Introduction: Patients with cancer receiving radio- or chemotherapy undergo many immunological stressors. Chronic regular exercise has been shown to positively influence the immune system in several populations, while exercise overload may have negative effects. Exercise is currently recommended for all patients with cancer. However, knowledge regarding the effects of exercise on immune markers in patients undergoing chemo- or radiotherapy is limited. The aim of this study is to systematically review the effects of moderate- and high-intensity exercise interventions in patients with cancer during chemotherapy or radiotherapy on immune markers. Methods: For this review, a search was performed in PubMed and EMBASE, until March 2023. Methodological quality was assessed with the PEDro tool and best-evidence syntheses were performed both per immune marker and for the inflammatory profile. Results: Methodological quality of the 15 included articles was rated fair to good. The majority of markers were unaltered, but observed effects included a suppressive effect of exercise during radiotherapy on some proinflammatory markers, a preserving effect of exercise during chemotherapy on NK cell degranulation and cytotoxicity, a protective effect on the decrease in thrombocytes during chemotherapy, and a positive effect of exercise during chemotherapy on IgA. Conclusion: Although exercise only influenced a few markers, the results are promising. Exercise did not negatively influence immune markers, and some were positively affected since suppressed inflammation might have positive clinical implications. For future research, consensus is needed regarding a set of markers that are most responsive to exercise. Next, differential effects of training types and intensities on these markers should be further investigated, as well as their clinical implications.
DOCUMENT
Psoriasis (Pso) is a chronic inflammatory skin disease, and up to 30% of Pso patients develop psoriatic arthritis (PsA), which can lead to irreversible joint damage. Early detection of PsA in Pso patients is crucial for timely treatment but difficult for dermatologists to implement. We, therefore, aimed to find disease-specific immune profiles, discriminating Pso from PsA patients, possibly facilitating the correct identification of Pso patients in need of referral to a rheumatology clinic. The phenotypes of peripheral blood immune cells of consecutive Pso and PsA patients were analyzed, and disease-specific immune profiles were identified via a machine learning approach. This approach resulted in a random forest classification model capable of distinguishing PsA from Pso (mean AUC = 0.95). Key PsA-classifying cell subsets selected included increased proportions ofdifferentiated CD4+CD196+CD183-CD194+ and CD4+CD196-CD183-CD194+ T-cells and reduced proportions of CD196+ and CD197+ monocytes, memory CD4+ and CD8+ T-cell subsets and CD4+ regulatory T-cells. Within PsA, joint scores showed an association with memory CD8+CD45RACD197- effector T-cells and CD197+ monocytes. To conclude, through the integration of in-depth flow cytometry and machine learning, we identified an immune cell profile discriminating PsA from Pso. This immune profile may aid in timely diagnosing PsA in Pso.
MULTIFILE
Systemic sclerosis (SSc) is an autoimmune disease which is characterized by vasculopathy, tissue fibrosis and activation of the innate and adaptive immune system. Clinical features of the disease consists of skin thickening and internal organ involvement. Due to the heterogeneous nature of the disease it is difficult to predict disease progression and complications. Despite the discovery of novel autoantibodies associated with SSc, there is an unmet need for biomarkers for diagnosis, disease progression and response to treatment. To date, the use of single (surrogate) biomarkers for these purposes has been unsuccessful. Combining multiple biomarkers in to predictive panels or ultimately algorithms could be more precise. Given the limited therapeutic options and poor prognosis of many SSc patients, a better understanding of the immune-pathofysiological profiles might aid to an adjusted therapeutic approach. Therefore, we set out to explore immunological fingerprints in various clinically defined forms of SSc. We used multilayer profiling to identify unique immune profiles underlying distinct autoantibody signatures. These immune profiles could fill the unmet need for prognosis and response to therapy in SSc. Here, we present 3 pathophysiological fingerprints in SSc based on the expression of circulating antibodies, vascular markers and immunomodulatory mediators.
DOCUMENT
