Faculty Bibliography

INTRODUCTION/BACKGROUND:Sleep, depression, stress, and neighborhood support are independently linked to cognition, but how these factors interact when sleep quality is poor remains understudied. METHODS:We analyzed cross-sectional baseline data from 233 adults aged ≥ 65 years in the Aging Adult Brain Connectome study. Sleep quality, depressive symptoms, stress, and neighborhood support were assessed with validated scales, and cognition was measured using the Preclinical Alzheimer's Cognitive Composite (PACC). Models tested two- and three-way interactions, adjusting for sociodemographics. RESULTS:Poor sleep quality was associated with lower PACC scores (β = -0.57, p = 0.002). This association was even more pronounced in older adults who also had depressive symptoms (β = -0.09, p < 0.001) or increased stress (β = -0.31, p < 0.001). This effect was attenuated by greater neighborhood support (interaction estimates 0.007-0.021, all p ≤ 0.014). DISCUSSION/CONCLUSIONS:Poor sleep quality was associated with lower cognition, compounded by psychosocial burden and buffered by neighborhood support. HIGHLIGHTS/CONCLUSIONS:Poor sleep quality worsened late-life cognitive performance in older adults. Depressive symptoms and stress further worsened the effect of poor sleep on cognitive performance. Neighborhood support buffered negative sleep-psychosocial impacts on cognitive performance.

BACKGROUND/UNASSIGNED:With rising patient volumes and a focus on quality, our health system had the objective to create a more efficient way to ensure accurate documentation of colorectal cancer (CRC) screening intervals from inbound colonoscopy reports to ensure timely follow-up. We developed an integrated end-to-end workflow solution using machine learning (ML) and robotic process automation (RPA) to extract and update electronic health record (EHR) follow-up dates from unstructured data. OBJECTIVE/UNASSIGNED:This study aimed to automate data extraction from external, free-text colonoscopy reports to identify and document recommended follow-up dates for CRC screening in structured EHR fields. METHODS/UNASSIGNED:As proof of concept, we outline the process development, validity, and implementation of an approach that integrates available tools to automate data retrieval and entry within the EHR of a large academic health system. The health system uses Epic Systems as its EHR platform, and the ML model used was trained on health system patient colonoscopy reports. This proof-of-concept process study consisted of six stages: (1) identification of gaps in documenting recommendations for follow-up CRC screening from external colonoscopy reports, (2) defining process objectives, (3) identification of technologies, (4) creation of process architecture, (5) process validation, and (6) health system-wide implementation. A chart review was performed to validate process outcomes and estimate impact. RESULTS/UNASSIGNED:We developed an automated process with 3 primary steps leveraging ML and RPA to create a fully orchestrated workflow to update CRC screening recall dates based on colonoscopy reports received from external sources. Process validity was assessed with 690 scanned colonoscopy reports. During process validation, the overall automated process achieved an accuracy of 80.7% (557/690, 95% CI 77.8%-83.7%) for correctly identifying the presence or absence of a valid follow-up date and a follow-up date false negative identification rate of 32.9% (130/395, 95% CI 29.4%-36.4%). From the organization-wide implementation to go-live until December 31, 2024, the system processed 16,563 external colonoscopy reports. Of these, 35.3% (5841/16,563) had a follow-up date meeting the relevant ML model threshold and thus were identified as ready for RPA processing. CONCLUSIONS/UNASSIGNED:Implementation of an automated workflow to extract and update CRC screening follow-up dates from colonoscopy reports is feasible and has the potential to improve accuracy in patient recall while reducing documentation burden. By standardizing data ingestion, extending this approach to various unstructured data types can address deficiencies in structured EHR documentation and solve for a lack of data integration and reporting for quality measures. Automated workflows leveraging ML and RPA offer practical solutions to overcome interoperability challenges and the use of unstructured data within health care systems.

