BACKGROUND: This retrospective study was planned to establish potential associations between circulating values of cardiac troponins and those of conventional blood lipids. METHODS: The study population consisted of patients attending an inpatient clinic of the University Hospital of Verona during the year 2015 as part of routine cardiovascular risk assessment. No exclusion criteria were applied. Serum lipids including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG) were measured using reference enzymatic techniques, whereas troponin T (TnT) was measured using a high-sensitivity (HS) immunoassay. A second analysis was also performed in the General Hospital of Verona, extracting data from the local laboratory database of all patients in whom troponin I (TnI) and blood lipids were simultaneously measured during the same year. RESULTS: In univariate analysis, HS-TnT was found to be associated with age, sex, TC, LDL-C, HDL-C, but not with TG. In multivariate linear regression analysis, age (positive correlation; P<0.001) and HDL-C (negative correlation; P=0.032) remained significantly associated with HS-TnT. The frequency of HS-TnT values >50 ng/L was higher in subjects with HDL-C <1 mmol/L than in those with HDL-C ≥1 mmol/L [odds ratio (OR), 1.84; 95% confidence interval (CI), 1.03-3.32]. The frequency of HS-TnT values >50 ng/L was also higher in elderly subjects than in younger ones (OR, 2.10; 95% CI, 1.15-3.84). The combination of age and HDL-C explained 35% of overall variability of TnT concentration. In the second analysis, HDL-C was also found to be an independent and negative predictor of TnI in multivariate linear regression analysis (P=0.010). The combination of age and HDL-C explained approximately 28% of the overall variability of TnI concentration. CONCLUSIONS: Our study suggests that HDL-C values inversely predict cardiac troponins concentration irrespective of age, sex and other blood lipids.
BACKGROUND: This retrospective study was planned to establish potential associations between circulating values of cardiac troponins and those of conventional blood lipids. METHODS: The study population consisted of patients attending an inpatient clinic of the University Hospital of Verona during the year 2015 as part of routine cardiovascular risk assessment. No exclusion criteria were applied. Serum lipids including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG) were measured using reference enzymatic techniques, whereas troponin T (TnT) was measured using a high-sensitivity (HS) immunoassay. A second analysis was also performed in the General Hospital of Verona, extracting data from the local laboratory database of all patients in whom troponin I (TnI) and blood lipids were simultaneously measured during the same year. RESULTS: In univariate analysis, HS-TnT was found to be associated with age, sex, TC, LDL-C, HDL-C, but not with TG. In multivariate linear regression analysis, age (positive correlation; P<0.001) and HDL-C (negative correlation; P=0.032) remained significantly associated with HS-TnT. The frequency of HS-TnT values >50 ng/L was higher in subjects with HDL-C <1 mmol/L than in those with HDL-C ≥1 mmol/L [odds ratio (OR), 1.84; 95% confidence interval (CI), 1.03-3.32]. The frequency of HS-TnT values >50 ng/L was also higher in elderly subjects than in younger ones (OR, 2.10; 95% CI, 1.15-3.84). The combination of age and HDL-C explained 35% of overall variability of TnT concentration. In the second analysis, HDL-C was also found to be an independent and negative predictor of TnI in multivariate linear regression analysis (P=0.010). The combination of age and HDL-C explained approximately 28% of the overall variability of TnI concentration. CONCLUSIONS: Our study suggests that HDL-C values inversely predict cardiac troponins concentration irrespective of age, sex and other blood lipids.
Authors: Eline P M Cardinaels; Alma M A Mingels; Leo H J Jacobs; Steven J R Meex; Otto Bekers; Marja P van Dieijen-Visser Journal: Clin Chem Lab Med Date: 2012-03-16 Impact factor: 3.694
Authors: M M Arrebola; J A Lillo; M J Diez De Los Ríos; M Rodríguez; A Dayaldasani; R Yahyaoui; V Pérez Journal: Clin Biochem Date: 2010-05-02 Impact factor: 3.281
Authors: Ivo Casagranda; Mario Cavazza; Aldo Clerico; Marcello Galvani; Filippo Ottani; Martina Zaninotto; Luigi Maria Biasucci; Gianfranco Cervellin; Tiziano Lenzi; Giuseppe Lippi; Mario Plebani; Marco Tubaro Journal: Clin Chem Lab Med Date: 2013-09 Impact factor: 3.694
Authors: Kristian Thygesen; Joseph S Alpert; Allan S Jaffe; Maarten L Simoons; Bernard R Chaitman; Harvey D White; Hugo A Katus; Bertil Lindahl; David A Morrow; Peter M Clemmensen; Per Johanson; Hanoch Hod; Richard Underwood; Jeroen J Bax; Robert O Bonow; Fausto Pinto; Raymond J Gibbons; Keith A Fox; Dan Atar; L Kristin Newby; Marcello Galvani; Christian W Hamm; Barry F Uretsky; Ph Gabriel Steg; William Wijns; Jean-Pierre Bassand; Phillippe Menasché; Jan Ravkilde; E Magnus Ohman; Elliott M Antman; Lars C Wallentin; Paul W Armstrong; Maarten L Simoons; James L Januzzi; Markku S Nieminen; Mihai Gheorghiade; Gerasimos Filippatos; Russell V Luepker; Stephen P Fortmann; Wayne D Rosamond; Dan Levy; David Wood; Sidney C Smith; Dayi Hu; José-Luis Lopez-Sendon; Rose Marie Robertson; Douglas Weaver; Michal Tendera; Alfred A Bove; Alexander N Parkhomenko; Elena J Vasilieva; Shanti Mendis Journal: Circulation Date: 2012-08-24 Impact factor: 29.690