Multifactorial Risk Assessment: LDL Level, Fasting Blood Glucose, Uric Acid, Triglycerides, and TG/HDL Ratio as Predictors of Framingham Risk Score for Hard Coronary Heart Disease

Authors

  • Andria Priyana Tarumanagara University
  • Alexander Halim Santoso Tarumanagara University
  • Ayleen Nathalie Jap Tarumanagara University
  • Jonathan Andersan Tarumanagara University
  • Jonathan Hadi Warsito Tarumanagara University

DOI:

https://doi.org/10.55606/jurrikes.v4i2.5056

Keywords:

Framingham, Hard coronary heart disease, Lipid panel, Predictor parameter

Abstract

. The Framingham Risk Score (FRS) assesses coronary heart disease (CHD) risk and predicts acute coronary events. Metabolic markers like LDL cholesterol, fasting blood glucose, uric acid, triglycerides, and TG/HDL ratio play critical roles in atherosclerosis and cardiovascular risk. Elevated LDL cholesterol, fasting blood glucose, and uric acid contribute to plaque formation, inflammation, and vascular damage, while high triglycerides and low HDL cholesterol exacerbate atherogenesis. This study explores the relationship between these markers and FRS to enhance CHD risk prediction and support targeted cardiovascular interventions. This study analyzed LDL cholesterol, fasting blood glucose, uric acid, triglycerides, and TG/HDL ratio with Framingham Risk Score in 85 participants, excluding those with incomplete data or chronic illnesses. The analysis found significant correlations between metabolic parameters and the 10-year myocardial infarction risk. LDL cholesterol, triglycerides, and uric acid showed moderate positive associations with cardiovascular outcomes, while the triglyceride-to-HDL ratio and fasting blood glucose had weaker but significant correlations. These findings highlight lipid profiles and metabolic markers as key contributors to cardiovascular risk. This study highlights significant correlations between LDL cholesterol, fasting blood glucose, uric acid, triglycerides, and the triglyceride/HDL ratio with 10-year cardiovascular risk. These findings emphasize the importance of lipid profiles, glycemic control, and metabolic markers in predicting coronary outcomes and guiding targeted preventive interventions for improved cardiovascular risk management.

Downloads

Download data is not yet available.

References

Ajoolabady, A., Pratico, D., Lin, L., Mantzoros, C. S., Bahijri, S., Tuomilehto, J., & Ren, J. (2024). Inflammation in atherosclerosis: Pathophysiology and mechanisms. Cell Death & Disease, 15(11), 1–16. https://doi.org/10.1038/s41419-024-07166-8

Akashi, N., Kuwabara, M., Matoba, T., Kohro, T., Oba, Y., Kabutoya, T., Imai, Y., Kario, K., Kiyosue, A., Mizuno, Y., Nochioka, K., Nakayama, M., Iwai, T., Nakao, Y., Iwanaga, Y., Miyamoto, Y., Ishii, M., Nakamura, T., Tsujita, K., … Nagai, R. (2023). Hyperuricemia predicts increased cardiovascular events in patients with chronic coronary syndrome after percutaneous coronary intervention: A nationwide cohort study from Japan. Frontiers in Cardiovascular Medicine, 9, 1062894. https://doi.org/10.3389/fcvm.2022.1062894

Barr, E. L. M., Zimmet, P. Z., Welborn, T. A., Jolley, D., Magliano, D. J., Dunstan, D. W., Cameron, A. J., Dwyer, T., Taylor, H. R., Tonkin, A. M., Wong, T. Y., McNeil, J., & Shaw, J. E. (2007). Risk of cardiovascular and all-cause mortality in individuals with diabetes mellitus, impaired fasting glucose, and impaired glucose tolerance: The Australian Diabetes, Obesity, and Lifestyle Study (AusDiab). Circulation, 116(2), 151–157. https://doi.org/10.1161/circulationaha.106.685628

Bjørnholt, J. V., Nitter-Hauge, S., Erikssen, G., Jervell, J., Aaser, E., Erikssen, J., Sandvik, L., & Thaulow, E. (1999). Fasting blood glucose: An underestimated risk factor for cardiovascular death. Results from a 22-year follow-up of healthy nondiabetic men. Diabetes Care, 22(1), 45–49. https://doi.org/10.2337/diacare.22.1.45

Bosomworth, N. J., & Fcfp, C. (2011). Practical use of the Framingham risk score in primary prevention: Canadian perspective. Canadian Family Physician, 57(4), 417–423.

