Exploring Existing Data Resources 2023 Awardees

Effects of Outdoor Artificial Light on Circadian Rhythms


$25,000 Awardee

Team: Adriane Soehner, PhD, (PI)

Abstract: Over the past century, the boundary between day and night has become progressively more obscured by the widespread adoption of artificial light at night (ALAN). It has also become increasingly apparent that excessive ALAN exposure increases the risk for most major physical and mental health conditions via disruption of the human biological clock. Our biological (circadian) clock synchronizes most bodily processes to a 24-hr day based on the light-dark cycle. ALAN weakens and delays biological clock signaling, resulting in dysregulation of most human biobehavioral systems and, over time, the development of disease. While it is now widely recognized that personal devices and indoor lighting contribute to ALAN exposure, a more difficult-to-modify ALAN exposure also occurs at the environment level (outdoor ALAN) from streetlights, automobiles, buildings, and other sources. Laboratory studies demonstrate that adolescence is a developmental period in which the biological clock’s sensitivity to evening light is intensified. However, it is unclear whether the extent of circadian disruption experienced by adolescents due to outdoor ALAN exceeds that of adults. We propose to test the hypotheses that 1) greater outdoor ALAN levels will be associated with greater circadian activity rhythm disruption and 2) the strength of this relationship will be moderated by age and/or self-reported pubertal stage, such that relationships between outdoor ALAN and circadian outcomes will grow weaker with age. Characterizing developmentally specific effects of an environmental pollutant – outdoor ALAN – on circadian disruption will provide a foundation for focused public health efforts to mitigate this potentially underrecognized source of health disparities in youth.

Exposome Wide Association of Complex Phenotypes in UK Biobank


$25,000 Awardee

Team: Peng Gao, Ph.D., (PI) Sai Zhang, Ph.D., (co-I)

Abstract: UK Biobank (UKBB) is a large-scale biobank study that investigates the influences of genetic predisposition and environmental exposure (including nutrition, lifestyle, medications, etc.) on human diseases and traits. Genome-wide association studies have been widely carried out based on the UKBB genetic data to investigate the associations between genetic variations and traits or diseases. However, since complex diseases are also impacted by environmental factors and the environmental data in UKBB has been previously underused, we propose to utilize the environmental-related data in UKBB to study the associations between environmental exposures and complex phenotypes by exposome-wide association studies (EWAS). The lack of information on how the exposome (-omes of all the exposures) contributes to complex phenotypes and the underused environmental data in UKBB motivated us to propose a pilot study to develop a highly innovative data-driven framework that 1) defines, categorizes, and quantifies the environmental factors that are related to human diseases and traits, 2) apply EWAS to identify the exposome-phenotype associations for complex diseases and traits, and 3) build an individualized phenotype predictor from exposome based on deep learning. We have identified 750 environmental-related factors and targeted complex respiratory diseases (e.g., asthma) as case studies of applying EWAS based on UKBB data. This pilot study can demonstrate the feasibility of using the proposed data-driven framework to study the impact of personal exposome on the etiology of complex phenome and support the submission of a competitive multiple-PI R01 grant.

Geospatial Analysis of disparities in Access to and Use of Metabolic Disease


$25,000 Awardee

Team:  Margaret Zupa, Ph.D., (PI), Amber Johnson, MD, (co-I), Ann-Marie Rosland, MD, (co-I), Scott Rothenberger, Ph.D., (co-I)

Abstract: Disparities in quality of care and outcomes for adults with type 2 diabetes and atherosclerotic cardiovascular disease (T2D and ASCVD) who live in rural areas, have low socioeconomic status, or are members of racial and ethnic minority groups are well-established. Care from specialists, including endocrinologists and cardiologists, is associated with improved care quality, which could both reduce the risk for and ameliorate disparities in adverse cardiovascular outcomes. However, it is unclear how geographic, transportation, and internet access barriers impact the use of specialty metabolic disease care for adults with T2D and ASCVD, and how the widespread uptake of telemedicine since 2020 has impacted disparities in care utilization. This study will newly link geographic data sources, including US Census Bureau, Federal Communications Commission, and Environmental Protection Agency, with patient-level health information through geospatial analysis, in order to 1) Assess the prevalence and magnitude of barriers to metabolic disease specialty care in the form of drive time, public transportation access, and broadband internet access for adults with T2D and ASCVD and 2) Evaluate the impact of these factors on specialty metabolic disease care use before and after the widespread availability of telemedicine. This work will foster new collaborations within the study team, which includes two early career investigators and allow the PI to gain new skills in the use of public data resources and geospatial analysis. Results from this study using UPMC data will inform local care and facilitate future grant applications to examine and address geographic contributors to disparities in metabolic disease care in national datasets.

