air pollution, noise, and chemical exposures. In addition, urban infrastructure and provisioning systems influence multiple domains of health risk, including behaviors, psychological stress, pollution, and nutrition through various pathways (eg, physical inactivity, air pollution, noise, heat stress, food systems, the availability of green space, and contaminant exposures). Beyond cardiometabolic health, city design may also affect climate change through energy and material consumption that share many of the same drivers with cardiometabolic diseases. Integrated spatial planning focusing on developing sustainable compact cities could simultaneously create heart-healthy and environmentally healthy city designs. This article reviews current evidence on the associations between the urban exposome (totality of exposures a person experiences, including environmental, occupational, lifestyle, social, and psychological factors) and cardiometabolic diseases within a systems science framework, and examines urban planning principles (eg, connectivity, density, diversity of land use, destination accessibility, and distance to transit). We highlight critical knowledge gaps regarding built-environment feature thresholds for optimizing cardiometabolic health outcomes. Last, we discuss emerging models and metrics to align urban development with the dual goals of mitigating cardiometabolic diseases while reducing climate change through cross-sector collaboration, governance, and community engagement. This review demonstrates that cities represent crucial settings for implementing policies and interventions to simultaneously tackle the global epidemics of cardiovascular disease and climate change.

Association of Environmental Injustice and Cardiovascular Diseases and Risk Factors in the United States

BACKGROUND

While the impacts of social and environmental exposure on cardiovascular risks are often reported individually, the combined effect is poorly understood.

METHODS AND RESULTS

Using the 2022 Environmental Justice Index, socio-environmental justice index and environmental burden module ranks of census tracts were divided into quartiles (quartile 1, the least vulnerable census tracts; quartile 4, the most vulnerable census tracts). Age-adjusted rate ratios (RRs) of coronary artery disease, strokes, and various health measures reported in the Prevention Population-Level Analysis and Community Estimates data were compared between quartiles using multivariable Poisson regression. The quartile 4 Environmental Justice Index was associated with a higher rate of coronary artery disease (RR, 1.684 [95% CI, 1.660-1.708]) and stroke (RR, 2.112 [95% CI, 2.078-2.147]) compared with the quartile 1 Environmental Justice Index. Similarly, coronary artery disease 1.057 [95% CI,1.043-1.0716] and stroke (RR, 1.118 [95% CI, 1.102-1.135]) were significantly higher in the quartile 4 than in the quartile 1 environmental burden module. Similar results were observed for chronic kidney disease, hypertension, diabetes, obesity, high cholesterol, lack of health insurance, sleep <7 hours per night, no leisure time physical activity, and impaired mental and physical health >14 days.

CONCLUSIONS

The prevalence of CVD and its risk factors is highly associated with increased social and environmental adversities, and environmental exposure plays an important role independent of social factors.

Public Health Relevance of US EPA Air Quality Index Activity Recommendations

Importance

Reducing exposure to fine particulate matter (<2.5 μm [PM2.5]) air pollution improves cardiopulmonary morbidity and mortality. However, the public health relevance of air quality index (AQI) activity guidelines under present-day environmental conditions in the US has not been critically assessed.

Objective

To evaluate the public health relevance of following PM2.5 AQI activity guidance in preventing serious atherosclerotic cardiovascular disease (ASCVD) and pulmonary events among adults in the US.

Design, Setting, and Participants

This cross-sectional modeling study involved the general adult population and sensitive individuals as designated by the US Environmental Protection Agency (EPA), including adults with preexisting ASCVD or lung disease (asthma or chronic obstructive pulmonary disease). The study was conducted between August 1, 2023, and January 31, 2024.

Exposures

Daily AQI strata for PM2.5 and the corresponding activity recommendations.

Main Outcomes and Measures

The main outcome was the number needed to treat (NNT) per day by following activity guidance across daily AQI strata to prevent 1 serious ASCVD or pulmonary event among relevant populations. To calculate PM2.5-induced excess disease event rates per day, estimated baseline disease-specific daily event rates for each group were multiplied by the increase in risks due to PM2.5 levels at each AQI stratum. The number of events prevented per day was calculated by multiplying each excess disease event rate by the percentage in exposure reduction plausibly incurred by following population-specific activity guidance at each AQI level. The NNT is the reciprocal of the number of events prevented.

Results

The NNT to prevent ASCVD events was high for the general population and for patients with ASCVD across all AQI strata. The range of values was comparatively lower to prevent pulmonary events among adults with lung disease. During most days (96%) when activity recommendations were promulgated due to elevated PM2.5 (AQI, 101-200), the NNT to prevent a serious disease event remained very high for the general population (>18 million), patients with ASCVD (approximately 1.6-5 million), and adults with lung disease (approximately 66 000-202 000).

Conclusions and Relevance

These findings suggest that existing PM2.5 AQI activity recommendations are of questionable public health relevance in present-day conditions and merit consideration for updating to improve their potential effectiveness.

Primary Prevention of Cardiovascular Disease in Women

Cardiovascular disease (CVD) remains a leading cause of mortality in women, necessitating innovative primary prevention strategies. Contemporary guidelines on primary prevention of CVD highlight the increasing prevalence of CVD risk factors and emphasize the significance of female-specific risk enhancers that substantially augment the future risk of CVD. These risk factors occur throughout a woman's life cycle, such as hormonal contraception, hypertensive disorders of pregnancy, and menopause, all of which confer an added layer of risk in women beyond the conventional risk factors. Despite this, current methods may not fully capture the nuanced vulnerabilities in women that increase their risk of CVD. In this review, we highlight gender-specific risk enhancers and subsequent prevention as well as strategies to improve primary prevention of CVD in women.