Plasma proteomic profiles associated with subclinical somatic mutations in blood cells may offer insights into downstream clinical consequences. Here we explore these patterns in clonal hematopoiesis of indeterminate potential (CHIP), which is linked to several cancer and non-cancer outcomes, including coronary artery disease (CAD). Among 61,833 participants (3881 with CHIP) from TOPMed and UK Biobank (UKB) with blood-based DNA sequencing and proteomic measurements (1,148 proteins by SomaScan in TOPMed and 2917 proteins by Olink in UKB), we identify 32 and 345 proteins from TOPMed and UKB, respectively, associated with CHIP and most prevalent driver genes (DNMT3A, TET2, and ASXL1). These associations show substantial heterogeneity by driver genes, sex, and race, and were enriched for immune response and inflammation pathways. Mendelian randomization in humans, coupled with ELISA in hematopoietic Tet2-/- vs wild-type mice validation, disentangle causal proteomic perturbations from TET2 CHIP. Lastly, we identify plasma proteins shared between CHIP and CAD.

BACKGROUND:Current primary prevention guidelines recommend estimation of long-term cardiovascular disease (CVD) risk among younger adults to enable preventive efforts earlier in the life course. However, conceptualizing absolute risk estimates over this time horizon is challenging for both clinicians and patients. Framing risk relative to peers (ie, "population-based percentiles") as complementary information may have utility for risk communication. OBJECTIVES/OBJECTIVE:We sought to develop population-based age- and sex-specific percentiles of 30-year absolute risk estimates for CVD, atherosclerotic cardiovascular disease (ASCVD), and heart failure (HF) with the PREVENT (Predicting Risk of CVD EVENTs) equations. METHODS:We used data from a nationally representative sample of U.S. adults aged 30 to 59 years without prevalent CVD in the National Health and Nutrition Examination Survey (NHANES) 2011 to March 2020. We calculated 30-year risk estimates with PREVENT for eligible participants and derived age- and sex-specific percentiles at each age (using rolling windows of ±5 years) for each outcome of CVD, ASCVD, and HF. RESULTS:Among 8,686 NHANES participants, representing approximately 91 million U.S. adults aged 30 to 59 years in the final sample, mean age was 44.8 ± 0.2 years among female participants and 44.2 ± 0.2 years among male participants. The median 30-year absolute CVD risk in the overall sample was 13.1%. Then, corresponding percentile values were developed and evaluated for each age among women and men aged 30 to 59 years for 30-year risk of CVD, ASCVD, and HF. For example, at age 45 years, the 50th percentile of 30-year CVD risk for was 9.9% for women and 16.2% for men, and the 75th percentile was 14.7% for women and 21.2% for men at that age. Similar patterns were observed for ASCVD and HF, with higher 30-year absolute risk in men compared with women. An online tool was developed to present risk percentiles alongside absolute 30-year risk for CVD, ASCVD, and HF. CONCLUSIONS:Population-based age- and sex-specific percentiles for 30-year CVD risk with PREVENT may offer a complementary tool for clinicians and patients in addition to communicating absolute CVD risk estimates. Prospective studies should test whether this approach improves risk perception or decision-making in younger adults.

Experimental evidence shows that organophosphate esters (OPEs), common flame retardants and plasticizers, can cause developmental neurotoxicity. We investigated the extent to which prenatal OPE exposure was associated with child cognition. Participants were 831 mother-child pairs from 3 sites in the Environmental influences on Child Health Outcomes (ECHO) Cohort with data on urinary levels of 9 OPE analytes during gestation (2009-2019) and child cognition. Based on detection frequencies, analytes were modeled continuously (adjusted for urinary dilution and log2-transformed), categorically (high/low/non-detect), or dichotomously (detect/non-detect). Children's cognition was measured using the Wechsler Preschool and Primary Scale of Intelligence or Wechsler Intelligence Scale for Children at mean age 5.7 years (SD=0.7). We examined associations of OPE analyte with age- and sex-standardized cognition scores using linear regression with generalized estimating equations to account for clustering within sites. We also tested for effect measure modification by sex. The analyte with the highest detection frequency (96.4%) was diphenyl phosphate (DPHP), a primary metabolite of triphenyl phosphate (TPHP). Higher concentrations of DPHP were associated with lower cognition scores ( per doubling of concentrations=-0.46, 95%CI: -0.90, -0.02). Bis(butoxyethyl) phosphate (BBOEP), bis(1-chloro-2-propyl) phosphate (BCPP), and bis(2-methylphenyl) phosphate (BMPP) above the detection limit (vs. below) were associated with higher cognition scores mainly in boys; but, sex interaction with BMPP was not significant. Prenatal exposure to DPHP, a widely detected OPE, was associated with lower cognitive functioning, though the effect size was small. Given widespread exposure, findings related to this and other OPEs should be further examined in mechanistic studies.

