The attitudes toward genomics and precision medicine (AGPM) measure examines attitudes toward activities such as genetic testing, gene editing, and biobanking. This is a useful tool for research on the ethical, legal, and social implications of genomics, a major program within the National Institutes of Health. We updated the AGPM to explore controversies over mRNA vaccines. This brief report examines the factor structure of the updated AGPM using a sample of 4939 adults in the USA. The updated AGPM’s seven factors include health benefits, knowledge benefits, and concerns about the sacredness of life, privacy, gene editing, mRNA vaccines, and social justice.

Objectives/Goals: Our goal is to enhance communication and documentation in collaborative biostatistics by refining data management and metadata processes. We aim to capture critical data collection and generation information, improve transparency and reproducibility, and foster stronger researcher partnerships for more effective collaborations. Methods/Study Population: Traditional statistical analysis plans (SAP) often miss essential contextual knowledge from collaborators, leading to gaps that hinder reproducibility and limit future data use. Biostatistics teams at the University of Kentucky have updated their strategies to better capture important details about data origins and collection processes. By focusing on clear, comprehensive documentation early in the research process, we aim to preserve foundational data insights and improve collaboration efficiency. Our Biostatistics, Epidemiology, and Research Design (BERD) team has established best practices for addressing data management structures with collaborators across medical and healthcare fields – covering all project stages, from initial data collection to metadata creation and dataset finalization. Results/Anticipated Results: We will detail the processes used to improve data management structures and the observed results of these processes. For example, initiating deeper discussions about data origins and collection processes as early as possible in the collaboration has resulted in a more comprehensive project narrative that lays the foundation for effective collaboration. By engaging with project leaders early in the process, we can confirm that critical details about how data were collected and processed are documented, improving both the transparency and reproducibility of research findings. Streamlining the processes of capturing this information makes it more accessible and useful for those with limited statistical backgrounds, which is particularly relevant for faculty and staff in BERD communities and Clinical and Translational Science Awards Programs. Discussion/Significance of Impact: Nuanced data documentation structures are crucial for transforming raw data into meaningful, reusable datasets. Our initiatives promote clear communication, enhanced efficiency, and streamlined workflows. Translational science researchers can benefit from improving data management and metadata to boost long-term collaborative success.

Objectives/Goals: 1) Determine the association between wildfire smoke-driven PM2.5 and risk of persistent respiratory symptoms and repeated asthma exacerbations after the acute wildfire period among adults with asthma. 2) Examine how measures to reduce personal exposure to wildfire smoke, including avoiding outdoor activities, modify this association. Methods/Study Population: This is a retrospective study of adults with asthma in WHAT-NOW, a cohort study of people living in Northern California during the 2018 Camp Fire. Daily smoke-driven PM2.5 was estimated for each participant based on their home address or evacuation location. We examined the association between mean PM2.5 exposure and the presence of respiratory symptoms at both the time of the survey (6–16 months post-wildfire) and at least one other post-wildfire time-period, as well as whether they had a medically attended respiratory illness (saw a doctor, visited the ER, or were hospitalized for a respiratory symptom). We examined the interaction of PM2.5 with spending time outdoors during the wildfires. Poisson regression models with robust standard errors were adjusted for age, sex, race, smoking, allergies, and education. Results/Anticipated Results: Among 337 adults with asthma in the WHAT-NOW cohort, one standard deviation higher smoke-driven PM2.5 was associated with higher risk of any persistent respiratory symptom (risk ratio (RR) 1.38, 95% CI 1.07 – 1.78) and having at least one medically attended respiratory illness (RR 1.33, 95% CI 1.07 – 1.65), but not significantly associated with repeated asthma exacerbations (RR 1.30, 95% CI 0.92 – 1.81). However, there was a significant interaction between PM2.5 and outdoor activities during the wildfire on the outcomes of any persistent respiratory symptoms (p = 0.041) and repeated asthma exacerbations (p = 0.028). The association between PM2.5 and repeated asthma exacerbations was greater among people who spent time outdoors (RR 3.36, 95% CI 1.47 – 10.23) than those who did not (RR 1.00, p = 0.99). Discussion/Significance of Impact: This study provides evidence that exposure to wildfire smoke increases respiratory morbidity among adults with asthma beyond the acute wildfire period. Additionally, it suggests that avoiding outdoor activities on smoky days can significantly decrease the risk of future repeated asthma exacerbations associated with smoke exposure.