Air pollution exposure and cardiometabolic risk

The Global Burden of Disease assessment estimates that 20% of global type 2 diabetes cases are related to chronic exposure to particulate matter (PM) with a diameter of 2·5 μm or less (PM2·5). With 99% of the global population residing in areas where air pollution levels are above current WHO air quality guidelines, and increasing concern in regard to the common drivers of air pollution and climate change, there is a compelling need to understand the connection between air pollution and cardiometabolic disease, and pathways to address this preventable risk factor. This Review provides an up to date summary of the epidemiological evidence and mechanistic underpinnings linking air pollution with cardiometabolic risk. We also outline approaches to improve awareness, and discuss personal-level, community, governmental, and policy interventions to help mitigate the growing global public health risk of air pollution exposure.

Impact of Residential Social Deprivation on Prediction of Heart Failure in Patients With Type 2 Diabetes: External Validation and Recalibration of the WATCH-DM Score Using Real World Data

BACKGROUND

Patients with type 2 diabetes are at risk of heart failure hospitalization. As social determinants of health are rarely included in risk models, we validated and recalibrated the WATCH-DM score in a diverse patient-group using their social deprivation index (SDI).

METHODS

We identified US Veterans with type 2 diabetes without heart failure that received outpatient care during 2010 at Veterans Affairs medical centers nationwide, linked them to their SDI using residential ZIP codes and grouped them as SDI <20%, 21% to 40%, 41% to 60%, 61% to 80%, and >80% (higher values represent increased deprivation). Accounting for all-cause mortality, we obtained the incidence for heart failure hospitalization at 5 years follow-up; overall and in each SDI group. We evaluated the WATCH-DM score using the C statistic, the Greenwood Nam D'Agostino test χ2 test and calibration plots and further recalibrated the WATCH-DM score for each SDI group using a statistical correction factor.

RESULTS

In 1 065 691 studied patients (mean age 67 years, 25% Black and 6% Hispanic patients), the 5-year incidence of heart failure hospitalization was 5.39%. In SDI group 1 (least deprived) and 5 (most deprived), the 5-year heart failure hospitalization was 3.18% and 11%, respectively. The score C statistic was 0.62; WATCH-DM systematically overestimated heart failure risk in SDI groups 1 to 2 (expected/observed ratios, 1.38 and 1.36, respectively) and underestimated the heart failure risk in groups 4 to 5 (expected/observed ratios, 0.95 and 0.80, respectively). Graphical evaluation demonstrated that the recalibration of WATCH-DM using an SDI group-based correction factor improved predictive capabilities as supported by reduction in the χ2 test results (801-27 in SDI groups I; 623-23 in SDI group V).

CONCLUSIONS

Including social determinants of health to recalibrate the WATCH-DM score improved risk prediction highlighting the importance of including social determinants in future clinical risk prediction models.

Food Environment Quality and Cardiovascular Disease Mortality in the United States: a County-Level Analysis from 2017 to 2019

BACKGROUND

Upstream socioeconomic circumstances including food insecurity and food desert are important drivers of community-level health disparities in cardiovascular mortality let alone traditional risk factors. The study assessed the association between differences in food environment quality and cardiovascular mortality in US adults.

DESIGN

Retrospective analysis of the association between cardiovascular mortality among US adults aged 45 and above and food environment quality, measured as the food environment index (FEI), in 2615 US counties. FEI was measured by equal weights of food insecurity (limited access to a reliable food source) and food desert (limited access to healthy food), ranging from 0 (worst) to 10 (best). Age-adjusted cardiovascular mortality rates per 100,000 adults aged 45 and above in the calendar year 2017-2019. County-level association between CVD mortality rate and FEI was modeled using generalized linear regression. Data were weighted using county population.

RESULT

Median CVD deaths per 100,000 population were 645.4 (IQR 561.5, 747.0) among adults aged 45 years and above across US counties in 2017-2019. About 12.8% (IQR 10.7%, 15.1%) of residents were food insecure and 6.3% (IQR 3.6%, 9.9%) were living in food desert areas. Comparing counties by FEI quartiles, the CVD mortality rate was higher in the least healthy FE counties (704.3 vs 598.6 deaths per 100,000 population) compared to the healthiest FE counties. One unit increase in FEI was associated with - 12.95 CVD deaths/100,000 population. In the subgroup analysis of counties with higher income inequality, the healthiest food environment was associated with 46.4 lower CVD deaths/100,000 population than the least healthy food environment. One unit increase in FEI in counties with higher income inequality was associated with a fivefold decrease in CVD mortality difference in African American counties (- 18.4 deaths/100,000 population) when compared to non-African American counties (- 3.63 deaths/100,000 population).

CONCLUSION

In this retrospective multi-county study in the USA, a higher food environment index was significantly associated with lower cardiovascular mortality.

