Faculty Bibliography

INTRODUCTION/BACKGROUND:Skeletal muscle loss is associated with cognitive decline, but whether neuroimaging-derived muscle characteristics predict incident dementia remains unclear. METHODS:We evaluated associations of deep learning-derived temporalis muscle (TM) cross-sectional area (CSA) and radiomic texture features from baseline T1-weighted magnetic resonance imaging (MRI) with incident dementia in dementia-free participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (n = 750) and the Atherosclerosis Risk in Communities (ARIC) study (n = 532). TM was segmented using a convolutional neural network trained in ADNI and externally validated in ARIC. Radiomic features were reduced using least absolute shrinkage and selection operator-penalized Cox models to generate a composite score. Multivariable Cox regression adjusted for demographics, apolipoprotein E ε4, baseline cognition, body mass index, and physical performance. RESULTS:Higher TM radiomic scores were associated with increased dementia risk in ADNI (hazard ratio per SD, 1.32) and ARIC (1.64). Smaller TM CSA predicted dementia in ADNI but not ARIC. DISCUSSION/CONCLUSIONS:TM texture patterns from routine brain MRI are associated with dementia risk, supporting TM phenotyping as a scalable marker of systemic biological vulnerability.

BACKGROUND:We investigated whether OPE urinary concentrations during pregnancy were associated with child's autism-related outcomes. METHODS:We included 4159 mother-child pairs from 15 cohorts in the NIH Environmental influences on Child Health Outcomes (ECHO) Consortium, with children born from 2006-2020 (median age [interquartile range]: 6 [4,10] years). Nine OPE biomarkers were measured in urine samples collected mid- to late pregnancy. Dilution-adjusted biomarkers were modeled continuously, categorically (high [>median], moderate [≤median], nondetect), or as detect/nondetect depending on their detection frequency. We assessed child autism-related traits via a) parent report on the Social Responsiveness Scale (SRS) and b) clinical autism diagnosis. We examined associations of OPEs with child outcomes, including modification by child sex, using generalized estimating equations to account for clustering by ECHO cohort. RESULTS:Compared with nondetectable concentrations, high exposure to bis-(butoxyethyl) phosphate (BBOEP) was associated with higher autistic trait scores (adj-β 0.97, 95% confidence interval [CI]: 0.42, 1.52) and greater odds of autism diagnosis (adjusted odds ratio [adj-OR]: 1.27, 95% CI: 1.07, 1.50). Bis-(1-chloro-2-propyl) phosphate (BCPP) showed associations with autistic trait scores (BCPP adj-β for high exposure vs nondetect: 0.34, 95% CI: -0.46, 1.13; BCPP adj-β for moderate exposure vs nondetect: 0.72, 95% CI: 0.24, 1.20). High exposure to bis-(2-chloroethyl) phosphate (BCETP) was associated with lower odds of autism diagnosis (adj-OR: 0.76, 95% CI: 0.60, 0.95). Other OPEs showed no associations in adjusted models. Associations between BBOEP and higher autistic trait scores were stronger in males than females. DISCUSSION:Prenatal exposure to OPEs, specifically BCPP and BBOEP, may be associated with a higher risk of autism diagnosis and related traits in childhood.

BACKGROUND/UNASSIGNED:The American Heart Association Predicting Risk of Cardiovascular Disease EVENT (PREVENT) equations were developed from observational research cohorts and electronic health record data and provide sex-specific risk estimates for cardiovascular disease (CVD), atherosclerotic CVD (ASCVD), and heart failure (HF). External validation in large contemporary samples across multiple health systems in the United States is needed. METHODS/UNASSIGNED:We assembled a national electronic health record-based cohort of US adults with individual-level patient data pooled from a collective of 30 health systems (Truveta) to externally validate the outcome-specific 10-year PREVENT equations (PREVENT-CVD, PREVENT-ASCVD, and PREVENT-HF). We included patients aged 30 to 79 years without a history of prior CVD and with an ambulatory encounter in the electronic health record between 2013 and 2018. The outcomes were defined as total CVD (composite of ASCVD and HF), ASCVD, and HF through December 2024 using diagnosis codes. Model performance of the outcome-specific PREVENT base equations was assessed with the Harrell C statistic and calibration slope, stratified by sex. RESULTS/UNASSIGNED:Of the 680 864 adults included, the mean (SD) age was 55 (13) years, and 56% were female. Over a mean (SD) follow-up of 6.8 (2.3) years, there were 29 535 incident CVD events, 19 280 incident ASCVD events, and 16 824 incident HF events. The median (interquartile range) 10-year predicted risk of PREVENT-CVD among women was 3.6% (1.3%-8.8%), and among men was 5.8% (2.5%-11.7%). The C statistic (95% CI) was 0.788 (0.786-0.790), and the calibration slope (95% CI) was 0.98 (0.95-1.01) for PREVENT-CVD. PREVENT-ASCVD and PREVENT-HF demonstrated similar C statistics (0.774 [0.771-0.777] and 0.824 [0.820-0.828]) and calibration slopes (1.07 [1.04-1.10] and 1.01 [0.97-1.04]) for prediction of the 10-year risk of ASCVD and HF, respectively. CONCLUSIONS/UNASSIGNED:The PREVENT equations accurately and precisely estimate the 10-year risk of CVD, ASCVD, and HF in a large sample of US adults. These findings support the generalizability of the PREVENT equations to inform guideline-recommended risk assessment and preventive efforts.

