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Background Individuals with both atrial fibrillation (AF) and myocardial infarction (MI) have higher mortality compared with individuals with only 1 condition. Whether mortality differs according to the temporal order of AF and MI is unclear. Methods and Results We included participants from the FHS (Framingham Heart Study) from 1960 and onwards. We assessed the hazard ratio (HR) of new‐onset AF and MI, and mortality according to MI and AF status (prevalent and interim) using multivariable‐adjusted Cox proportional hazards models. Interim diseases were modeled as time‐varying variables. For the analysis of new‐onset AF, 10 923 participants (55% women; mean±SD age, 54±8 years) were included. For new‐onset MI, 10 804 participants (55% women; mean±SD age, 54±8 years) were included. Compared with no MI, the hazard of new‐onset AF was higher in participants with prevalent (HR, 1.60 [95% CI, 1.32–1.94]) and interim MI (HR, 3.96 [95% CI, 3.18–4.91]). Both ST‐segment–elevation MI and non–ST‐segment–elevation MI were associated with new‐onset AF. Interim AF, not prevalent AF, was associated with higher hazard rate of new‐onset MI (HR, 2.21 [95% CI, 1.67–2.92]). Interim AF was associated with both ST‐segment–elevation MI and non–ST‐segment–elevation MI. Mortality was significantly greater among participants with AF and MI compared with participants with 1 of the 2, regardless of temporal order. Conclusions We report a bidirectional association between AF and MI, which was observed for both non–ST‐segment–elevation MI and ST‐segment–elevation MI. Participants with both AF and MI had considerably higher mortality compared with participants with only 1 of the 2 conditions, regardless of order.

Background Smartphone‐based digital technology is increasingly being recognized as a cost‐effective, scalable, and noninvasive method of collecting longitudinal cognitive and behavioral data. Accordingly, a state‐of‐the‐art 3‐year longitudinal project focused on collecting multimodal digital data for early detection of cognitive impairment was developed. Methods and Results A smartphone application collected 2 modalities of cognitive data, digital voice and screen‐based behaviors, from the FHS (Framingham Heart Study) multigenerational Generation 2 (Gen 2) and Generation 3 (Gen 3) cohorts. To understand the feasibility of conducting a smartphone‐based study, participants completed a series of questions about their smartphone and app use, as well as sensory and environmental factors that they encountered while completing the tasks on the app. Baseline data collected to date were from 537 participants (mean age=66.6 years, SD=7.0; 58.47% female). Across the younger participants from the Gen 3 cohort (n=455; mean age=60.8 years, SD=8.2; 59.12% female) and older participants from the Gen 2 cohort (n=82; mean age=74.2 years, SD=5.8; 54.88% female), an average of 76% participants agreed or strongly agreed that they felt confident about using the app, 77% on average agreed or strongly agreed that they were able to use the app on their own, and 81% on average rated the app as easy to use. Conclusions Based on participant ratings, the study findings are promising. At baseline, the majority of participants are able to complete the app‐related tasks, follow the instructions, and encounter minimal barriers to completing the tasks independently. These data provide evidence that designing and collecting smartphone application data in an unsupervised, remote, and naturalistic setting in a large, community‐based population is feasible.

ABSTRACT Most research using digital technologies builds on existing methods for staff‐administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home‐based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early‐stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow‐up possible. Through the American Heart Association–funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.

Background Smartphone‐based cognitive assessments have emerged as promising tools, bridging gaps in accessibility and reducing bias in Alzheimer disease and related dementia research. However, their congruence with traditional neuropsychological tests and usefulness in diverse cohorts remain underexplored. Methods and Results A total of 406 FHS (Framingham Heart Study) and 59 BHS (Bogalusa Heart Study) participants with traditional neuropsychological tests and digital assessments using the Defense Automated Neurocognitive Assessment (DANA) smartphone protocol were included. Regression models investigated associations between DANA task digital measures and a neuropsychological global cognitive Z score (Global Cognitive Score [GCS]), and neuropsychological domain‐specific Z scores. FHS participants’ mean age was 57 (SD, 9.75) years, and 44% (179) were men. BHS participants' mean age was 49 (4.4) years, and 28% (16) were men. Participants in both cohorts with the lowest neuropsychological performance (lowest quartile, GCS1) demonstrated lower DANA digital scores. In the FHS, GCS1 participants had slower average response times and decreased cognitive efficiency scores in all DANA tasks (P

Abstract INTRODUCTION Alzheimer's disease (AD) is a heterogeneous disorder characterized by complex underlying neuropathology that is not fully understood. This study aimed to identify cognitive progression subtypes and examine their correlation with clinical outcomes. METHODS Participants of this study were recruited from the Framingham Heart Study. The Subtype and Stage Inference (SuStaIn) method was used to identify cognitive progression subtypes based on eight cognitive domains. RESULTS Three cognitive progression subtypes were identified, including verbal learning (Subtype 1), abstract reasoning (Subtype 2), and visual memory (Subtype 3). These subtypes represent different domains of cognitive decline during the progression of AD. Significant differences in age of onset among the different subtypes were also observed. A higher SuStaIn stage was significantly associated with increased mortality risk. DISCUSSION This study provides a characterization of AD heterogeneity in cognitive progression, emphasizing the importance of developing personalized approaches for risk stratification and intervention. Highlights We used the Subtype and Stage Inference (SuStaIn) method to identify three cognitive progression subtypes. Different subtypes have significant variations in age of onset. Higher stages of progression are associated with increased mortality risk.