Health position paper and redox perspectives - Disease burden by transportation noise

Transportation noise is a ubiquitous urban exposure. In 2018, the World Health Organization concluded that chronic exposure to road traffic noise is a risk factor for ischemic heart disease. In contrast, they concluded that the quality of evidence for a link to other diseases was very low to moderate. Since then, several studies on the impact of noise on various diseases have been published. Also, studies investigating the mechanistic pathways underlying noise-induced health effects are emerging. We review the current evidence regarding effects of noise on health and the related disease-mechanisms. Several high-quality cohort studies consistently found road traffic noise to be associated with a higher risk of ischemic heart disease, heart failure, diabetes, and all-cause mortality. Furthermore, recent studies have indicated that road traffic and railway noise may increase the risk of diseases not commonly investigated in an environmental noise context, including breast cancer, dementia, and tinnitus. The harmful effects of noise are related to activation of a physiological stress response and nighttime sleep disturbance. Oxidative stress and inflammation downstream of stress hormone signaling and dysregulated circadian rhythms are identified as major disease-relevant pathomechanistic drivers. We discuss the role of reactive oxygen species and present results from antioxidant interventions. Lastly, we provide an overview of oxidative stress markers and adverse redox processes reported for noise-exposed animals and humans. This position paper summarizes all available epidemiological, clinical, and preclinical evidence of transportation noise as an important environmental risk factor for public health and discusses its implications on the population level.

AI prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from CT calcium score

Background

Recent studies have used basic epicardial adipose tissue (EAT) assessments (e.g., volume and mean HU) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACE).

Objectives

Create novel, hand-crafted EAT features, "fat-omics", to capture the pathophysiology of EAT and improve MACE prediction.

Methods

We segmented EAT using a previously-validated deep learning method with optional manual correction. We extracted 148 radiomic features (morphological, spatial, and intensity) and used Cox elastic-net for feature reduction and prediction of MACE.

Results

Traditional fat features gave marginal prediction (EAT-volume/EAT-mean-HU/BMI gave C-index 0.53/0.55/0.57, respectively). Significant improvement was obtained with 15 fat-omics features (C-index=0.69, test set). High-risk features included volume-of-voxels-having-elevated-HU-[-50, -30-HU] and HU-negative-skewness, both of which assess high HU, which as been implicated in fat inflammation. Other high-risk features include kurtosis-of-EAT-thickness, reflecting the heterogeneity of thicknesses, and EAT-volume-in-the-top-25%-of-the-heart, emphasizing adipose near the proximal coronary arteries. Kaplan-Meyer plots of Cox-identified, high- and low-risk patients were well separated with the median of the fat-omics risk, while high-risk group having HR 2.4 times that of the low-risk group (P<0.001).

Conclusion

Preliminary findings indicate an opportunity to use more finely tuned, explainable assessments on EAT for improved cardiovascular risk prediction.

Noise and mental health: evidence, mechanisms, and consequences

The recognition of noise exposure as a prominent environmental determinant of public health has grown substantially. While recent years have yielded a wealth of evidence linking environmental noise exposure primarily to cardiovascular ailments, our understanding of the detrimental effects of noise on the brain and mental health outcomes remains limited. Despite being a nascent research area, an increasing body of compelling research and conclusive findings confirms that exposure to noise, particularly from sources such as traffic, can potentially impact the central nervous system. These harms of noise increase the susceptibility to mental health conditions such as depression, anxiety, suicide, and behavioral problems in children and adolescents. From a mechanistic perspective, several investigations propose direct adverse phenotypic changes in brain tissue by noise (e.g. neuroinflammation, cerebral oxidative stress), in addition to feedback signaling by remote organ damage, dysregulated immune cells, and impaired circadian rhythms, which may collectively contribute to noise-dependent impairment of mental health. This concise review linking noise exposure to mental health outcomes seeks to fill research gaps by assessing current findings from studies involving both humans and animals.

Cumulative social disadvantage and cardiovascular disease burden and mortality

AIMS

To investigate the association between cumulative social disadvantage and cardiovascular burden and mortality in a large cohort of the general population.

METHODS AND RESULTS

Cross-sectional (n = 15 010, aged 35 to 74 years, baseline investigation period 2007 to 2012) and longitudinal data (5- and 10-year follow-ups from 2012 to 2022) from the Gutenberg Health Study were used to investigate the association between individual socioeconomic status (SES, measured via a validated questionnaire) and cardiovascular disease (CVD, composite of atrial fibrillation, coronary artery disease, myocardial infarction, stroke, chronic heart failure, peripheral artery disease, and/or venous thromboembolism) risk and mortality. Subjects with prevalent CVD had a lower SES sum score, as well as lower education, occupation, and household net-income scores (all P < 0.0001). Logistic regression analysis showed that a low SES (vs. high, defined by validated cut-offs) was associated with 19% higher odds of prevalent CVD [odds ratio (OR) 1.19, 95% CI 1.01; 1.40] in the fully adjusted model. At 5-year follow-up, low SES was associated with both increased cardiovascular [hazard ratio (HR) 5.36, 2.24; 12.82] and all-cause mortality (HR 2.23, 1.51; 3.31). At 10-year follow-up, low SES was associated with a 68% higher risk of incident CVD (OR 1.68, 1.12; 2.47) as well as 86% higher all-cause mortality (HR 1.86, 1.55; 2.24). In general, the education and occupation scores were stronger related to risk of CVD and death than the household net-income score. Low SES was estimated to account for 451.45 disability-adjusted life years per 1000 people (years lived with disability 373.41/1000 and years of life lost 78.03/1000) and an incidence rate of 11 CVD cases and 3.47 CVD deaths per 1000 people per year. The population attributable fraction for CVD incidence after 5 years was 4% due to low SES.

CONCLUSION

Despite universal healthcare access, cumulative social disadvantage remains associated with higher risk of CVD and mortality. Dimensions of education and occupation, but not household net income, are associated with outcomes of interest.

