Objectives/Goals: To culturally adapt and validate the Spanish version of the UCLA Loneliness Scale-10 for the Puerto Rican population and to determine the prevalence and characteristics of loneliness among individuals aged 60 and older across various regions of the island. Methods/Study Population: A quantitative, descriptive, cross-sectional study was conducted including cultural adaptation and psychometric validation of the Spanish UCLA Loneliness Scale-10. A pilot with 47 older adults from northeastern Puerto Rico preceded a stratified random sample of 400 adults aged 60+ from public housing in 24 municipalities, covering Puerto Rico’s eight regions. Sample size was calculated with 99% confidence and 5% error margin. Recruitment occurred Dec 2024–Mar 2025. Inclusion: age ≥60, Spanish-speaking, residing in Puerto Rico. Cognitive function was screened using MMSE. Results/Anticipated Results: Most participants were female (78.3%) and aged 60-70 (59.5%). Over half lived alone (54.3%), and 82% earned under $10,000 annually. The UCLA Loneliness Scale showed high validity (CVI=0.99) and reliability (α=0.88). Loneliness prevalence was 39.8%, mostly moderate, with 4.25% severe. No significant differences in loneliness by gender, marital status, or family composition were found. However, low income, limited family accessibility, poor neighborhood relations, and mobility issues were significantly linked to higher loneliness. Social activity showed a trend toward reducing loneliness but was not statistically significant. Discussion/Significance of Impact: This study with 400 Puerto Rican older adults confirms the culturally adapted UCLA-10 scale’s reliability (α=0.88), crucial for accurately measuring loneliness in this population. Findings reveal 39.8% loneliness prevalence, influenced by socioeconomic and social factors, underscoring the need for culturally sensitive interventions.

Objectives/Goals: To (1) determine concordance (matched clinical and pathological AD diagnoses) and discordance (mismatched) between clinical and neuropathological findings in AD and (2) determine whether educational attainment differentiates individuals with concordant versus discordant AD profiles. Methods/Study Population: Secondary analyses will use data from the National Alzheimer’s Coordinating Center (NACC), which include baseline and follow-up cognitive assessments, educational history, and postmortem neuropathological evaluations. Participants will be classified into four clinico-pathological groups: two concordant groups – (1) true AD (both clinically and pathologically confirmed AD) and (2) true healthy (no clinical or pathological evidence of AD); and two discordant groups – (3) resilient (pathologically confirmed AD without clinical symptoms) and (4) symptomatic without pathology (clinical AD with absent or minimal pathological confirmation). Multinomial logistic regression will be used to examine associations between educational attainment and group classification. Results/Anticipated Results: We hypothesize that higher educational attainment will be associated with an increased likelihood of clinico-pathological discordance. Specifically, individuals with higher education are expected to be overrepresented in the resilient group (high pathology, no clinical AD) compared to the true AD group (both clinical and pathological AD). Conversely, lower education levels may be more common in the true AD and symptomatic without pathology groups, reflecting reduced cognitive reserve or potential diagnostic misclassification. We further anticipate that within the discordant groups, higher education will correlate with better baseline and follow-up cognitive performance despite comparable levels of neuropathological burden. Discussion/Significance of Impact: Findings would suggest that education enhances cognitive reserve, mitigating the clinical effects of AD pathology and helping explain why some individuals remain cognitively intact despite significant disease. Recognizing this role highlights the need to integrate neuropathological and reserve-related factors in AD research and diagnosis.

Objectives/Goals: The objectives of this project were to (1) develop a multidimensional well-being index, reflecting personal capacities and environmental conditions, and (2) evaluate its association with preterm birth. The goal of this investigation was to illustrate the value of multidimensional well-being for understanding health outcomes. Methods/Study Population: The LIFE study was a retrospective cohort of postpartum US-born Black women (ages 18+) recruited 2009–2011 from a suburban Detroit hospital. Eligible participants self-identified as African American, had singleton births, and spoke English; exclusions included incarceration, cognitive impairment, or serious mental health conditions. Trained interviewers collected survey and medical data during postpartum stays. Of 1,410 recruits (71% response), 1,177 had complete data. Key variables were binarized; logistic regression models assessed associations with preterm birth. Results/Anticipated Results: Few of the 21 binarized variables showed individual statistical significance. Significant predictors included having someone to offer advice about a crisis or to listen when needing to talk about oneself or one’s problems, and self-rated physical and mental health satisfaction. Of the dimension scales, only the physical health scale (physical health rating, mental health rating, and mental health satisfaction) was significant. The overall well-being index was borderline significant for lower odds of preterm birth (OR=0.934, 95% CI: 0.887–1.002, p=0.0593), reaching significance when adjusted for maternal age (OR=0.938 95% CI: 0.883-0.998, p=0.0414). Discussion/Significance of Impact: This study’s findings underscore that well-being is best understood as a holistic interconnected construct. By centering context, this approach advances health equity, encourages systemic accountability and change, and inspires innovative public health research.