Brindle, P., Ebrahim, S., Jonathan, E., Lampe, F., Walker, M., Whincup, P., & Fahey, T. (2003). Predictive accuracy of the Framingham coronary risk score in British men: Prospective cohort study. BMJ, 327(7426), 1267. https://doi.org/10.1136/bmj.327.7426.1267

Burger, P. M., Dorresteijn, J. A. N., Koudstaal, S., Holtrop, J., Kastelein, J. J. P., Jukema, J. W., Ridker, P. M., Mosterd, A., & Visseren, F. L. J. (2024). Course of the effects of LDL-cholesterol reduction on cardiovascular risk over time: A meta-analysis of 60 randomized controlled trials. Atherosclerosis, 396, 118540. https://doi.org/10.1016/j.atherosclerosis.2024.118540

Chien, K. L., Hsu, H. C., Sung, F. C., Su, T. C., Chen, M. F., & Lee, Y. T. (2005). Hyperuricemia as a risk factor on cardiovascular events in Taiwan: The Chin-Shan Community Cardiovascular Cohort Study. Atherosclerosis, 183(1), 147–155. https://doi.org/10.1016/j.atherosclerosis.2005.01.018

Da Luz, P. L., Favarato, D., Faria-Neto, J. R., Lemos, P., & Chagas, A. C. P. (2008). High ratio of triglycerides to HDL-cholesterol predicts extensive coronary disease. Clinics, 63(4), 427–432. https://doi.org/10.1590/s1807-59322008000400003

Feig, D. I., Kang, D.-H., & Johnson, R. J. (2008). Uric acid and cardiovascular risk. The New England Journal of Medicine, 359(17), 1811–1821. https://doi.org/10.1056/NEJMra0800885

Ference, B. A., Ginsberg, H. N., Graham, I., Ray, K. K., Packard, C. J., Bruckert, E., Hegele, R. A., Krauss, R. M., Raal, F. J., Schunkert, H., Watt, G. F., Borén, J., Fazio, S., Horton, J. D., Masana, L., Nicholls, S. J., Nordestgaard, B. G., Van De Sluis, B., Taskinen, M. R., … Catapano, A. L. (2017). Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. European Heart Journal, 38(32), 2459–2472. https://doi.org/10.1093/eurheartj/ehx144

Gaziano, T. A., Young, C. R., Fitzmaurice, G., Atwood, S., & Gaziano, J. M. (2008). Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: The NHANES I Follow-up Study cohort. The Lancet, 371(9616), 923–931. https://doi.org/10.1016/S0140-6736(08)60418-3

Irving, J. (2019). Hyperlipidemia: Causes, types, symptoms and treatment. Nova Science Publishers.

Jørgensen, A. P., Frikke-Schmidt, R., Nordestgaard, B. G., & Tybjærg-Hansen, A. (2014). Loss-of-function mutations in APOC3 and risk of ischemic vascular disease. New England Journal of Medicine, 371(1), 32–41. https://doi.org/10.1056/NEJMoa1308027

Kappelle, P. J. W. H., Gansevoort, R. T., Hillege, H. L., Wolffenbuttel, B. H. R., Dullaart, R. P. F., & PREVEND Study Group. (2011). Apolipoprotein B/apolipoprotein A-I ratio and risk of cardiovascular disease in the general population: The PREVEND study. Clinical Chemistry, 57(2), 263–273. https://doi.org/10.1373/clinchem.2010.155929

Kinoshita, M., Yokote, K., Arai, H., Iida, M., Ishigaki, Y., Ishibashi, S., Umemoto, S., & Japan Atherosclerosis Society (JAS). (2018). Japan Atherosclerosis Society (JAS) guidelines for prevention of atherosclerotic cardiovascular diseases 2017. Journal of Atherosclerosis and Thrombosis, 25(9), 846–984. https://doi.org/10.5551/jat.CR002

Knopp, R. H. (2005). Drug treatment of lipid disorders. New England Journal of Medicine, 353(17), 1933–1947. https://doi.org/10.1056/NEJMra043040

Lemieux, I., Pascot, A., Couillard, C., Lamarche, B., Tchernof, A., Alméras, N., Bergeron, J., Gaudet, D., Tremblay, G., Prud’homme, D., & Després, J. P. (2000). Hypertriglyceridemic waist: A marker of the atherogenic metabolic triad (hyperinsulinemia; hyperapolipoprotein B; small, dense LDL) in men? Circulation, 102(2), 179–184. https://doi.org/10.1161/01.CIR.102.2.179

