Dataset Information

Markers of angiogenesis and macrophage products for predicting disease course and monitoring vascular inflammation in giant cell arteritis.

ABSTRACT: OBJECTIVE:GCA, a systemic vasculitis, is characterized by an IL-6-dependent acute-phase response. This response is typically suppressed by treatment rendering CRP/ESR unreliable for monitoring vascular inflammation. Also, there are no accurate biomarkers predicting a non-favourable disease course. Here we investigated macrophage products and markers of angiogenesis as biomarkers for prognosis and monitoring of vascular inflammation. METHODS:Forty-one newly diagnosed, glucocorticoid-naive GCA patients were prospectively followed for relapses and glucocorticoid requirement for a median of 30 months (range 0-71). Serum markers at baseline and during follow-up were compared with 33 age-matched healthy controls and 13 infection controls. Concentrations of IL-6, serum amyloid A, soluble CD163, calprotectin, YKL-40, VEGF, angiopoietin-1 and -2 and sTie2 were determined by ELISA/Luminex assay. RESULTS:Serum concentrations of all markers, but not angiopoietin-1, were elevated in GCA patients at baseline when compared with healthy controls. High VEGF (P = 0.0025) and angiopoietin-1 (P = 0.0174) and low YKL-40 (P = 0.0369) levels at baseline were predictive of a short time to glucocorticoid-free remission. Elevated angiopoietin-2 levels were associated with an imminent relapse during treatment (P < 0.05). IL-6 correlated strongly with acute-phase markers and soluble CD163 but not with markers of angiogenesis, YKL-40 or calprotectin. Glucocorticoid treatment down-modulated all markers except for calprotectin and YKL-40. Tissue expression of markers in temporal arteries was confirmed. CONCLUSION:Markers of angiogenesis at baseline and during treatment predict GCA disease course, suggesting utility in patient stratification for glucocorticoid-sparing therapy. Calprotectin and YKL-40 are candidate markers for monitoring vessel wall inflammation.

SUBMITTER: van Sleen Y

PROVIDER: S-EPMC6649803 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Markers of angiogenesis and macrophage products for predicting disease course and monitoring vascular inflammation in giant cell arteritis.

van Sleen Yannick Y Sandovici Maria M Abdulahad Wayel H WH Bijzet Johan J van der Geest Kornelis S M KSM Boots Annemieke M H AMH Brouwer Elisabeth E

Rheumatology (Oxford, England) 20190225

<h4>Objective</h4>GCA, a systemic vasculitis, is characterized by an IL-6-dependent acute-phase response. This response is typically suppressed by treatment rendering CRP/ESR unreliable for monitoring vascular inflammation. Also, there are no accurate biomarkers predicting a non-favourable disease course. Here we investigated macrophage products and markers of angiogenesis as biomarkers for prognosis and monitoring of vascular inflammation.<h4>Methods</h4>Forty-one newly diagnosed, glucocorticoi ...[more]

PMID: 30805622

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Project description:BackgroundInferential statistical methods failed in identifying reliable biomarkers and risk factors for relapsing giant cell arteritis (GCA) after glucocorticoids (GCs) tapering. A ML approach allows to handle complex non-linear relationships between patient attributes that are hard to model with traditional statistical methods, merging them to output a forecast or a probability for a given outcome.ObjectiveThe objective of the study was to assess whether ML algorithms can predict GCA relapse after GCs tapering.MethodsGCA patients who underwent GCs therapy and regular follow-up visits for at least 12 months, were retrospectively analyzed and used for implementing 3 ML algorithms, namely, Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF). The outcome of interest was disease relapse within 3 months during GCs tapering. After a ML variable selection method, based on a XGBoost wrapper, an attribute core set was used to train and test each algorithm using 5-fold cross-validation. The performance of each algorithm in both phases was assessed in terms of accuracy and area under receiver operating characteristic curve (AUROC).ResultsThe dataset consisted of 107 GCA patients (73 women, 68.2%) with mean age ( ± SD) 74.1 ( ± 8.5) years at presentation. GCA flare occurred in 40/107 patients (37.4%) within 3 months after GCs tapering. As a result of ML wrapper, the attribute core set with the least number of variables used for algorithm training included presence/absence of diabetes mellitus and concomitant polymyalgia rheumatica as well as erythrocyte sedimentation rate level at GCs baseline. RF showed the best performance, being significantly superior to other algorithms in accuracy (RF 71.4% vs LR 70.4% vs DT 62.9%). Consistently, RF precision (72.1%) was significantly greater than those of LR (62.6%) and DT (50.8%). Conversely, LR was superior to RF and DT in recall (RF 60% vs LR 62.5% vs DT 47.5%). Moreover, RF AUROC (0.76) was more significant compared to LR (0.73) and DT (0.65).ConclusionsRF algorithm can predict GCA relapse after GCs tapering with sufficient accuracy. To date, this is one of the most accurate predictive modelings for such outcome. This ML method represents a reproducible tool, capable of supporting clinicians in GCA patient management.

Dataset Information

Markers of angiogenesis and macrophage products for predicting disease course and monitoring vascular inflammation in giant cell arteritis.

Publications

Markers of angiogenesis and macrophage products for predicting disease course and monitoring vascular inflammation in giant cell arteritis.

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