Dyslipidemia and coronary artery calcium: From association to development of a risk-prediction nomogram.

Nutr Metab Cardiovasc Dis

Division of Nephrology, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan; Big Data Center, China Medical University Hospital and College of Medicine, Taichung, Taiwan. Electronic address:

Published: August 2022

Background And Aims: The associations between dyslipidemia and coronary artery calcium (CAC) are controversial. We investigated their cross-sectional relationships and developed a predictive scoring system for prognostically significant coronary calcification (PSCC).

Methods And Results: This study evaluated the lipid profiles and the CAC score (CACS) measured through multidetector computed tomography (MDCT) among Taiwanese adult patients in a tertiary hospital between 2011 and 2016. Patients with CACS higher than 100 were classified as having PSCC. Dyslipidemia for each lipid component was defined based on the clinical cutoffs or the use of the lipid-lowering agents. Multivariable logistic regression was used to assess the association between dyslipidemia and PSCC and the model performance was assessed using calibration plot, discrimination, and a decision curve analysis. Of the 3586 eligible patients, 364 (10.2%) had PSCC. Increased age, male sex, higher body mass index (BMI), and higher level of triglyceride (TG) were associated with PSCC. The adjusted odds ratios (95% confidence intervals) of PSCC was 1.15 (0.90-1.47) for dyslipidemia defined by total cholesterol (TC) ≥200 mg/dL, 1.06 (0.83-1.35) for low-density-lipoprotein-cholesterol (LDL-C) ≥130 mg/dL, and 1.36 (1.06-1.75) for TG ≥ 200 mg/dL. The positive association between TG ≥ 200 mg/dL and PSCC was not modified by sex. Incorporating hypertriglyceridemia did not significantly improve the predictive performance of the base model comprising of age, sex, BMI, smoking, hypertension, diabetes, estimated glomerular filtration rate, and fasting glucose.

Conclusions: Hypertriglyceridemia was significantly associated with the prevalent odds of PSCC. Our proposed predictive model may be a useful screening tool for PSCC.

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http://dx.doi.org/10.1016/j.numecd.2022.05.006DOI Listing

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