Sarju Ganatra

Background: Cardiovascular disease (CVD) remains the leading cause of morbidity and death worldwide. Risk scores such as the pooled cohort equations often underpredict risk in individuals with low socioeconomic status. Methods: We used the Centers for Disease Control and Prevention Population Level Analysis and Community Estimates data set to obtain zip code–level prevalence of hypertension, obesity, stroke, diabetes, and coronary artery disease. The Distressed Communities Index (DCI) collates community socioeconomic status and social determinants/drivers of health metrics into a numerical score. Linear regression adjusted for region and rurality assessed associations between DCI and CVD prevalence. Results: We included 26 058 US zip codes (2016–2020). The median DCI was 50.9 (interquartile range, 25.4–75.9). Each 1‐unit increase in DCI was associated with increased prevalence of obesity (β=0.10 [95% CI, 0.09–0.10]), hypertension (β=0.08 [95% CI, 0.08–0.09]), diabetes (β=0.06 [95% CI, 0.06–0.06]), coronary artery disease (β=0.03 [95% CI, 0.03–0.03]), and stroke (β=0.02 [95% CI, 0.02–0.02]; all P <0.001). Rurality and region were independently associated with CVD and risk factor prevalence, with rural and southern zip codes having the highest prevalence. Conclusions: DCI, geographic region, and rurality are independently associated with greater prevalence of CVD and its risk factors in the United States. The DCI is a practical, scalable surrogate for socioeconomic status and social determinants/drivers of health that may enhance CVD risk stratification on the basis of an area's economic resilience or disadvantage. Using DCI with the Preventing Cardiovascular Events score may capture economic and social challenges it omits, warranting further study.

Cardio-oncology has emerged as a critical discipline in modern medicine due to the growing population of cancer survivors and the increasing recognition of cardiovascular disease as a major cause of morbidity and mortality in this population. While there have been significant advances in chemotherapy such as advances in chemotherapy, targeted therapies, immunotherapies, and radiation therapy, these treatments are linked to a wide range of cardiovascular toxicities such as myocarditis, arrhythmias, and progressive fibrotic remodeling. These toxicities tend to be caused by interrelated and multifactorial cellular processes including endothelial damage, immunological dysregulation and mitochondrial dysfunction. Therefore, early diagnosis of subclinical harm using multimodal surveillance strategies integrating clinical risk assessment, specialized clinical imaging, and circulating biomarkers has replaced reactive care of overt cardiotoxicity in modern cardio-oncology. Echocardiographic techniques such as longitudinal strain, with the help of biomarker-guided surveillance of natriuretic peptides and cardiac troponins, have made the early detection of cardiac dysfunction more effective. However, the current prediction risk models are still constrained and limited by their dependence on static clinical variables and their partial integration of biological mechanisms. This review examines the current methods for risk stratification and surveillance throughout the cancer care continuum, including baseline assessment, monitoring during active therapy, and long-term survivorship surveillance. Future approaches for tailored cardiovascular care are also highlighted, including new advances in precision cardio-oncology such as multi-omics profiling, molecular biomarkers, artificial intelligence, and mechanism-guided preventative strategies. Therefore, advancing biologically informed and risk-adapted monitoring frameworks may enhance early diagnosis, maximize cardioprotective measures, and lower long-term cardiovascular consequences.

Aims: The integration of point-of-care ultrasound (POCUS) by non-specialists and the shortage of trained sonographers highlights the need for scalable training approaches. This study aimed to evaluate the learning curve of novice operators performing artificial intelligence (AI)-guided limited transthoracic echocardiography (TTE) and to assess whether acquired images were sufficient for diagnostic interpretation of structural cardiac disease. Methods and results: In this multicentre, prospective secondary analysis, nine novice operators performed limited TTE scans on 159 patients using a handheld device with AI-based acquisition guidance. Following eight hours of standardized training, novices independently obtained six standard TTE views. Three blinded expert reviewers graded image quality on a 1–5 scale and assessed diagnostic adequacy. Image scores were used to generate learning curves, and subgroup analyses examined the influence of patient characteristics. Of 954 novice-acquired images, 97.7% met the diagnostic threshold (score ≥3). After training, all operators achieved mean scores ≥3 across patients. AI-guidance consistently enabled high-quality imaging across all views, with minimal impact from sex, age, or pathology. Body mass index (BMI) showed a significant effect (P = 0.0029), though all subgroups exceeded diagnostic thresholds: 4.44 ± 0.17 (BMI 30). Experts reliably ruled out left ventricular dysfunction (99.4%) and hypertrophy (98.7%); agreement was lower for wall motion abnormalities (80.7%) and atrial dilation (86.6%). Conclusion: Novices with no prior POCUS experience achieved diagnostic-quality TTE images after one day of AI-guided training. AI may supplement conventional echocardiography training, and future research should evaluate its integration into routine clinical workflows.