Synthetic multi-contrast late gadolinium enhancement imaging using post-contrast magnetic resonance fingerprinting

Late gadolinium enhancement (LGE) MRI is the non-invasive reference standard for identifying myocardial scar and fibrosis but has limitations, including difficulty delineating subendocardial scar and operator dependence on image quality. The purpose of this work is to assess the feasibility of generating multi-contrast synthetic LGE images from post-contrast T1 and T2 maps acquired using magnetic resonance fingerprinting (MRF). Fifteen consecutive patients with a history of prior ischemic cardiomyopathy (12 men; mean age 63  ±  13 years) were prospectively scanned at 1.5 T between Oct 2020 and May 2021 using conventional LGE and MRF after injection of gadolinium contrast. Three classes of synthetic LGE images were derived from MRF post-contrast T1 and T2 maps: bright-blood phase-sensitive inversion recovery (PSIR), black- and gray-blood T2 -prepared PSIR (T2 -PSIR), and a novel "tissue-optimized" image to enhance differentiation among scar, viable myocardium, and blood. Image quality was assessed on a 1-5 Likert scale by two cardiologists, and contrast was quantified as the mean absolute difference (MAD) in pixel intensities between two tissues, with different methods compared using Kruskal-Wallis with Bonferroni post hoc tests. Per-patient and per-segment scar detection rates were evaluated using conventional LGE images as reference. Image quality scores were highest for synthetic PSIR (4.0) and reference images (3.8), followed by synthetic tissue-optimized (3.3), gray-blood T2 -PSIR (3.0), and black-blood T2 -PSIR (2.6). Among synthetic images, PSIR yielded the highest myocardium/scar contrast (MAD = 0.42) but the lowest blood/scar contrast (MAD = 0.05), and vice versa for T2 -PSIR, while tissue-optimized images achieved a balance among all tissues (myocardium/scar MAD = 0.16, blood/scar MAD = 0.26, myocardium/blood MAD = 0.10). Based on reference mid-ventricular LGE scans, 13/15 patients had myocardial scar. The per-patient sensitivity/accuracy for synthetic images were the following: PSIR, 85/87%; black-blood T2 -PSIR, 62/53%; gray-blood T2 -PSIR, 100/93%; tissue optimized, 100/93%. Synthetic multi-contrast LGE images can be generated from post-contrast MRF data without additional scan time, with initial feasibility shown in ischemic cardiomyopathy patients.

Unfavorable social determinants of health and mortality risk by cardiovascular disease status: Findings from a National Study of United States Adults

BACKGROUND

The association between cumulative burden of unfavorable social determinants of health (SDoH) and all-cause mortality has not been assessed by atherosclerotic cardiovascular disease (ASCVD) status on a population level in the United States.

METHODS

We assessed the association between cumulative social disadvantage and all-cause mortality by ASCVD status in the National Health Interview Survey, linked to the National Death Index.

RESULTS

In models adjusted for established clinical risk factors, individuals experiencing the highest level of social disadvantage (SDoH-Q4) had over 1.5 (aHR = 1.55; 95%CI = 1.22, 1.96) and 2-fold (aHR = 2.21; 95% CI = 1.91, 2.56) fold increased risk of mortality relative to those with the most favorable social profile (SDoH-Q1), respectively for adults with and without ASCVD; those experiencing co-occurring ASCVD and high social disadvantage had up to four-fold higher risk of mortality (aHR = 3.81; 95%CI = 3.36, 4.32).

CONCLUSIONS

These findings emphasize the importance of a healthcare model that prioritizes efforts to identify and address key social and environmental barriers to health and wellbeing, particularly in individuals experiencing the double jeopardy of clinical and social risk.

Supporting and implementing the beneficial parts of the exposome: The environment can be the problem, but it can also be the solution

In 2005, Christopher P. Wild introduced the exposome concept, encompassing the biochemical changes in the organism in response to the totality of all environmental exposures throughout the entire lifespan and their association with health outcomes. The exposome concept also aimed at to completing the genome, that describes the genetic predisposition as a determinant of disease and death as well as potential targets of intervention. The exposome can be subdivided into multiple pollutomes related to specific chemical and physical pollutants (or other forms of environmental risks), periods of life (infancy, childhood, adolescence, adulthood, and old age) or geographical locations. While exposome research and, in general, health research of the last decades has predominantly focused on what factors contribute to and initiate morbidity and mortality, little is done on factors that will help to develop, maintain, or even increase human health. We want to contribute to this reorientation by supporting and implementing the beneficial exposome, comprising all environmental exposures with the potential to promote health.

Geographically Weighted Modeling to Explore Social and Environmental Factors Affecting County-Level Cardiovascular Mortality in People With Diabetes in the United States: A Cross-Sectional Analysis

Disparities exist in the cardiovascular mortality rates among people with type 2 diabetes (T2D). Research has established that these disparities are often related to the environmental and social determinations of health. This study explores the spatial variation between air pollution, social determinants of health and T2D related age-adjusted cardiovascular mortality (aa-CVM) in the United States. We obtained county-level T2D related to aa-CVM (per 100,000 residents) from Centers for Disease Control and Prevention WONDER (Wide-ranging Online Data for Epidemiologic Research) (2010 to 2019). We fit a geographically weighted linear regression with aa-CVM as the outcome and the following covariates (ambient air pollution [particulate matter of 2.5 µm size], median annual household income, racial/ethnic minorities, higher education, rurality, food insecurity, and primary health care access) were included. Overall, the median aa-CVM rate was 92.9 and highest in the South (102.2). In the West, aa-CVM was significantly associated with particulate matter of 2.5 µm size, annual median household income, racial minority status and primary health care access. Food insecurity was the most significant exposure in the Midwest and Northeast, while in the South, annual median household income and food insecurity were significant. In conclusion, this study demonstrated a substantial regional variation of exposure to determinants of T2D related aa-CVM in the United States. These findings should be considered in policy frameworks and interventions as part of community-level approaches to addressing T2D related aa-CVM, and within broader state and national discussions of the importance of population health.