Objectives/Goals: Amyloid deposition in the ligamentum flavum (LF) is associated with LF hypertrophy and resulting lumbar stenosis (LS) in some patients. Whether amyloid causes inflammation and subsequent fibrosis in the LF is unclear. An association between amyloid and markers of inflammation may present an opportunity to treat early stenosis with medical therapy. Methods/Study Population: LF specimens from 66 patients with lumbar stenosis (LS) were analyzed, including 33 amyloid-positive and 33 amyloid-negative patients matched by age and diagnosis. We performed immunohistochemistry on 7 tissue sections per specimen for biomarkers of inflammation (IL-6, C9) and fibrosis (Collagen, Elastin, TGF-β1, α-SMA, ROCK1). All biomarkers were quantified using a validated random forest classifier in QuPath. In Aim 1, we compared the collagen-to-elastin ratio between groups as an indicator of fibrosis, with secondary comparison of the collagen and elastin percent (α=0.025 each, Bonferroni corrected). In Aim 2, we will compare expression of IL-6, C9, TGF-β1, α-SMA, and ROCK1 using a multivariate mixed-effects regression model to identify inflammatory and fibrotic patterns linked to amyloid deposition. Results/Anticipated Results: This study tests the hypothesis that amyloid deposition in the ligamentum flavum (LF) drives fibrosis, and the resulting LF hypertrophy and lumbar stenosis (LS), via an inflammatory pathway. Aim 1 analyses are complete, and amyloid-positive LF demonstrated a higher collagen-to-elastin ratio, with a mean difference of 0.31 (0.07–0.55, p = 0.014). This difference is driven by elastin, which covers 7.5% less tissue area in amyloid-positive LF (p = 0.008). This is the first histological evidence that amyloid is associated with altered extracellular matrix composition in the LF. We hypothesize that amyloid-positive LF will show elevated expression of IL-6 and C9, comparable TGF-β1 and α-SMA, and lower ROCK1, supporting an inflammatory pathway to LF hypertrophy distinct from pathways triggered by mechanical stress. Discussion/Significance of Impact: This work will provide evidence on a possible role of amyloid in the pathophysiology of LS in a subset of patients. Our results will guide more robust quantitative analyses, including proteomics and transcriptomics, and for the first time, may support trial of anti-amyloid medical therapies to slow the progression of this primarily surgical disorder.

Objectives/Goals: Emerging studies demonstrate association of hearing loss with adverse mental and cognitive outcomes, warranting the need for reliable measures of hearing in epidemiological research. This study compared the prevalence of hearing loss among US adults based on objective and self-reported measures, as well as hearing aid use from the HRS. Methods/Study Population: This is a cross-sectional study utilizing data from the 2016/18 Health and Retirement Study (HRS), a nationally representative cohort of US adults aged ≥50 years. Participants who completed both the objective hearing screening using the HearCheck device and hearing-related questionnaires were included (n=13,087). Survey weights were applied to account for the complex sampling design to estimate the prevalence of hearing loss as well as hearing aid use among those with objective hearing loss. Agreement between objective and self-reported hearing status was assessed using Cohen’s κ. Multivariable regression analyses examined demographic factors (age, sex, race/ethnicity, income, and education) associated with under-reporting of hearing loss and hearing aid use. Results/Anticipated Results: In this nationally representative sample of US adults aged ≥50 years, the prevalence of objective hearing loss was 52.6% [95% CI: 51.5-53.7] and self-reported hearing loss was 26.9% [25.9-27.9%]. Hearing aid use rate was 9.7% [8.9-10.5] among those with any objective hearing loss (score<6) and 44.9% [41.1-48.9] among those with moderate or worse hearing loss (score<3). Agreement between objective and self-reported hearing was low (Cohen’s κ=0.2), with 61.6% [60.1-63.0] under-reporting their hearing loss. In a multivariable model accounting for demographic factors, under-reporting was more likely among females (OR: 1.8 [1.6-2.1]) and individuals with higher education (OR: 2.0 [1.6-2.6]). Hearing aid use was associated with higher education (OR: 2.9 [2.0-4.2]) and higher income (OR: 2.8 [1.9-4.0]). Discussion/Significance of Impact: Hearing loss is common in older US adults, yet hearing aid use remains low. Frequent under-reporting of hearing loss highlights the need for objective measures in epidemiology studies to elucidate the impact of hearing loss on health outcomes. Addressing costs and awareness may improve access and lessen negative health effects related to hearing loss.

