Publication details

Arterial Stiffness and Cardiometabolic-Based Chronic Disease: The Kardiovize Study

Authors

PAVLOVSKA Iuliia MECHANICK J. I. NETO G. A. M. INFANTE-GARCIA M. M. NIETO-MARTINEZ R. KUNZOVÁ Šárka POLCROVÁ Anna VYSOKÝ Robert MEDINA-INOJOSA J. R. LOPEZ-JIMENEZ F. STOKIN G. B. GONZALEZ-RIVAS J. P.

Year of publication 2021
Type Article in Periodical
Magazine / Source Endocrine Practice
MU Faculty or unit

Faculty of Medicine

Citation
Web https://www.sciencedirect.com/science/article/pii/S1530891X21000859?via%3Dihub
Doi http://dx.doi.org/10.1016/j.eprac.2021.03.004
Keywords adiposity; atherosclerosis; cardio-ankle vascular index; diabetes; obesity; type 2 diabetes
Description Objective: Arterial stiffness (ArSt) describes a loss of arterial wall elasticity and is an independent predictor of cardiovascular events. A cardiometabolic-based chronic disease model integrates concepts of adiposity-based chronic disease (ABCD), dysglycemia-based chronic disease (DBCD), and cardiovascular disease. We assessed if ABCD and DBCD models detect more people with high ArSt compared with traditional adiposity and dysglycemia classifiers using the cardio-ankle vascular index (CAVI). Methods: We evaluated 2070 subjects aged 25 to 64 years from a random population-based sample. Those with type 1 diabetes were excluded. ABCD and DBCD were defined, and ArSt risk was stratified based on the American Association of Clinical Endocrinologists criteria. Results: The highest prevalence of a high CAVI was in stage 2 ABCD (18.5%) and stage 4 DBCD (31.8%), and the lowest prevalence was in stage 0 ABCD (2.2%). In univariate analysis, stage 2 ABCD and all DBCD stages increased the risk of having a high CAVI compared with traditional classifiers. After adjusting for age and gender, only an inverse association between obesity (body mass index >= 30 kg/m(2)) and CAVI remained significant. Nevertheless, body mass index was responsible for only 0.3% of CAVI variability. Conclusion: The ABCD and DBCD models showed better performance than traditional classifiers to detect subjects with ArSt; however, the variables were not independently associated with age and gender, which might be explained by the complexity and multifactoriality of the relationship of CAVI with the ABCD and DBCD models, mediated by insulin resistance. (C) 2021 AACE. Published by Elsevier Inc. All rights reserved.

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