tbuckley/cabot-search huggingface dataset:
Abstract P3091: Participant Perceptions of Diet-Related Feedback Messages: Insights from the SMARTER Trial Focus Groups
Authors: Britney Beatrice, Jacob Kariuki, Susan Zickmund, Patrick Galyean, Elisabeth Kimball, Jeanette Young, Jessica Cheng, Molly Conroy, Maribel Cedillo, Susan Sereika, Zhadyra Bizhanova, Lora Burke
Journal: Circulation (2025)

Background: Mobile health (mHealth) interventions may provide a more accessible approach to a broader segment of the population needing weight loss treatment. Little is known about individuals’ perceptions of remotely-delivered diet-related feedback (FB) messages. Methods: The aim of this analysis was to assess perceptions of diet-related FB among participants randomized to the FB intervention (n = 251) of SMARTER, a randomized controlled trial that examined the efficacy of a 12-month remotely-delivered mHealth intervention targeting weight loss. Participants were asked to self-monitor (SM) diet using a commercial app and received FB messages in real-time in response to the SM data at least once daily. We conducted six focus groups (N=23) via Zoom to discuss participants’ perceptions of the FB intervention. Trained experts in qualitative research used a semi-structured focus group guide to facilitate discussion on how study participants interacted with technology and the FB they received. Participants were invited to participate in one of two focus groups based on weight loss at 12 months and average step counts during the 30 days prior to their 12-month assessment. After the final study visit, eligible participants were invited to participate in the focus groups and provided noteworthy comments. The data were analyzed using ATLAS.ti (version 9). Results: Four key themes emerged from the content analysis pertaining to FB and broader study issues: timing of the FB notifications, personalization of FB messages, issues with the SM app, and social aspect of the study design. Participants found FB helpful to engage in SM behaviors and reported a desire for more personalized FB messages related to diet. Some participants voiced a desire to set their own schedule for FB message delivery to ensure FB could be viewed when received. They also identified two challenges: the limited selection of foods in the diet SM app which in turn may have affected their SM and FB messaging and the absence of a social community. Conclusions: Our results suggest that giving participants the flexibility for FB notifications to align with their schedules and phone accessibility may improve receipt of FB and engagement. There is a need to enhance the personalization of FB while maintaining the scalability for a broader population reach. Incorporating contact with a coach to supplement remotely-delivered FB may further improve participant engagement in changing nutrition habits.
Abstract P1025: Overall Effect of Stress Reduction Interventions on Cardiovascular Outcomes (Both Qualitative and Quantitative): A Systematic Review and Meta-Analysis.
Authors: Nanush Damarlapally, Nia Uswanti Binti, TANUSHA WINSON, Prithvi Basu, Vitrag Tejani, Keerthika Damarlapally, Vijaylaxmi Thimmappa, Naga Siva Naveen Chodisetti, Ayesha Firdous Khan, Dr.Khena .
Journal: Circulation (2025)