Objectives/Goals: This study evaluates whether BT2-mediated reduction of branched-chain amino acids enhances cognitive performance and alters cortical gene expression in transgenic Alzheimer’s disease mouse models, clarifying BCAA’s role in AD pathology and assessing BT2’s potential as a disease-modifying therapy. Methods/Study Population: Four groups of mice were used: wild-type vehicle, wild-type with 20 mg BT2, AD vehicle, and AD with 20 mg BT2(3,6-dichlorobenzobthiophene2-carboxylic acid). BT2, an inhibitor of branched-chain aminotransferase 2, was administered intraperitoneally to reduce BCAA accumulation implicated in mTOR overactivation and tau hyperphosphorylation. Cortical tissue samples were collected for RT-qPCR analysis of 17 genes linked to neurodegeneration. Y-Maze behavioral assays assessed spatial memory and cognition. ANOVA with post-hoc Tukey’s tests (α = 0.05) assessed intergroup significance between behavioral and transcriptional outcomes. Additionally, a Random Forest Regressor Model was utilized in order to identify gene expression patterns predictive of cognitive improvement following BT2 treatment. Results/Anticipated Results: BT2 (3,6-dichlorobenzo[b]thiophene-2-carboxylic acid) treatment restored Beclin-1, VPS-26, NRF2, and NF-κB expression to near wild-type levels, correlating with improved Y-Maze performance. These changes enhanced autophagy, reduced inflammation, and improved mitochondrial resilience. Partial recovery of BACE1, MFN2, and PSD-95 indicated moderate restoration of amyloid processing and synaptic plasticity. Enrichr analysis highlighted pathways in autophagy, mitochondrial biogenesis, and miRNA regulation, suggesting BT2 exerts neuroprotective effects by modulating metabolic and inflammatory homeostasis. The Random Forest Regressor Model’s Feature importance mapping further revealed that changes in Beclin-1, NRF2, and NF-κB expression most strongly correlated with behavioral recovery. Discussion/Significance of Impact: BT2 shows strong potential in mitigating Alzheimer’s pathology through branched-chain amino acid modulation. It normalizes NRF2, Beclin-1, and NF-κB, promoting neuroprotection and cognitive improvement. This supports BT2 as a promising metabolic therapy warranting further long-term studies.

Objectives/Goals: Widespread COVID-19 vaccine hesitancy was a critical barrier to managing the pandemic. Rural populations represent a key demographic in this challenge, yet the specific drivers of their hesitancy remain underexplored. To address this gap, we analyzed a survey of 454 participants in rural Mississippi to identify key predictors of vaccine hesitancy. Methods/Study Population: We employed six machine learning models and a logistic regression model, using feature importance and partial dependence analyses to understand the influence of demographic, behavioral, and belief-based factors. Results/Anticipated Results: Among participants, 98 (21.6%) reported vaccine hesitancy. The Bayesian Additive Regression Trees (BART) model identified vaccine safety as the most critical predictor, with hesitancy probabilities dropping from 0.4 to below 0.1 among those who agreed that vaccines are safe. Different from some other studies, factors such as vaccine accessibility and healthcare provider support had limited influence. Belief in vaccine safety and education level were the primary drivers of vaccine hesitancy in the rural population. Discussion/Significance of Impact: These findings suggest that public health interventions targeting rural communities must better communicate vaccine safety to effectively combat hesitancy.