BACKGROUND:Data on patient-centered outcomes of mobile health cardiac rehabilitation (mHealth-CR) for older adults with ischemic heart disease are limited. The RESILIENT (Rehabilitation at Home Using Mobile Health in Older Adults After Hospitalization for Ischemic Heart Disease) trial, the largest randomized study of mHealth-CR in this population, found no significant improvements in functional capacity, health status, angina, or disability compared with usual care. OBJECTIVES/OBJECTIVE:The purpose of this study was to evaluate whether mHealth-CR affects personalized goal attainment-a prespecified secondary endpoint of RESILIENT-using goal attainment scaling (GAS). METHODS:A total of 400 patients (≥65 years) with ischemic heart disease were randomized to mHealth-CR or usual care. Participants specified goals for CR at baseline using the five-category goal attainment scale: much-less-than-expected (-2), less-than-expected (-1), expected (0), better-than-expected (+1), and much-better-than-expected (+2). Goal attainment was assessed at 3 months. RESULTS:Of 400 patients (median age, 71.0 years [range 65.0-91.0]; 72.8% male; 65.2% prefrail/frail) randomized to mHealth-CR (n = 298) or usual care (n = 102), 353 (88.3%) completed GAS. Most goals addressed physical activity (54.0% mHealth-CR vs 59.0% usual care), health care behaviors (14.4% vs 11.9%), or symptom management (13.1% vs 9.0%). Rates of attaining or exceeding goals (GAS ≥0) were similar between groups (80.5% vs 77.6%; P = 0.492). However, in the intervention arm, there was a higher rate of exceeding expected level of goal attainment (GAS +1, +2) compared with usual care (52.6% vs 34.2%; P = 0.006). CONCLUSIONS:In a trial that did not demonstrate differences on traditional endpoints, those receiving mHealth-CR were more likely to exceed personalized CR goals. These findings suggest the intervention facilitated greater progress toward individualized goals and underscore the importance of patient-centered outcomes in CR.

BACKGROUND:Adolescents in low- and middle-income countries (LMICs) face significant mental health challenges, yet their perspectives are often underrepresented in the design of preventive strategies. Co-design approaches, such as human-centered design (HCD), offer a promising way to tailor interventions and implementation strategies to adolescents’ needs and local context. In LMICs, these methods require careful adaptation to address resource constraints, limited design literacy, and cultural norms. This study documents how HCD was adapted to engage adolescents in northern Ghana as co-designers of school-based mental health preventive strategies. METHODS:Guided by the first two phases of HCD, we conducted two workshops with 24 students from 12 public senior high schools in Tamale, Ghana. Workshop 1 (Inspiration) used structured, case-based discussions informed by the Consolidated Framework for Implementation Research (CFIR) to explore adolescents’ perspectives on mental health. Workshop 2 (Ideation) used interactive choice-based activities to elicit youth-generated strategies. To align with cognitive and sociocultural factors, we incorporated scaffolded facilitation, hands-on activities, and peer-led engagement. Qualitative data from facilitator notes, artifacts, and audio-confirmed summaries were synthesized using structured rapid qualitative analysis. RESULTS:Adolescents identified key mental health concerns, including stigma, peer and family influences, and fears about confidentiality. Gender-specific discussions revealed culturally rooted concerns, such as peer pressure and substance use among boys and limited support-seeking among girls. Adolescents prioritized five school strategies: teacher training, curricular integration, mentorship programs, activities that promote positive thinking and mindfulness, and entertainment-based mental health education. Youth demonstrated a conceptual shift from viewing mental health as an individual problem to a shared responsibility across schools and communities. Formation of an Adolescent Advisory Board reflected youth interest in sustained leadership and co-design. CONCLUSIONS:Contextualized co-design methods can meaningfully engage adolescents in LMIC settings and support the development of culturally grounded, feasible, and youth-prioritized mental health strategies. Structured facilitation enhances both the inclusivity and authenticity of adolescent engagement. This study contributes to implementation science by presenting a replicable co-design framework with policy relevance and providing a foundation for multilevel intervention development in resource-constrained educational systems. SUPPLEMENTARY INFORMATION:The online version contains supplementary material available at 10.1186/s12889-025-25012-0.