Mind Gaps and Bone Snaps: Exploring the Connection Between Alzheimer's Disease and Osteoporosis

Abstract

Purpose of review: This comprehensive review discusses the complex relationship between Alzheimer's disease (AD) and osteoporosis, two conditions that are prevalent in the aging population and result in adverse complications on quality of life. The purpose of this review is to succinctly elucidate the many commonalities between the two conditions, including shared pathways, inflammatory and oxidative mechanisms, and hormonal deficiencies. Recent findings: AD and osteoporosis share many aspects of their respective disease-defining pathophysiology. These commonalities include amyloid beta deposition, the Wnt/β-catenin signaling pathway, and estrogen deficiency. The shared mechanisms and risk factors associated with AD and osteoporosis result in a large percentage of patients that develop both diseases. Previous literature has established that the progression of AD increases the risk of sustaining a fracture. Recent findings demonstrate that the reverse may also be true, suggesting that a fracture early in the life course can predispose one to developing AD due to the activation of these shared mechanisms. The discovery of these commonalities further guides the development of novel therapeutics in which both conditions are targeted. This detailed review delves into the commonalities between AD and osteoporosis to uncover the shared players that bring these two seemingly unrelated conditions together. The discussion throughout this review ultimately posits that the occurrence of fractures and the mechanism behind fracture healing can predispose one to developing AD later on in life, similar to how AD patients are at an increased risk of developing fractures. By focusing on the shared mechanisms between AD and osteoporosis, one can better understand the conditions individually and as a unit, thus informing therapeutic approaches and further research. This review article is part of a series of multiple manuscripts designed to determine the utility of using artificial intelligence for writing scientific reviews.