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Clinical parameters and biomarkers for anti-TNF treatment prognosis in rheumatoid arthritis patients

Miguel Cuchacovich1, Daniel Bueno, Rodrigo Carvajal

  • 1Department of Medicine, Clinical Hospital University of Chile, San Pío X, N 2460, Off 607, Providencia, Santiago, Chile, mcuchaco@gmail.com.

Clinical Rheumatology
|August 3, 2014
PubMed

Insights

Predicting rheumatoid arthritis (RA) treatment response to TNF blockers remains challenging. While some clinical factors and autoantibodies show promise, no single marker reliably predicts individual patient outcomes for this common RA therapy.

Area of Science:

  • Rheumatology
  • Immunology
  • Pharmacology

Background:

  • Tumor necrosis factor (TNF) is central to rheumatoid arthritis (RA) pathogenesis.
  • TNF blockers are effective RA treatments, but patient response is variable.
  • Predicting which patients will respond to anti-TNF therapy is crucial for personalized medicine.

Purpose of the Study:

  • To review current knowledge on predicting treatment response to TNF inhibitors in RA.
  • To evaluate the utility of clinical factors, disease activity, comorbidities, and autoantibodies (RF, ACPA) as predictive markers.
  • To assess the role of TNF promoter polymorphisms in anti-TNF therapy response.

Main Methods:

  • Review of existing literature on predictors of anti-TNF therapy response in RA.
  • Analysis of clinical factors such as promptness/magnitude of response, disease activity, comorbidities, tobacco use, and glucocorticoid treatment.
  • Examination of serological markers including rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA) titers.
  • Consideration of genetic factors like TNF promoter polymorphisms.

Main Results:

  • Prompt and significant early response and low baseline disease activity predict better outcomes.
  • Comorbidities, smoking, glucocorticoid use, and high baseline radiological scores are associated with poorer response.
  • High baseline RF titers may correlate with decreased response, with titers often decreasing upon successful treatment.
  • The role of TNF polymorphisms is controversial, and consensus on ACPA levels as predictors is lacking.
  • Current single markers are insufficient for predicting individual patient response.

Conclusions:

  • Predicting anti-TNF therapy response in RA is complex, with multiple factors influencing outcomes.
  • While clinical and serological markers offer some insights, their individual predictive power is limited.
  • Further research is needed to identify robust biomarkers for personalized anti-TNF treatment selection in RA.

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