Predictive Modeling of Osteoarthritis Using Biochemical Markers: A Cross-Sectional Analysis

Mani Arumugam; Chacko Paul Joseph P; Naveen Jeyaraman; Swaminathan Ramasubramanian; Rajeswary Hari; Madhan Jeyaraman

doi:10.13107/jocr.2024.v14.i12.5084

Predictive Modeling of Osteoarthritis Using Biochemical Markers: A Cross-Sectional Analysis

J Orthop Case Rep. 2024 Dec;14(12):237-245. doi: 10.13107/jocr.2024.v14.i12.5084.

Authors

Mani Arumugam¹, Chacko Paul Joseph P¹, Naveen Jeyaraman², Swaminathan Ramasubramanian³, Rajeswary Hari⁴, Madhan Jeyaraman²

Affiliations

¹ Department of Orthopaedics, Melmaruvathur Adhiparasakthi Institute of Medical Sciences, Melmaruvathur, Tamil Nadu, India.
² Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai, Tamil Nadu, India.
³ Department of Orthopaedics, Government Medical College, Omandurar Government Estate, Chennai, Tamil Nadu, India.
⁴ Department of Biotechnology, Dr MGR Educational and Research Institute, Chennai, Tamil Nadu, India.

Abstract

Introduction: Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by cartilage deterioration, joint pain, and reduced mobility. This study aimed to quantify the association between specific biochemical markers and OA and to develop a predictive model for assessing OA risk based on these markers.

Materials and methods: A total of 200 participants (mean age: 55.2 years) were recruited for this cross-sectional analysis, conducted at Sri Lalithamabigai Medical College and Hospital between June and September 2023. The study included 100 patients with X-ray-confirmed knee OA and 100 healthy controls. Biochemical markers - Vitamin D, parathyroid hormone (PTH), alkaline phosphatase (ALP), calcium, magnesium, chromium, and selenium - were measured using high-performance liquid chromatography tandem mass spectrometry, immunoassays, and inductively coupled plasma mass spectrometry. Statistical analysis involved independent t-tests and logistic regression, with model performance assessed by accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (AUC-ROC).

Results: The mean levels of the measured markers in the OA group were 19.82 ng/mL for Vitamin D, 58.10 pg/mL for PTH, and 135.70 IU/L for ALP, compared to 43.24 ng/mL, 23.73 pg/mL, and 53.13 IU/L, respectively, in controls. Significant differences were observed in Vitamin D, PTH, and ALP levels between OA patients and controls (P < 0.05). The predictive model demonstrated excellent performance, with an AUC-ROC of 1.00.

Conclusion: The study identified significant associations between specific biochemical markers and OA. However, given the systemic influences on these markers, their role in predicting OA risk should be interpreted with caution. Further, research is needed to validate these findings and explore their potential in personalized OA management strategies.

Keywords: Osteoarthritis; biochemical markers; cross-sectional analysis; personalized medicine; predictive modeling.