Lusis, A. J. (2000). Atherosclerosis. Nature, 407(6801), 233–241. https://doi.org/10.1038/35025203

Matsushita, K., Mahmoodi, B. K., Woodward, M., Emberson, J. R., Jafar, T. H., Jee, S. H., Polkinghorne, K. R., Shankar, A., Smith, D. H., Tonelli, M., Warnock, D. G., Wen, C. P., Coresh, J., Gansevoort, R. T., Hemmelgarn, B. R., Levey, A. S., Levin, A., & Chronic Kidney Disease Prognosis Consortium. (2012). Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate. JAMA, 307(18), 1941–1951. https://doi.org/10.1001/jama.2012.3954

Mora, S., Wenger, N. K., Demicco, D. A., Beckman, J. A., & Cannon, C. P. (2012). Determinants of residual risk in secondary prevention patients treated with high-versus low-dose statin therapy: The Treating to New Targets (TNT) study. Circulation: Cardiovascular Quality and Outcomes, 5(6), 904–912. https://doi.org/10.1161/CIRCOUTCOMES.112.964015

Nordestgaard, B. G., Chapman, M. J., Ray, K., Borén, J., Andreotti, F., Watts, G. F., Ginsberg, H. N., Amarenco, P., Catapano, A., Descamps, O. S., Fisher, E., Kovanen, P. T., Kuivenhoven, J. A., Lesnik, P., Masana, L., Reiner, Ž., Taskinen, M. R., Tokgözoğlu, L., & Tybjærg-Hansen, A. (2010). Lipoprotein(a) as a cardiovascular risk factor: Current status. European Heart Journal, 31(23), 2844–2853. https://doi.org/10.1093/eurheartj/ehq386

Patel, A. P., Wang, M., Pirruccello, J. P., Ellinor, P. T., & Kathiresan, S. (2018). Lp(a) (Lipoprotein[a]) concentrations and incident atherosclerotic cardiovascular disease: New insights from a large national biobank. Arteriosclerosis, Thrombosis, and Vascular Biology, 38(3), 591–598. https://doi.org/10.1161/ATVBAHA.117.310578

Rashid, S., & Genest, J. (2007). Lipid modification to prevent cardiovascular disease: The evidence-based strategy for primary and secondary prevention. Vascular Health and Risk Management, 3(3), 411–421. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1993986/

Rhee, E. J., Kim, H. C., Kim, J. H., Lee, E. Y., Kim, B. J., Kim, E. M., Song, Y., Lim, J. H., Kim, D. J., Choi, S., Moon, M. K., Park, J. H., Jeong, I. K., & Korean Society of Lipid and Atherosclerosis. (2022). 2022 Guidelines for the management of dyslipidemia. Journal of Lipid and Atherosclerosis, 11(1), 1–51. https://doi.org/10.12997/jla.2022.11.1.1

Ridker, P. M. (2016). From C-reactive protein to interleukin-6 to interleukin-1: Moving upstream to identify novel targets for atheroprotection. Circulation Research, 118(1), 145–156. https://doi.org/10.1161/CIRCRESAHA.115.306656

Rosenson, R. S., Davidson, M. H., Pourfarzib, R., & Underberg, J. A. (2010). Underappreciated opportunities for lipid management in patients with cardiometabolic risk. American Journal of Medicine, 123(10), 863.e1–863.e9. https://doi.org/10.1016/j.amjmed.2010.04.027

Shai, I., Rimm, E. B., Hankinson, S. E., Curhan, G., Rifai, N., & Ma, J. (2004). Multivariate assessment of lipid parameters as predictors of coronary heart disease among postmenopausal women: Prospective cohort study. BMJ, 329(7459), 261. https://doi.org/10.1136/bmj.38133.637310.63

Sniderman, A. D., & Toth, P. P. (2021). Secondary prevention in atherosclerosis: The way forward. Atherosclerosis, 334, 1–9. https://doi.org/10.1016/j.atherosclerosis.2021.06.012

Sniderman, A. D., Scantlebury, T., & Cianflone, K. (2001). Hypertriglyceridemic hyperapo B: The unappreciated atherogenic dyslipoproteinemia in type 2 diabetes mellitus. Annals of Internal Medicine, 135(6), 447–459. https://doi.org/10.7326/0003-4819-135-6-200109180-00011

