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Updated: May 27, 2026

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint
Published on: July 22, 2021
Joint Disease and Demographic Factors in Clinical Populations of Medically Underserved Communities
Blake C Martin1, Juan Lopez-Alvarenga1, Michael D Sander2
1The University of Texas Rio Grande Valley.
Background:
The Rio Grande Valley is a medically underserved, demographically unique population that warrants the exploration of various conditions.
Objectives:
We sought to analyze the demographic disparities between the different types of common joint conditions in this community. We hypothesized that osteoarthritis would be the most common joint condition and that the demographics between the various joint conditions would vary depending on age, sex, BMI, and ethnicity.
Methods:
This was a retrospective cohort, from January 1, 2018, to September 4, 2024. Patients' charts were obtained using the ICD-10 diagnosis codes M15-M19 for osteoarthritis, M05-M06 for rheumatoid arthritis, M10 or M1A for gout, M45 for AS, L40.5 for psoriatic arthritis, M08 for juvenile idiopathic arthritis, and M00.8 for septic arthritis. We analyzed the data via descriptive statistics and panel regression models.
Results:
Osteoarthritis was the most common diagnosis, accounting for 87% of cases (7,418 patients) who have this diagnosis alone. Hispanics showed a strong positive association with BMI compared to non-Hispanics (p < 0.001). The largest difference of age was in septic arthritis, with non-Hispanic patients having an adjusted mean age of 71.3 years (95% CI: 57.9, 84.8) compared to 48.2 years (95% CI: 41.8, 54.7) for Hispanic patients. The associations between BMI and Gout, Osteoarthritis, and Rheumatoid Arthritis were statistically significant (p-values: 0.015, 0.014, and 0.030).
Conclusion:
These findings underscore the importance of targeting obesity and age-related joint conditions in this medically underserved population and may suggest different patterns of disease onset, healthcare access, or lifestyle factors by ethnicity that could inform tailored interventions.
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