Q2-2025 Research Roundup
Title: Sex/Gender Influences on Periodontal Disease and Diabetes: A Population Science Approach, with Software
Amount: $55,774
Funding Agency: National Institutes of Health (NIH)
Period: 09/01/2022-08/31/2025
Abstract: Periodontal disease (PD), which contributes to eventual tooth loss, continues to remain a major global oral health (OH) burden, particularly in the US. However, PD is also associated with an ever-growing list of multi-comorbidities, such as Type-2 diabetes, cardiovascular disease, etc. Manifestation and progression of PD is multifactorial. Primarily driven by the baby-boomer generation, there is a significant increase in the number of immunosuppressed elderly in the US, who are also at an elevated risk of compromised OH. With recent biomedical advancements, human life expectancy has been increasing at a rapid rate, contributing to an aging population, with a growing burden of non-communicable diseases, such as diabetes. With the rising cost of dental healthcare in the US (a staggering US$124B in 2016), there is a pressing need to develop novel statistical tools for accurate risk quantification of PD for specific comorbid subgroups, disentangle causality of PD treatments/interventions, and administer treatments.
Title: Putting the Sampling Design to Work: Enhancing Monitoring Programs for Improved Management and Inference of Ecological Responses to Changes in Climate
Amount: $396,729
Funding Agency: US Geological Survey (USGS)
Period: 10/01/2022-09/30/2025
Abstract: The goal of this work is to develop statistical methods to enhance and/or modify existing monitoring programs’ abilities to understand climate effects on fish and wildlife populations. Specifically, given existing monitoring programs, our objectives are to (1) develop statistical models that quantify and account for the impacts of the sampling design in understanding the relationship between climate and species abundance or occupancy, and (2) develop an optimal supplemental sampling design that factors in spatial and temporal effects, precision, and cost tradeoffs to enhance the monitoring program’s ability to track climate change and provide early indicators for fish and wildlife responses. With partners at both state and federal agencies, these approaches can be extended to improve the existing monitoring programs of other fish, wildlife, and habitats in making informed management decisions in the face of climate change.
Title: Characterizing Gene-Environment Interactions that Affect Individual Susceptibility to an Expanding Chemical Exposome
Amount: $1,260,340
Funding Agency: National Institutes of Health (NIH)
Period: 07/08/2022-04/30/2027
Abstract: Exposure to environmental chemicals has been linked to increases in cancer incidence, birth defects, impaired cognitive development, and neurodegenerative disease. Unfortunately, the gap between the ever-expanding number of chemicals in the environment and data on their potential health hazards continues to widen. Although recent advancements that use in vitro, high-throughput screening technologies may speed the pace of chemical testing, those platforms cannot detect adverse health effects diagnosable only at a systemic level, such as abnormal development or aberrant behavior. Additionally, an in vivo context is needed to quantify the contribution of interindividual genetic variation to susceptibility differences in developmental or behavioral consequences of exposure. There is strong evidence that gene-environment interactions related to individual genetic variation play an important role in health outcomes, and that these interactions are likely a major source of the heterogeneity.
Title: Integrative Modeling and Dynamic Prediction of Alzheimers’ Disease
Amount: $379,624
Funding Agency: National Institutes of Health (NIH)
Period: 09/15/2020-05/31/2025
Abstract: The proposal develops novel integrative statistical methods for modeling and predicting Alzheimer’s disease progression by integrating data of different types or sources including multiple longitudinal outcomes, brain imaging data and genetic data.
Title: Methods to improve efficiency and robustness of clinical trials using information from real-world data with hidden bias
Amount: $2.6 million
Funding Agency: FDA 1U01FD007934
Period: 2023-2026
Abstract: North Carolina State University (PI: Shu Yang) and Duke University (PI: Xiaofei Wang) have secured up to $2.6 million in funding from the U.S. Food and Drug Administration to spearhead cutting-edge research that transforms the design and analysis of clinical trials. This prestigious three-year award (September 1, 2023 – August 31, 2026), issued under FOA RFA-FD-23-025, supports the development of advanced statistical methods that integrate real-world data (RWD) while rigorously addressing hidden biases that can compromise evidence quality. The project tackles key challenges at the forefront of regulatory science, aiming to elevate the quality and utilization of RWD, deepen the understanding of real-world evidence (RWE) study designs, and deliver innovative analytic tools that enhance the credibility of RWE used in regulatory decision-making. The research team has already produced significant results, including publications in leading journals such as Biometrika, Biometrics, Lifetime Data Science, and proceedings of the International Conference on Machine Learning (ICML), along with the development of open-source R software to facilitate adoption of these new methods. The investigators have also been invited to deliver short courses at major national and international conferences, expanding the impact and visibility of their work across academic, regulatory, and industry communities.
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