2024 RAISE Pilot Grant Awardees

Improving cardiovascular clinical trial representativeness among women

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PIs: Rachel Kohn, MD, MSCE; Scott D. Halpern, MD, PhD

Women experience suboptimal treatment for, and greater mortality from cardiovascular diseases. This stems in part from underrepresentation of women in cardiovascular trials, limiting the generalizability of advancements to women. Stakeholders identified communication as a key factor for mitigating trial enrollment discomfort, and financial incentives reduce racial gaps in trial accrual. Key unanswered questions include (1) which behavioral economic (BE)-informed communication strategies reduce participation gaps, (2) whether these strategies produce adverse ethical effects, and (3) whether communication strategies and incentives work synergistically to promote RCT enrollment. Therefore, we propose tests of these key questions to improve cardiovascular RCT representativeness among women. In Aim 1, we will evaluate combinations of messaging frameworks and financial incentives to maximize cardiovascular trial enrollment among women. In Aim 2, we will evaluate undue (i.e., decreasing participants’ sensitivity to risk) and unjust inducements (i.e., more influence among poorer patients) for BE strategies among women. Using Amazon mechanical turk, we will recruit 250 participants. Participants will receive 10 of 48 possible versions containing a hypothetical cardiovascular trial prompt that only varies in messaging framework, financial incentive, and risk. Participants will provide their willingness to participate (WTP) in each scenario on a five-point Likert scale. We will test associations of BE strategy combinations with WTP. We will test associations of interaction terms between the risk variable and BE combinations with WTP (testing for undue inducement), and between annual income and financial incentive with WTP (testing for unjust inducement). We will use multivariable ordinal logistic regression for all analyses. 

X-chromosome mediated mechanisms mediating sex-specific differences in Neonatal Lung Injury

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PIs: Krithika Lingappan, MD, PhD, MS; Montserrat Anguera, PhD

Despite improved neonatal care and increased overall survival of preterm neonates, the male sex has been identified as a risk factor for the development of many prematurity-related morbidities. Bronchopulmonary Dysplasia (BPD) is the leading cause of morbidity affecting premature babies, and survivors of BPD have long-standing deficits in lung function and may be at risk for the development of additional lung diseases as adults. The increasing evidence of the role of sex as a biological variable in disease outcome, pathophysiology, and response to therapy has highlighted the gap in neonatal basic, clinical, and translational studies. We were the first lab to highlight the crucial role of sex-specific differences in neonatal hyperoxic lung injury in a murine model and have continued to elucidate molecular mechanisms contributing to sexual dimorphism in the neonatal lung. Fundamental questions remain about the mechanisms by which sex as a biological variable modulates lung injury, repair, and recovery. The scientific premise is based on our recent work that shows that sex chromosomes and not sex hormones mediate the differences in the neonatal lung phenotype following injury. The research proposal addresses these gaps by extending our work in elucidating X-chromosome-mediated epigenetic mechanisms mediating sex-specific differences in neonatal lung injury. This paradigm-shifting work is necessary to identify the sex-biased molecular forces that modify disease in the neonatal period and to explain the resilience and/or susceptibility based on biological sex in human BPD.

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A deep reinforcement learning-based system to guide treatment regimens for osteoporosis based on sex and sex hormone dependencies

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PI: Xiaowei Liu, PhD 

Osteoporosis is a disease characterized by significant reductions in overall bone density and structural integrity, causing skeletal fragility and an increased risk of bone fractures. Current treatments for osteoporosis focus on either inhibiting bone resorption using anti-catabolic agents, such as bisphosphonates, or promoting bone formation using anabolic agents, such as intermittent parathyroid hormone (PTH). However, a therapeutic pause after 3-5 years of bisphosphonate use is recommended. Moreover, there is a limit of 18-24 months on the use of anabolic treatments, and their treatment benefit can be reversed upon discontinuation. Nevertheless, osteoporosis is a life-long chronic condition and there is a pressing need for novel therapeutic regimens to provide continuous osteoporosis management and maintain treatment benefit. Our recent investigation unexpectedly discovered that skeletal responses to the anabolic treatment and discontinuation is sex- and gonadal function-dependent. However, osteoporosis not only affects postmenopausal women, but also aged men. Moreover, though rare, young adults may also suffer from osteoporosis and require treatments. Therefore, inspired by recent successes of machine learning in healthcare, in this pilot grant, we propose to develop a deep reinforcement learning (DRL)-based treatment decision making system to (1) elucidate the influence of sex and gonadal function on skeletal responses to sequential treatment regimens combining anabolic and anti-resorptive agents (2) validate a proof-of-concept framework for personalized treatment regimens to achieve the highest treatment benefit and extend treatment duration by considering subjects’ sex and gonadal functions.