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Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning.

ABSTRACT: After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-?1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints.

SUBMITTER: Bianchi J

PROVIDER: S-EPMC7228972 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning.

Bianchi Jonas J de Oliveira Ruellas Antônio Carlos AC Gonçalves João Roberto JR Paniagua Beatriz B Prieto Juan Carlos JC Styner Martin M Li Tengfei T Zhu Hongtu H Sugai James J Giannobile William W Benavides Erika E Soki Fabiana F Yatabe Marilia M Ashman Lawrence L Walker David D Soroushmehr Reza R Najarian Kayvan K Cevidanes Lucia Helena Soares LHS

Scientific reports 20200515 1

After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (ra ...[more]

PMID: 32415284

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Project description:Background Extracellular matrix (ECM) protein malfunction or defect may lead to temporomandibular joint osteoarthritis (TMJ OA). Dentin sialophophoprotein (DSPP) is a mandibular condylar cartilage ECM protein, and its deletion impacted cell proliferation and other extracellular matrix alterations of postnatal condylar cartilage. However, it remains unclear if long-term loss of function of DSPP leads to TMJ OA. The study aimed to test the hypothesis that long-term haploinsufficiency of DSPP causes TMJ OA. Materials and Methods To determine whether Dspp+/- mice exhibit TMJ OA but no severe tooth defects, mandibles of wild-type (WT), Dspp+/-, and Dspp homozygous (Dspp-/-) mice were analyzed by Micro-computed tomography (micro-CT). To characterize the progression and possible mechanisms of osteoarthritic degeneration over time in Dspp+/- mice over time, condyles of Dspp+/- and WT mice were analyzed radiologically, histologically, and immunohistochemically. Results Micro-CT and histomorphometric analyses revealed that Dspp+/- and Dspp-/- mice had significantly lower subchondral bone mass, bone volume fraction, bone mineral density, and trabecular thickness compared to WT mice at 12 months. Interestingly, in contrast to Dspp-/- mice which exhibited tooth loss, Dspp+/- mice had minor tooth defects. RNA sequencing data showed that haplodeficency of DSPP affects the biological process of ossification and osteoclast differentiation. Additionally, histological analysis showed that Dspp+/- mice had condylar cartilage fissures, reduced cartilage thickness, decreased articular cell numbers and severe subchondral bone cavities, and with signs that were exaggerated with age. Radiographic data showed an increase in subchondral osteoporosis up to 18 months and osteophyte formation at 21 months. Moreover, Dspp+/- mice showed increased distribution of osteoclast in the subchondal bone and increased expression of MMP2, IL-6, FN-1, and TLR4 in the mandibular condylar cartilage. Conclusions Dspp+/- mice exhibit TMJ OA in a time-dependent manner, with lesions in the mandibular condyle attributed to hypomineralization of subchondral bone and breakdown of the mandibular condylar cartilage, accompanied by upregulation of inflammatory markers.

Dataset Information

Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning.

Publications

Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning.

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