Abstract

Aim. To assess the significance of troponin I (hs-cTnI) for cardio­vascular risk stratification in a population of Russian working-age men and women of.Material and methods. The data of 13976 men and women aged 25-64 from the ESSE-RF1 and ESSE-RF2 multicenter studies were analyzed. The examination included following points: standard questionnaire, measurements of height, weight, waist circumference, body mass index, blood pressure, pulse rate, biochemical tests. The level of hs-cTnI in blood serum samples stored at -70оC in the Biobank of the National Medical Research Center for Therapy and Preventive Medicine was determined by chemiluminescent immunoassay analysis. Individual cardiovascular risk was calculated using the SCORE scale: values <1% — low risk; 1-4% — moderate, 5-9% — high and ≥10% very high. Similar to the BiomarCaRE study, participants were divided into 3 sex-adjusted cTnI levels: men <6, 6 to 12, and >12 pg/ml; women <4, 4 to 10, and >10 pg/ml. We analyzed following hard endpoints: cardiovascular death or non-fatal myocardial infarction, and composite endpoint: the same and stroke.Results. The prospective analysis of the relationship between troponin levels and endpoints and composite endpoints showed that in participants without prior CVD, hs-cTnI levels >12 pg/ml in men and >10 pg/ml in women are associated with an increased risk of endpoints and composite endpoints by 3,39 (1,91-6,03) and 2,69 (1,6-4,52) times (p<0,001), respectively. Similarly to BiomarCaRE, the SCORE-calculated risk reclassification was validated by adding the hs-cTnI value. The net reclassification improvement index (NRI) for endpoints and composite endpoints was 13% and 11% (p<0,001), respectively. Among men, the reclassification for NRI composite endpoints was more accurate than among women: 19% and 11%, respectively.Conclusion. The hs-cTnI level is an independent predictor of myo­cardial infarction, stroke, and death from CVD in people aged 35-64 years without prior CVD. Adding the hs-cTnI level to the SCORE model makes the risk prediction more accurate.

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