Correlation between serum cartilage oligomeric matrix protein and major adverse cardiovascular events within 30 days in patients with acute coronary syndrome

Cardiovascular disease risk in early rheumatoid arthritis: the impact of cartilage oligomeric matrix protein (COMP) and disease activity

BACKGROUND

To investigate whether baseline serum cartilage oligomeric matrix protein (COMP), patient characteristics, traditional cardiovascular disease (CVD) risk factors and disease activity over time predict CVD, in early rheumatoid arthritis (RA).

METHODS

This study included patients with early RA (< 12 months disease duration) (n = 233) recruited 1995-2005. Potential predictors of CVD and coronary artery disease (CAD) were assessed using Cox regression.

RESULTS

A first ever diagnosis of CVD occurred in 70 patients, and CAD in 52. Age, sex, hypertension and diabetes predicted CVD and CAD. COMP was associated with increased risk of CVD and CAD [crude hazard ratios (HRs) per SD 1.45; 95% CI 1.17-1.80 and 1.51; 95% CI 1.18-1.92, respectively]. When adjusted for age, sex, hypertension, diabetes and ESR, results where similar but did not reach significance [HRs 1.32, 95% CI 0.99-1.74 and 1.35, 95% CI 0.99-1.86]. Baseline disease activity did not independently predict CVD. High DAS28 (> 5.1) at two years was associated with increased risk of subsequent CVD [adjusted HR 2.58; 95% CI 1.10-6.04] and CAD. ESR and CRP at two years as well as cumulative disease activity over 2 years independently predicted CVD and CAD.

CONCLUSION

COMP may be a novel predictor of CVD and CAD in RA. Active disease two years after RA diagnosis, as well as cumulative disease activity, was associated with increased risk of CVD and CAD, independent of traditional CVD risk factors. Awareness of the particularly increased CVD risk among difficult to treat patients is important in order to further reduce CVD in RA.

Development of an in vitro Model of Human Gut Microbiota for Screening the Reciprocal Interactions With Antibiotics, Drugs, and Xenobiotics

Altering the gut microbiota can negatively affect human health. Efforts may be sustained to predict the intended or unintended effects of molecules not naturally produced or expected to be present within the organism on the gut microbiota. Here, culture-dependent and DNA-based approaches were combined to UHPLC-MS/MS analyses in order to investigate the reciprocal interactions between a constructed Human Gut Microbiota Model (HGMM) and molecules including antibiotics, drugs, and xenobiotics. Our HGMM was composed of strains from the five phyla commonly described in human gut microbiota and belonging to Firmicutes, Bacteroidetes, Proteobacteria, Fusobacteria, and Actinobacteria. Relevantly, the bacterial diversity was conserved in our constructed human gut model through subcultures. Uneven richness distribution was revealed and the sensitivity of the HGMM was mainly affected by antibiotic exposure rather than by drugs or xenobiotics. Interestingly, the constructed model and the individual cultured strains respond with the same sensitivity to the different molecules. UHPLC-MS/MS analyses revealed the disappearance of some native molecules in the supernatants of the HGMM as well as in those of the individual strains. These results suggest that biotransformation of molecules occurred in the presence of our gut microbiota model and the coupled approaches performed on the individual cultures may emphasize new bacterial strains active in these metabolic processes. From this study, the new HGMM appears as a simple, fast, stable, and inexpensive model for screening the reciprocal interactions between the intestinal microbiota and molecules of interest.