Abstract
Background: Cardiometabolic Index (CMI) was associated with several risk factors for stroke; however, few studies assessed the role of CMI in stroke risk.
Objective: This study aimed to assess the association between CMI and stroke in a population- based cross-sectional study.
Methods: This study included 4445 general residents aged ≥40 years selected by multistage stratified random cluster sampling. CMI was calculated as the product of the ratio of waist circumference to height (WHtR) and the ratio of triglyceride levels to high-density lipoprotein cholesterol levels (TG/HDL-C). Participants were categorized according to CMI quartiles: quartile 1 (Q1), quartile 2 (Q2), quartile 3 (Q3), and quartile 4 (Q4). Multivariate logistic regression analysis and receiver operating characteristic (ROC) curves were used to assess the association between CMI and stroke.
Results: A total of 4052 participants were included in the study, with an overall stroke prevalence of 7.2%. The prevalence of stroke increased with CMI quartiles, ranging from 4.4% to 9.2% (p for trend <0.001). Compared with Q1, stroke risk for Q2, Q3, and Q4 were 1.550-, 1.693-, and 1.704- fold, respectively. The area under the ROC curve (AUC) (95% CI) was 0.574 (0.558-0.589) for CMI, 0.627 (0.612-0.642) for WHtR, 0.556 (0.540-0.571) for TG/HDL-C. CMI was inferior to WHtR (p=0.0024), but CMI had a marginal advantage over TG/HDL-C (p<0.0001) in terms of its stroke discrimination ability.
Conclusion: Although there was a strong and independent association between CMI and stroke in the general population, CMI had limited discriminating ability for stroke. Thus, new parameters should be developed.
Keywords: Cardiometabolic index, stroke, ischemic stroke, waist-to-height ratio, triglyceride, high-density lipoprotein cholesterol.
[1]
Feigin VL, Krishnamurthi RV, Parmar P, et al. Update on the global burden of ischemic and hemorrhagic stroke in 1990-2013: The GBD 2013 study Neuroepidemiology 2015; 45(3): 161-76.
[2]
Wang W, Jiang B, Sun H, et al. Prevalence, incidence, and mortality of stroke in China: results from a nationwide population-based survey of 480 687 adults Circulation 2017; 135(8): 759-71.
[3]
Machado M, Alves M, Fior A, et al. Functional outcome after mechanical thrombectomy with or without previous thrombolysis. 2021; 30(2): 105495.
[4]
Yang X, Li C, Li J, et al. Insulin resistance is significantly related with worse clinical outcomes in non-diabetic acute ischemic stroke patients treated with intravenous thrombolysis. 2021; 30(3): 105526.
[5]
Derbisz J, Nowak K, Wnuk M, et al. Prognostic Significance of Stroke-Associated Infection and other Readily Available Parameters in Acute Ischemic Stroke Treated by Intravenous Thrombolysis. 2021; 30(2): 105525.
[6]
Suzuki K, Matsumaru Y, Takeuchi M, et al. Effect of mechanical thrombectomy without vs with intravenous thrombolysis on functional outcome among patients with acute ischemic stroke: the skip randomized clinical trial. JAMA 2021; 325(3): 244-53.
[7]
Zi W, Qiu Z, Li F, et al. Effect of endovascular treatment alone vs intravenous alteplase plus endovascular treatment on functional independence in patients with acute ischemic stroke: the devt randomized clinical trial. JAMA 2021; 325(3): 234-43.
[8]
Zhou M, Wang H, Zeng X, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2019; 394(10204): 1145-58.
[9]
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.
[10]
Wakabayashi I, Sotoda Y, Hirooka S, Orita H. Association between cardiometabolic index and atherosclerotic progression in patients with peripheral arterial disease. 2015; 446: 231-6.
[11]
Acosta-García E, Concepción-Páez M. Cardiometabolic index as a predictor of cardiovascular risk factors in adolescents. Clin Rheumatol 2018; 20(3): 340-5.
[12]
Shi WR, Wang HY, Chen S, Guo XF, Li Z, Sun YX. Estimate of prevalent diabetes from cardiometabolic index in general Chinese population: a community-based study 2018.