Tsimikas, S. (2017). A test in context: Lipoprotein(a): Diagnosis, prognosis, controversies, and emerging therapies. Journal of the American College of Cardiology, 69(6), 692–711. https://doi.org/10.1016/j.jacc.2016.11.042

Tsimikas, S., Fazio, S., Ferdinand, K. C., Ginsberg, H. N., Koschinsky, M. L., Marcovina, S. M., Moriarty, P. M., Nordestgaard, B. G., Santos, R. D., & Watts, G. F. (2019). NHLBI Working Group Recommendations to Reduce Lipoprotein(a)-Mediated Risk of Cardiovascular Disease and Aortic Stenosis. Journal of the American College of Cardiology, 74(24), 2874–2892. https://doi.org/10.1016/j.jacc.2019.10.046

van der Valk, F. M., Bekkering, S., Kroon, J., Yeang, C., Van den Bossche, J., van Buul, J. D., Rezaee, F., Drexhage, J. A. R., Versloot, M., Storm, G., de Winther, M. P. J., Duncker, D. J., Van der Heiden, K., Lutgens, E., Tsimikas, S., & Stroes, E. S. G. (2016). Oxidized phospholipids on lipoprotein(a) elicit arterial wall inflammation and an inflammatory monocyte response in humans. Circulation, 134(8), 611–624. https://doi.org/10.1161/CIRCULATIONAHA.116.021223

Vasquez-Casilla, M. L., & Banchs, H. (2021). Lipoprotein(a): A review for the clinical cardiologist. Texas Heart Institute Journal, 48(3), e197749. https://doi.org/10.14503/THIJ-19-7749

Vaverkova, H., Karasek, D., Halenka, M., Hrbkova, R., & Novotny, D. (2020). Lipoprotein(a): A risk factor for atherosclerosis and an indicator of residual vascular risk. Vnitrní Lékarství, 66(11), 632–639. https://doi.org/10.36290/vnl.2020.105

Wang, H., Yuan, Z., Heng, W., Zhang, H., Wang, F., & Pan, X. (2019). Lipoprotein(a) and cardiovascular outcomes in Chinese patients with stable coronary artery disease. Frontiers in Cardiovascular Medicine, 6, 142. https://doi.org/10.3389/fcvm.2019.00142

Wang, X., Zhang, X., Liu, J., Wu, H., Chen, Y., & Wang, W. (2018). Lipoprotein(a) as a risk factor for stroke: A meta-analysis of prospective studies. Journal of Clinical Neuroscience, 47, 6–11. https://doi.org/10.1016/j.jocn.2017.09.005

Yeang, C., Witztum, J. L., & Tsimikas, S. (2015). ‘LDL-C’ and lipoprotein(a) — The role of apo(a) in atherosclerosis. Nature Reviews Cardiology, 12, 633–643. https://doi.org/10.1038/nrcardio.2015.94

Zhao, Y., Liu, J., & Wang, W. (2020). Lipoprotein(a) and cardiovascular disease: Current understanding and future prospects. Clinical Cardiology, 43(12), 1332–1341. https://doi.org/10.1002/clc.23478

Zhou, Y., Zhao, X., Wang, J., Zhang, J., Gu, L., Yang, Y., & Lu, Z. (2021). Association of lipoprotein(a) with atherosclerotic cardiovascular disease and implications for clinical practice. Journal of Atherosclerosis and Thrombosis, 28(5), 463–472. https://doi.org/10.5551/jat.58101

Downloads

Published

2025-05-26

How to Cite

Priyana, A., Santoso, A. H., Jap, A. N., Andersan, J., & Warsito, J. H. (2025). Multifactorial Risk Assessment: LDL Level, Fasting Blood Glucose, Uric Acid, Triglycerides, and TG/HDL Ratio as Predictors of Framingham Risk Score for Hard Coronary Heart Disease. JURNAL RISET RUMPUN ILMU KESEHATAN, 4(2), 01–13. https://doi.org/10.55606/jurrikes.v4i2.5056

Issue

Vol. 4 No. 2 (2025): Agustus : Jurnal Riset Rumpun Ilmu Kesehatan

Section

Articles

License

Copyright (c) 2025 JURNAL RISET RUMPUN ILMU KESEHATAN

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.