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  • Title: Troponin I and T in relation to cardiac injury detected with electrocardiography in a population-based cohort - The Maastricht Study.
    Author: Kimenai DM, Martens RJH, Kooman JP, Stehouwer CDA, Tan FES, Schaper NC, Dagnelie PC, Schram MT, van der Kallen CJH, Sep SJS, van Suijlen JDE, Kroon AA, Bekers O, van Dieijen-Visser MP, Henry RMA, Meex SJR.
    Journal: Sci Rep; 2017 Jul 26; 7(1):6610. PubMed ID: 28747765.
    Abstract:
    Interest in high-sensitivity cardiac troponin I(hs-cTnI) and T(hs-cTnT) has expanded from acute cardiac care to cardiovascular disease(CVD) risk stratification. Whether hs-cTnI and hs-cTnT are interchangeable in the ambulant setting is largely unexplored. Cardiac injury is a mechanism that may underlie the associations between troponin levels and mortality in the general population. In the population-based Maastricht Study, we assessed the correlation and concordance between hs-cTnI and hs-cTnT. Multiple regression analyses were conducted to assess the association of hs-cTnI and hs-cTnT with electrocardiographic (ECG) changes indicative of cardiac abnormalities. In 3016 eligible individuals(mean age,60 ± 8years;50.6%,men) we found a modest correlation between hs-cTnI and hs-cTnT(r = 0.585). After multiple adjustment, the association with ECG changes indicative of cardiac abnormalities was similar for both hs-cTn assays(OR,hs-cTnI:1.72,95%CI:1.40-2.10;OR,hs-cTnT:1.60,95%CI:1.22-2.11). The concordance of dichotomized hs-cTnI and hs-cTnT was κ = 0.397(≥sex-specific 75th percentile). Isolated high levels of hs-cTnI were associated with ECG changes indicative of cardiac abnormalities(OR:1.93,95%CI:1.01-3.68), whereas isolated high levels of hs-cTnT were not(OR:1.07,95%CI:0.49-2.31). In conclusion, there is a moderate correlation and limited concordance between hs-cTnI and hs-cTnT under non-acute conditions. These data suggest that associations of hs-cTnI and hs-cTnT with cardiac injury detected by ECG are driven by different mechanisms. This information may benefit future development of CVD risk stratification algorithms.
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