Derivation and Internal Validation of a Disease-Specific Cardiovascular Risk Prediction Model for Patients With Psoriatic Arthritis and Psoriasis.

OBJECTIVE: To address suboptimal cardiovascular risk prediction in patients with psoriatic disease (PsD), we developed and internally validated a five-year disease-specific cardiovascular risk prediction model. METHODS: We analyzed data from a prospective cohort of participants with PsD without a history of cardiovascular events. Traditional cardiovascular risk factors and PsD-related measures of disease activity were considered as potential predictors. The study outcome included nonfatal and fatal cardiovascular events. A base prediction model included 10 traditional cardiovascular risk factors. Eight PsD-related factors were assessed by adding them to the base model to create expanded models, which were controlled for PsD therapies. Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression with 10-fold cross-validation. Model performance was assessed using measures of discrimination and calibration and measures of sensitivity and specificity. RESULTS: Between 1992 and 2020, 85 of 1,336 participants developed cardiovascular events. Discrimination of the base model (with traditional cardiovascular risk factors alone) was excellent, with an area under the receiver operator characteristic curve (AUC) of 85.5 (95% confidence interval [CI] 81.9-89.1). Optimal models did not select any of the tested disease-specific factors. In a sensitivity analysis, which excluded lipid lowering and antihypertensive treatments, the number of damaged joints was selected in the expanded model. However, this model did not improve risk discrimination compared to the base model (AUC 85.5, 95% CI 82.0-89.1). CONCLUSION: Traditional cardiovascular risk factors alone are effective in predicting cardiovascular risk in patients with PsD. A risk score based on these factors performed well, indicating excellent discrimination and calibration.

Association of Cardiac Biomarkers With Cardiovascular Outcomes in Patients With Psoriatic Arthritis and Psoriasis: A Longitudinal Cohort Study.

OBJECTIVE: In patients with psoriatic disease (PsD), we determined whether cardiac troponin I (cTnI) and N-terminal pro-brain natriuretic peptide (NT-proBNP) were associated with carotid plaque burden and the development of cardiovascular events independent of the Framingham Risk Score (FRS). METHODS: Among 1,000 patients with PsD, carotid total plaque area (TPA) was measured in 358 participants at baseline. Cardiac troponin I and NT-proBNP were measured using automated clinical assays. The association between cardiac biomarkers and carotid atherosclerosis was assessed by multivariable regression after adjusting for cardiovascular risk factors. Improvement in the prediction of cardiovascular events beyond the FRS was tested using measures of risk discrimination and reclassification. RESULTS: In univariate analyses, cTnI (ß coefficient 0.52 [95% confidence interval (95% CI) 0.3, 0.74], P < 0.001) and NT-proBNP (ß coefficient 0.24 [95% CI 0.1, 0.39], P < 0.001) were associated with TPA. After adjusting for cardiovascular risk factors, the association remained statistically significant for cTnI (adjusted ß coefficient 0.21 [95% CI 0, 0.41], P = 0.047) but not for NT-proBNP (P = 0.21). Among the 1,000 patients with PsD assessed for cardiovascular risk prediction, 64 patients had incident cardiovascular events. When comparing a base model (with the FRS alone) to expanded models (with the FRS plus cardiac biomarkers), there was no improvement in predictive performance. CONCLUSION: In patients with PsD, cTnI may reflect the burden of atherosclerosis, independent of traditional cardiovascular risk factors. Cardiac troponin I and NT-proBNP are associated with incident cardiovascular events independent of the FRS, but further study of their role in cardiovascular risk stratification is warranted.

Targeted metabolomic profiling and prediction of cardiovascular events: a prospective study of patients with psoriatic arthritis and psoriasis.

