Comparison of scores for the classification of cardiometabolic risk in adult patients enrolled in a Venezuelan program for chronic non-communicable diseases

a cross-sectional study

Authors

Keywords:

Cardiovascular diseases, Diabetes mellitus, Metabolic syndrome, Risk factors

Abstract

BACKGROUND: Several continuous measurements of cardiometabolic risk (CMR) have emerged as indexes or scores. To our knowledge, there are no published data on its application and validation in Latin America. OBJECTIVE: To evaluate four continuous measurements of metabolic status and CMR. We established its predictive capacity for four conditions associated with CMR. DESIGN AND SETTING: Cross-sectional study conducted at a healthcare center in the state of Carabobo, Venezuela. METHODS: The sample comprised 176 Venezuelan adults enrolled in a chronic disease care program. Four CMR scores were calculated: metabolic syndrome (MetS) Z-score; cardiometabolic index (ICMet); simple method for quantifying MetS (siMS) score; and siMS risk score. CMR biomarkers, proinflammatory status and glomerular function were assessed. MetS was established in accordance with a harmonized definition. RESULTS: Patients with MetS showed higher levels of all scores. All scores increased as the number of MetS components rose. The scores showed significant correlations with most CMR biomarkers, inflammation and glomerular function after adjusting for age and sex. In the entire sample, MetS Z-score, siMS score and siMS risk score showed the ability to detect MetS, reduced glycemic control, proinflammatory status and decreased estimated glomerular filtration rate. ICMet only discriminated MetS and proinflammatory state. There were some differences in the predictive capacity of the scores according to sex. CONCLUSIONS: The findings support the use of the scores assessed here. Follow-up studies should evaluate the predictive capacity of scores for cardiovascular events and diabetes in the Venezuelan population.

Downloads

Download data is not yet available.

Author Biographies

Nelina Alejandra Ruíz-Fernández, Faculty of Health Sciences, Universidad de Carabobo

PhD. Medical Laboratory Technician and Professor, Department of Morphophysiopathology, School of Bioanalysis, Faculty of Health Sciences, Universidad de Carabobo, Valencia, Carabobo, Venezuela; and Principal Researcher, Institute of Nutritional Research, Faculty of Health Sciences, Universidad de Carabobo, Valencia, Carabobo, Venezuela.

Ulises Leal, Faculty of Health Sciences, Universidad de Carabobo

MD. Physician and Internal Medicine Specialist, Integral Medical Care Unit, University of Carabobo, Valencia, Carabobo, Venezuela; and Specialist Physician type II, Outpatient Clinic of the Municipality of San Diego, Carabobo, Venezuela.

Milagros Espinoza, Faculty of Health Sciences, Universidad de Carabobo

PhD. Medical Laboratory Technician and Professor, Department of Research and Professional Development, School of Bioanalysis, Faculty of Health Sciences, Universidad de Carabobo, Valencia, Carabobo, Venezuela.

References

World Health Organization. Global status report on noncommunicable diseases 2014. Geneva: World Health Organization; 2014. Available from: Available from: http://apps.who.int/iris/bitstream/handle/10665/149296/WHO_NMH_NVI_15.1_spa.pdf;jsessionid=CCCE0230DD8F8CB4027A460C3E9080DB?sequence=1 Accessed in 2019 (Jun 27).

Ministerio para el Poder Popular para la Salud. Anuario de Mortalidad 2013. Caracas: MPPS. Available from: Available from: https://www.ovsalud.org/descargas/publicaciones/documentos-oficiales/Anuario-Mortalidad-2013.pdf Accessed in 2019 (Jun 27).

Kaur J. A Comprehensive review on metabolic syndrome. Cardiol Res Pract. 2014;2014:943162. PMID: 24711954; doi: 10.1155/2014/943162.

Kahn R, Buse J, Ferrannini E, et al. The metabolic syndrome: time for a critical appraisal. Diabetes Care. 2005;28(9):2289-304. PMID: 16123508; doi: 10.2337/diacare.28.9.2289.