Alzheimer`s disease mortality in the United States: Cross-sectional analysis of county-level socio-environmental factors

BACKGROUND

Geographical disparities in mortality among Alzheimer`s disease (AD) patients have been reported and complex sociodemographic and environmental determinants of health (SEDH) may be contributing to this variation. Therefore, we aimed to explore high-risk SEDH factors possibly associated with all-cause mortality in AD across US counties using machine learning (ML) methods.

METHODS

We performed a cross-sectional analysis of individuals ≥65 years with any underlying cause of death but with AD in the multiple causes of death certificate (ICD-10,G30) between 2016 and 2020. Outcomes were defined as age-adjusted all-cause mortality rates (per 100,000 people). We analyzed 50 county-level SEDH and Classification and Regression Trees (CART) was used to identify specific county-level clusters. Random Forest, another ML technique, evaluated variable importance. CART`s performance was validated using a "hold-out" set of counties.

RESULTS

Overall, 714,568 individuals with AD died due to any cause across 2,409 counties during 2016-2020. CART identified 9 county clusters associated with an 80.1% relative increase of mortality across the spectrum. Furthermore, 7 SEDH variables were identified by CART to drive the categorization of clusters, including High School Completion (%), annual Particulate Matter 2.5 Level in Air, live births with Low Birthweight (%), Population under 18 years (%), annual Median Household Income in US dollars ($), population with Food Insecurity (%), and houses with Severe Housing Cost Burden (%).

CONCLUSION

ML can aid in the assimilation of intricate SEDH exposures associated with mortality among older population with AD, providing opportunities for optimized interventions and resource allocation to reduce mortality among this population.

The Global Burden of premature cardiovascular disease, 1990-2019

Aims

Premature cardiovascular disease (pCVD) definition varies in literature, with age cut-offs ranging from 50-65 years. While there is some literature available on pCVD in North America, comprehensive data on its global burden is still lacking which hinders the development of efficient strategies for early detection and prevention. In this study we aimed to investigate the global trends in pCVD related morbidity and mortality from 1990 to 2019.

Methods

The 1990-2019 Global Burden of Disease (GBD) database was utilized to examine global trends in cardiovascular disease-related total mortality, mortality rates, and Disability-Adjusted Life Years (DALYs) within individuals aged 15-49 years. The findings were further analyzed based on factors such as age, sex, and Socio-Demographic Index (SDI).

Results

From 1990 to 2019, the number of global annual pCVD deaths increased by 25%, from 992,067 (95% UI 1,042,261 - 946,383) to 1,241,484 (95% UI 1,339,193 -1,146,252). The rate of associated mortality decreased by 13%. Metabolic conditions were the most significant risk factors for pCVD mortality. Ischemic heart disease and stroke are the leading causes of death across all age groups. pCVD mortality presented progressive widening between high and low SDI regions. Additionally, sex-specific disparities in CVD mortality were significantly greater in the premature age group as compared to all-age groups.

Conclusion

pCVD is an increasingly significant global cause of morbidity and mortality that disproportionately affects males and individuals living in less privileged regions. Furthermore, ischemic heart disease and stroke were identified as the main drivers of pCVD global burden.

Coronary artery calcium scoring for cardiovascular risk assessment in patients with inflammatory bowel disease

BACKGROUND

Inflammatory bowel disease (IBD) is associated with higher incidence of atherosclerotic cardiovascular disease (ASCVD). Data investigating the role of coronary artery calcium (CAC) scoring in identifying subclinical atherosclerotic disease in IBD patients is scarce.

METHODS

Using data obtained from the CLARIFY registry, a prospective study of no-charge coronary artery calcium (CAC) testing at University Hospitals, we reviewed patients with ulcerative colitis (UC) or Crohn's disease (CD) who underwent CAC scoring from 2014 to 2020. We investigated the concordance between CAC risk and 10-year estimated ASCVD risk by AHA/ACC pooled cohort equation using pre-established thresholds for statin prescription (CAC≥100, 10-year ASCVD risk ≥7.5%). We additionally investigated the association between CAC, preventive therapy initiation and Major Adverse Cardiovascular Events (MACE).

RESULTS

A total of 369 patients with IBD were included (174 UC, 195 CD), with median age of 60 years. The median CAC score was 14.9 with no significant difference between UC and CD (P = .76). Overall, 151 (41%) had CAC of 0, 108 (29%) had CAC 1-99, 61 (17%) had CAC 100 to 399, and 49 (13%) had CAC ≥400 with no difference in CAC distribution between CD and UC (P = .17). There was no difference in median CAC between IBD or age/sex-matched controls (P = .34). Approximately half of the patients (52%) with IBD had 10-year estimated ASCVD risk of 7.5% or higher. Among patients with ASCVD risk <7.5% (n = 163), 29 (18%) had CAC≥100 and among patients with ASCVD risk ≥7.5% (n = 178), 102 (57%) had CAC <100. There was no difference between CAC<100 vs CAC≥100 with respect to CRP, use of immunosuppressive or amino-salicylate therapy, IBD severity or complications. CAC score (AUROC 0.67 [0.56-0.78]), but not PCE ASCVD risk (AUROC 0.60 [0.48-0.73]), was predictive of MACE. The best cut-off for CAC score was 76 (sensitivity = 60%, specificity = 69%), and was associated with 4-fold increase in MACE (Hazard Ratio 4.0 [2.0-8.1], P < .001).