Objectives/Goals: Using secure systems for sharing documents with external collaborators is essential for all researchers. These documents may include protected health information (PHI) or sensitive materials like protocols, study reports, DSMB reports, publications, presentations, abstracts, and statistical analysis plans (SAPs). Methods/Study Population: We surveyed the ACTS Biostatistics, Epidemiology, and Research Design Special Interest Group (BERD-SIG) to gather information about the systems they are currently using or have used in the past for document sharing with external collaborators. The survey focused on the security of these systems, particularly in relation to sharing documents containing PHI. In addition, the survey included questions about various system features of interest. These features included version control, simultaneous editing by multiple users, and access rights management, such as the ability to assign different permissions (e.g., read-only, write, and download) to different individuals. We also invited participants to provide feedback on any additional positive or negative aspects of the systems they use. Results/Anticipated Results: We received 28 completed survey responses. Respondents had an option for choosing more than one system. The top current systems reported were Microsoft Teams (OneDrive, SharePoint) (n = 16), Box (n = 11), Google Docs/Drive (n = 10), and Dropbox (n = 6). Among other systems listed individually were Filelocker, REDCap, Slack, Website, Significant Media Shuttle, and Zulip. Notably, 15 responses indicated the respondents were unsure if their system is secure for sharing documents containing PHI. Respondents also offered feedback on both the positive and negative aspects of these systems. For example, a key advantage of Box was its password-controlled access. However, its incompatibility with office tools and the challenges for external collaborators attempting to access the system were noted as drawbacks. Discussion/Significance of Impact: Utilizing secure institutional document-sharing systems and understanding their features significantly affects the effectiveness and security of collaborations among researchers, particularly with external partners. This knowledge is especially crucial when sharing documents containing sensitive patient and study data.

Converting knowledge from basic research into innovations that improve clinical care requires a specialized workforce that converts a laboratory invention into a product that can be developed and tested for clinical use. As the mandate to demonstrate more real-world impact from the national investment in research continues to grow, the demand for staff that specialize in product development and clinical trials continues to outpace supply. In this study, two academic medical institutions in the greater Houston–Galveston region termed this population the “bridge and clinical research professional” (B + CRP) workforce and assessed its turnover before and after the onset of the COVID-19 pandemic . Both institutions realized growth (1.2 vs 2.3-fold increase) in B + CRP-specific jobs from 2017 to 2022. Turnover increased 1.5–2-fold after the onset of the pandemic but unlike turnover in the larger clinical and translational research academic workforce, the instability did not resolve by 2022. These results are a baseline measurement of the instability of our regional B + CRP workforce and have informed the development of a regional alliance of universities, academic medical centers, and economic development organizations in the greater Houston–Galveston region to increase this highly specialized and skilled candidate pool.

Objectives/Goals: Poor visual memory and perceptual organization task performance predicts cognitive decline and is sensitive to dementia severity. No genome-wide association study (GWAS) has assessed the genomic basis of cognitive visual-spatial phenotypes. We aimed to identify common genetic variants associated with visual memory and spatial organization. Methods/Study Population: We included dementia- and stroke-free participants aged 45 years or older from up to seven cohorts in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, who performed cognitive tasks assessing delayed visual memory (e.g., Benton Visual Retention Test (BVRT, n = 10,934) and visual reproductions (VR, n = 5,527)) or spatial organization (i.e., Hooper Visual Organization Test (HVOT, n = 5,024)). Each cohort used linear regression models to relate common genetic variants imputed to the 1000 Genomes panel to each cognitive phenotype, adjusting for age, sex, population stratification, and education. Summary statistics for the BVRT were meta-analyzed using METAL. Combined GWAS was used for a joint analysis of all traits. Results/Anticipated Results: We identified a genome-wide significant variant related to BVRT performance located near the TSHZ3 gene (rs10425277, p = 6.76×10–9). TSHZ3 is important for the development and function of cortical projecting neurons and may be implicated in Alzheimer’s disease progression by repressing CASP4 transcription. Multitrait analyses, including BVRT, VR, and HVOT, identified two additional variants of interest in SMYD3 gene (rs10802275, p = 5.58×10–7) and near ZFPM2 (rs2957459, p = 2.03×10–7), both of which are overexpressed in the brain and have important implications for neurodevelopment. SMYD3 may be directly involved in synaptic dysfunction and has been shown to be upregulated in the prefrontal cortex of Alzheimer’s disease patients. Discussion/Significance of Impact: Our findings suggest that variants related to visual memory and spatial organization are involved in neurodevelopmental and degenerative pathways. This GWAS adds to the growing body of GWAS literature on the genetic basis of cognitive function. Additional analyses are underway to replicate these findings and extend functional annotation.