OBJECTIVE: In patients with psoriatic disease (PsD), we sought serum metabolites associated with cardiovascular (CV) events and investigated whether they could improve CV risk prediction beyond traditional risk factors and the Framingham Risk Score (FRS). METHODS: Nuclear magnetic resonance metabolomics identified biomarkers for incident CV events in patients with PsD. The association of each metabolite with incident CV events was analysed using Cox proportional hazards regression models first adjusted for age and sex, and subsequently for traditional CV risk factors. Variable selection was performed using penalisation with boosting after adjusting for age and sex, and the FRS. RESULTS: Among 977 patients with PsD, 70 patients had incident CV events. In Cox regression models adjusted for CV risk factors, alanine, tyrosine, degree of unsaturation of fatty acids and high-density lipoprotein particles were associated with decreased CV risk. Glycoprotein acetyls, apolipoprotein B and cholesterol remnants were associated with increased CV risk. The age-adjusted and sex-adjusted expanded model with 13 metabolites significantly improved prediction of CV events beyond the model with age and sex alone, with an area under the receiver operator characteristic curve (AUC) of 79.9 versus 72.6, respectively (p=0.02). Compared with the FRS alone (AUC=73.9), the FRS-adjusted expanded model with 11 metabolites (AUC=75.0, p=0.72) did not improve CV risk discrimination. CONCLUSIONS: We identify novel metabolites associated with the development of CV events in patients with PsD. Further study of their underlying causal role may clarify important pathways leading to CV events in this population.

Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules.

We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development.

Human Mesenchymal Stem Cells Impact Th17 and Th1 Responses Through a Prostaglandin E2 and Myeloid-Dependent Mechanism.

: Human mesenchymal stem cells (hMSCs) are being increasingly pursued as potential therapies for immune-mediated conditions, including multiple sclerosis. Although they can suppress human Th1 responses, they reportedly can reciprocally enhance human Th17 responses. Here, we investigated the mechanisms underlying the capacity of hMSCs to modulate human Th1 and Th17 responses. Human adult bone marrow-derived MSCs were isolated, and their purity and differentiation capacity were confirmed. Human venous peripheral blood mononuclear cells (PBMC) were activated, alone, together with hMSC, or in the presence of hMSC-derived supernatants (sups). Cytokine expression by CD4+ T-cell subsets (intracellular staining by fluorescence-activated cell sorting) and secreted cytokines (enzyme-linked immunosorbent assay) were then quantified. The contribution of prostaglandin E2 (PGE2) as well as of myeloid cells to the hMSC-mediated regulation of T-cell responses was investigated by selective depletion of PGE2 from the hMSC sups (anti-PGE2 beads) and by the selective removal of CD14+ cells from the PBMC (magnetic-activated cell sorting separation). Human MSC-secreted products could reciprocally induce interleukin-17 expression while decreasing interferon-γ expression by human CD4+ T cells, both in coculture and through soluble products. Pre-exposure of hMSCs to IL-1ß accentuated their capacity to reciprocally regulate Th1 and Th17 responses. Human MSCs secreted high levels of PGE2, which correlated with their capacity to regulate the T-cell responses. Selective removal of PGE2 from the hMSC supernatants abrogated the impact of hMSC on the T cells. Selective removal of CD14+ cells from the PBMCs also limited the capacity of hMSC-secreted PGE2 to affect T-cell responses. Our discovery of a novel PGE2-dependent and myeloid cell-mediated mechanism by which human MSCs can reciprocally induce human Th17 while suppressing Th1 responses has implications for the use of, as well as monitoring of, MSCs as a potential therapeutic for patients with multiple sclerosis and other immune-mediated diseases. SIGNIFICANCE: Although animal studies have generated a growing interest in the anti-inflammatory potential of mesenchymal stem cells (MSCs) for the treatment of autoimmune diseases, MSCs possess the capacity to both limit and promote immune responses. Yet relatively little is known about human-MSC modulation of human disease-implicated T-cell responses, or the mechanisms underlying such modulation. The current study reveals a novel prostaglandin E2-dependent and myeloid cell-mediated mechanism by which human MSCs can reciprocally regulate human Th17 and Th1 responses, with implications for the use of MSCs as a potential therapeutic for patients with multiple sclerosis and other immune-mediated diseases.