Complement lectin pathway protein levels reflect disease activity in juvenile idiopathic arthritis: a longitudinal study of the Nordic JIA cohort

Does distance to the hospital affect the course of treatment and follow-up for children with Acute Lymphoblastic Leukemia?

PURPOSE

Centralization of specialized healthcare including treatment of children with acute lymphoblastic leukemia (ALL) has increased in high-income countries. We aimed to clarify whether the distance to the hospital for children with ALL affects the course of treatment regarding the number and duration of hospitalizations, outpatient clinic visits, missed appointments in the outpatient clinic, the duration of follow-up, or survival.

METHODS

We performed a retrospective cohort study of children diagnosed with ALL aged 0-14 at Aalborg University Hospital from 1996 to 2018. Data was collected through registries and medical records. Comparisons of cancer- and sociodemographic characteristics in children with long versus short distance were made, divided at 42 km. Outcome variables were assessed by correlation and regression analyses. We compared overall survival using Kaplan-Meier.

RESULTS

The cohort consisted of 95 children. Cancer- and sociodemographic characteristics of the children with long versus short distance were similar, though the children with long distance were older and had less infections during treatment. There was no significant difference between the two groups regarding the number of hospitalizations, outpatient clinic visits, missed appointments, the duration of follow-up, or survival. Though, the summarized length of hospitalization was shorter for the group with long distance, when adjusting for age (-23 days (95% CI -45;-1)).

CONCLUSION

No significant differences between the course of treatment and follow-up for children with long and short distance to the hospital was revealed, except for a shorter summarized length of hospitalization for children with long distance.

Inflammatory biomarkers predicting long-term remission and active disease in juvenile idiopathic arthritis: a population-based study of the Nordic JIA cohort

OBJECTIVES

To assess the ability of baseline serum biomarkers to predict disease activity and remission status in juvenile idiopathic arthritis (JIA) at 18-year follow-up (FU) in a population-based setting.

METHODS

Clinical data and serum levels of inflammatory biomarkers were assessed in the longitudinal population-based Nordic JIA cohort study at baseline and at 18-year FU. A panel of 16 inflammatory biomarkers was determined by multiplexed bead array assay. We estimated both univariate and multivariate logistic regression models on binary outcomes of disease activity and remission with baseline variables as explanatory variables.

RESULTS

Out of 349 patients eligible for the Nordic JIA cohort study, 236 (68%) had available serum samples at baseline. We measured significantly higher serum levels of interleukin 1β (IL-1β), IL-6, IL-12p70, IL-13, MMP-3, S100A9 and S100A12 at baseline in patients with active disease at 18-year FU than in patients with inactive disease. Computing receiver operating characteristics illustrating the area under the curve (AUC), we compared a conventional prediction model (gender, age, joint counts, erythrocyte sedimentation rate, C reactive protein) with an extended model that also incorporated the 16 baseline biomarkers. Biomarker addition significantly improved the ability of the model to predict activity/inactivity at the 18-year FU, as evidenced by an increase in the AUC from 0.59 to 0.80 (p=0.02). Multiple regression analysis revealed that S100A9 was the strongest predictor of inactive disease 18 years after disease onset.

CONCLUSION

Biomarkers indicating inflammation at baseline have the potential to improve evaluation of disease activity and prediction of long-term outcomes.

Inflammatory Biomarkers Can Differentiate Acute Lymphoblastic Leukemia with Arthropathy from Juvenile Idiopathic Arthritis Better Than Standard Blood Tests

OBJECTIVE

To evaluate the predictive value of biomarkers of inflammation like phagocyte-related S100 proteins and a panel of inflammatory cytokines in order to differentiate the child with acute lymphoblastic leukemia (ALL) from juvenile idiopathic arthritis (JIA).

STUDY DESIGN

In this cross-sectional study, we measured S100A9, S100A12, and 14 cytokines in serum from children with ALL (n = 150, including 27 with arthropathy) and JIA (n = 236). We constructed predictive models computing areas under the curve (AUC) as well as predicted probabilities in order to differentiate ALL from JIA. Logistic regression was used for predictions of ALL risk, considering the markers as the respective exposures. We performed internal validation using repeated 10-fold cross-validation and recalibration, adjusted for age.