Objectives/Goals: The objectives of this project aim to (1) feature strengthened areas of integration between Biostatistics, Epidemiology & Research Design (BERD) and Biomedical Informatics (BMI) Cores, (2) exemplify an inter-core approach to maximize CTSA data service resources, and (3) discuss opportunities to adapt this approach in other CTSA hubs. Methods/Study Population: BERD and BMI Cores are partnered disciplines integral to the mission of CTSA hubs but often operate independently of each other which can result in unfulfilled data resource linkages. In 2023, the University of Kentucky Center for Clinical and Translational Science integrated joint operations between the two cores to maximize research data support services. This project defines three areas of inter-core collaboration between BERD and BMI in CTSA hubs in academic medical centers: (1) collaborative needs assessment for research data support services, (2) joint consultations for study design, analysis, and data extraction, and (3) partnered joint data resource navigation. Process comparisons of previous and current workflows are presented to show the advantages of utilizing collaborative core efforts. Results/Anticipated Results: When operations are integrated, BERD and BMI Cores leverage CTSA resources to a higher capacity, with data-intensive research support becoming more effective and expedited for project timelines and workflows across the CTSA, especially in support of translational science and research. Early evaluation results support improved operation performance metrics and inter-core teamwork satisfaction. With this partnership, BERD and BMI cores strengthen CTSA data research support services including functional datasets, higher-level service, and leveraging institutional resources. Common service issues including cycles of data re-extractions and limited data support are now avoided when BERD and BMI cores address investigator needs with a joint approach. Discussion/Significance of Impact: CTSAs aiming to improve research navigation efficiency might approach joint BERD and BMI core collaborations and adapt these strategies to fit institutional infrastructure. This collaboration can serve as a case study in translational science innovation, addressing persistent CTSA workflow, hiring, and resource allocation.

Objectives/Goals: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with unclear biological causes, potentially influenced by the gut-brain axis. This study sought to explore the infant gut microbiome to identify whether specific changes in the microbiota during early infancy are associated with later diagnosis of ASD. Methods/Study Population: Study participants included a subset of children from MARBLES (Markers of Autism Risk in Babies – Learning Early Signs), an ASD enriched-risk cohort study. Infant stool samples were collected from these children between 0 and 7 months of age. At 36 months of age, children were clinically classified as having ASD (n=42), or non-typically developing without ASD (Non-TD, n=16), or typically developing (TD, n=79). The fecal microbiome was investigated using 16S rRNA gene sequencing to evaluate whether the gut microbial composition during early infancy was associated with later neurodevelopmental diagnoses. Results/Anticipated Results: Overall, no significant differences in alpha diversity or beta diversity, or bacterial abundance, were found between groups of infants who developed ASD or Non-TD compared to those who went on to have TD. However, our findings highlight some early alterations in gut microbial composition during infancy that may relate to later neurodevelopmental outcomes. For example, infants who developed ASD showed slightly lower Veillonella and Flavonifractor levels, suggesting minor early microbial distinctions potentially linked to neurodevelopment. These differences are less pronounced than the effects of delivery mode or diet on the infant gut microbiome. Discussion/Significance of Impact: Although we found no significant differences in bacterial abundance in early infancy linked to later neurodevelopmental outcomes, our results highlight minor microbial shifts that align with findings in older children with ASD, which may indicate early gut microbiome dysbiosis, but further research is needed.

Objectives/Goals: Although the Deepest Vertical Pocket (DVP) method is increasingly used to assess amniotic fluid volume due to its simplicity and lower intervention rates, its relationship with neonatal outcomes remains underexplored. This study aims to determine if DVP measurements are associated with Apgar scores at 1 and 5 minutes in low-risk term pregnancies. Methods/Study Population: An IRB-approved retrospective cohort study of 171 singleton term pregnancies delivered at Penn State Hershey Medical Center between 2019 and 2024 was performed. Inclusion criteria included delivery at ≥37 weeks, birth weight between the 10th and 90th percentiles, absence of fetal anomalies, spontaneous labor or scheduled Cesarean, and classification as low risk based on ACOG 2019 Levels of Maternal Care guidelines. A single second-trimester DVP measurement was obtained from anatomy ultrasounds. Apgar scores at 1 and 5 minutes were abstracted from delivery records. Statistical analysis included ANOVA and Spearman correlation to assess relationships between DVP and Apgar scores. Results/Anticipated Results: Among 171 patients, the mean DVP was 4.79 cm (SD 0.94). Apgar scores at 1 minute were most commonly 8 (73%) or 9 (15%), and 94% of neonates had a score of 9 at 5 minutes. There was no statistically significant correlation between DVP and Apgar scores at 1 minute (Spearman r = –0.11, p = 0.14) or 5 minutes (r = 0.10, p = 0.19). ANOVA results also revealed no significant differences in DVP across Apgar score categories at 1 minute (p = 0.12) or 5 minutes (p = 0.41). Discussion/Significance of Impact: In this cohort of low-risk term pregnancies, DVP was not significantly associated with Apgar scores at 1 or 5 minutes. This study is one of the few to isolate DVP rather than AFI and to apply it to routine obstetric care. These findings support the use of DVP as a non-invasive, reliable fluid assessment method without concern for adverse neonatal outcomes.