CONCLUSION

Subclinical atherosclerosis, as evaluated by CAC scoring, is prevalent in patients with IBD, and is associated with cardiovascular events. Further studies are needed to understand underlying biological processes of increased atherosclerotic disease risk among adults with IBD.

Cardiac Graft Versus Host Disease: A Rare or Underrecognized Pathology?

Allogeneic hematopoietic stem cell transplantation is a potentially curative procedure for a variety of hematologic and immunologic disorders. Despite the powerful therapeutic potential, both acute, and chronic toxicities including graft versus host disease (GVHD) and cardiovascular disease can lead to significant short- and long-term morbidity and mortality. While GVHD can affect any organ, cardiac involvement is rarely described in the literature. In this review, we review available literature, and allude to pathophysiology and therapeutic approaches in cardiac GVHD.

Acute exposure to simulated nocturnal traffic noise and cardiovascular complications and sleep disturbance-results from a pooled analysis of human field studies

OBJECTIVES

A series of human field studies demonstrated that acute exposure to simulated nocturnal traffic noise is associated with cardiovascular complications and sleep disturbance, including endothelial dysfunction, increased blood pressure, and impaired sleep quality. A pooled analysis of these results remains to be established and is of tremendous interest to consolidate scientific knowledge.

METHODS

We analyzed data from four randomized crossover studies (published between 2013 to 2021 and conducted at the University Medical Center Mainz, Germany). A total of 275 subjects (40.4% women, mean age 43.03 years) were each exposed to one control scenario (regular background noise) and at least to one traffic noise scenario (60 aircraft or train noise events) in their homes during nighttime. After each night, the subjects visited the study center for comprehensive cardiovascular function assessment, including the measurement of endothelial function and hemodynamic and biochemical parameters, as well as sleep-related variables.

RESULTS

The pooled analysis revealed a significantly impaired endothelial function when comparing the two different noise sequences (0-60 vs. 60-0 simulated noise events, mean difference in flow-mediated dilation -2.00%, 95% CI -2.32; -1.68, p < 0.0001). In concordance, mean arterial pressure was significantly increased after traffic noise exposure (mean difference 2.50 mmHg, 95% CI 0.54; 4.45, p = 0.013). Self-reported sleep quality, the restfulness of sleep, and feeling in the morning were significantly impaired after traffic noise exposure (all p < 0.0001).

DISCUSSION

Acute exposure to simulated nocturnal traffic noise is associated with endothelial dysfunction, increased mean arterial pressure, and sleep disturbance.

Cardiovascular disease burden attributable to non-optimal temperature: analysis of the 1990-2019 global burden of disease

AIMS

Extreme temperatures are increasingly experienced as a result of climate change. Both high and low temperatures, impacted by climate change, have been linked with cardiovascular disease (CVD). Global estimates on non-optimal temperature-related CVD are not known. The authors investigated global trends of temperature-related CVD burden over the last three decades.

METHODS AND RESULTS

The authors utilized the 1990-2019 global burden of disease methodology to investigate non-optimal temperature, low temperature- and high temperature-related CVD deaths, and disability-adjusted life years (DALYs) globally. Non-optimal temperatures were defined as above (high temperature) or below (low temperature) the location-specific theoretical minimum-risk exposure level or the temperature associated with the lowest mortality rates. Analyses were later stratified by sociodemographic index (SDI) and world regions. In 2019, non-optimal temperature contributed to 1 194 196 (95% uncertainty interval [UI]: 963 816-1 425 090) CVD deaths and 21 799 370 (95% UI: 17 395 761-25 947 499) DALYs. Low temperature contributed to 1 104 200 (95% UI: 897 783-1 326 965) CVD deaths and 19 768 986 (95% UI: 16 039 594-23 925 945) DALYs. High temperature contributed to 93 095 (95% UI: 10 827-158 386) CVD deaths and 2 098 989 (95% UI: 146 158-3 625 564) DALYs. Between 1990 and 2019, CVD deaths related to non-optimal temperature increased by 45% (95% UI: 32-63%), low temperature by 36% (95% UI: 25-48%), and high temperature by 600% (95% UI: -1879-2027%). Non-optimal temperature- and high temperature-related CVD deaths increased more in countries with low income than countries with high income.

CONCLUSION

Non-optimal temperatures are significantly associated with global CVD deaths and DALYs, underscoring the significant impact of temperature on public health.

Prediction of stent under-expansion in calcified coronary arteries using machine learning on intravascular optical coherence tomography images

It can be difficult/impossible to fully expand a coronary artery stent in a heavily calcified coronary artery lesion. Under-expanded stents are linked to later complications. Here we used machine/deep learning to analyze calcifications in pre-stent intravascular optical coherence tomography (IVOCT) images and predicted the success of vessel expansion. Pre- and post-stent IVOCT image data were obtained from 110 coronary lesions. Lumen and calcifications in pre-stent images were segmented using deep learning, and lesion features were extracted. We analyzed stent expansion along the lesion, enabling frame, segmental, and whole-lesion analyses. We trained regression models to predict the post-stent lumen area and then computed the stent expansion index (SEI). Best performance (root-mean-square-error = 0.04 ± 0.02 mm2, r = 0.94 ± 0.04, p < 0.0001) was achieved when we used features from both lumen and calcification to train a Gaussian regression model for segmental analysis of 31 frames in length. Stents with minimum SEI > 80% were classified as "well-expanded;" others were "under-expanded." Under-expansion classification results (e.g., AUC = 0.85 ± 0.02) were significantly improved over a previous, simple calculation, as well as other machine learning solutions. Promising results suggest that such methods can identify lesions at risk of under-expansion that would be candidates for intervention lesion preparation (e.g., atherectomy).

