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Dyslipidemia and coronary artery calcium: From association to development of a risk-prediction nomogram |
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| Institution: | 1. Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan;2. Division of Nephrology, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan;3. Big Data Center, China Medical University Hospital and College of Medicine, Taichung, Taiwan;4. Division of Cardiology, Department of Internal Medicine, Asia University Hospital and College of Medicine, Taichung, Taiwan;5. Department of Family Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan;6. Department of Laboratory Medicine, China Medical University Hospital, Taichung, Taiwan;1. Association for Cardiac Research, Rome, Italy;2. EA 4650, Signalisation, électrophysiologie et imagerie des lésions d''ischémie reperfusion myocardique, UNICAEN, 14000 Caen, France;3. Research Centre for Food and Nutrition, Council for Agricultural Research and Economics (CREA-AN), Rome, Italy;1. Department of Cardiology, Rigshospitalet, University Hospital, Copenhagen, Denmark;2. Department of Cardiology, Zealand University Hospital, Roskilde, Denmark;3. Novartis Healthcare, Denmark;4. I2minds, Aarhus, Denmark;5. Danish Institute for Health Services Research, Copenhagen, Denmark;1. Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, Ghent University, C.Heymanslaan 10 - 4K3, 9000 Ghent, Belgium;2. Departments of Public Health Nutrition, Faculty of Public Health, Mulawarman University, Samarinda, East Kalimantan, Indonesia;3. Univ. Lille, Inserm, CHU Lille, CIC 1403 - Centre D''investigation Clinique, F-59000 Lille, France;4. Univ. Lille, Inserm, CHU Lille, U1286 - INFINITE – Institute for Translational Research in Inflammation, F-59000 Lille, France;5. GENUD (Growth, Exercise, Nutrition and Development) Research Group, Faculty of Health Science, University of Zaragoza, C/Pedro Cerbuna 12, 50009 Zaragoza, Spain;6. Instituto Agroalimentario de Aragón (IA2) and Instituto de Investigación Sanitaria de Aragón (IIS Aragón), Spain;7. Centro de Investigación Biomédica en Red de Fisiopatología de La Obesidad y Nutrición (CIBEROBN), Spain;8. Instituto de Salud Carlos III, Madrid, Spain;1. Division of Endocrinology, Diabetology and Metabolic Diseases, Department of General and Specialty Medicine, Molinette Hospital, University of Turin - Cso Dogliotti, 14-10126, Turin, Italy;2. Radiology Unit, Department of Diagnostic Imaging and Interventional Radiology, Molinette Hospital, University of Turin - Cso Dogliotti, 14-10126, Turin, Italy;3. Unit of Infectious Diseases, Department of Medical Sciences, Amedeo di Savoia Hospital, University of Turin - Corso Svizzera, 164-10149, Turin, Italy;1. Centre for Research Ethics & Bioethics, Uppsala University, Uppsala, Sweden;2. Department of Surgical Sciences, Medical Epidemiology, Uppsala University, Uppsala, Sweden;3. Erasmus School of Health Policy & Management, Erasmus University, Rotterdam, the Netherlands;4. Erasmus Choice Modelling Centre, Erasmus University, Rotterdam, the Netherlands;5. Department of Medical Sciences, Cardiology, Uppsala University, Uppsala, Sweden |
| Abstract: | Background and aimsThe 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 resultsThis 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.ConclusionsHypertriglyceridemia was significantly associated with the prevalent odds of PSCC. Our proposed predictive model may be a useful screening tool for PSCC. |
| Keywords: | Coronary artery calcium Multidetector computed tomography Prognostically significant coronary calcification Dyslipidemia Apolipoprotein CAC"} {"#name":"keyword" "$":{"id":"kwrd0040"} "$$":[{"#name":"text" "_":"Coronary artery calcium CACS"} {"#name":"keyword" "$":{"id":"kwrd0050"} "$$":[{"#name":"text" "_":"Coronary artery calcium score MDCT"} {"#name":"keyword" "$":{"id":"kwrd0060"} "$$":[{"#name":"text" "_":"Multidetector computed tomography PSCC"} {"#name":"keyword" "$":{"id":"kwrd0070"} "$$":[{"#name":"text" "_":"Prognostically significant coronary calcification TC"} {"#name":"keyword" "$":{"id":"kwrd0080"} "$$":[{"#name":"text" "_":"Total cholesterol TG"} {"#name":"keyword" "$":{"id":"kwrd0090"} "$$":[{"#name":"text" "_":"Triglyceride LDL-C"} {"#name":"keyword" "$":{"id":"kwrd0100"} "$$":[{"#name":"text" "_":"Low-density lipoprotein cholesterol HDL-C"} {"#name":"keyword" "$":{"id":"kwrd0110"} "$$":[{"#name":"text" "_":"High-density lipoprotein cholesterol CMUH"} {"#name":"keyword" "$":{"id":"kwrd0120"} "$$":[{"#name":"text" "_":"China Medical University Hospital CRDR"} {"#name":"keyword" "$":{"id":"kwrd0130"} "$$":[{"#name":"text" "_":"Clinical Research Data Repository HU"} {"#name":"keyword" "$":{"id":"kwrd0140"} "$$":[{"#name":"text" "_":"Hounsfield units 4-AAP"} {"#name":"keyword" "$":{"id":"kwrd0150"} "$$":[{"#name":"text" "_":"4-aminoantipyrin NCEP"} {"#name":"keyword" "$":{"id":"kwrd0160"} "$$":[{"#name":"text" "_":"AT III National Cholesterol Education Program–Adult Treatment Panel III BMI"} {"#name":"keyword" "$":{"id":"kwrd0170"} "$$":[{"#name":"text" "_":"Body mass index eGFR"} {"#name":"keyword" "$":{"id":"kwrd0180"} "$$":[{"#name":"text" "_":"Estimated glomerular filtration rate CKD-EPI"} {"#name":"keyword" "$":{"id":"kwrd0190"} "$$":[{"#name":"text" "_":"Chronic Kidney Disease Epidemiology Collaboration DM"} {"#name":"keyword" "$":{"id":"kwrd0200"} "$$":[{"#name":"text" "_":"Diabetes mellitus HTN"} {"#name":"keyword" "$":{"id":"kwrd0210"} "$$":[{"#name":"text" "_":"Hypertension ICD"} {"#name":"keyword" "$":{"id":"kwrd0220"} "$$":[{"#name":"text" "_":"International Classification of Diseases CVD"} {"#name":"keyword" "$":{"id":"kwrd0230"} "$$":[{"#name":"text" "_":"Cardiovascular disease EMR"} {"#name":"keyword" "$":{"id":"kwrd0240"} "$$":[{"#name":"text" "_":"Electronic medical records CKD"} {"#name":"keyword" "$":{"id":"kwrd0250"} "$$":[{"#name":"text" "_":"Chronic kidney disease AUC"} {"#name":"keyword" "$":{"id":"kwrd0260"} "$$":[{"#name":"text" "_":"Area under the curve ROC"} {"#name":"keyword" "$":{"id":"kwrd0270"} "$$":[{"#name":"text" "_":"Receiver operating characteristic CVC"} {"#name":"keyword" "$":{"id":"kwrd0280"} "$$":[{"#name":"text" "_":"Calcified vascular cells LXR"} {"#name":"keyword" "$":{"id":"kwrd0290"} "$$":[{"#name":"text" "_":"Liver X receptor ApoC-III"} {"#name":"keyword" "$":{"id":"kwrd0300"} "$$":[{"#name":"text" "_":"Apolipoprotein C-III |
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