Osteoarthritis year in review 2018: mechanics

Abstract

Objective: To review recent biomechanics literature focused on the interactions between biomechanics and articular cartilage health, particularly focused on macro-scale and human studies.

Design: A literature search was conducted in PubMed using the search terms (biomechanics AND osteoarthritis) OR (biomechanics AND cartilage) OR (mechanics AND osteoarthritis) OR (mechanics AND cartilage) for publications from April 2017 to April 2018.

Results: Abstracts from the 559 articles generated from the literature search were reviewed. Due to the wide range of topics, 62 full texts with a focus on in vivo biomechanical studies were included for further discussion. Several overarching themes in the recent literature were identified and are summarized, including 1) new methods to detect early osteoarthritis (OA) development, 2) studies describing healthy and OA cartilage and biomechanics, 3) ACL injury and OA development, 4) meniscus injury and OA development, and 5) OA prevention, treatment, and management.

Conclusions: Mechanical loading is a critical factor in the maintenance of joint health. Abnormal mechanical loading can lead to the onset and progression of OA. Thus, recent studies have utilized various biomechanical models to better describe the etiology of OA development and the subsequent effects of OA on the mechanics of joint tissues and whole body biomechanics.

Keywords: Cartilage; Mechanics; Osteoarthritis.

Figure 1:

Ultrasound has been utilized as a tool to measure cartilage thickness in vivo. In this study, cartilage thickness was measured in three locations within a single image (lateral condyle, intercondylar space, and medial condyle) and was defined as the distance between the cartilage-bone and synovium-cartilage borders. Image from Harkey et al. [23].

Figure 2:

Mechanical indentation and analysis schematic (A), microscopic analysis via Fourier transform infrared imaging, digital densitometry, and polarized light microscopy (B), and pericellular matrix analysis based on optical density profiles for proteoglycan content (C) from Han et al. [39].

Figure 3:

The Experimental Dynamic Gait Arena for Rodents (EDGAR) was used to obtain spatiotemporal and dynamic gait data. This schematic shows a side view of EDGAR (A), floor design with instrumentation panels (B), and a single frame from a representative trial video (C). Image from Jacobs et al. [58].