[13]
Wang H. Validity of cardiometabolic index, lipid accumulation product, and body adiposity index in predicting the risk of hypertension in Chinese population. BMC Cardiovasc Disord 2018; 130(3): 325-33.
[14]
Wang Z. Capacity of different anthropometric measures to predict diabetes in a Chinese population in southwest China: a 15-year prospective study. Sao Paulo medical journal =. Rev Paul Med 2019; 36(10): 1261-7.
[15]
Li HH, Wang JM, Ji YX, et al. Association of visceral adiposity surrogates with impaired fasting glucose in nonobese individuals. Metab Syndr Relat Disord 2020; 18(3): 128-33.
[16]
Zidi W, Zayani Y, Abbes A, et al. Which obesity index is more compatible in predicting metabolic syndrome? A population based study? Archives of Cardiovascular Diseases Supplements 2020; 12(1): 160-1.
[17]
Wang H, Chen Y, Guo X. Usefulness of cardiometabolic index for the estimation of ischemic stroke risk among general population in rural China. Postgrad Med 2017; 129(8): 834-41.
[18]
Technical specification of stroke screening and prevention in China. Chinese Journal of the Frontiers of Medical Science 2013; (9): 44-50. [Electronic Version].
[19]
Feigin VL, Mensah GA, Norrving B, Murray CJ, Roth GA. Atlas of the Global Burden of Stroke (1990-2013): The GBD 2013 Study. Neuroepidemiology 2015; 45(3): 230-6.
[20]
Li JL, Wang LD, Chao BH, Liu YL. Prevalence of stroke in China: an epidemiological study based on the National Stroke Screening Survey. Lancet 2015; 386: 49.
[21]
Zhang FL, Guo ZN, Wu YH, et al. Prevalence of stroke and associated risk factors: a population based cross sectional study from northeast China. BMJ Open 2017; 7(9): e015758.
[22]
Zhang P, Sun X, Jin H, Zhang FL, Guo ZN, Yang Y. Comparison of the Four Anthropometric Indexes and Their Association With Stroke: A Population-Based Cross-Sectional Study in Jilin Province, China. Front Neurol 2019; 10: 1304.
[23]
Chen HX, Wang LJ, Yang Y, Yue FX, Chen LM, Xing YQ. The prevalence of intracranial stenosis in patients at low and moderate risk of stroke. Ther Adv Neurol Disord 2019; 12: 1756286419869532.
[24]
Li FE, Zhang FL, Zhang P, et al. Sex-based differences in and risk factors for metabolic syndrome in adults aged 40 years and above in Northeast China: Results from the cross-sectional China national stroke screening survey. BMJ Open 2021; 11(3): e038671.
[25]
Aho K, Harmsen P, Hatano S, Marquardsen J, Smirnov VE, Strasser T. Cerebrovascular disease in the community: results of a WHO collaborative study. Bull World Health Organ 1980; 58(1): 113-30.
[26]
James PA, Oparil S, Carter BL, et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA 2014; 311(5): 507-20.
[27]
American Diabetes A. Diagnosis and classification of diabetes mellitus. Diabetes Care 2014; 37(Suppl. 1): S81-90.
[28]
Willenbring ML, Massey SH, Gardner MB. Helping patients who drink too much: an evidence-based guide for primary care clinicians. Am Fam Physician 2009; 80(1): 44-50.
[29]
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44(3): 837-45.
[30]
Wakabayashi I. A U-shaped relationship between alcohol consumption and cardiometabolic index in middle-aged men. Lipids Health Dis 2016; 15: 50.
[31]
Wakabayashi I. Relationship between Smoking and Cardiometabolic Index in Middle-Aged Men. Clin Lab 2016; 62(6): 1045-51.
[32]
Wakabayashi I. Inverse association of light-to-moderate alcohol drinking with cardiometabolic index in men with diabetes mellitus. Diabetes Metab Syndr 2018; 12(6): 1013-7.
