Sammendrag
Introduction: Several prospective trials have established the relationship between lipids and apolipoproteins in the prediction of atherosclerotic cardiovascular disease. Data from trials have also been used to develop prediction equations that quantify the risk for atherosclerotic cardiovascular disease (ASCVD). We used one of the most recent prediction equations, the pooled cohort equation, to evaluate the clinical utility of a variety of lipid metrics.
Methodology: The pooled cohort equation was used to calculate 10-year risk of cardiovascular disease among a group of apparently healthy Nigerian participants. The score was then compared with their values of fasting plasma total cholesterol (TC), Triglycerides (TG), High density lipoprotein cholesterol (HDL–C), Apolipoprotein B, Apolipoprotein A1 as well as LDL-C/HDL-C and Apo B/Apo A1 ratios. The risk score and the lipid metrics were divided into 2 groups based on association with high risk of CVD.
Results: Out of the 157 participants, 30 (19.1%), participants had an estimated risk score > 7.5%. Apo B/Apo A1 ratio, LDL-C/HDL-C and TG had significant positive correlations and ApoA1 and HDL-C had significant negative correlations with ASCVD risk score, all with p <0.01. LDL-C and TC did not show a significant linear relationship with ASCVD risk score. The odds ratios for the Apo B/Apo A1 and LDL-C/HDL-C ratio had the strongest associations with the ASCVD risk category.
Conclusion: Risk prediction equations may be used in the evaluation of the clinical utility of lipid metrics, reaching conclusions similar to those from prospective studies.
Keywords: Lipids, pooled cohort equation, clinical utility
Résumé
Introduction : Plusieurs essais prospectifs ont établi la relation entre les lipides et les apolipoprotéines dans la prédiction de maladie cardiovasculaires athérosclérotique. Les données des essais ont également été utilisées pour développer des équations de prédiction qui quantifient le risque de maladie cardiovasculaire athérosclérotique (MCVAS). Nous avons utilisé l’une des équations de prédiction les plus récentes, l’équation de cohorte regroupée, pour évaluer l’utilité clinique d’une variété de paramètres lipidiques.
Méthodologie : L’équation de cohorte regroupée a été utilisée pour calculer le risque de maladie cardiovasculaire sur 10 ans parmi un groupe de participants nigérians apparemment en bonne santé.Le score a ensuite été comparé à leurs valeurs de cholestérol total à jeun (CT), Triglycérides (TG), cholestérol à lipoprotéines de haute densité (HDL –C), ApolipoprotéineB, ApolipoprotéineA1 ainsi que les ratios LDL-C / HDL-C et Apo B / Apo A1.Le score de risque et les métriques lipidiques ont été divisés en 2 groupes en fonction de l’association avec un risque élevé de MCV.
Résultats:Sur les 157 participants, 30 (19,1%) participants avaient un score de risque estimé>7,5%.Le ratio Apo B / Apo A1, LDLC / HDL-C et TG avait des corrélations positives significatives et Apo A1 et HDL-C avaient des corrélations négatives significatives avec le score de risque MCVAS, tous avec p <0,01.LDL-C et CT n’ont pas montré de relation linéaire significative avec le score de
risque MCVAS.Les rapports de cotes pour les ratios Apo B / Apo A1 et LDL-C / HDL-C étaient les plus fortement associés à la catégorie de risque MCVAS.
Conclusion : Les équations de prédiction du risque peuvent être utilisées dans l’évaluation de l’utilité clinique des métriques lipidiques, aboutissant à des conclusions similaires à celles d’études prospectives.
Mots - clés : Lipides, équation de cohorte regroupée, utilité clinique
Correspondence: Dr. M.A. Kuti, Department of Chemical Pathology, College of Medicine, University of Ibadan, Ibadan, Nigeria. E-mail: modupekuti@yahoo.com
Referanser
Castelli WP, Anderson K, Wilson PW and Levy D. Lipids and risk of coronary heart disease. The Framingham Study. Annals of epidemiology. 1992; 2 (1-2):23-28.
Holme I, Aastveit AH, Jungner I and Walldius G. Relationships between lipoprotein components and risk of myocardial infarction: age, gender and short versus longer follow-up periods in the Apolipoprotein MOrtality RISk study (AMORIS). Journal of internal medicine. 2008;264(1):30-38.
Kozarevic D, McGee D, Vojvodic N, et al. Serum cholesterol and mortality: the Yugoslavia Cardiovascular Disease Study. American journal of epidemiology. 1981;114(1):21-28.
Szatrowski TP, Peterson AV, Jr., Shimizu Y, et al. Serum cholesterol, other risk factors, and cardiovascular disease in a Japanese cohort. Journal of chronic diseases. 1984;37(7):569-584.
D’Agostino RB, Sr., Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743-753.
Goff DC, Jr., Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25 Suppl 2):S49-73.
Preiss D and Kristensen SL. The new pooled cohort equations risk calculator. The Canadian journal of cardiology. 2015;31(5):613-619.
Muntner P, Colantonio LD, Cushman M, et al. Validation of the atherosclerotic cardiovascular disease pooled cohort risk equations. JAMA. 2014;311(14):1406-1415.
Friedewald WT, Levy RI and Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clinical chemistry. 1972;18(6):499-502.
American College of Cardiology. ASCVD risk estimator plus 2017 [updated August 2017; cited 2017]. Available from: http://tools.acc.org/ASCVD-Risk-Estimator-Plus/#!/calculate/estimate/.
Millan J, Pinto X, Munoz A, et al. Lipoprotein ratios: Physiological significance and clinical usefulness in cardiovascular prevention. Vascular health and risk management. 2009;5:757-65.
Burtis CA, Ashwood ER, Bruns DE, Tietz NW. Tietz textbook of clinical chemistry and molecular diagnostics. 5th ed. St. Louis, Mo.: Saunders; 2013. xviii, 2,238 p. p.
Ryoo JH, Ha EH, Kim SG, Ryu S and Lee DW. Apolipoprotein B is highly associated with the risk of coronary heart disease as estimated by the Framingham risk score in healthy Korean men. Journal of Korean medical science. 2011;26(5):631-636.
Meisinger C, Loewel H, Mraz W and Koenig W. Prognostic value of apolipoprotein B and A-I in the prediction of myocardial infarction in middle-aged men and women: results from the MONICA/KORA Augsburg cohort study. European heart journal. 2005;26(3):271-8.
Lind L, Vessby B and Sundstrom J. The apolipoprotein B/AI ratio and the metabolic syndrome independently predict risk for myocardial infarction in middle-aged men. Arteriosclerosis, thrombosis, and vascular biology. 2006;26(2):406-10.
McQueen MJ, Hawken S, Wang X, et al. Lipids, lipoproteins, and apolipoproteins as risk markers of myocardial infarction in 52 countries (the INTERHEART study): a case-control study. Lancet. 2008;372(9634):224-233.
Lima LM, Carvalho MdG and Sousa MO. Apo B/apo AI ratio and cardiovascular risk prediction. Arquivos brasileiros de cardiologia. 2007;88(6):e187-e90.
Kasper D, Fauci A, Hauser S, Longo D and Jameson J. Harrison’s Principles of Internal Medicine. New York: McGraw-Hill Education; 2015.
Schiros CG, Denney TS, Jr. and Gupta H. Interaction analysis of the new pooled cohort equations for 10-year atherosclerotic cardiovascular disease risk estimation: a simulation analysis. BMJ open. 2015;5(4):e006468.