Importance: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are associated with increased risk of diabetic retinopathy (DR) and nonarteritic anterior ischemic optic neuropathy (NAION). The risk of sight-threatening complications associated with GLP-1 RAs is underexamined.Objective: To investigate whether the use of GLP-1 RAs in patients with T2D is associated with the development of DR, NAION, or DR complications.Design, Setting, and Participants: This retrospective cohort study of adults (aged ≥18 years) with T2D and a recent hemoglobin A_1c level of 6.5% or higher was conducted between January 1, 2015, and September 30, 2022, using the TriNetX database. The cohort was divided into 2 groups, adjusted for baseline characteristics through propensity score matching (PSM), based on whether the individuals received prescriptions for a GLP-1 RA. The statistical analysis was conducted on October 10, 2024.Exposures: At least 2 prescriptions of a GLP-1 RA given 6 months apart.Main Outcomes and Measures: Cox proportional hazard regression models were used to evaluate the primary outcome: association between GLP-1 RAs and the risk of incident DR, NAION, or sight-threatening complications over a 2-year follow-up period.Results: After PSM, 185 066 individuals (mean [SD] age, 59.0 [12.5] years; 93 389 females [50.5%]) were prescribed GLP-1 receptor agonists. Use of GLP-1 RAs was associated with an increased incidence of DR (hazard ratio [HR], 1.07; 95% CI, 1.03-1.11), while no statistically significant difference was observed in the risk of NAION (HR, 1.26; 95% CI, 0.94-1.70). In a subgroup analysis of 32 695 patients with preexisting DR, GLP-1 RAs were not associated with progression to proliferative DR (HR, 1.06; 95% CI, 0.97-1.15) or diabetic macular edema (HR, 0.98; 95% CI, 0.95-1.01) but were associated with a lower occurrence of vitreous hemorrhages (HR, 0.74; 95% CI, 0.68-0.80), neovascular glaucoma (HR, 0.78; 95% CI, 0.68-0.88), or blindness (HR, 0.77; 95% CI, 0.73-0.82).Conclusions and Relevance: In this cohort study of individuals with T2D, GLP-1 RA use was associated with a modestly increased risk of incident DR; however, fewer patients experienced sight-threatening DR complications, including blindness, even among those with preexisting DR. These findings suggest that all patients with T2D treated with GLP-1 RAs, regardless of preexisting DR, should be regularly screened and monitored for potential complications of T2D.

Background: Although micro‐ and nanoplastics are an emerging risk factor for the development of cardiovascular diseases, the association of marine microplastic levels (MMLs) with the burden of cardiometabolic diseases at a population level remains unexplored. We investigated the relationship between the mean microplastic concentration in ocean water within 200 nautical miles of the US coastline and the prevalence of type 2 diabetes, coronary artery disease, and stroke in nearby counties. Methods: Microplastic concentration data within 200 nautical miles of the US coastline were geospatially analyzed to calculate mean MMLs. The coastal counties were stratified into 4 categories based on MMLs in the adjacent ocean water body: low, medium, high, or very high. The Behavioral Risk Factor Surveillance System 2019 to 2020 survey data were used to identify the prevalence of type 2 diabetes, coronary artery disease, and stroke in these counties. Univariate and population‐weighted multivariate quasi‐Poisson regression, adjusted for county‐level confounding variables, was used to evaluate the unadjusted and adjusted associations between disease prevalence and MMLs. Results: The mean age of the population was 43±6 years. After adjusting for age, sex, physician access, and socioenvironmental vulnerabilities at the county level, counties with very high MMLs exhibited an 18%, 7%, and 9% higher adjusted prevalence of type 2 diabetes, coronary artery disease, and stroke, respectively, compared with those with low levels. Conclusion: Marine microplastic levels were significantly associated with the prevalence of cardiometabolic diseases in coastal counties. Comprehensive studies at an individual level are needed to further investigate the health impacts of microplastic pollution.

Background: There is an incomplete understanding of the impact of climate change on cardiovascular‐kidney‐metabolic (CKM) syndrome. Although climate change affects all Americans, certain communities and individuals may suffer a disproportionate burden. Therefore, this study's objective was to explore the relationship between the Climate Vulnerability Index domains and the prevalence of CKM components in the United States. Methods: This cross‐sectional study obtained health outcomes from Centers for Disease Control and Prevention Population Level Analysis and Community Estimates (2021) and assessed the census tract‐level prevalence of coronary heart disease, chronic kidney disease, obesity, diabetes, dyslipidemia, and hypertension. ANOVA was used to assess statistically significant differences in the means of CKM components between quartiles of each climate change domain. Linear regression models assessed the association between each domain and outcome, followed by models adjusted by baseline Climate Vulnerability Index components. Fully adjusted models included all 3 climate change domains and the baseline Climate Vulnerability Index components. Results: Data for 70 300 census tracts, covering 299.8 million individuals, were included. Statistically significant differences ( P <0.001) in CKM prevalence were found between quartiles of all climate change domains. These associations persisted after adjusting for baseline domains (environment, social/economic, and infrastructure) and in the full model including all domains. Conclusions: The Climate Vulnerability Index's climate change component was associated with the prevalence of CKM components (coronary heart disease, chronic kidney disease, obesity, diabetes, dyslipidemia, and hypertension). These findings suggest that populations with CKM may be associated with increased climate vulnerability, underscoring the need for a deeper understanding of climate change as a determinant of health.