Klein BE, Klein R, Lee KE. Components of the metabolic syndrome and risk of cardiovascular disease and diabetes in Beaver Dam. Diabetes Care. 2002;25(10):1790-4. PMID: 12351479; doi: 10.2337/diacare.25.10.1790.

Agarwal S, Jacobs DR Jr, Vaidya D, et al. Metabolic syndrome derived from principal component analysis and incident cardiovascular events: the multi-ethnic study of atherosclerosis (MESA) and health, aging, and body composition (Health ABC). Cardiol Res Pract. 2012;2012:919425. PMID: 22536533; doi: 10.1155/2012/919425.

O’Neill S, O’Driscoll L. Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies. Obes Rev. 2015;16(1):1-12. PMID: 25407540; doi: 10.1111/obr.12229.

DeBoer MD, Gurka MJ. Clinical utility of metabolic syndrome severity scores: considerations for practitioners. Diabetes Metab Syndr Obes. 2017;10:65-72. PMID: 28255250; doi: 10.2147/DMSO.S101624.

Giampaoli S, Palmieri L, Mattiello A, Panico S. Definition of high risk individuals to optimise strategies for primary prevention of cardiovascular diseases. Nutr Metab Cardiovasc Dis. 2005;15(1):79-85. PMID: 15871855; doi: 10.1016/j.numecd.2004.12.001.

Organización Mundial de la Salud. El Estado Físico: Uso e Interpretación de la Antropometría. WHO Technical Report Series, 854. Geneva: World Health Organization ; 1995. Available from: Available from: http://apps.who.int/iris/bitstream/handle/10665/42132/WHO_TRS_854_spa.pdf;jsessionid=6FC4A30C4FE2956CF4B11829C1FF3B79?sequence=1 Accessed in 2019 (Jun 28).

Alberti KG, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640-5. PMID: 19805654; doi: 10.1161/CIRCULATIONAHA.109.192644.

Ashwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr. 2005;56(5):303-7. PMID: 16236591; doi: 10.1080/09637480500195066.

Chobanian AV, Bakris GL, Black HR, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: The JNC 7 Report. JAMA. 2003;289(19):2560-71. PMID: 12748199; doi: 10.1001/jama.289.19.2560.

Okorodudu DO, Jumean MF, Montori VM, et al. Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. Int J Obes (Lond). 2010;34(5):791-9. PMID: 20125098; doi: 10.1038/ijo.2010.5.

Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604-12. PMID: 19414839; doi: 10.7326/0003-4819-150-9-200905050-00006.

Gracia S, Montañés R, Bover J, et al. Recomendaciones sobre la utilización de ecuaciones para la estimación del filtrado glomerular en adultos [Recommendations for the use of equations to estimate glomerular filtration rate in adults. Spanish Society of Nefrology]. Nefrologia. 2006;26(6):658-65. PMID: 17227242.

American Diabetes Association. 2. Classification and diagnosis of diabetes. Diabetes Care. 2015;38(Suppl):S8-S16. PMID: 25537714; doi: 10.2337/dc15-S005.

National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002;106(25):3143-421. PMID: 12485966.

Millán J, Pintó X, Muñoz A, et al. Lipoprotein ratios: Physiological significance and clinical usefulness in cardiovascular prevention. Vasc Health Risk Manag. 2009;5:757-65. PMID: 19774217.

Baez-Duarte BG, Zamora-Gínez I, González-Duarte R, et al. Triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) index as a reference criterion of risk for metabolic syndrome (MetS) and low insulin sensitivity in apparently healthy subjects. Gac Med Mex. 2017;153(2):152-8. PMID: 28474700.

Expert Dyslipidemia Panel, Grundy SM. An International Atherosclerosis Society Position Paper: global recommendations for the management of dyslipidemia. J Clin Lipidol. 2013;7(6):561-5. PMID: 24314355; doi: 10.1016/j.jacl.2013.10.001.

Salazar J, Martínez MS, Chávez M, et al. C-reactive protein: clinical and epidemiological perspectives. Cardiol Res Pract. 2014;2014:605810. PMID: 24653858; doi: 10.1155/2014/605810.