Investigating Neurodevelopmental Pathway to Juvenile Delinquent Behavior


$25,000 Awardee

Team: Ashely Parr, PhD, (PI)

Abstract: Adolescence is a period of neurodevelopment marked by increased sensation-seeking that arises due to asynchronous maturation of reward- and cognitive- systems. Although adolescent sensation-seeking is adaptive, it can lead to risk-taking, which may be instantiated as delinquency that transgresses legal boundaries (e.g., vandalism). Approximately 700,000 U.S. youth enter the juvenile justice system annually, which itself can disrupt psychosocial, behavioral, and neurocognitive development, and can elevate risk for maladaptive trajectories that culminate in significant personal, societal, and health-related costs. Furthermore, how environmental and personality factors interact with neurodevelopmental mechanisms to confer risk for trajectories in delinquency remains unknown. Studies from our group and others have demonstrated that adolescent risk-taking is supported by increased frontostriatal connectivity, and age-related decreases in connectivity are mediated by striatal dopamine,6 supporting improvements in decision-making and decreases in risk-taking into adulthood. Motivated by these results, we aim to understand how these critical decision-making systems contribute to delinquency in the first-of-its-kind Adolescent Brain Cognitive Development – Social Development (ABCD-SD) dataset (N = 2,426, age 9-14 at enrollment followed for 10 years). The ABCD-SD is a well-powered, multi-site, longitudinal cohort that combines neuroimaging with detailed phenotyping of delinquency, risk-taking, environmental, and personality-based risk factors, providing an unprecedented level of detail that will allow us to identify robust and reproducible relationships between neurobiological mechanisms, delinquency, and risk-factors during adolescence. These findings will advance our understanding of neurobiological factors that support delinquency, which can inform policy regarding appropriate system responses to adolescent delinquency, and rehabilitative strategies that support healthy neurocognitive trajectories.

Leveraging VA MVP to Study Complement Protection


$25,000 Awardee

Team: William Bain, MD, (PI) Eleanor Feingold, Ph.D., (co-I)

Abstract: Pneumonia is a leading cause of death globally. Severe pneumonia is often complicated by sepsis and acute respiratory distress syndrome (ARDS), both of which carry high rates of mortality. There are few therapeutic measures available for patients with severe pneumonia other than antimicrobials, which are increasingly threatened by emerging pathogens and rising antimicrobial resistance. Therefore, novel therapies to improve clinical outcomes during severe pneumonia are desperately needed. The complement system is a crucial component of the host defense against pneumonia. We have recently demonstrated that preserved functional capacity of the alternative complement pathway is associated with decreased mortality during ARDS caused by pneumonia and sepsis. Furthermore, genetic deficiency of key alternative complement proteins increases the risk of pneumonia in both mice and humans, suggesting a potential mechanistic relationship. Yet, the complement system and its potential genetic determinants have not been well studied during severe pneumonia and its complications. The VA Million Veterans Program (MVP) has recently conducted a genomewide x phenome-wide association study among 650,000 veterans and released summary data to VA researchers. We propose to utilize this unprecedented resource to study the association between genetic variation in the complement system and relevant phenotypes of pneumonia. The proposed research will establish a collaboration between a bench-translational early-career investigator with complement expertise and a senior expert in genetics and biostatistics. That collaboration would enable not only a novel investigation into complement biology but also would support the development of research infrastructure for use of the MVP at the University of Pittsburgh and Pittsburgh VA.

Using AI to Predict Response to Therapy in Adult and Pediatric Sarcomas


$25,000 Awardee

Team:  Kurt Weiss, MD, (PI), Ahmad Pahlavan Tafti, Ph.D., (co-I)