Background: Cardiovascular diseases (CVDs) are a leading cause of global mortality, with chronic stress being a significant risk factor. This review examines the effectiveness of stress reduction interventions (SRIs) on cardiovascular health outcomes in adults. Methods: A systematic literature search was conducted using PubMed, Cochrane Library, ERIC, and Google Scholar between 2015 and 2024 for the effect of SRIs on various CVDs. A random-effects model was used to analyse data for mean difference (MD) and 95% CI using Review Manager software 5.4.1 version. SRIs like cognitive behavioural therapy (CBT), Yoga, mindfulness-based stress reduction (MBSR), heart rate variability biofeedback (HRV-BF), Tai-Chi, Mindful Awareness Practices (MAP), and coping skills training (CST) were examined in various CVDs like CHF, CAD, MI and HTN. The RoB 2 tool, funnel plots were used for quality assessment and publication bias respectively. Heterogeneity was determined by I 2 statistics and leave one out analysis study. A P-value &lt;0.05 was considered significant. Results: A total of 14 studies were included with 1,180 participants and a mean age of 54.64 years. Qualitative outcomes such as quality of life (QoL), health related quality of life (HRQL), heart rate variability (HRV), left ventricular ejection fraction (LVEF), NTproBNP, cognitive functions, and hostility reduction were systematically reviewed. Quantitative outcomes including systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), C-reactive protein (CRP), and high frequency power (HFP) which reflects the changes in heart rate with respiration were subjected to statistical analysis. SRIs significantly lowered SBP [MD = −9.70 mmHg, 95% CI (−14.39, −5.01), p &lt; 0.0001] and DBP [MD = −5.52 mmHg, 95% CI (−10.57, −0.47), p &lt; 0.0001] respectively but did not significantly affect HR (MD = −0.01 bpm, 95% CI [−2.87, 2.86], p = 0.17) or CRP levels (MD = 0.58 mg/L, p = 0.70) or HFP (MD = 4.22 ms^2, p = 0.92) [Image 1]. Additionally, yoga was associated with reduced levels of CRP, NTproBNP and improved QoL, LVEF. Tai chi was linked to enhanced cognitive function, and QoL while MAP and MBSR lowered perceived stress and anger levels [Image 2]. Conclusion: SRIs are associated with lower SBP, DBP pressure, heart rate, CRP levels, HFP levels, and improvement in qualitative outcomes. Larger prospective studies are required to assess these outcomes to improve preventive measures and decrease cardiac mortality.
Abstract P1020: Relationship Between CHA <sub>2</sub> DS <sub>2</sub> -VASc, Health Literacy, and Atrial Fibrillation Knowledge
Authors: Tanvi Nayak, Preeti Kansal, Kenzie Cameron, Elissa Ozanne, Geoffrey Barnes, Shiza Jeewa, Mandy Pershing, Angie Fagerlin, Kerri Cavanaugh, Elizabeth Jackson, Victor Montori, Chaorong Wu, Rod Passman, STEP-UP AF Writing Group
Journal: Circulation (2025)

Introduction: It is unknown how comorbid diagnoses affect patients’ disease-specific knowledge or how patients’ health literacy or numeracy affects this relationship. In patients with atrial fibrillation (AF), comorbidities can be quantified by the CHA 2 DS 2 -VASc score. We explore the association of AF/stroke knowledge with CHA 2 DS 2 -VASc score, health literacy, and numeracy for usual care participants in the multi-center Randomized Evaluation of Decision Support Interventions for Atrial Fibrillation (RED-AF) trial evaluating decision aids to initiate (initiation cohort) or continue (monitor cohort) oral anticoagulation (OAC) in AF patients. Methods: This study was conducted at six academic medical centers in the United States. Participants were aged ≥18, diagnosed with non-valvular AF, and at risk for stroke (CHA 2 DS 2 -VASc ≥1 for men, ≥2 for women). Patient demographics were obtained via questionnaires after a baseline clinical encounter; knowledge was evaluated by patient responses to 7 questions about OAC and AF. Health literacy was measured by a validated self-reported screening tool; numeracy by a validated subjective numeracy scale; CHA 2 DS 2 -VASc was calculated from medical records. We applied generalized estimating equations for a binomial outcome to jointly relate patient knowledge to CHA 2 DS 2 -VASc score, health literacy and numeracy, controlling for 5 additional patient factors and accounting for clustering by clinician. Results: We report on 306 patients randomized to the usual care group across six participating sites. Average age 68.7 9, 61.6% male, mean CHA 2 DS 2 -VASc 2 1.5. Patient knowledge was independently associated with numeracy (5% higher odds of correct response per 1 unit increase in numeracy, 95% CI 0-10%, p=0.05) and membership in the monitor cohort (40% odds of correct response, 4-89%, p=0.03) but not health literacy or increased stroke risk. Conclusion: In the RED-AF study , although health literacy and increasing stroke risk were not independently associated with patient knowledge about AF, numeracy had a minimal positive association. Those already on OAC had greater disease knowledge than patients newly starting OAC. These results suggest a need to broadly increase patient education regardless of baseline comorbidity burden and degree of health literacy. Future studies should be performed to better understand other factors that drive patient knowledge regarding AF and stroke.
Abstract 003: Transgender Adults Experience Earlier and Elevated Cardiovascular-Kidney-Metabolic Multimorbidity Compared to Cisgender Peers
Authors: Julianne Mercer, Benjamin Encino, Laurajo Ryan, Justina Lipscomb, Chanhyun Park, Grace Lee
Journal: Circulation (2025)