Abstract Aims The HERMES (HEart failure Molecular Epidemiology for Therapeutic targetS) consortium aims to identify the genomic and molecular basis of heart failure. Methods and results The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome‐wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow‐up following heart failure diagnosis ranged from 2 to 116 months. Forty‐nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34–90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of ≥1.10 for common variants (allele frequency ≥ 0.05) and ≥1.20 for low‐frequency variants (allele frequency 0.01–0.05) at P

Background Increased neck circumference, a proxy for upper‐body subcutaneous fat, is associated with cardiovascular risk and metabolic risk factors, accounting for body mass index (BMI) and waist circumference. The association between neck circumference and incident atrial fibrillation (AF) is unclear. The aim of current study was to evaluate the association between neck circumference and incident AF. Methods and Results We selected participants from the Framingham Heart Study aged ≥55 years without diagnosed AF and with available neck circumference, BMI, and waist circumference measurements. We defined high neck circumference as ≥14 inches in women and ≥17 inches in men on the basis of the Contal and O’Quigley changepoint method. We used Fine‐Gray models to estimate subdistribution hazards ratios (sHRs) for the association between neck circumference and incident AF accounting for the competing risk of death. We adjusted models for clinical risk factors. We then additionally adjusted separately for BMI, waist circumference, and height/weight. The study sample included 4093 participants (mean age 64±7 years, 55% female). During 11.2±5.7 mean years of follow‐up, incident AF occurred in 571 participants. High neck circumference was associated with incident AF (sHR for high versus low: 1.58; 95% CI, 1.32–1.90, P

Background Proteomic biomarkers related to cardiovascular disease risk factors may offer insights into the pathogenesis of cardiovascular disease. We investigated whether modifiable lifestyle risk factors for cardiovascular disease are associated with distinctive proteomic signatures. Methods and Results We analyzed 1305 circulating plasma proteomic biomarkers (assayed using the SomaLogic platform) in 897 FHS (Framingham Heart Study) Generation 3 participants (mean age 46±8 years; 56% women; discovery sample) and 1121 FOS (Framingham Offspring Study) participants (mean age 52 years; 54% women; validation sample). Participants were free of hypertension, diabetes mellitus, and clinical cardiovascular disease. We used linear mixed effects models (adjusting for age, sex, body mass index, and family structure) to relate levels of each inverse‐log transformed protein to 3 lifestyle factors (ie, smoking, alcohol consumption, and physical activity). A Bonferroni‐adjusted P value indicated statistical significance (based on number of proteins and traits tested, P

Background Prior studies relating proteomics markers to incident AF screened for limited numbers of proteins. Methods and Results We performed proteomics assays among participants from the Framingham Heart Study Offspring attending their fifth examination. Plasma protein levels (n=1373) were measured by the SOMAscan proteomic profiling platform. We used robust inference for the Cox proportional hazards model to relate each protein level with incident AF. In addition, we examined the association between AF‐related genetic loci and levels of proteins associated with AF. Our study included 1885 participants (mean age 55±10 years, 54% women) who had proteomic profiles measured. A total of 349 participants developed AF during follow‐up (mean follow‐up 18.3 years). We observed that 8 proteins were significantly associated with incident AF after adjusting for age, sex, technical covariates, and correction for multiple testing (P

BackgroundShort‐term exposure to elevated air pollution has been associated with higher risk of acute cardiovascular diseases, with systemic oxidative stress induced by air pollution hypothesized as an important underlying mechanism. However, few community‐based studies have assessed this association. Methods and ResultsTwo thousand thirty‐five Framingham Offspring Cohort participants living within 50 km of the Harvard Boston Supersite who were not current smokers were included. We assessed circulating biomarkers of oxidative stress including blood myeloperoxidase at the seventh examination (1998–2001) and urinary creatinine‐indexed 8‐epi‐prostaglandin F2α (8‐epi‐PGF2α) at the seventh and eighth (2005–2008) examinations. We measured fine particulate matter (PM2.5), black carbon, sulfate, nitrogen oxides, and ozone at the Supersite and calculated 1‐, 2‐, 3‐, 5‐, and 7‐day moving averages of each pollutant. Measured myeloperoxidase and 8‐epi‐PGF2α were loge transformed. We used linear regression models and linear mixed‐effects models with random intercepts for myeloperoxidase and indexed 8‐epi‐PGF2α, respectively. Models were adjusted for demographic variables, individual‐ and area‐level measures of socioeconomic position, clinical and lifestyle factors, weather, and temporal trend. We found positive associations of PM2.5 and black carbon with myeloperoxidase across multiple moving averages. Additionally, 2‐ to 7‐day moving averages of PM2.5 and sulfate were consistently positively associated with 8‐epi‐PGF2α. Stronger positive associations of black carbon and sulfate with myeloperoxidase were observed among participants with diabetes than in those without. ConclusionsOur community‐based investigation supports an association of select markers of ambient air pollution with circulating biomarkers of oxidative stress.