Objectives/Goals: Longitudinal studies capture dynamic host–environment interactions crucial to microbiome research. Commonly used mixed-effects models can struggle with small clinical samples, like many endometrial cancer studies. We will compare them to simpler fixed-effects models, aiming to develop a new fixed-effects differential abundance alternative. Methods/Study Population: Starting with simulated data for linear models with a single treatment effect, we will incrementally increase complexity to reflect real-world microbiome data. To observe conditions of misspecification, simulated data of repeated measures will be generated using different distributions, while varying parameters and inter-class correlation. In each condition, linear fixed- and mixed-effects models will be fitted 1000 times to empirically determine p-value, type 1 error rate, and power. We will increase the verisimilitude of our approach by using endometrial cancer data from our lab as a real background, while adding simulated signals. This data will be used to assess extant differential abundance models against our novel fixed-effects model. Results/Anticipated Results: Our initial findings indicate that fixed-effects models maintain control over type 1 error rates in small samples, while mixed-effects models do not perform as reliably. We are now exploring the effects of confounders on type 1 error rates and power in linear models, while our next step will evaluate generalized linear models in a differential abundance context. We expect that the fixed-effects models will continue to be as reliable as mixed-effects models, while being less computationally complex. After establishing our theoretical basis, our novel model should perform comparably to mixed-effects models on pseudo-simulated endometrial cancer data and better at small sample sizes. We will finally apply our model to real endometrial cancer data to identify microbial markers of disease predisposition. Discussion/Significance of Impact: By characterizing and improving these statistical tools, we seek to increase the robustness and power of microbiome study results (especially at small sample sizes), ultimately supporting better-informed translational research decisions. This work underscores the importance of statistical methods in advancing microbiome research.

Objectives/Goals: This study demonstrates the utility of the CBID biodesign process for identifying and prioritizing high-impact neurosurgical needs. The research emphasizes the process’s role in developing innovative medical technologies that align with the healthcare ecosystem’s demands and stakeholder priorities. Methods/Study Population: The CBID Spiral Innovation Model, integrating clinical, technical, business, and strategic considerations across clinical challenges in neurosurgery was employed over a 15-week period at a tertiary care center. The process involved three phases: (1) needs identification through 8 weeks of clinical immersion, (2) 7–8 weeks of stakeholder engagement via informational interviews, surveys, and conferences, and (3) iterative refinement based on evidence generation and market value. Stakeholders included over 70 clinicians (neurosurgeons, neurocritical care specialists, neurologists, etc.) across 15 institutions as well as more than 10 payers and hospital administrators. Data collection encompassed direct observation, structured interviews, and comprehensive literature review. Results/Anticipated Results: The initial list of 300+ identified neurosurgical needs was reduced to 271 after clinician and market input. High-level market and clinical evidence assessments further reduced this to 74 needs. Finally, through iterative evaluation of evidence generation, market opportunity, and stakeholder feedback, five critical unmet needs in stroke, traumatic brain injury, hydrocephalus, and epilepsy were identified for technological innovation. These needs met the criteria for clinical importance, economic viability, and market accessibility. The findings highlight the effectiveness of the biodesign process in creating a roadmap for innovation that is both clinically relevant and commercially viable. Discussion/Significance of Impact: This study underscores the effectiveness of structured need-finding and prioritization within neurosurgery. Integrating stakeholder perspectives and rigorous analysis, it provides a replicable framework for medical innovation to accelerate the development of impactful solutions across medicine.

