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Approved Research

The impact of estrogen, bone density, muscle fat distribution, and gender roles on knee pain: A population-based longitudinal study of postmenopausal women

Principal Investigator: Dr Andy Wong
Approved Research ID: 76691
Approval date: April 25th 2022

Lay summary

Knee osteoarthritis (OA) is a disease where the joints natural cushioning, cartilage and bone, starts to break down, resulting in joint pain, swelling, stiffness, and decreased ability to move. Knee OA impacts over 3 million adults in Canada and over 302 million people worldwide and is one of the most diagnosed diseases in general practice. Although the main risk factors for knee OA are obesity and excessive weight placed on knees, it also affects many non-overweight individuals. Research suggests that other possible risk factors for knee OA include changes in hormone levels (i.e., estrogen), bone structure (i.e., bone mineral density), and muscle fat distributions. This may be why many non-overweight postmenopausal women have knee OA. A rapid drop in estrogen levels after menopause drives development of the following diseases in women: osteoporosis (bone loss), sarcopenia (muscle loss), and sarcopenic obesity (increased distribution of fat within muscle). However, it is unclear whether these estrogen-driven bone and muscle effects also causes changes in local bones and muscles around the knee that may cause knee OA and pain. In addition, most studies of women and knee OA only consider the role of sex, yet many symptoms of OA may be influenced by social factors related to gender. Therefore, the aim of this study is to identify pathways linking estrogen levels, bone mineral density, muscle fat distribution, and gender with knee OA and pain. A population-based cohort study of postmenopausal women using data from the United Kingdom Biobank will be conducted. The expected duration to conduct this study is 3-years.

We will determine the associations between estrogen, bone density, muscle fat distribution, and gender with knee pain using statistical analysis. We will also use statistical models to identify the pathways leading from estrogen to knee pain. In addition, we will develop a method to represent knee bone mineral density for scenarios where medical imaging (e.g., X-rays, MRIs) is not available to obtain direct measurements. Overall, this proposed work fills a critical knowledge gap by identifying the pathways that cause knee pain in a priority population of postmenopausal women. Current treatment options only manage OA symptoms (i.e., pain and inflammation) rather than targeting the origin of symptoms (e.g., estrogen). To our knowledge, this study will be the first to examine the effect of sex- and gender-related factors on knee OA. Findings will identify targets for intervention that directly address the sources of knee pain.