OBJECTIVE:The quality of discharge processes impacts adverse outcomes post hospitalization for children with medical complexity (CMC). Perspectives of caregivers at risk for communication challenges (eg, non-English speakers, those with limited health literacy, immigrants) on understandability and feasibility of discharge instructions for CMC are understudied. Our objective was to explore their perspectives on barriers and facilitators to comprehension and implementation of discharge instructions for CMC. PATIENTS AND METHODS/METHODS:In this qualitative, descriptive study, we used maximum variation sampling to enroll caregivers of CMC (N = 40) discharged from acute or intensive care units of 2 urban hospitals. English- and Spanish-speaking caregivers of CMC (Pediatric Medical Complexity Algorithm) aged 18 years or older and discharged on 1 or more daily medications were eligible. We conducted semistructured interviews, audio-recorded and transcribed interviews, and performed content analysis. Two team members applied codes from a codebook (developed based on prior literature and preliminary analyses) and identified emerging themes. RESULTS:Equal numbers of participants spoke English and Spanish (n = 20 per language) and were recruited from each hospital (n = 20 per hospital). Half (47.5%) had low health literacy; 85% were of non-US birth. Themes emerged across 4 categories: (1) clinician and medication access; (2) patient- and family-centered approaches to discharge planning and education; (3) caregiver prior experience; and (4) resources and support systems. CONCLUSIONS:Caregivers identified barriers and facilitators affecting discharge plan comprehension and implementation. Optimizing postdischarge care for CMC is complex and requires family-centered, language-concordant, and health literacy-informed processes. Future interventions should focus on systems-level changes that enhance access and account for caregiver perspectives.

INTRODUCTION/UNASSIGNED:Young Black children experience poorer sleep health than children from other racial/ethnic groups. Tailored family-centered interventions are needed to improve sleep health among young Black children. Tailoring requires information about parent perceptions, practices and context. The purpose of this study was to (1) understand the contextual definition of sleep health for Black families; (2) bedtime routines and sleep practices among Black families; and (3) parent preferences that may inform intervention design. METHODS/UNASSIGNED:We engaged 30 Black parents of 3-8-year-old children with mild sleep problems (e.g., child takes >30 min to fall asleep at bedtime) in this study. Parents completed three ratings scales about sleep health practices and child bedtime behavior and a semi-structured interview. Statistical analysis included descriptive statistics and correlations. We used an implementation science rapid qualitative analysis approach to analyze qualitative data from the interviews. RESULTS/UNASSIGNED:The final sample identified as 52% African-American, 14% Jamaican, 10% Haitian, and 26% mixed multi-ethnic groups. Parent mean age was 41 years and child mean age were 5 years old. About 80% of responders were women. About one-third of the sample held a doctoral degree, and half the sample had a bachelor's or Master's degree. Parents answered 56% of sleep knowledge questions accurately (ranging from 32 to 100%) and reported an average of two sleep problems. Parents described healthy sleep as involving flexibility in sleep timing and bedtime routines. More than half of parents reported co-sleeping practices, with reasons ranging from a strategy to address night wakings, to a way to preserve bonding. Later child bedtime (after 9 pm) was associated with bedtime resistance, permissive parenting, and parent stress. Many parents reported that they had poor sleep quality and duration. DISCUSSION/UNASSIGNED:Findings elucidate a range of factors to consider in tailoring sleep health interventions for Black families of young children. These include the common practice of co-sleeping, parents' value for flexibility related to bedtime and bedtime routines, and parents' own challenges with getting enough and good quality sleep. Tailored family-centered interventions will benefit from considering these factors.