Introduction: Cardiovascular disease (CVD) is a key driver of cardiovascular-kidney-metabolic multimorbidity (CKMM) and mortality. Transgender adults appear to face a higher prevalence of CVD risk factors than cisgender peers, but their elevated risk correlates with CKMM remain unclear. This study examines the association between CKMM and transgender identity. Methods: We used nationally representative data from the 2017-2022 Behavioral Risk Factor Surveillance System, including adults aged ≥18 with gender identity data. The primary outcome was CKMM (yes/no), defined as CVD (coronary heart disease, stroke, or myocardial infarction), chronic kidney disease, and metabolic risk factors (BMI ≥ 25 kg/m2 or diabetes mellitus). Multivariable logistic regression models assessed the odds of CKMM, with transgender status as the main independent variable. Covariates included sociodemographics, behavioral risk factors, BMI, age, and age-gender interaction. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) are reported, accounting for complex survey design. Results: The study included 649,106 transgender adults (median age 33 years, IQR 22-53), consisting of 347,539 transgender men and 301,577 transgender women. About 61% of transgender adults exhibited ≥1 CKMM feature, overweight/obesity being the most common. Transgender adults had a threefold higher likelihood of CKMM compared to cisgender peers (aOR 3.09, 95% CI, 1.73-5.52; p&lt;.001). Elevated CKMM odds in transgender adults were driven by significantly higher rates of myocardial infarction, stroke, coronary heart disease, and chronic kidney disease. Age was the only significant predictor of CKMM in transgender adults, while multiple factors—BMI, health coverage, physical activity, heavy drinking, income, and employment—were significant for cisgender adults. A significant age-transgender status interaction (p&lt;.001) demonstrated that transgender adults had a higher predicted probability of CKMM across the age continuum with risk convergence around 80 years. For example, the predicted probability of CKMM in a 22-year-old transgender adult was equivalent to that of a 66-year-old cisgender adult. Conclusion: CKMM is strongly linked to transgender identity, with transgender adults facing higher risks at substantially younger ages. This underscores the need for improved screening and preventive measures in transgender populations to address early onset of CKMM and reduce premature morbidity and mortality.
Abstract P3175: Navigating Stroke Recovery: The Association of Insurance Coverage on Hospital Discharge Disposition in Rural versus Urban Settings in North Carolina
Authors: Mahesh Pinapaka, Mehul Patel, Wayne Rosamond
Journal: Circulation (2025)

Introduction/Hypothesis: Hospital discharge disposition of stroke patients is influenced by various factors, including health status, recovery needs, and healthcare resource availability. Health insurance coverage as a proxy for access to post-stroke care may reveal disparities in discharge outcomes. We examined the association between insurance status and discharge outcomes for stroke patients in rural and urban North Carolina, emphasizing the importance of stratifying by rurality to explore potential differences in access to care. Methods: We obtained demographics, county of residence, insurance, admission date, and discharge disposition of adult patients with principal discharge diagnosis codes for stroke (ICD-10-CM I60.xx-I63.xx) from an inpatient database of hospitalizations from January 2019 to September 2020 across 115 NC hospitals. We categorized patient insurance status as Private, Self-pay, Medicare, Medicaid, or Other. We dichotomized discharge disposition to Home or Healthcare Facility. Multivariable logistic regression estimated associations between insurance and discharge outcomes, adjusting for age, sex, stroke type, and rurality. Separate models for rural and urban populations explored differences in discharge patterns. Results: Among 31,935 patients with a mean age of 69, 38% lived in rural counties, 50% were female, and 60% were discharged home. Medicaid (OR=0.64, 95% CI: 0.58-0.70) and Medicare (OR=0.75, 95% CI: 0.71-0.80) patients had lower odds, and Self-pay (OR=2.07, 95% CI: 1.83-2.35) patients had higher odds of being discharged home compared to patients with private insurance. Discharge outcomes based on insurance status were consistent across rural and urban subgroups. Conclusions: Insurance status is significantly associated with discharge outcomes, with Medicaid and Medicare patients less likely and Self-Pay patients more likely to be discharged home compared to private insurance, regardless of rurality. Understanding discharge dynamics can inform policies to improve access and equity in post-stroke care. Further research should explore discharge disparities for vulnerable patient groups.
Abstract P3089: Predicting Reductions In Hemoglobin A1c From Large Dietary Datasets Using Artificial Intelligence Pipeline With Interpretable Encoder-Decoder Deep Neural Networks
Authors: Martha Tamez, Luis Soenksen, Leonard Boussioux, Josiemer Mattei
Journal: Circulation (2025)