Objectives/Goals: Our study’s primary objective is to develop a multivariate random effects meta-analysis model that estimates risk ratio at a series of clinically relevant time points. Our goal in doing so is to address gaps in traditional hazard ratio (HR) meta-analyses due to observed time-varying treatment effects. Methods/Study Population: Our model estimates pooled risk ratios (RRs), the ratio of cumulative event probabilities, at predefined timepoints (e.g., 12, 24, 36 months). We derive the variance of the log-RR for each trial at each time point by applying the delta method to the Kaplan-Meier (KM) estimator function. Furthermore, we will derive the covariance to account for the correlation between RR estimates at different time points within the same trial. We would then like to incorporate these derivations of within-trial variance and covariance at each available time milestone into our multivariate model to increase the precision of our pooled RR estimated for both Overall Survival (OS) and Progression-Free Survival (PFS). Results/Anticipated Results: We identified 7 trials by consulting 5 systematic reviews published through 2021-2025 to ensure use of updated data. We reconstructed the pseudo-Individual Patient Data of these trials by applying PDFs of published KM curves to a validated survival curve reconstruction algorithm. For the outcome of overall survival, the max amount of follow-up time recorded is 108 months. On the other hand, there are 96 months of available follow-up for the outcome of progression-free survival. In a univariate random effects meta-analysis of HR for OS, the pooled HR was found to be 0.81 (CI: 0.74 – 0.89). Additionally, the pooled HR for PFS was found to be 0.68 (CI: 0.59 – 0.79). For both outcomes, we anticipate the multivariate random effects analysis of Risk Ratio to yield more precise confidence intervals than those of HR. Discussion/Significance of Impact: The proposed multivariate meta-analysis model will improve precision of treatment effect estimates which will add to the ongoing evaluation of immunotherapies as a current standard of care. This is significant to translational science as it provides time-specific treatment effect estimates that could be more interpretable in a clinical setting.

Objectives/Goals: Safely and effectively translating AI classification models requires robust post-deployment monitoring. Yet there is little guidance about how to do so. Here, we outline how standard machine learning model development study designs are insufficient for post-deployment monitoring, and what specific study designs are needed. Methods/Study Population: We made original linkages between machine learning methodology and biostatistics, particularly to diagnostic testing, for study design guidance on post-deployment model performance and fairness assessment. Results/Anticipated Results: The kind of case–control sampling typically used for model development cannot give valid estimates of the Positive Predictive Value (precision) and may suffer from verification bias; in a pragmatic clinical trial, or under deployment, only the PPV can be measured, unless there is random confirmatory testing of predicted negatives. This is important for measuring sensitivity, specificity, and False Omission Rate as a fairness metric. Discussion/Significance of Impact: By linking well-understood biostatistics principles (e.g., diagnostic testing) to machine learning classifier evaluation, we show what is required for rigorous evaluation of models in clinical practice. This will establish clear guidance and rigorous standards of practice for study design and analytic aspects of post-deployment monitoring.

Objectives/Goals: Within the machine learning “fairness” literature, models audits are point estimates, and only effect size is considered. The well-developed frameworks in biostatistics for diagnostic testing apply directly to classifiers more generally; these principles can be extended to have statistically valid fairness audits. Methods/Study Population: We made original linkages between machine learning methodology and biostatistics, particularly to diagnostic testing, to get analytic biostatistical methods for model evaluation that are applicable to fairness testing. Results/Anticipated Results: The use of odds ratios to compare ratios of binomial proportions is superior to taking ratios of binomial proportions directly, as is currently standard in machine learning. Odds ratios also have an analytic asymptotic approximation for standard errors, by which we can test if a point estimate (effect size) is greater than what we would expect from noise. In diagnostic testing, the odds ratios considered are generally across the cells of a confusion matrix, but we can form odds ratios for specific marginal quantities (positive predictive value, negative predictive value, true positive rate, true negative rate) for specific aspects of model performance or fairness. Discussion/Significance of Impact: Biostatistics can make direct contributions to instantly improve the state of machine learning practice, giving ready-made methods that can take account generations of hard-won lessons about the importance of uncertainty quantification and sample size in making claims and conclusions.