BACKGROUND:Care coordination between primary care providers and nephrologists is crucial for preemptive kidney transplantation (KT), which confers health advantages over KT after dialysis. Residence in areas with limited primary care (Medically Underserved Areas [MUAs]/Health Professional Shortage Areas [HPSAs]) and nephrology access may differentially affect preemptive listing/KT. OBJECTIVE:To quantify access to preemptive KT by residence in limited primary care/nephrology access areas. DESIGN/METHODS:Retrospective cohort study from the US national registry. PARTICIPANTS/METHODS:A total of 353,636 adult KT candidates (age ≥ 18) listed between 2005-2020. EXPOSURES/METHODS:ZIP-code level MUA and HPSA information (HRSA), and distance to nearest nephrologist (CMS; urbanicity-specific thresholds for "far" from nephrologists: suburban, > 5.8 km; urban, > 2.3 km; small town, > 19.4 km; rural, > 25.2 km). MAIN MEASURES/METHODS:Poisson regression with robust variance estimator quantified adjusted prevalence ratios (aPRs) of preemptive listing, and cause-specific hazards models quantified adjusted hazard ratios (aHRs) of preemptive KT by MUAs/HPSAs/distance to nephrologists. Interaction terms quantified differences in the aforementioned associations by race and ethnicity/neighborhood urbanicity/socioeconomic determinants. KEY RESULTS/RESULTS: < 0.05). Lastly, there were no associations between distance to nephrologists and preemptive listing/KT. CONCLUSIONS:Limited primary care access may impede KT access. Greater investment in primary care within MUAs/HPSAs, addressing geographic/linguistic barriers, and improved nephrology care coordination may increase transplant equity. CLINICAL TRIAL NUMBER/BACKGROUND:Not applicable.

Genome-wide association studies identify cancer susceptibility loci, but downstream protein mechanisms remain incompletely defined. We integrate polygenic risk scores (PRSs) for 21 cancers with 4,955 plasma proteins measured in cancer-free Atherosclerosis Risk in Communities (ARIC) participants to prioritize cancer-related proteins and protein networks. The protein quantitative trait score (pQTS) approach assesses associations between cancer PRS and individual protein levels, while ARCHIE partitions cancer risk variants into trans-regulated protein-network components using sparse canonical correlation analysis. Across cancers, pQTS identifies 90 protein associations, including 53 distal trans associations, and ARCHIE identifies 19 components spanning 433 proteins. Downstream analyses connect prioritized proteins to cancer driver genes, somatic alterations, immune cell populations, CRISPR dependency, and cancer-relevant pathways. Cervical cancer and basal cell carcinoma illustrate immune, human papillomavirus (HPV)-related, pigmentation, and inflammatory mechanisms. These findings show that PRS-proteome integration can reveal circulating protein networks underlying inherited cancer susceptibility.

INTRODUCTION/UNASSIGNED:Music-based interventions (MBIs) are widely used in dementia care, but objective methods for characterizing participant responses during intervention sessions remain limited. Synchronized datasets combining wearable physiological signals and behavioral observations are particularly scarce. METHODS/UNASSIGNED:We conducted a pilot feasibility study involving five individuals with Alzheimer's disease and related dementias (ADRD) who participated in 13 formal MBI sessions. Physiological signals, including photoplethysmography (PPG), electrodermal activity (EDA), skin temperature (TEMP), and accelerometry (ACC), were collected using a wrist-worn wearable sensor and synchronized with intervention playlists and time-stamped behavioral observations. Exploratory analyses examined physiological responses across intervention phases, participant-specific response patterns, time-of-day effects, and music-preference effects. RESULTS/UNASSIGNED:The dataset contains 13 intervention sessions, 99 music segments, and 248 behavioral observations. PPG, ACC, TEMP, and behavioral observations were available for all sessions, while EDA quality varied because of sensor-contact challenges. Behavioral responses were highly heterogeneous across participants, with engagement and calm behaviors observed most frequently. Physiological responses also showed substantial inter-individual variability, and case studies demonstrated that physiological and behavioral responses were not always concordant. CONCLUSION/UNASSIGNED:This study demonstrates the feasibility of collecting synchronized physiological, behavioral, and intervention-context data during MBIs in people living with dementia. The resulting publicly available multimodal dataset provides a foundation for future investigations of participant-specific responses and adaptive music-based interventions.