At present, to help clinical decision-making, a model that full successfully predicts UA outcome, easily assessable in clinical practice, is lacking. However, in order to address the goal of early identification of UA patients with high risk to develop RA, prediction model has been proposed. All these models estimate the frequency of progression to RA related to the calculated score attributed to defined clinical and laboratory parameter. Up to now, in all proposed models, the assessment of soluble biomarkes included are limited to RF and anti-CCP antibody evaluation. Model proposed by Van der Helm appears to be the main algorithm that has been extensively applied in different cohort of UA patients in order to evaluated the accuracy in outcome prediction (Van der Helm-Van Mil AHN et al, Arthritis Rheum, 2007: Van der Helm-Van Mil AHN et al, Arthritis Rheum, 2008). This algorithm appear to have a good accuracy for predicting outcome of UA patients presenting respectively low (=/<6.0) (91% did not develop RA) and high (=/>8) score (84% develop RA). Unfortunately, this model appear to be inadequate in estimating the risk in 25% of UA with an intermediate score. More recently, in 2010, the American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR) developed new criteria for facilitating the early identification of UA patient with highest probability to develop persistent or erosive RA. Recent studies, aiming to evaluate the diagnostic performance of ACR/EULAR 2010 criteria, showed good predictive value for UA patient scored =/>6, overlapping accuracy of Van der Helm algorithm (Alves C et al., Ann Rheum Dis, 2011; Cader MZ, Ann Rheum Dis, 2011). On the other hand, in these cohorts of patient a relevant rate of patients who needed to be treated during follow-up, did not fulfill the ACR/EULAR 2010 criteria at baseline. Thus, misclassification may be a relevant issue when treatment decision are taken according to this algorithm. These evidence underline that predicting power improvement of clinical useful model, easily assessable in clinical practice, is greatly needed. ACR/EULAR working group for defining 2010 classification criteria stated that genetic, proteomic, serological or imaging biomarkers that provide a more robust basis for risk stratification will be considered for a modification or amendment of the 2010 criteria. To reach this goal, data concerning multiple evaluation of a large set of molecules in UA patients are needed. Therefore, evaluation of a composite (genetic and soluble) biomarker profile in a cohort of recent onset UA patients (prospectively assessed), utilizing multiplex-detection technology approach, appear to be original and innovative because: - allow to analyze a vast number of candidate biomarkers - may identify a reliable soluble biomarker profile in recent onset UA patients in relationship to clinical outcome - may define the genetic network of epistatic interaction that underlie the susceptibility to develop RA in early UA patients. - may add power in predicting early UA outcome comparing to prognostic model currently followed in clinical practice Combined detection of multiple indicators may define a molecular signature that allows the simultaneously identification of diagnostic/prognostic features of individuals patients, thus leading forward to the practice of personalized medicine.
Prognostic value of a combined panel of soluble and genetic biomarkers in patients with early arthritis
RUBINI, Michele;
2012
Abstract
At present, to help clinical decision-making, a model that full successfully predicts UA outcome, easily assessable in clinical practice, is lacking. However, in order to address the goal of early identification of UA patients with high risk to develop RA, prediction model has been proposed. All these models estimate the frequency of progression to RA related to the calculated score attributed to defined clinical and laboratory parameter. Up to now, in all proposed models, the assessment of soluble biomarkes included are limited to RF and anti-CCP antibody evaluation. Model proposed by Van der Helm appears to be the main algorithm that has been extensively applied in different cohort of UA patients in order to evaluated the accuracy in outcome prediction (Van der Helm-Van Mil AHN et al, Arthritis Rheum, 2007: Van der Helm-Van Mil AHN et al, Arthritis Rheum, 2008). This algorithm appear to have a good accuracy for predicting outcome of UA patients presenting respectively low (=/<6.0) (91% did not develop RA) and high (=/>8) score (84% develop RA). Unfortunately, this model appear to be inadequate in estimating the risk in 25% of UA with an intermediate score. More recently, in 2010, the American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR) developed new criteria for facilitating the early identification of UA patient with highest probability to develop persistent or erosive RA. Recent studies, aiming to evaluate the diagnostic performance of ACR/EULAR 2010 criteria, showed good predictive value for UA patient scored =/>6, overlapping accuracy of Van der Helm algorithm (Alves C et al., Ann Rheum Dis, 2011; Cader MZ, Ann Rheum Dis, 2011). On the other hand, in these cohorts of patient a relevant rate of patients who needed to be treated during follow-up, did not fulfill the ACR/EULAR 2010 criteria at baseline. Thus, misclassification may be a relevant issue when treatment decision are taken according to this algorithm. These evidence underline that predicting power improvement of clinical useful model, easily assessable in clinical practice, is greatly needed. ACR/EULAR working group for defining 2010 classification criteria stated that genetic, proteomic, serological or imaging biomarkers that provide a more robust basis for risk stratification will be considered for a modification or amendment of the 2010 criteria. To reach this goal, data concerning multiple evaluation of a large set of molecules in UA patients are needed. Therefore, evaluation of a composite (genetic and soluble) biomarker profile in a cohort of recent onset UA patients (prospectively assessed), utilizing multiplex-detection technology approach, appear to be original and innovative because: - allow to analyze a vast number of candidate biomarkers - may identify a reliable soluble biomarker profile in recent onset UA patients in relationship to clinical outcome - may define the genetic network of epistatic interaction that underlie the susceptibility to develop RA in early UA patients. - may add power in predicting early UA outcome comparing to prognostic model currently followed in clinical practice Combined detection of multiple indicators may define a molecular signature that allows the simultaneously identification of diagnostic/prognostic features of individuals patients, thus leading forward to the practice of personalized medicine.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