Introduction: Artificial intelligence algorithms can help understand and predict the complex interactions between dietary intake and health outcomes, especially from large datasets. Precision nutrition leverages machine learning techniques to analyze such patterns, but the methods and uses need deeper examination. Objective: Demonstrate the viability of fully unsupervised training of a β-Variational Autoencoder (β-VAE, a fully unsupervised and robust framework for capturing latent features from high-dimensional data) to predict improved hemoglobin A1c from complex population-level dietary data. Methods: Our β-VAE neural network architecture (Figure 1) was trained using 10 cycles of cross-sectional datasets from the US National Health and Nutrition Examination Survey from 1999 to 2018. Dietary data were collected with 24-hour recalls. A cumulative 102,957 participants and 5885 food/nutrient items were aggregated into 547 interpretable latent variables. Results: The trained network performance was evaluated through an aggregated multi-participant study case (n=1000) that explored the relationship between the target hemoglobin A1c change percentage and associated potential changes in participants' diet. Among the top five dietary changes required to achieve a 20% reduction in the latent space value corresponding to hemoglobin A1c were increasing coffee/tea and seafood intake and decreasing sweets, pizza, and salt intake (Figure 1). Conclusion: The prediction model showed a reasonable alignment with dietary recommendations despite being trained in a fully unsupervised way. β-VAE may be a first-in-class, efficient, and interpretable framework to analyze complex population-level dietary data for precision nutrition research.
Abstract P3086: Future Heart Motion Measurements in Deep Learning-based Predictive Imaging
Authors: Lucy Lu, Viswanathan Rajagopalan, Richard Segall
Journal: Circulation (2025)

Introduction: Predicting the motion of heartbeats and the progression of cardiovascular diseases offers potential opportunities to provide early warnings and timely prevention, diagnosis, and/or treatment. However, quantitative measurement for predicting subsequent heart motion in medical imaging is unclear. We hypothesized that amplitude measurement in the frequency domain can be used to detect subsequent heart motion in a recurrent neural network architecture. Methods: We performed image annotations on two-dimensional echocardiographic videos from the public domain and defined the Regions of Interest from diseased/control left ventricular (LV) areas of beating human hearts (20 videos with 1500 frames each). Pathologies included myocardial infarction and aortic aneurysms. Following the reduction of video frames into one-dimensional sequences, we converted the sequences into the frequency domain using Fast Fourier Transform. We also used a deep learning-based recurrent neural network architecture, especially Long Short-Term Memory model to perform both one-class and two-class Time-Series Analyses. We subsequently performed quantitative evaluation of the analyses. Results: The Time-Series Analysis of amplitude measurements was able to successfully predict subsequent frames. For one-class Time-Series Analysis, the average accuracy for high-quality videos was 0.82 for both control and diseased regions. For all videos, the accuracy was 0.81 for control regions and 0.82 for diseased regions. For two-class abnormality detection, the average precision was 0.9996 for high-quality videos and 0.9353 for all videos, the average recall was 0.98116 for high-quality videos and 0.9215 for all videos, the average specificity was 0.9995 for high-quality videos and 0.9353 for all videos, the average sensitivity was 0.9996 for high-quality videos and 0.9353 for all videos, and the average accuracy was 0.8918 for high-quality videos and 0.8378 for all videos. We also generated the Area Under the Receiver Operating Characteristic curve (AUROC), and its average was 0.95. Conclusions: Using Fast-Fourier Transform and Long Short-Term Memory model, we successfully measured future heartbeats in both diseased and control LV walls. The results showed high precision, recall, sensitivity, specificity, and Receiver Operating Characteristics. These suggest that the progression of regional wall motion abnormalities may be predicted in cardiovascular diseases.
Abstract MP20: Evaluating Discrimination of Cardiovascular Disease Risk Prediction Models Among Diverse Socioeconomic Groups: the All of Us Cohort
Authors: Ashley Lewis, Adrian Bacong, Latha Palaniappan, Tina Hernandez-Boussard
Journal: Circulation (2025)