Objectives/Goals: Telehealth is a key solution to improving access to healthcare and disease detection, especially in rural areas. Telehealth access relies on the presence of broadband in an area, which has shifted with the growing importance of internet. We aim to assess the distribution of US county-level broadband availability from 2017 to 2020 overall and by rurality. Methods/Study Population: We employed an ecologic study design to examine the distributions of two measures of broadband availability across all US counties from 2017 to 2020. Broadband presence was defined as counties meeting the Federal Communications Commission (FCC) definition of broadband with at least one high-speed internet provider and an average download/upload speed of at least 25/3 megabits per second using 2017–2020 FCC Broadband Deployment Data. Broadband access, or county-year proportion of households with broadband, was defined using 2017–2020 data from the American Community Survey. We used log-binomial and linear regression models and a difference-in-difference analytical approach to estimate the difference in the change in broadband presence and access from 2017 to 2020 between rural and urban counties. Results/Anticipated Results: Overall, broadband presence increased across the study period, where 66.82% of counties had broadband present in 2017 versus 92.57% in 2020. A major jump occurred from 2018 to 2019, where 64.96% of counties had broadband present in 2018 compared to 91.09% in 2019. Broadband access also rose, where the average proportion of households with broadband was 69.43% in 2017 and 78.24% in 2020. The increase in both broadband presence and access from 2017 to 2020 was larger in rural counties compared to urban counties. Specifically, the increase in the probability of having broadband present from 2017 to 2020 was 30% higher in rural compared to urban counties (95% CI: 1.24, 1.38). The increase in the proportion of households with broadband was 1.21% higher from 2017 to 2020 in rural compared to urban counties (95% CI: 0.004, 0.021). Discussion/Significance of Impact: While broadband presence and access both increased from 2017 to 2020, the observed increases were larger in rural compared to urban counties. Improvements in broadband access in the US are continuously needed to increase the use of telehealth and, subsequently, lessen rural/urban disparities in healthcare access and disease detection.

To facilitate and sustain community-engaged research (CEnR) conducted by academic-community partnerships (ACPs), a Clinical Translational Science Award (CTSA)-funded Community Engagement Core (CEC) and Community Partner Council (CPC) co-created two innovative microgrant programs. The Community Health Grant (CHG) and the Partnership Development Grant (PDG) programs are designed to specifically fund ACPs conducting pilot programs aimed at improving health outcomes. Collectively, these programs have engaged 94 community partner organizations while impacting over 55,000 individuals and leveraging $1.2 million to fund over $10 million through other grants and awards. A cross-sectional survey of 57 CHG awardees demonstrated high overall satisfaction with the programs and indicated that participation addressed barriers to CEnR, such as building trust in research and improving partnership and program sustainability. The goal of this paper is to (1) describe the rationale and development of the CHG and PDG programs; (2) their feasibility, impact, and sustainability; and (3) lessons learned and best practices. Institutions seeking to implement similar programs should focus on integrating community partners throughout the design and review processes and prioritizing projects that align with specific, measurable goals.

Objectives/Goals: Approximately 250,000 anterior cruciate ligament (ACL) tears occur annually in the USA. Symptoms generally improve after ACL reconstruction (ACLR), but 20% of athletes do not return to sport. It is not clear how biomechanical function and mental health impact return to activity, so the purpose of this study is to evaluate their effect on return to activity following ACLR. Methods/Study Population: Patients age 18 years and older who have undergone primary ACLR at a single institution who are one year out from their initial procedure will be recruited by email. Patients will be excluded if they had a concomitant or subsequent ligamentous knee injury in the follow-up period since their index procedure. Additionally, patients will be excluded if they do not have access to a mobile phone with video recording capability. The primary outcome will be joint angle kinematics and postural balance metrics derived from patient recorded mobile-phone videos while performing several provocative exercises (sit-to-stand, Star Excursion Balance test). Patients will also be given surveys assessing knee symptoms, psychological readiness for return to sport, mental health, athletic history, and current return to sport level. Results/Anticipated Results: We predict that psychological readiness for return to sport following ACL injury and biomechanical postural stability will each be independently associated with return to sport timing following ACLR. Additionally, we anticipate that psychological readiness, as measured by higher ACL-Return to Sport Index scores, will be more strongly associated with return to sport compared to biomechanical movement quality (better gross postural control on skeletal modeling). In other words, higher psychological readiness for return to sport will have a stronger independent association with return to sport level compared to biomechanical movement quality measures. Discussion/Significance of Impact: This study also aims to define return to sport in terms of biomechanics and psychological readiness in an athletically heterogenous population. Additionally, we will test the feasibility of patient-led, remote, mobile, marker less motion capture for assessment of biomechanical function and distribution of patient-reported outcome measures.

