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Approved Research

Development of risk models related to wellness and musculoskeletal health

Principal Investigator: Dr Christopher Cassa
Approved Research ID: 77928
Approval date: February 8th 2022

Lay summary

Musculoskeletal health and wellness are areas which affect many individuals, but have not yet fully benefitted from broad genetics-based population screening. We plan to analyze a broad range of musculoskeletal disorders or injuries, risk factors to musculoskeletal disorders or injuries, along with wellness phenotypes which might impact overall health and risk or recovery from such disorders (e.g. sleep, exercise, mobility, pain management, exercise, occupational details, mental health and wellness, and nutrition). We are seeking to develop personalized wellness recommendations for individuals, informed by such analysis. We plan to develop population scores which can be used to inform individuals about what might work best to manage their health or recover from a musculoskeletal issue such as an injury. We also hope to find ways to encourage people to use this information to improve their health in other meaningful ways, including improving their sleep and other lifestyle habits.

Scope extension: gWell Health is focused on personalized wellness recommendations for individuals, informed by genomics. We plan to develop polygenic risk scores and population models to provide guidance related to musculoskeletal health and general wellness. We plan to analyze predispositional risk factors to musculoskeletal disease or injury, as well as wellness phenotypes including sleep, exercise, mobility, pain management, healthy lifestyle habits, mindfulness, and nutrition. For each phenotype, we will identify patient cohorts using diagnosis codes, patient survey data, and laboratory measurement values. For each phenotype with sufficient individuals, we will make use of the genotype array data to develop polygenic risk scores or other risk models for each condition. We will then use these to better understand and inform our patients, and to provide population context for each score and area. Additionally, we will develop statistical models which can be used to understand personal risk factors for disease onset, exacerbation, or severe outcomes. The data used in these models could include clinical diagnostic codes and medical interventions, laboratory measurements, and behavioral/lifestyle characteristics. These models will be used to counsel patients on the risk of disease onset and potential associated outcomes, methods to prevent disease, and behaviors or methods to improve disease management, and will span patient characteristics such as obesity, selected common conditions such as osteoarthritis or NAFLD, and acute events (e.g., infectious disease). For example, one chronic condition we propose to develop a model for is osteoporosis. Our model would make use of diagnosis codes or self reports of disease, medical interventions, sex, BMI, and lifestyle or behavioral survey data to model risk for those who have not yet developed disease, and would also be used to counsel patients on methods to improve management and reduce sequelae. For an infectious disease such as COVID-19, we intend to model risk of disease using personal and genetic risk factors (based on those already developed and published by consortia such as the COVID-HGI), medical or public health interventions (e.g. vaccination), lifestyle habits (e.g., activity level), to promote risk reducing health behaviors or public health interventions. These models will be used to counsel patients about individual-level or group risk, and to understand their risk in the context of other individuals.