[33]
Wakabayashi I. Relationship between age and cardiometabolic index in Japanese men and women. Obes Res Clin Pract 2018; 12(4): 372-7.
[34]
Wang H, Sun Y, Li Z, et al. Gender-specific contribution of cardiometabolic index and lipid accumulation product to left ventricular geometry change in general population of rural. In: BMC Cardiovasc Disord. 2018; 18: p. (1)62.
[35]
Meschia JF, Bushnell C, Boden-Albala B, et al. Guidelines for the primary prevention of stroke: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2014; 45(12): 3754-832.
[36]
Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol 2019; 74(10): 1376-414.
[37]
Petry N, Rohner F, Phall MC, et al. Prevalence and co-existence of cardiometabolic risk factors and associations with nutrition-related and socioeconomic indicators in a national sample of Gambian women. Sci Rep 2021; 11(1): 12057.
[38]
Kahn HS. The “lipid accumulation product” performs better than the body mass index for recognizing cardiovascular risk: a population-based comparison. BMC Cardiovasc Disord 2005; 5: 26.
[39]
Liu X, Zhang D, Liu Y, et al. A J-shaped relation of BMI and stroke: Systematic review and dose-response meta-analysis of 4.43 million participants. Nutrition, metabolism, and cardiovascular diseases. Nutr Metab Cardiovasc Dis 2018; 28(11): 1092-9.
[40]
Lu M, Ye W, Adami HO, Weiderpass E. Prospective study of body size and risk for stroke amongst women below age 60. J Intern Med 2006; 260(5): 442-50.
[41]
Bodenant M, Kuulasmaa K, Wagner A, et al. Measures of abdominal adiposity and the risk of stroke: the MOnica Risk, Genetics, Archiving and Monograph (MORGAM) study. Stroke 2011; 42(10): 2872-7.
[42]
Winter Y, Pieper L, Klotsche J, Riedel O, Wittchen HU. Obesity and Abdominal Fat Markers in Patients with a History of Stroke and Transient Ischemic Attacks. 2016; 25(5): 1141-7.
[43]
Barzi F, Patel A, Woodward M, et al. A comparison of lipid variables as predictors of cardiovascular disease in the Asia Pacific region. Ann Epidemiol 2005; 15(5): 405-13.
[44]
Kurth T, Everett BM, Buring JE, Kase CS, Ridker PM, Gaziano JM. Lipid levels and the risk of ischemic stroke in women. Neurology 2007; 68(8): 556-62.
[45]
Pikula A, Beiser AS, Wang J, et al. Lipid and lipoprotein measurements and the risk of ischemic vascular events: Framingham Study. Neurology 2015; 84(5): 472-9.
[46]
O’Donnell MJ, Chin SL, Rangarajan S, et al. Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study. Lancet 2016; 388(10046): 761-75.
[47]
McLaughlin T, Reaven G, Abbasi F, et al. Is there a simple way to identify insulin-resistant individuals at increased risk of cardiovascular disease? Am J Cardiol 2005; 96(3): 399-404.
[48]
Cordero A, Laclaustra M, Leon M, et al. Comparison of serum lipid values in subjects with and without the metabolic syndrome. Am J Cardiol 2008; 102(4): 424-8.
[49]
Bhalodkar NC, Blum S, Enas EA. Accuracy of the ratio of triglycerides to high-density lipoprotein cholesterol for predicting low-density lipoprotein cholesterol particle sizes, phenotype B, and particle concentrations among Asian Indians. Am J Cardiol 2006; 97(7): 1007-9.
[50]
Cai Z, Huang J, Chen H, et al. The triglyceride:high-density lipoprotein-cholesterol ratio and steno-occlusive disease in the intracranial arteries. J Diabetes Investig 2011; 32(1): 103-9.
Related Books
Frontiers in Clinical Drug Research - CNS and Neurological Disorders
Frontiers in Clinical Drug Research - CNS and Neurological Disorders
Frontiers in Clinical Drug Research - CNS and Neurological Disorders
Frontiers in Clinical Drug Research-Dementia
Frontiers in Clinical Drug Research - CNS and Neurological Disorders
Article Metrics
17
1