Objectives/Goals: Physical activity (PA) is a well-documented protective factor against many cardiovascular diseases. PA guidelines to reduce these risks and the impact of variability are unclear, and most studies only examine a 7-day activity window. This study aimed to examine factors related to variability in step counts in a 3-year study of adults aged ≥18 years. Methods/Study Population: Included were 6,525 participants from the Michigan Predictive Ability and Clinical Trajectories study, a prospective cohort of community-dwelling adults enrolled between 8/14/2018 and 12/19/2019 who received care at Michigan Medicine and were followed for 3 years. Data were collected from Apple Watches provided to participants via the HealthKit. This secondary analysis included those with ≥4 valid weeks of data (≥4 days with at least 8 hours of wear time). Season was defined as Spring (March 20–June 20), Summer (June 21–September 21), Fall (September 22–December 20), and Winter (December 21–March 19). GEE models against the outcome of variability, defined as weekly standard deviation of step counts, and the predictor of season were adjusted for age, sex, race/ethnicity, weekly average step count, diabetes, and body mass index. Results/Anticipated Results: The average (standard deviation (SD) step counts by season were 7101 (3434) in Spring, 7263 (3354) in Summer, 6863 (3236) in Fall, and 6555 (3211) in Winter. Compared to winter, there was statistically significantly higher variability in all other seasons (p Discussion/Significance of Impact: In this cohort of community-dwelling adults, we found significant differences in variability of physical activity by season, age, and BMI. Future work will examine how this variability impacts the risk of development of cardiovascular disease, incorporating the impact and recovery trajectories of COVID-19 and other acute respiratory infections.

Objectives/Goals: Aim 1: Assess the correlation between Tuft cell T2R functionality and Th2 sinonasal inflammation, disease burden, and post-surgical outcomes in AERD patients. Aim 2: Determine if hyperfunctional Tuft cell T2Rs enhance denatonium-stimulated inflammatory responses in nasal epithelial air–liquid interface (ALI) cultures from AERD patients. Methods/Study Population: Aim 1: We will conduct a prospective cohort study with 82 AERD patients. Taste sensitivity to denatonium (DB), serving as a proxy for tuft cell T2R functionality, will be assessed using a validated 13-point scale, and correlations with clinical outcomes – SNOT-22, histopathologic, CT, and endoscopic scores – will be analyzed using linear regression. Aim 2: Sinonasal epithelial cells will be collected from AERD patients either hyper- or hyposensitive to DB and from healthy controls. We will establish ALI cultures and expose them to varying DB concentrations. Secretions will be analyzed for antimicrobial peptide release via bacterial kill assays and for IL-25 and β-defensin 2 via ELISA. Tuft cell frequency and baseline IL-25 mRNA expression will be assessed using immunofluorescence and quantitative real-time polymerase chain reaction, respectively. Results/Anticipated Results: We expect that higher DB taste sensitivity in AERD patients will correlate with worse clinical outcomes, reflected by elevated 6-month postoperative SNOT-22 scores, indicating increased symptoms. Additionally, we anticipate that preoperative Lund-MacKay and Lund-Kennedy scores, along with histopathological metrics, will be worse in DB-hypersensitive patients, establishing a link between taste sensitivity and disease burden. In vitro, we predict that AERD patients with DB hypersensitivity will demonstrate significantly higher IL-25 and β-defensin 2 secretion and reduced bacterial colonies in kill assays. We also expect increased tuft-cell frequency and baseline IL-25 mRNA in AERD-derived cultures compared to healthy controls, highlighting T2R functionality’s role in AERD pathogenesis. Discussion/Significance of Impact: This project aims to investigate a putative new role for Tuft cells in AERD pathogenesis by correlating Tuft cell T2R functionality with outcomes in AERD patients and with inflammatory response in vitro. Findings could lead to predictive clinical taste tests and future genotyping studies to identify T2R polymorphisms correlated with AERD severity.