Background: The American Heart Association Predicting Risk of cardiovascular disease EVENTs (AHA PREVENT) were introduced as an updated method to guide cardiovascular disease (CVD) risk prediction. These equations included new CVD outcomes (e.g., heart failure) and performed well among diverse race groups. Given that many development and validation studies often include individuals of higher socioeconomic backgrounds, the generalizability of PREVENT provides further opportunities to evaluate its predictive ability in other diverse socioeconomic groups. Objective: To evaluate the generalizability of PREVENT on an ongoing diverse U.S. cohort by income and educational attainment. Methods: We analyzed PREVENT-eligible participants from the All of Us (AoU) Study, an ongoing longitudinal cohort study of individuals living in the U.S. (n = 4,924). We calculated the PREVENT risk scores for 5-years of follow-up for total CVD and atherosclerotic CVD (ASCVD). We then examined the discriminatory ability of PREVENT for 5 years of follow up using Harrell’s C-Statistic for the full sample and by race, educational attainment, and annual income categories. Results: PREVENT Total CVD (C-statistic = 0.765), ASCVD (C-statistic = 0.736), and Heart Failure (C-statistic = 0.811) performed adequately in the examined AoU cohort (Table). For income, Total CVD performance was best among people who made $50k-100k (C-statistic = 0.874) and worse among people who made &lt; $50k (C-statistic = 0.706). However, people who made &gt; $100k had the largest uncertainty in equation performance (C-statistic = 0.713, 95% CI: 0.231-0.909). For education, total CVD performance was best among people with a college degree or higher (C-statistic = 0.821) and worse among people with a high school diploma or lower (C-statistic = 0.709). Conclusion: The AHA PREVENT equations showed continued robustness when applied to a diverse, ongoing national cohort and when stratified by educational attainment and annual income. While we show that PREVENT has good generalizability, the inconsistency for certain income and educational attainment groups highlights the need to recruit more socioeconomically diverse populations for cohort studies.
Abstract MP13: Insomnia with Objective Short Sleep Duration is Associated with Hypertension in Adolescents
Authors: Axel Robinson, Kristina Lenker, Susan Calhoun, Alexandros Vgontzas, Duanping Liao, Edward Bixler, Steven Beltecas, Julio Fernandez-Mendoza
Journal: Circulation (2025)

Introduction: Several studies have examined the association between insomnia and short sleep duration with elevated blood pressure (eBP) and stage 2 hypertension (HTN) in middle-aged adults. However, no study to date has examined the joint effect of self-reported insomnia and objective short sleep on eBP and HTN in adolescents. Methods: 421 adolescents (16.5±2.3 years old, 53.9% male, 21.9% racial/ethnic minority) from the Penn State Child Cohort, a randomly-selected population-based sample, underwent 9-hour polysomnography (PSG) and physical examinations. Insomnia was defined by a self-report of difficulties falling and/or staying asleep, while objective short sleep duration was defined based on the median PSG-measured total sleep time (i.e., &lt; 7.7 hours of sleep). BP levels were measured 3 consecutive times in the seated position and the average of the last 2 was used to ascertain the presence of eBP (i.e., systolic ≥120 and diastolic &lt;80 mmHg) and HTN (i.e., systolic ≥140 or diastolic ≥90 mmHg). Logistic regression models assessed the association of each group based on insomnia and objective sleep duration with eBP and HTN, while adjusting for age, sex, race/ethnicity, body mass index, apnea/hypopnea index, periodic limb movement index, socioeconomic status, and working status. Results: Compared to the reference group (n=126), adolescents who reported insomnia and slept objectively short (n=77) had 5-fold odds (95%CI=1.1-23.0, p-value=0.041) of having HTN. Adolescents who did not report insomnia but slept objectively short (n=136) had 2.7-fold odds (95%CI=1.5-4.8, p-value=0.001) of having eBP. Adolescents who reported insomnia and slept objectively normal (n=82) did not show any significant associations with either eBP or HTN. Conclusions: Insomnia with objective short sleep duration in adolescents is associated with stage 2 HTN, while short sleep alone is associated with incipient eBP. These findings further support that insomnia with short sleep duration is a more biologically severe phenotype of the disorder requiring different therapeutic approaches to improve sleep and reduce cardiovascular risk.