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A Bayesian Model to Analyze the Association of Rheumatoid Arthritis With Risk Factors and Their Interactions.

Leon Lufkin1, Marko Budišić2, Sumona Mondal2

  • 1The Clarkson School, Clarkson University, Potsdam, NY, United States.

Frontiers in Public Health
|September 6, 2021
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Summary
This summary is machine-generated.

This study reveals that interactions between rheumatoid arthritis (RA) risk factors, like age and body mass index (BMI), significantly impact disease prediction, aiding early diagnosis strategies.

Keywords:
BayesianNHANEScomorbiditiesfactor analysis of mixed datagenetic algorithminteractionspredictionrheumatoid arthritis

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Area of Science:

  • Rheumatology and autoimmune disease research.
  • Biostatistics and predictive modeling in healthcare.
  • Public health and epidemiology of chronic conditions.

Background:

  • Rheumatoid arthritis (RA) is a chronic autoimmune disease causing joint inflammation and systemic effects.
  • RA pathogenesis is complex, influenced by comorbidities, demographics, and socioeconomic factors.
  • Understanding these factors and their interactions is crucial for RA management and prediction.

Purpose of the Study:

  • To quantitatively assess individual and interactive effects of RA risk factors on disease risk.
  • To develop and validate a predictive model for rheumatoid arthritis using diverse risk factors.
  • To explore the role of multi-order interactions in RA pathogenesis and prediction.

Main Methods:

  • Utilized Bayesian logistic regression and Factor Analysis of Mixed Data (FAMD) on NHANES data.
  • Selected 11 known RA risk factors (e.g., age, gender, BMI, depression) for analysis.
  • Employed a genetic algorithm (GA) for model optimization based on AUC, considering up to third-order interactions.

Main Results:

  • The optimized model achieved a smoothed AUC of 0.826 on validation and 0.805 on test datasets.
  • Confirmed the influence of individual RA risk factors and identified significant second- and third-order interactions.
  • Interactions involving age and BMI were particularly associated with RA risk.

Conclusions:

  • Risk factor interactions play a potentially significant role in rheumatoid arthritis mechanisms.
  • The predictive model incorporating interactions offers insights for early RA diagnosis strategies.
  • Findings highlight the importance of considering complex interactions for a comprehensive understanding of RA risk.