Abstract: Sarcomas are rare mesenchymal cancers that arise from the connective tissues including bone, muscle, and fat. Sarcomas affect patients of all ages and disproportionately affect children, adolescents, and young adults. Most sarcoma patients receive neo-adjuvant (pre-operative) treatment of some kind. This is most often chemotherapy in the case of young patients with bone sarcomas and radiation in the case of older patients with soft tissue sarcoma. The degree to which the pre-operative treatment has been successful greatly impacts how extensive the surgeons must be with their removal of the sarcoma. This results in a significant role of the assessment of the efficacy of pre-operative treatment. Combining existing UPMC datasets of pre-treatment imaging, pre-operative imaging, and histopathological evaluation of adult and pediatric sarcoma patients, the current project mainly aims to build, train, test, and validate AI and deep learning medical image analysis algorithm(s) to predict the degree of treatment effect. This information would revolutionize sarcoma treatment and enable an unprecedented level of precise and personalized medicine for patients with these rare tumors. The main objective of the present project is to: 1) Computationally construct a retrospective cohort of adult and pediatric sarcoma patients (n = 579 surgeries) who have undergone CT and/or MRI imaging. Develop, train, test, and validate deep learning computer vision model(s) to predict response to therapy in sarcoma, localizing and characterizing CT and MRI findings to predict response to the therapy.

Past Awardees

Multidisciplinary Pediatric Diabetes Care


$25,000 Awardee

Team: Christine March, MD (PI), Ingrid Libman, MD, PhD, Elizabeth Miller, MD, PhD, Julie Donahue, PhD, Scott Rothenberger, PhD

Abstract: Diabetes is one of the most common chronic diseases of childhood, and the incidence of both type 1 (T1D) and type 2 (T2D) diabetes is increasing. Youth with diabetes are at risk for higher morbidity and mortality compared to healthy peers. The best predictor of long-term complications is cumulative glycemic exposure. By and large, most youths with diabetes fail to meet recommended glycemic targets, despite modern treatment advances, with clear inequities in outcomes by diagnosis type, race/ethnicity, and socioeconomic status. Contributing to this landscape is access to multidisciplinary care from diabetes providers, diabetes educators, dieticians, and behavioral health providers. Though single-center reports have examined missed appointments with select aspects of the diabetes care team in both T1D and T2D, fidelity to the multidisciplinary care model as a whole has never been studied in a generalizable cohort. Using Optum’s Integrated database, a robust national dataset composed of claims and electronic health data, we aim to describe and compare patterns of multidisciplinary care use among youth with T1D and T2D. We will identify characteristics of youth who experience multidisciplinary care and those with gaps in care using multivariable regression modeling. Finally, we will explore the relationship between multidisciplinary care and two key outcomes, glycemic control (Hemoglobin A1c) and acute care utilization using similar analyses. Completion of these aims will help to characterize issues in health care utilization facing this population, the initial step to designing novel, community-partnered interventions to strengthen support systems and bridge gaps in care.

Urine metabolomics exploring existing data in the clinical toxicology laboratory


$25,000 Awardee

Team: Kenichi Tamama MD, PhD (PI), Dhivyaa Rajasundaram, PhD

Abstract: Substance abuse or drug addiction has been one of the significant health hazards worldwide. Drug screening is routinely used in the clinical management of drug addicts, but biomarkers of drug addiction itself or chronic drug exposure are largely unavailable to clinicians currently. The availability of such biomarkers should help clinicians identify patients with a high risk of developing drug addiction and better predict the disease trajectories and responses of the patients to treatments. The overall objective of this proposal is to determine novel substance abuse-associated biomarker candidates by exploring existing unused data acquired in urine comprehensive drug screening (CDS) at the Clinical Toxicology Laboratory. More than 99% of the acquired raw mass spectral data in urine CDS are untouched and wasted. In this proposal, we will perform the following specific aims in this pilot study to determine substance abuse-associated novel biomarker candidates. Aim 1: Identify the metabolites that are significantly associated with drug addiction. Aim 2: Determine the differential metabolomics profiles of the patients receiving buprenorphine or methadone. Upon successful completion of the proposed research, we expect our contribution to determine urinary metabolites associated with drug addiction (Aim 1) and the future retention of the patients in medication-assisted treatment (Aim 2). This contribution will be significant because the availability of such biomarkers should help clinicians predict the disease trajectories and responses of the patients to treatments, ultimately leading to increased social productivity and reduced future overdose events of the patients through better clinical management.