The cardiovascular disease burden attributable to particulate matter pollution in South America: analysis of the 1990-2019 global burden of disease

OBJECTIVES

Fine particulate matter <2.5 microns (PM2.5) is the most studied air pollutant. Both short- and long-term exposure to PM2.5 have been linked to cardiovascular disease (CVD). Studies evaluating air pollution in South America are scarce. Therefore, the impact of exposure to PM2.5, household air pollution (HAP), and ambient air pollution (AAP) on CVD mortality and CVD disability-adjusted life years (DALYs) in South American countries from 1990 to 2019 was explored.

STUDY DESIGN AND METHODS

The Global Burden of Disease initiative exposure-response function was used to analyze the total PM2.5, ambient PM2.5, and household PM2.5-related CVD deaths and DALYs rates, per 100,000 individuals, in 12 South American countries between 1990 and 2019. The relative change in burden was also assessed by comparing the 1990-1994 to 2015-2019 periods.

RESULTS

In 2019, 70,668 deaths and 1,736,414 DALYs due to CVD were attributed to total PM2.5 exposure in South America. Substantial regional heterogeneity was observed concerning the absolute change in PM2.5 concentration levels comparing 1990 to 2019. All South American countries observed a relative decline in CVD deaths and DALYs comparing the 1990-1994 to 2015-2019 periods. No country was able to reach the current World Health Organization 5 μg/m3 recommended limit in 2019. Predominantly, AAP was the greatest contributor to the CVD burden.

CONCLUSION

Air pollution substantially impacted CVD in South America; however, this impact was heterogenous, and the relative reduction of HAP and AAP burden was also not uniform. Recognizing PM2.5 importance is key for developing target population and individual-level interventions, which could ultimately alleviate its burden.

Environmental exposures and blood pressure in adolescents and adults in the T1D exchange clinic registry

AIMS

To examine the associations between environmental determinants of health and blood pressure and whether age, sex, or race moderated the associations among 18,754 adolescents and adults from the type 1 diabetes (T1D) Exchange Clinic Registry.

METHODS

We used multivariable linear regression. Environmental determinants included exposure to ambient fine particulate matter (PM2.5, obtained from an integrated model), nitrogen dioxide (NO2), noise and light pollution, and the normalized difference vegetation index (NDVI, a marker of green space) at the ZIP code level of residence.

RESULTS

Higher exposure to PM2.5 and NO2, and lower NDVI, was associated with higher systolic and diastolic blood pressure, and higher light pollution exposure were similarly associated with higher diastolic blood pressure. These associations between environmental exposures and blood pressure remained significant after accounting for other covariates (age, sex, race/ethnicity, BMI, and T1D duration). With aging, the negative association between NDVI and blood pressure weakened.

CONCLUSIONS

These findings emphasize the significance of minimizing exposure to environmental pollutants, including PM2.5 and NO2, as well as ensuring access to areas with higher NDVI, to promote cardiovascular health in individuals with T1D.

Immunologic profiles in patients with chronic limb-threatening ischemia undergoing endovascular revascularization

BACKGROUND

Inflammation and immune dysregulation have been associated with adverse outcomes in cardiovascular disease. There is limited understanding of the association of different profiles of white blood cell (WBC) subsets and red cell distribution width (RDW) in patients with chronic limb-threatening ischemia (CLTI).

METHODS

Patients with CLTI undergoing endovascular revascularization in our single-center, tertiary care hospital from 2017 to 2019, who had a preceding complete blood count (CBC) with WBC differentials (n =213), were included in the analysis. Patient characteristics, laboratory values, and clinical outcomes were collected. Cox proportional hazards regression models were used to assess for associations between all-cause mortality and leukocyte subset; multivariate analysis was used to account for confounders. Kaplan-Meier curves were generated to depict survival censored at 1 year postrevascularization using baseline CBC indices.

RESULTS

Adjusting for confounders, elevated RDW was associated with increased mortality (continuous per % increase, adjusted hazard ratio [HR] 1.33, p < 0.001). Baseline lymphopenia was associated with mortality in univariate analysis. Other leukocyte subtypes were not associated with mortality outcomes in our population. Exploratory analysis showed negative deflections in ∆WBC from pre- to postprocedure day 1 were affiliated with increased mortality when adjusted for age, sex, race, chronic kidney disease, and baseline hemoglobin (∆WBC HR 1.16, p = 0.004). Further exploratory analysis showed an association between RDW and all-comers readmission.

CONCLUSIONS

The utilization of a periprocedural WBC subset differential can be a useful adjunct to risk-stratify patients with CLTI undergoing endovascular revascularization. Further studies are needed to understand potential ways to modulate immune dysregulation so as to improve mortality outcomes.

Neighbourhood-level social deprivation and the risk of recurrent heart failure hospitalizations in type 2 diabetes

BACKGROUND

The importance of type 2 diabetes mellitus (T2D) in heart failure hospitalizations (HFH) is acknowledged. As information on the prevalence and influence of social deprivation on HFH is limited, we studied this issue in a racially diverse cohort.