Objectives/Goals: Conception to early childhood is reported to be one of the most critical developmental periods in human development. This study examines the role of adversity experienced by both mom and child during the pre-and post-natal periods on children’s cardiometabolic disease risk (CMDR) outcomes. Methods/Study Population: Data include responses from 852 biological mother-child dyads from the Family Life Project. Adversity during pregnancy was measured using the Life Experiences Survey. The number of life experiences that mothers reported as “bad” was included in the analyses. Children’s experiences with adversity were assessed using cumulative risk, a composite measure of adversity captured at four time points: 6, 15, 24, and 35 months. These variables were used to predict children’s obesity and mean arterial pressure, markers of cardiometabolic disease risk. Children’s obesity status was measured using standardized body mass index (BMIZ) values, assessed in children when they were 24, 35, 48, 58, 90, and 154 months. Mean arterial pressure was captured at 154 months. Results/Anticipated Results: Less than half of mothers (42.4%) and children (44.6%) identified as Black. Roughly half of the children (49.1%) were female. Most (78.2%) mothers were categorized as being of low economic status. Preliminary analyses revealed that number of negative life experiences reported by mothers were not significantly correlated with MAP. Children’s cumulative risk (CR) at 6 (r=0.11, p<0.01), 15 (r=0.11, p<0.01), 24 (r=0.11, p<0.01), and 35 (r=0.10, p<0.01) months were significantly correlated mean arterial pressure (MAP) at age 14. Associations between CR and MAP remained significant among girls at all time points and among boys only at months 6,15, and 24 months. There were no significant correlations between adversity experienced in utero on children’s BMIZ values at any timepoint. Discussion/Significance of Impact: The American Heart Association states that MAP is often used as an indicator of CMDR. Preliminary results from this study suggest a potential pathway from adversity experienced in infancy and adolescent MAP. Future studies should further explore these associations to better understand the pathways from early life adversity to high CMDR.

Objectives/Goals: Hepatocellular carcinoma (HCC) is the third leading cause of cancer deaths worldwide. The epidemiology of HCC has been changing over the past several decades. The aim of this study is to examine changes in etiology, incidence, and survival of HCC in a population-based cohort in the Upper Midwest. Methods/Study Population: The Rochester Epidemiology Project (REP) is a medical records linkage system between healthcare systems that centralizes patient records to a database for investigators to access for approved research projects. The REP now includes 27 counties in southeastern Minnesota and western Wisconsin, with access to 2.9 million medical records linked to 1.4 million persons who resided in this region at some point between 2010-2024. In this study, we used ICD-9 and ICD-10 codes to identify any patient in the REP with one or more instances of the relevant HCC codes. Charts were manually reviewed to confirm diagnosis, residency, and clinical information. All patient data were analyzed as an overall cohort and stratified by era of diagnosis or demographic characteristics. Results/Anticipated Results: Metabolic disorders were the most prevalent HCC risk factor in this cohort, with hypertension, nonalcoholic steatohepatitis, and hyperlipidemia rates significantly increasing over the study period. Age- and sex-adjusted HCC incidence also increased over the study period from 5.39 (95% CI: 3.63, 7.98) per 100,000 person-years in 2010-2013 to 8.17 (95% CI: 6.10, 11.07) per 100,000 person-years in 2018-2021 (p=1.1844x10-4). Age, sex, and race stratification shows that this increase was driven by men, those age 65+ years, and non-Hispanic White individuals. The median survival in this cohort was 23.41 (95% CI: 19.81, 26.97) months, with surveillance status, BCLC stage at diagnosis, hypertension, and hypertriglyceridemia being associated with a significantly decreased hazard ratio of death. Discussion/Significance of Impact: This study highlights the changing landscape of HCC etiology and increasing incidence using a population-based Midwestern cohort. Overall survival remains low, highlighting the need for more robust prevention and screening in at-risk groups.