Radio-Omics and Deep Learning Analysis for Hepatocellular Carcinoma


$25,000 Awardee

Team: Michele Molinari, MD (PI), Shandong Wu, PhD, Marta Minervini, MD, Alessandro Furlan, MD, Nick D’Ardenne, MD, Christopher Buros, MD

Abstract: Adults (>18 years) treated for HCC with hepatic resections at the University of Pittsburgh Medical Center between January 1, 2011, and December 31st, 2020, will be candidates for this study. Patients will be excluded if they were treated with palliative surgeries, if they had a diagnosis of mixed cholangio-HCC, or if they had previous hepatic resections for malignancies. The study population will be divided into two groups: patients with HCC in the settings of NAFLD and patients with HCC from other risk factors (e.g., viral hepatitis, (B or C), alcoholic cirrhosis, cryptogenic cirrhosis, others). Baseline demographic, clinical, pathological, and radiological characteristics between the two groups will be analyzed. The most recent contrast-enhanced computerized tomographic (CT) scans or magnetic resonance imaging (MRI) performed before the index operations will be retrieved and de-identified. Staging radiological studies will be analyzed by experienced body-imaging radiologists to determine the maximum size of the largest HCC, the total number of tumors, the presence of vascular tumor invasion, and the presence of peri-hepatic lymphatic metastases. For each participant, radiomics analysis of the largest tumor and surrounding liver parenchyma will be performed and analyzed with the assistance of artificial intelligence and deep learning technologies. The primary aim of the study is to determine if there are radiological differences between patients with HCC and NAFLD versus patients with HCC and other predisposing factors.

A3ST: AI-based Automated Fidelity Assessment for Strategy Training in Inpatient Rehabilitation


$25,000 Awardee

Team Members: Yanshan Wang, Ph.D. (PI), Leming Zhou, Ph.D., Elizabeth Skidmore, Ph.D., Alexandra Harper, PhD

Abstract: Stroke is a leading cause of disability in the world. Cognitive impairments occur in as many as half of adults who sustain a first-time stroke and are associated with significant long-term disability. Efficacious interventions that promote independence could significantly improve the quality of life for these individuals and reduce healthcare expenses. Strategy training, a multidisciplinary rehabilitation approach developed by the team, teaches skills that can be used to significantly reduce disability. Randomized controlled clinical trials have shown that strategy training is a more feasible and efficacious intervention to promote independence than traditional rehabilitation approaches. To assess the implementation of strategy training and treatment integrity, the team has developed a standardized fidelity assessment approach. This approach requires a highly trained independent evaluator to use a validated fidelity checklist to assess instructional cues (i.e., guided and directed verbal cues and gestures) from video and audio recordings of rehabilitation sessions. This fidelity assessment approach is valid and feasible in single-site studies, but it is labor-intensive, time-consuming, expensive, and not scalable as the trial is expanding to 30 inpatient rehabilitation facilities across the nation. To address the challenge of examining the fidelity of intervention delivery in large clinical trials, we plan to leverage advanced artificial intelligence (AI) techniques to automate the fidelity assessment approach, which has the potential to propel and translate to rehabilitation intervention practice and research forward in new directions previously untapped. The proposed project will be among the first to develop automated deep neural network-based AI methods for characterizing the delivery of clinical rehabilitation interventions.

Novel Methods for Single-Cell DNA Methylation Data


$25,000 Awardee

Team Members: Jiebiao Wang, Ph.D. (PI), Manqi Cai, Brandon McKinney, MD, Ph.D., Chris McKennan, PhD

Abstract: In the last decade, single-cell multi-modal omics data has been explosively collected and thus was selected as Method of the Year 2019 by Nature Methods, after single-cell sequencing was selected as Method of the Year 2013. As funded by NIH, the BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative constructed the Cell Census Network (BICCN) to conduct a multi-modal cell census and atlas of the brain. The initial product of BICCN for millions of cells of the primary motor cortex is published in Nature 2021. The rapid development and continued expansion of sequencing technologies produced a huge amount of data. Over one thousand computational tools have been developed to analyze single-cell RNA-sequencing (scRNAseq) data. However, little attention has been paid to analyzing single-cell DNA methylation (scDNAm) data, which is key to regulating gene transcription and disease development. The scDNAm data are sparse and noisy, thus providing an unprecedented opportunity for developing novel statistical and computation methods. Unlike existing work that mainly focused on data integration and cell clustering, we propose statistical methods to analyze the regulatory mechanisms between single-cell epigenetics (DNAm, chromatin accessibility, and conformation) and transcriptomics data and detect multi-omics cell type marker genes. With robust cell type markers, we will build a cell type signature matrix for DNAm and deconvolve tissue-level DNAm to infer cellular fractions. This can enhance the cell-type-specific interpretation of bulk DNAm data and reduce the confounding in tissue-level DNAm analysis introduced by cellular heterogeneity.