National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002;39(2 Suppl 1):S1-266. PMID: 11904577.

Gurka MJ, Lilly CL, Oliver MN, DeBoer MD. An examination of sex and racial/ethnic differences in the metabolic syndrome among adults: a confirmatory factor analysis and a resulting continuous severity score. Metabolism. 2014;63(2):218-25. PMID: 24290837; doi: 10.1016/j.metabol.2013.10.006.

Wakabayashi I, Daimon T. The “cardiometabolic index” as a new marker determined by adiposity and blood lipids for discrimination of diabetes mellitus. Clin Chim Acta. 2015;438:274-8. PMID: 25199852; doi: 10.1016/j.cca.2014.08.042.

Soldatovic I, Vukovic R, Culafic D, Gajic M, Dimitrijevic-Sreckovic V. siMS Score: Simple Method for Quantifying Metabolic Syndrome. PLoS One. 2016; 11(1):e0146143. PMID: 26745635; doi: 10.1371/journal.pone.0146143.

Templeton, GF. A two-step approach for transforming continuous variables to normal: implications and recommendations for IS research. CAIS. 2011;28(1):41-58.

DeBoer MD, Gurka MJ, Woo JG, Morrison JA. Severity of the metabolic syndrome as a predictor of type 2 diabetes between childhood and adulthood: the Princeton Lipid Research Cohort Study. Diabetologia. 2015;58(12):2745-52. PMID: 26380985; doi: 10.1007/s00125-015-3759-5.

DeBoer MD, Gurka MJ, Golden SH, et al. Independent associations between metabolic syndrome severity & future coronary heart disease by sex and race. J Am Coll Card. 2017;69(9):1204-5. PMID: 28254184; doi: 10.1016/j.jacc.2016.10.088.

Gurka MJ, Golden SH, Musani SK, et al. Independent associations between a metabolic syndrome severity score and future diabetes by sex and race: the Atherosclerosis Risk in Communities Study and Jackson Heart Study. Diabetologia. 2017;60(7):1261-70. PMID: 28378033; doi: 10.1007/s00125-017-4267-6.

Gurka MJ, Guo Y, Filipp SL, DeBoer MD. Metabolic syndrome severity is significantly associated with future coronary heart disease in Type 2 diabetes. Cardiovasc Diabetol. 2018;17(1):17. PMID: 29351794; doi: 10.1186/s12933-017-0647-y.

DeBoer MD, Filipp SL, Musani SK, et al. Metabolic Syndrome Severity and Risk of CKD and Worsened GFR: The Jackson Heart Study. Kidney Blood Press Res. 2018;43(2):555-67. PMID: 29642060; doi: 10.1159/000488829.

Wakabayashi I. Relationship between Smoking and Cardiometabolic Index in Middle-Aged Men. Clin Lab. 2016;62(6):1045-51. PMID: 27468566.

Wakabayashi I, Sotoda Y, Hirooka S, Orita H. Association between cardiometabolic index and atherosclerotic progression in patients with peripheral arterial disease. Clin Chim Acta. 2015;446:231-6. PMID: 25920694; doi: 10.1016/j.cca.2015.04.020.

Wang H, Chen Y, Sun G, et al. Validity of cardiometabolic index, lipid accumulation product, and body adiposity index in predicting the risk of hypertension in Chinese population. Postgrad Med. 2018;130(3):325-33. PMID: 29478365; doi: 10.1080/00325481.2018.1444901.

Downloads

Published

2020-02-06

How to Cite

1.
Ruíz-Fernández NA, Leal U, Espinoza M. Comparison of scores for the classification of cardiometabolic risk in adult patients enrolled in a Venezuelan program for chronic non-communicable diseases: a cross-sectional study. Sao Paulo Med J [Internet]. 2020 Feb. 6 [cited 2025 Mar. 9];138(1):69-78. Available from: https://periodicosapm.emnuvens.com.br/spmj/article/view/583

Issue

Section

Original Article