Objectives/Goals: The Wake Forest Clinical and Translational Science Institute (WF CTSI) provides many services, but little is known about how utilization of services affects research outcomes. Our goal is to understand long-term outcomes of recurrent service use from existing resources with an eye towards creating new processes to track research outcomes. Methods/Study Population: The Dissemination, Implementation, and Continuous Quality Improvement team partnered with the Biostatistics, Epidemiology, and Research Design (BERD) program for this project. Initially, we aimed to track abstract and paper publications, and grant submissions (unfunded and funded) for all service requests coming through the BERD from 2017-2024. This was quickly deemed infeasible using prior CTSI data collection processes; however, detailed follow-up data did exist on funded CTSI pilots in the same time window. Consequently, we aimed to test associations between project attributes provided on the service request form and downstream research outcomes, including peer-reviewed publications and grant funding. We used chi-squared tests in the Python package “tableone” to test these associations. Results/Anticipated Results: Our sample only included WF CTSI pilot-funded projects; though, unexpectedly we found information on pilot projects funded by different sources including center/institutional pilots, federal funding, foundation grants, and internal/departmental funding. We found it most effective to link service requests with IRB numbers, because different principle investigators can submit requests on behalf of the same project. N=206 total projects were included in the analysis. Regarding peer-reviewed publications, associations were found with type of service, principal investigator rank, and IRB approval status. Regarding grant funding, associations were found with service type, principal investigator career stage, and previous principal investigator grant funding. Discussion/Significance of Impact: As part of a large academic learning health system, the WF CTSI provides support to various investigators and tracking long-term research outcomes is a priority. This investigation identified important predictors of successful publications and grants in pilot projects, though it also identified gaps in processes to track research outcomes.

Objectives/Goals: The goal was to create and deploy an intuitive, easy-to-use tool that clinical investigators can use to make or modify high-quality figures for publication. Methods/Study Population: We developed GoFiguR, an interactive application built with RShiny, to help researchers and analysts visualize their data with ease. Users upload data files, choose a plot type (histogram, scatterplot, boxplot, bar plot, or Kaplan-Meier curve), and then specify variables, labels, and font sizes to generate the figure. Alternatively, users can upload a ggplot2 object to modify labels, font sizes, or plot themes in an existing figure. All figures can be exported either as ggplot2 object for additional modification or as one of multiple file formats with user-defined dimensions and resolution. GoFiguR is hosted on a secure, university-maintained website with access limited to university personnel. Instructional step-by-step handouts were developed to assist investigators in the use of the app. Results/Anticipated Results: We plan to integrate GoFiguR into the biostatistics consultative services offered by our Clinical and Translational Science Program. In practice, we anticipate several use cases: investigators and analysts can use GoFiguR to generate mock figures during project planning, analysts can create visualizations following data analysis, and investigators can apply final edits to figures prior to dissemination. Through this collaborative process, investigators are expected to gain a deeper understanding of appropriate figure types for various data visualizations. Discussion/Significance of Impact: Creating publication-quality figures is often a time-intensive process requiring multiple iterations, between the investigator and analyst. GoFiguR enables more efficient development of figures as investigators can directly modify figures to their specification.

Objectives/Goals: Ocular adverse events (OAEs) have been associated with GLP-1 use in clinical studies. This study aims to describe the proportion and distribution of ocular adverse events by treatment arm, including serious and non-serious events, in phase 2-4 clinical trials of GLP-1 drugs using publicly available ClincialTrials.gov data. Methods/Study Population: The ClinicalTrials.gov database was searched on 9/11/25 for interventional studies with GLP-1 as treatment (n= 1545). The search was filtered to terminated and completed studies with results (n= 349). Adverse event reports from Clinical Trials Transformation Initiative (CTTI) Aggregate Analysis of ClinicalTrials.gov (AACT) database were restricted to phase 2-4 drug studies (n= 240), of which 167 involved a GLP1-1 drug with 64 studies reporting ocular adverse events. Ocular adverse events were summarized as weighted and unweighted mean proportions by arm and seriousness, with chi-square, Fisher’s exact, and Wilcoxon tests assessing significance. Study characteristics were included in statistical models to further assess differences in ocular adverse event proportions. Results/Anticipated Results: Ocular adverse events ranked 10 among top organ systems affected in GLP-1 drug trials. Among 64 studies reporting ocular adverse events, serious ocular event proportions were higher in GLP-1 arms compared to placebo (weighted: p < 0.01; unweighted: p = 0.02). Most studies (n = 51) systemically assessed ocular events; 10 were non-systematic, and 3 did not classify reporting. The top 10 ocular events highlighted retinal detachment at significantly higher proportions in drug arms than placebo arms (weighted: 0.11% vs. 0.02%, p = 0.04). Cataract and diabetic retinopathy events were observed less frequently in drug arm than placebo arm (weighted: p = 0.02; p = 0.03). Discussion/Significance of Impact: Serious and non-serious ocular adverse events occurred more often in GLP-1 drug arms, driven by specific conditions such as retinal detachment. Using the AACT database enables large-scale evaluation of adverse event patterns, providing broader context than single-study analyses.

