Use of acoustic emission to identify novel candidate biomarkers for knee osteoarthritis (OA)


Autoři: Daniela K. Schlüter aff001;  Lucy Spain aff003;  Wei Quan aff005;  Harry Southworth aff006;  Nicola Platt aff007;  Joe Mercer aff008;  Lik-Kwan Shark aff005;  John C. Waterton aff009;  Mike Bowes aff010;  Peter J. Diggle aff001;  Mandy Dixon aff007;  Jane Huddleston aff007;  John Goodacre aff003
Působiště autorů: CHICAS, Lancaster Medical School, Lancaster University, Lancaster, England, United Kingdom aff001;  Institute of Population Health Science, Liverpool University, Liverpool, England, United Kingdom aff002;  Faculty of Health and Medicine, Lancaster University, Lancaster, England, United Kingdom aff003;  Institute of Science, Natural Resources and Outdoor Studies, University of Cumbria, Carlisle, England, United Kingdom aff004;  Applied Digital Signal and Image Processing Research Centre, University of Central Lancashire, Preston, England, United Kingdom aff005;  Data Clarity Consulting Ltd, Altrincham, England, United Kingdom aff006;  Lancaster Health Hub, Lancaster University, Lancaster, England, United Kingdom aff007;  The Christie NHS Foundation Trust, Manchester, England, United Kingdom aff008;  Centre for Imaging Sciences, University of Manchester, Manchester Academic Health Sciences Centre, Manchester, England, United Kingdom aff009;  Imorphics Ltd, Manchester, England, United Kingdom aff010
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223711

Souhrn

Our objective was to determine the efficacy and feasibility of a new approach for identifying candidate biomarkers for knee osteoarthritis (OA), based on selecting promising candidates from a range of high-frequency acoustic emission (AE) measurements generated during weight-bearing knee movement. Candidate AE biomarkers identified by this approach could then be validated in larger studies for use in future clinical trials and stratified medicine applications for this common health condition. A population cohort of participants with knee pain and a Kellgren-Lawrence (KL) score between 1-4 were recruited from local NHS primary and secondary care sites. Focusing on participants’ self-identified worse knee, and using our established movement protocol, sources of variation in AE measurement and associations of AE markers with other markers were explored. Using this approach we identified 4 initial candidate AE biomarkers, of which “number of hits” showed the best reproducibility, in terms of within-session, day to day, week to week, between-practitioner, and between-machine variation, at 2 different machine upper frequency settings. “Number of hits” was higher in knees with KL scores of 2 than in KL1, and also showed significant associations with pain in the contralateral knee, and with body weight. “Hits” occurred predominantly in 2 of 4 defined movement quadrants. The protocol was feasible and acceptable to all participants and professionals involved. This study demonstrates how AE measurement during simple sit-stand-sit movements can be used to generate novel candidate knee OA biomarkers. AE measurements probably reflect a composite of structural changes and joint loading factors. Refinement of the method and increasing understanding of factors contributing to AE will enable this approach to be used to generate further candidate biomarkers for validation and potential use in clinical trials.

Klíčová slova:

Acoustic signals – Biomarkers – Cartilage – Clinical trials – Knee joints – Knees – Musculoskeletal system – Osteoarthritis


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Článek vyšel v časopise

PLOS One


2019 Číslo 10