A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women

Lazzarini, Nicola; Runhaar, Jos; Bay‐Jensen, Anne‐Christine; Thudium, Christian S.; Bierma-Zeinstra, Sita M A; Henrotin, Yves; Bacardit, Jaume

doi:10.1016/j.joca.2017.09.001

Cited by 79 publications

(77 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Combining biochemical markers with other markers (i.e., imaging, genetic, and clinical markers) and bioinformatics may facilitate earlier detection of OA. Using data of the prevention of knee osteoarthritis in overweight females study, a randomized controlled trial of middle-aged women with high BMI, authors generated 5 predictive models for KOA incidence including clinical variables, food and pain questionnaire data, as well as biochemical markers and imaging based information 12 . Several biomarkers of extracellular matrix tissue turnover provided information on the risk of KOA incidence.…”

Section: Generating Oa Prediction Modelsmentioning

confidence: 99%

Osteoarthritis year in review 2018: biomarkers (biochemical markers)

Hosnijeh

Bierma-Zeinstra

Bay‐Jensen

2019

Osteoarthritis and Cartilage

Self Cite

View full text Add to dashboard Cite

The aim of this narrative review is to summarize important findings from biochemical marker studies relevant to osteoarthritis (OA) in the context of new discoveries and clinical and scientific need. Design: We conducted a systematic search of electronic medical databases (Embase, Medline, Web of Science, Cochrane central) between 01-03-2017 and 31-03-2018. The search was restricted to human studies, English language and full text available publications while reviews were excluded. Only papers describing protein based biomarkers measured in human body fluids (blood, urine and synovial fluid (SF)) were included. Of the 992 papers, 86 were reviewed here, with inclusion primarily based on relevance to OA biochemical markers. Results: This review highlights a selection of studies based on their quality and perceived importance to the field mainly including those that 1 evaluate prognostic value of biomarkers for OA progression (i.e., biomarkers reflecting change in composition of joint tissues and biomarkers of inflammation) 2 , help in assessment of intervention efficacy, and 3 are innovative and uncover new candidate biomarkers, or use new approaches in biomarker discovery. Conclusions: Key findings and implications for possible clinical utility of biochemical markers are summarized and discussed. Given the paucity of robust biomarkers within the field, and the heterogeneity of the condition, enormous works are needed for development and validation of novel and clinically applicable biomarkers to reduce the impact of this highly prevalent and debilitating condition.

show abstract

Section: Generating Oa Prediction Modelsmentioning

confidence: 99%

Osteoarthritis year in review 2018: biomarkers (biochemical markers)

Hosnijeh

Bierma-Zeinstra

Bay‐Jensen

2019

Osteoarthritis and Cartilage

Self Cite

View full text Add to dashboard Cite

show abstract

“…[2][3][4][5][6][7][8]58 Because clinical data are not routinely available until patients seek care for symptoms, most classification and staging algorithms are primarily informed by late-stage disease, which leads to ambiguity, overlap, and generalization, relegating physicians and patients to incomplete, possibly inaccurate, data for decision making regarding type and timing of treatment. [59][60][61][62][63][64][65][66][67][68] As such, more nuanced, earlier, longitudinal analytical methods, ideally including controls for relevant cohorts, which incorporate articular cartilage lesions features, whole-joint status, and whole-patient variables are needed to fill this unmet need in orthopaedic health care. Unfortunately, current analytical methods for classification, staging, or prediction of joint disease suffer from key issues, including standardization for terminology and methods, lack of well-designed studies, subjectivity of classification or inputs, lack of analytical validation, and lack of relevance to clinical practice (i.e., external validity).…”

Section: Current Clinical Grading and Scoring Methodsmentioning

confidence: 99%

“…Although researchers have previously used ML methods to define or better assign risk for end-stage articular cartilage pathology in terms of OA progression, most ML algorithms have considered data inputs or outputs that are not routinely collected through regular clinic visits (e.g., genetic testing, radiological testing or longitudinal imaging, surgical considerations, gait testing, and biomechanics). 64,66,81 This also does not consider the cost-prohibitive and logistical hurdles for obtaining this information; due to these obstacles, most classification algorithms do not reflect data available through current clinical practice. Consequently, most algorithms are purely academic in nature and costly to implement, while being simultaneously burdensome to research participants and of limited utility for clinical practice.…”

Section: A Way Forwardmentioning

confidence: 99%

Classification, Categorization, and Algorithms for Articular Cartilage Defects

2020

View full text Add to dashboard Cite

There is a critical unmet need in the clinical implementation of valid preventative and therapeutic strategies for patients with articular cartilage pathology based on the significant gap in understanding of the relationships between diagnostic data, disease progression, patient-related variables, and symptoms. In this article, the current state of classification and categorization for articular cartilage pathology is discussed with particular focus on machine learning methods and the authors propose a bedside–bench–bedside approach with highly quantitative techniques as a solution to these hurdles. Leveraging computational learning with available data toward articular cartilage pathology patient phenotyping holds promise for clinical research and will likely be an important tool to identify translational solutions into evidence-based clinical applications to benefit patients. Recommendations for successful implementation of these approaches include using standardized definitions of articular cartilage, to include characterization of depth, size, location, and number; using measurements that minimize subjectivity or validated patient-reported outcome measures; considering not just the articular cartilage pathology but the whole joint, and the patient perception and perspective. Application of this approach through a multistep process by a multidisciplinary team of clinicians and scientists holds promise for validating disease mechanism-based phenotypes toward clinically relevant understanding of articular cartilage pathology for evidence-based application to orthopaedic practice.

show abstract

“…Kinetic variables from the hip and knee and the quality of life outcome score were combined to create a prediction model for predicting the risk of knee OA in post-traumatic individuals (with the least prediction error of 0.02). Imaging-based information incorporated in machine learning-based prediction models were found to improve their performance (Lazzarini et al, 2017). In Ashinsky et al (2017), a disease classification model was developed using a machine learning algorithm to select features of articular cartilage from MRI (performed in vivo) indicative of OA progression.…”

Section: Examplesmentioning

confidence: 99%

Use of Computational Modeling to Study Joint Degeneration: A Review

Mukherjee

Nazemi

Jonkers

et al. 2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Osteoarthritis (OA), a degenerative joint disease, is the most common chronic condition of the joints, which cannot be prevented effectively. Computational modeling of joint degradation allows to estimate the patient-specific progression of OA, which can aid clinicians to estimate the most suitable time window for surgical intervention in osteoarthritic patients. This paper gives an overview of the different approaches used to model different aspects of joint degeneration, thereby focusing mostly on the knee joint. The paper starts by discussing how OA affects the different components of the joint and how these are accounted for in the models. Subsequently, it discusses the different modeling approaches that can be used to answer questions related to OA etiology, progression and treatment. These models are ordered based on their underlying assumptions and technologies: musculoskeletal models, Finite Element models, (gene) regulatory models, multiscale models and data-driven models (artificial intelligence/machine learning). Finally, it is concluded that in the future, efforts should be made to integrate the different modeling techniques into a more robust computational framework that should not only be efficient to predict OA progression but also easily allow a patient's individualized risk assessment as screening tool for use in clinical practice.

show abstract

A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women

Cited by 79 publications

References 24 publications

Osteoarthritis year in review 2018: biomarkers (biochemical markers)

Osteoarthritis year in review 2018: biomarkers (biochemical markers)

Classification, Categorization, and Algorithms for Articular Cartilage Defects

Use of Computational Modeling to Study Joint Degeneration: A Review

Contact Info

Product

Resources

About