Objectives/Goals: The objective of my study is to examine the relationship between ultra-processed foods, sleep duration, and memory in adults aged 60 years and older. Diet and sleep are often studied independently as modifiable risk factors for cognitive decline; a growing body of evidence suggests a unique nexus between the two with regard to cognitive outcomes. Methods/Study Population: A cross-sectional analysis will be conducted using data from the National Health and Nutrition Examination Survey (NHANES) among participants aged 60 years and older from 2011 to 2014. The NOVA system, which classifies food according to the extent of processing, ranging from whole foods to ultra-processed foods, will be used to calculate dietary intake from two 24-hour recalls. Sleep duration will be analyzed using self-reported average hours of sleep per night, and cognitive performance will be evaluated using word recall and verbal fluency tests. Food insecurity, socioeconomic status, education, and race/ethnicity will be analyzed as covariates. Descriptive and multivariable regression analysis will be used to assess statistical associations. Results/Anticipated Results: As ultra-processed foods cause oxidative stress and neuroinflammation, higher consumption of these foods, which are high in fat, sodium, and sugar, is anticipated to be associated with shorter sleep duration and poor memory performance. A dose-response relationship is predicted. These associations are expected to be exacerbated in food-insecure environments, where ultra-processed foods are readily accessible, particularly among individuals with lower socioeconomic status. Data may also reveal differences based on race/ethnicity or educational attainment, highlighting important disparities in the social determinants of health. The study is expected to reveal trends in sleep inequity, where economic disadvantages and food insecurity contribute to shorter sleep durations. Discussion/Significance of Impact: This study seeks to identify modifiable behavioral risk factors for cognitive decline, particularly for older adults. By uncovering how diet and sleep interact to influence brain health, this study can guide practical interventions, such as nutrition and sleep education, to slow cognitive decline and improve quality of life for aging adults.

Objectives/Goals: The study aims to systematically characterize the frequency and distribution of serious and non-serious adverse events by intervention arm and organ system in clinical trials of GLP-1 drugs using the publicly available Aggregate Content of ClinicalTrials.gov (AACT) database. Methods/Study Population: The ClinicalTrials.gov database was searched on 9/11/25 for interventional studies with GLP-1 as treatment (n= 1545). The search was filtered to terminated and completed studies with results (n= 349). Study information, including adverse event reports, was downloaded from Clinical Trials Transformation Initiative (CTTI) AACT database. Data were filtered to phase 2-4 studies (n= 247), followed by drug intervention studies (n= 240). Of these, 167 studies involved the use of a GLP1-1 drug. Adverse events were summarized using study size-weighted mean proportions with chi-squared and Fisher’s exact tests for significance, stratified by intervention arm, seriousness, and organ system. Results/Anticipated Results: Across 167 GLP-1 trials, the weighted proportion of participants experiencing non-serious adverse events was higher in drug arms than in placebo (4.3% vs. 3.3%, p < 0.001). Serious events were uncommon but slightly higher in drug arms (0.1% vs. 0.1%, p = 0.01). The most frequently affected organ systems for non-serious events were gastrointestinal, metabolism/nutrition, and infections/infestations. Among serious events, cardiac disorders were most frequent, occurring less often in the drug arm (0.2% vs. 0.3%, p < 0.001). Discussion/Significance of Impact: Elevated adverse event rates in GLP-1 trials were primarily due to non-serious events across multiple organ systems. Leveraging the AACT database demonstrates the feasibility of harmonizing trial-level adverse event information to assess safety patterns across diverse trial populations.