Résumé
Objective To explore the association of (cardiometabolic index , CMI ) and ( lipid accumulation product , LAP ) with the prevalence of hypertension in adults in Pingshan District, Shenzhen, and to evaluate the predictive value of CMI and LAP for the prevalence of hypertension. Methods A cross-sectional survey was conducted, in which 1000 permanent residents aged 18-69 years in Pingshan District, Shenzhen were randomly selected as survey subjects. The data were collected using questionnaires, physical measurements and blood tests, and the relationship between CMI, LAP and hypertension prevalence was analyzed using logistic regression models. Results A total of 987 subjects were included in the analysis, including 471 (47.72%) males and 516 (52.28%) females, with an average age of 40.9 ± 11.1 years. Subjects of different genders were divided into groups Q1 to Q4 according to quartiles of CMI and LAP, and the prevalence of hypertension increased with increasing levels of CMI and LAP in both men and women (P for trend < 0.001). Logistic regression analysis revealed that after adjusting for variables such as age and occupation, the OR for hypertension was 1.251 (1.025-1.526) and 1.685 (1.001-2.836) for men and women, respectively, for each standard deviation increase in CMI; the OR for hypertension was 3.519 (1.343-9.222) for men in the Q4 group, compared to the CMI subgroup Q1. For each standard deviation increase in LAP, the ORs for hypertension were 1.355 (1.089-1.686) and 1.825 (1.023-3.254) for men and women, respectively; compared to LAP subgroup Q1, the ORs for hypertension in the male Q3 and Q4 groups were 2.554 (1.103-5.909) and 5.322 (2.393-11.834), while the OR for hypertension in the female Q4 group was 2.906 (1.096-7.703). ROC analysis revealed that the area under the curve (AUC) for CMI, LAP, and BMI was 0.671, 0.704, and 0.702 for males, and 0.660, 0.722, and 0.697 for females, respectively. The AUC for LAP was greater than that for CMI for different genders (P < 0.01). Conclusion The greater the CMI and LAP values in adults in Pingshan District, Shenzhen , the higher the prevalence of hypertension. CMI and LAP have certain values in predicting the prevalence of hypertension.
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Objective:To investigate the relationship between cardiometabolic index (CMI) and hyperuricemia (HUA) in the health examination population.Methods:It was a cross-sectional study. A total of 21 720 individuals who received health examinations in Xiangya hospital, Central South University between 2020 and 2021 were recruited in this study. Multivariate logistic regression analysis was used to determine the independent correlation between CMI and HUA, and stratified analysis was applied to check whether there were population differences. Then the predictive value of CMI for hyperuricemia in the health examination population was evaluated with the area under the receiver operator characteristic (ROC) curve.Results:Among the 21 720 subjects, 4 418 (20.34%) were detected with HUA. In the HUA group, the body mass index (BMI), waist-to-hip ratio, CMI, total cholesterol, triglyceride, systolic blood pressure, diastolic blood pressure, fasting blood glucose, 2-hour postprandial blood glucose, and blood creatinine levels were all significantly higher than those in the normal uric acid group, while high-density lipoprotein and epidermal growth factor receptor (eGFR) were significantly lower (all P<0.05). Multiple logistic regression analysis showed that after adjusting for relevant factors, CMI was significantly positively correlated with HUA ( OR=1.16, 95% CI: 1.129-1.192); and with the increase of CMI, the risk of HUA increased gradually. Stratified analysis and interaction test according to gender, age, BMI, hypertension, abnormal blood glucose and glomerular filtration rate indicated that CMI was positively associated with the occurrence of HUA in all populations. Compared with that in people with abnormal blood glucose, the correlation between CMI and HUA was more obvious in people with normal blood glucose. The area under the ROC curve (AUC) for CMI to predict HUA was 0.723(95% CI: 0.715-0.731), with a specificity of 0.636 and a sensitivity of 0.698, and the cut-point was 0.693. Conclusion:There was a significant positive correlation between CMI and HUA in the health examination population, which has good predictive value for HUA.
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Objective:To explore the association of cardiometabolic index(CMI) and other body fat evaluation indicators [body mass index(BMI), waist circumference(WC), waist to height ratio(WHtR), lipid accumulation index(LAP)] with the prevalence of metabolic syndrome(MS) as well as the predictive value of the above indicators for MS.Methods:A total of 10 140 residents over 40 years old in Guiyang city who participated in the " Epidemiological study on tumor risk of type 2 diabetes patients in China" in 2011 were recruited. The 2005 International Diabetes Federation diagnostic criteria were used to identify MS. Logistic regression was used to analyze the association of CMI and other body fat evaluation indicators with MS. Receiver operating characteristic(ROC) curve was used to evaluate the predictive value and the optimal cut-off point of different indicators. Taking the best cut-off point value of each index as the boundary, the prevalence of MS was evaluated again by Chi square test.Results:The prevalence of MS in the study population was 39.81%(27.23% for men and 44.39% for women). Logistic regression analysis showed that the risk of MS increased with increasing CMI and quartile level of other body fat evaluation indicators in both men and women( P<0.05). The risks of MS in CMI Q4 group were 17.15(95% CI 11.64-25.27) for male and 45.14(95% CI 37.07-54.96) for female compared with Q1 group. In male, the area under curve(AUC) of MS by predicted CMI was 0.761(sensitivity 79.8%, specificity 63.2%, optimal cut-off point 0.71). WC displayed the highest value of AUC among the body fat evaluation indicators. In women, the AUC value of MS predicted by CMI was 0.831(sensitivity 76.8%, specificity 75.7%, optimal cut-off point 0.65), higher than those of BMI and WHtR while lower than those of WC and LAP. Further calculating the prevalence of MS with the best cut-off point value of each index as the boundary, WC was still the best predictor for male, while CMI was only secondary to LAP for women. Conclusion:CMI and other body fat evaluation indicators are significantly associated with MS. CMI could be used to predict MS.
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Objective:To investigate the relationship between cardiometabolic index and metabolic syndrome in people aged 40 and beyond in Guiyang city.Methods:A total of 4 506 residents over 40 years(including 3 067 females and 1 439 males) were enrolled in the analysis from those who participated in the epidemiological study of cancer risk in Chinese patients with type 2 diabetes in 2011 in Guiyang City. The cardiometabolic index (CMI) is calculated by triglycerides(TG)/high-density lipoprotein-cholesterol(HDL-C)×waist-to-height ratio (WHtR). Multivariable logistic regression was used to analyze the correlation between cardiometabolic index and metabolic syndrome, and ROC was used to analyze the predictive ability of CMI on the incidence of metabolic syndrome. Results:The average follow-up period was 3 years. According to the International Diabetes Federation (IDF) diagnostic criteria for metabolic syndrome in 2005, 985 patients (774 women and 211 males) had metabolic syndrome. The incidence rate of metabolic syndrome in the general population was 21.86%, the incidence rate of male metabolic syndrome was 14.66%, and that of women was 25.24%, and the incidence of CMI increased with the increase of the number of women. After multivariable logistic regression analysis, the odds ratio of CMI for metabolic syndrome in women is 1.303(95% CI 1.263-1.344) and 1.724(95% CI 1.162-2.558) in men, respectively. ROC results showed that CMI had a good ability to predict the incidence (AUC: 0.759 for men and 0.852 for women). Conclusion:CMI is positively associated with the incidence of metabolic syndrome. It supports CMI as a useful method to screen metabolic syndrome in China′s general population.