METHODS

Linking data from US Veterans with stable T2D (without prevalent HF) with a zip-code derived population-level social deprivation index (SDI), we grouped them according to increasing SDI as follows: SDI: group I: ≤20; II: 21-40; III: 41-60; IV: 61-80; and V (most deprived) 81-100. Over a 10-year follow-up period, we identified the total (first and recurrent) number of HFH episodes for each patient and calculated the age-adjusted HFH rate [per 1000 patient-years (PY)]. We analysed the incident rate ratio between SDI groups and HFH using adjusted analyses.

RESULTS

In 1 012 351 patients with T2D (mean age 67.5 years, 75.7% White), the cumulative incidence of first HFH was 9.4% and 14.2% in SDI groups I and V respectively. The 10-year total HFH rate was 54.8 (95% CI: 54.5, 55.2)/1000 PY. Total HFH increased incrementally from SDI group I [43.3 (95% CI: 42.4, 44.2)/1000 PY] to group V [68.6 (95% CI: 67.8, 69.9)/1000 PY]. Compared with group I, group V patients had a 53% higher relative risk of HFH. The negative association between SDI and HFH was stronger in Black patients (SDI × Race pinteraction  < .001).

CONCLUSIONS

Social deprivation is associated with increased HFH in T2D with a disproportionate influence in Black patients. Strategies to reduce social disparity and equalize racial differences may help to bridge this gap.

Attractive deep morphology-aware active contour network for vertebral body contour extraction with extensions to heterogeneous and semi-supervised scenarios

Automatic vertebral body contour extraction (AVBCE) from heterogeneous spinal MRI is indispensable for the comprehensive diagnosis and treatment of spinal diseases. However, AVBCE is challenging due to data heterogeneity, image characteristics complexity, and vertebral body morphology variations, which may cause morphology errors in semantic segmentation. Deep active contour-based (deep ACM-based) methods provide a promising complement for tackling morphology errors by directly parameterizing the contour coordinates. Extending the target contours' capture range and providing morphology-aware parameter maps are crucial for deep ACM-based methods. For this purpose, we propose a novel Attractive Deep Morphology-aware actIve contouR nEtwork (ADMIRE) that embeds an elaborated contour attraction term (CAT) and a comprehensive contour quality (CCQ) loss into the deep ACM-based framework. The CAT adaptively extends the target contours' capture range by designing an all-to-all force field to enable the target contours' energy to contribute to farther locations. Furthermore, the CCQ loss is carefully designed to generate morphology-aware active contour parameters by simultaneously supervising the contour shape, tension, and smoothness. These designs, in cooperation with the deep ACM-based framework, enable robustness to data heterogeneity, image characteristics complexity, and target contour morphology variations. Furthermore, the deep ACM-based ADMIRE is able to cooperate well with semi-supervised strategies such as mean teacher, which enables its function in semi-supervised scenarios. ADMIRE is trained and evaluated on four challenging datasets, including three spinal datasets with more than 1000 heterogeneous images and more than 10000 vertebrae bodies, as well as a cardiac dataset with both normal and pathological cases. Results show ADMIRE achieves state-of-the-art performance on all datasets, which proves ADMIRE's accuracy, robustness, and generalization ability.

Psychosocial risk factors and outcomes in patients undergoing catheter ablation for atrial fibrillation

The association of psychosocial risk factors with cardiovascular disease is well-established, and there is a growing recognition of their influence on atrial fibrillation (AF) . A recent National Heart, Lung, and Blood Institute workshop called for transforming AF research to integrate social determinants of health. There is limited data examining the impact of psychosocial risk factors (PSRFs) on outcomes in patients with an established diagnosis of AF. Catheter ablation for AF has been shown to improve arrhythmia burden and quality of life compared with medical treatment alone. It is unknown how PSRFs affect clinical outcomes in patients undergoing AF ablation. It is important to understand this relationship, especially given the increasing adoption of catheter ablation in clinical practice.

Depression and incident cardiovascular disease among patients with chronic kidney disease

Background

Depression is associated with an increased risk of cardiovascular disease (CVD) and is prevalent among patients with chronic kidney disease (CKD). We aimed to identify the association of depression with incident CVD.

Methods

We studied patients with CKD stages 2-4 enrolled in the Chronic Renal Insufficiency Cohort (CRIC) and excluded participants with preexisting CVD. The Cox proportional hazard model was used to examine the association between baseline depression [Beck's Depression Inventory (BDI) score ≥11] and incidence of CVD (cerebrovascular accident, myocardial infarction, heart failure, or peripheral artery disease). Models were adjusted for age, sex, race, estimated glomerular filtration rate (eGFR), urine albumin-creatinine ratio (UACR), systolic and diastolic blood pressure, and 10-year estimated CVD risk.

Results

Among 2585 CRIC study participants, 640 (25%) patients had depression at study baseline. Compared to patients without depression, patients with depression were more likely to be women (56% vs. 46%), non-White (68% vs. 53%), with household income <$20,000 (53% vs. 26%), without a high school degree (31% vs. 15%), uninsured (13% vs. 7%), with lower eGFR (42 vs. 46 ml/min/1.73 m (Palmer et al., 2013 Jul)22), and with higher UACR (90 vs. 33 mg/g). In multivariate analyses, depression was associated with a 29% increased risk of developing CVD (adjusted hazard ratio 1.29, 95% confidence interval 1.03-1.62, p = 0.03). BDI (as a continuous variable) was associated with CVD (adjusted hazard ratio 1.017, 95% confidence interval 1.004-1.030, p = 0.012).

Conclusions

Among patients with CKD stages 2-4 enrolled in CRIC without preexisting CVD, depression was associated with a 29% increased risk of incident CVD.

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.