RESULTS

In ALL, the levels of S100A9, S100A12, interleukin (IL)-1 beta, IL-4, IL-13, IL-17, matrix metalloproteinase-3, and myeloperoxidase were low compared with JIA (P < .001). IL-13 had an AUC of 100% (95% CI 100%-100%) due to no overlap between the serum levels in the 2 groups. Further, IL-4 and S100A9 had high predictive performance with AUCs of 99% (95% CI 97%-100%) and 98% (95% CI 94%-99%), respectively, exceeding both hemoglobin, platelets, C-reactive protein, and erythrocyte sedimentation rate.

CONCLUSIONS

The biomarkers S100A9, IL-4, and IL-13 might be valuable markers to differentiate ALL from JIA.

Proteomic profiling of extracellular vesicles in synovial fluid and plasma from Oligoarticular Juvenile Idiopathic Arthritis patients reveals novel immunopathogenic biomarkers

Introduction

New early low-invasive biomarkers are demanded for the management of Oligoarticular Juvenile Idiopathic Arthritis (OJIA), the most common chronic pediatric rheumatic disease in Western countries and a leading cause of disability. A deeper understanding of the molecular basis of OJIA pathophysiology is essential for identifying new biomarkers for earlier disease diagnosis and patient stratification and to guide targeted therapeutic intervention. Proteomic profiling of extracellular vesicles (EVs) released in biological fluids has recently emerged as a minimally invasive approach to elucidate adult arthritis pathogenic mechanisms and identify new biomarkers. However, EV-prot expression and potential as biomarkers in OJIA have not been explored. This study represents the first detailed longitudinal characterization of the EV-proteome in OJIA patients.

Methods

Fourty-five OJIA patients were recruited at disease onset and followed up for 24 months, and protein expression profiling was carried out by liquid chromatography-tandem mass spectrometry in EVs isolated from plasma (PL) and synovial fluid (SF) samples.

Results

We first compared the EV-proteome of SF vs paired PL and identified a panel of EV-prots whose expression was significantly deregulated in SF. Interaction network and GO enrichment analyses performed on deregulated EV-prots through STRING database and ShinyGO webserver revealed enrichment in processes related to cartilage/bone metabolism and inflammation, suggesting their role in OJIA pathogenesis and potential value as early molecular indicators of OJIA development. Comparative analysis of the EV-proteome in PL and SF from OJIA patients vs PL from age/gender-matched control children was then carried out. We detected altered expression of a panel of EV-prots able to differentiate new-onset OJIA patients from control children, potentially representing a disease-associated signature measurable at both the systemic and local levels with diagnostic potential. Deregulated EV-prots were significantly associated with biological processes related to innate immunity, antigen processing and presentation, and cytoskeleton organization. Finally, we ran WGCNA on the SF- and PL-derived EV-prot datasets and identified a few EV-prot modules associated with different clinical parameters stratifying OJIA patients in distinct subgroups.

Discussion

These data provide novel mechanistic insights into OJIA pathophysiology and an important contribution in the search of new candidate molecular biomarkers for the disease.

M-ficolin: a valuable biomarker to identify leukaemia from juvenile idiopathic arthritis

OBJECTIVE

Distinction on clinical grounds between acute lymphoblastic leukaemia presenting with arthropathy (ALLarthropathy) and juvenile idiopathic arthritis (JIA) is difficult, as the clinical and paraclinical signs of leukaemia may be vague. The primary aim was to examine the use of lectin complement pathway proteins as markers to differentiate ALLarthropathy from JIA. The secondary aims were to compare the protein levels at baseline and follow-up in a paired number of children with ALL and to examine the correlation with haematology counts, erythrocyte sedimentation reaction (ESR), C-reactive protein (CRP), blasts, relapse and death.

STUDY DESIGN

In this observational study, we measured M-ficolin, CL-K1 and MASP-3 in serum from children with ALL (n=151) and JIA (n=238) by time-resolved immunofluorometric assays. Logistic regression was used for predictions of ALL risk, considering the markers as the respective exposures. We performed internal validation using repeated '10-fold cross-validation' with 100 repetitions computing the area under the curve (AUC) as well as positive and negative predictive values in order to evaluate the predictive performance.

RESULTS

The level of M-ficolin was higher in JIA than ALLtotal and the ALLarthropathy subgroup. The M-ficolin level normalised after remission of ALL. M-ficolin could differentiate ALL from JIA with an AUC of 94% and positive predictive value (PPV) of 95%, exceeding CRP and haemoglobin. In a dichotomised predictive model with optimal cut-offs for M-ficolin, platelets and haemoglobin, AUC was 99% and PPV 98% in detecting ALL from JIA.

CONCLUSION

M-ficolin is a valuable marker to differentiate the child with ALL from JIA.