C-peptide: A predictor of cardiovascular mortality in subjects with established atherosclerotic disease

Abstract

C-peptide was associated with cardiovascular mortality independently of known diabetes status in a cohort of patients with chronic atherosclerotic disease. Future studies using C-peptide into a reclassification approach might be undertaken to consider its potential as a cardiovascular disease biomarker.

Serum Uric Acid Levels as an Indicator for Metabolically Unhealthy Obesity in Children and Adolescents

“…Previously published results have shown C-peptide to be associated with insulin resistance, cardiometabolic abnormalities belonging to the metabolic syndrome, dyslipidemia, hypertension, hyperuricemia, and type 2 diabetes mellitus in both obese children and adults [42, 43]. Cardellini et al [44] demonstrated an important role of C-peptide levels in the chemotactic effect on the inflammatory cells and suggested that it promotes the onset of atherosclerosis. To the best of our knowledge, this is the first study addressing C-peptide as potential indicator of MUO in overweight/obese children and adolescents.…”

Soluble ST2 is a biomarker for cardiovascular mortality related to abnormal glucose metabolism in high-risk subjects

“…The Tor Vergata Atherosclerosis Registry (TVAR) (ISRCTN42405215) was previously described [21]. Here, we show a secondary analysis based on follow-up performed in 2017.…”

“…DST200-R&D Systems, Inc.); the average intra-assay coefficient of variation was 5.6% and interassay coefficient of variation was 17.3%. Other laboratory measurements were previously described [21]. GFR was estimated with the MDRD formula.…”

Prognostic assessment of repeatedly measured time-dependent biomarkers, with application to dilated cardiomyopathy

“…Here t is a time-horizon of interest, which is specified by the user, and so is threshold c. Examples in different areas of clinical research are reported in Pignatelli et al (2015), Iacovelli et al (2015), Riggio et al (2015), Basili et al (2017) and Cardellini et al (2017). Suppose now that we are actually measuring a time-dependent marker X (t), which has been repeatedly assessed for n subjects at pre-specified visit times…”