Swipe om te navigeren naar een ander artikel
Injury is an important risk factor for osteoarthritis (OA), a highly prevalent and disabling joint disease. Joint shape is linked to OA, but the interplay of injury and joint shape and their combined role in OA, particularly at the ankle, is not well known. Therefore, we explored cross-sectional associations between ankle shape and injury in a large community-based cohort.
Ankles without radiographic OA were selected from the current data collection of the Johnston County OA Project. Ankles with self-reported prior injury were included as injury cases (n = 108) along with 1:1 randomly selected non-injured ankles. To define ankle shape, a 68 point model on weight-bearing lateral ankle radiographs was entered into a statistical shape model, producing a mean shape and a set of continuous variables (modes) representing variation in that shape. Nineteen modes, explaining 80% of shape variance, were simultaneously included in a logistic regression model with injury status as the dependent variable, adjusted for intra-person correlation, sex, race, body mass index (BMI), baseline OA radiographic grade, and baseline symptoms.
A total of 194 participants (213 ankles) were included; mean age 71 years, BMI 30 kg/m2, 67% white and 71% women. Injured ankles were more often symptomatic and from whites. In a model adjusted only for intra-person correlation, associations were seen between injury status and modes 1, 6, 13, and 19. In a fully adjusted model, race strongly affected the estimate for mode 1 (which was no longer statistically significant).
This study showed variations in ankle shape and history of injury as well as with race. These novel findings may indicate a change in ankle morphology following injury, or that ankle morphology predisposes to injury, and suggest that ankle shape is a potentially important factor in the development of ankle OA.
Prevalence and most common causes of disability among adults--United States, 2005. MMWR. Morb Mortal Wkly Rep. 2009;58(16):421–6.
National Statistics All ED Visits [ https://hcupnet.ahrq.gov/#setup]. Accessed 6 June 2016.
Cootes TF, Taylor CJ, Cooper DH, Graham J. Active shape models - their training and application. Comput Vis Image Underst. 1995;61(1):38–59. CrossRef
Gregory JS, Waarsing JH, Day J, Pols HA, Reijman M, Weinans H, et al. Early identification of radiographic osteoarthritis of the hip using an active shape model to quantify changes in bone morphometric features: can hip shape tell us anything about the progression of osteoarthritis? Arthritis Rheum. 2007;56(11):3634–43. CrossRefPubMed
Barr RJ, Gregory JS, Reid DM, Aspden RM, Yoshida K, Hosie G, et al. Predicting OA progression to total hip replacement: can we do better than risk factors alone using active shape modelling as an imaging biomarker? Rheumatology (Oxford). 2012;51(3):562–70. CrossRef
Jordan JM, Helmick CG, Renner JB, Luta G, Dragomir AD, Woodard J, et al. Prevalence of knee symptoms and radiographic and symptomatic knee osteoarthritis in African Americans and Caucasians: the Johnston County osteoarthritis project. J Rheumatol. 2007;34(1):172–80. PubMed
U.S. Department of Health and Human Services: 2008 Physical Activity Guidelines for Americans. 2008;76. http://www.health.gov/paguidelines. Accessed 4 Nov 2017.
Rogers WH. Regression standard errors in clustered samples. Stata Tech Bull. 1993;13:19–23.
- Cross-sectional associations between variations in ankle shape by statistical shape modeling, injury history, and race: the Johnston County Osteoarthritis Project
Amanda E. Nelson
Yvonne M. Golightly
Jordan B. Renner
Joanne M. Jordan
Richard M. Aspden
Jennifer S. Gregory
- BioMed Central