In this community-based Framingham Heart Study, Schnabel et al. investigated 11 different blood biomarkers (C-reactive protein, fibrinogen, interleukin-6, intercellular adhesion molecule-1, lipoprotein-associated phospholipase-A2 [mass and activity], monocyte chemoattractant protein-1, myeloperoxidase, CD40 ligand, P-selectin, and tumor necrosis factor receptor II [TNFRII]) in 3035 participants (mean age 61+/- 9 years, 53% woman) and incident major CVD and mortality. Within 8,9 years follow-up 253 participants experienced a CVD event and 343 died.
Prediction of CVD: In the multivariable-adjusted association of the separate inflammatory biomarkers with major CVD incidence among the 2827 participants free of major CVD at the seventh examination cycle TNFRII and CRP were found to be individually associated with major incident CVD. With stepwise selection, CRP (hazard ratio [HR] per ln-biomarker SD, 1.18; 95% confidence interval [CI], 1.02–1.35; nominal P=0.02) and TNFRII (HR, 1.15; 95% CI, 1.01–1.32; nominal P=0.04) were retained as predictors of major CVD. After Bonferroni correction on the number of principal components, none of the biomarkers retained statistical significance. The 2 selected biomarkers did not significantly improve the discrimination ability for CVD of the model comprising traditional risk factors (c-statistic of 0.769; 95% CI, 0.743–0.796 before and 0.773; 95% CI, 0.746–0.80 after adding the biomarkers). The estimated increment of c-statistic was 0.0038 (95% CI, −0.0023 to 0.010; P=0.11). The Hosmer–Lemeshow statistic showed adequate calibration between observed and predicted mortality risk of the final model (X2=13.2; df=10; P=0.15).
Prediction of Mortality: The data for multivariable-adjusted single biomarker associations with mortality revealed 4 of the 11 markers to be individually associated with mortality: CRP, ICAM-1, interleukin-6, and TNFRII (all P<0.0001). In the stepwise biomarker model, the 3 markers selected were ICAM-1, interleukin-6, and TNFRII (all P<0.0001; Table 4). TNFRII revealed the highest HR (HR, 1.33; 95% CI, 1.19–1.49). The additional adjustment for interim CVD marginally decreased the estimated HRs, but the same set of biomarkers was selected in the final model. The most statistically significant biomarker, TNFRII alone in the model for death incorporating clinical risk factors had higher model discrimination (c-statistic of 0.789 [95% CI, 0.765–0.813] rose to 0.799 [95% CI, 0.776–0.823]). The 3 selected biomarkers combined only marginally improved the discrimination ability of the model for death beyond traditional risk factors, with c-statistic 0.811 (95% CI, 0.788– 0.834) after adding biomarkers. The estimated increment of c-statistic was 0.022 (95% CI, 0.011–0.033; P<0.0001). The Hosmer–Lemeshow statistic showed adequate calibration between observed and predicted mortality risk in the final model (X2=12.5; df=10; P=0.19). The authors did not observe significant interactions by age or sex for the two biomarkers in relation to incident CVD and the three biomarkers which were associated with mortality.
Of 11 inflammatory biomarkers, TNFRII was associated nominally with incident major CVD, and significantly with all-cause mortality over a follow-up period of 10 years. The combination of TNFRII with C-reactive protein in relation to CVD and with interleukin-6 to mortality increased the predictive ability in addition to CVD risk factors for total mortality but not for incident CVD.
Comments:In the present study Schnabel et al. identified in the community-based Framingham Heart Study TNFRII as nominally predictive of CVD and significantly predictive of mortality. However, even the combination of information on TNFRII and CRP for incident CVD and TNFRII and interleukin-6 for mortality increased the predictive ability of a model consisting of classical CVD risk factors only modestly, if at all. This observation substantiates the ongoing discussion that the evaluation of circulating blood biomarkers is not suitable for risk determination under the current clinical conditions. Importantly, it has to be mentioned that the detection of biomarkers in the blood does not necessarily imply their role in the etiopathogenesis of the various inflammatory heart diseases. Thus, it is required to correlate biomarkers with the underlying mechanisms of these heart diseases. It is very likely that the presence and magnitude of specific biomarkers in distinct stages of virus related heart diseases is rather different from that in e.g. autoimmune connective tissue diseases. Therefore, future investigations judging the value of biomarkers for risk determination have to involve the pathophysiological background and endurance of the disease in context of clinical and experimental settings.
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