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AHA risk model: A population based cross sectional study
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Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2018) xxx–xxx
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Comparison of abdominal obesity measures in predicting of 10-year cardiovascular risk in an Iranian adult population using ACC/AHA risk model: A population based cross sectional study K. Hajian-Tilakia,* , B. Heidarib a b
Dept of Biostatistics and Epidemiology, Babol University of Medical Sciences, Babol, Iran Dept of Internal Medicine, Ayatollah Rohani hospital, Babol University of Medical Sciences, Babol, Iran
A R T I C L E I N F O
A B S T R A C T
Article history: Available online xxx
Background: Several abdominal obesity measures have been used for prediction of 10-year cardiovascular disease (CVD) risk but the superiority of these measures remains controversial. The objective of this study was to assess the predictive ability of abdominal obesity measures for risk of CVD events in an Iranian adult population. Methods: We analyzed the data of population based cross-section study of 567 representative samples of adult population aged 40–70 years in Babol, the north of Iran. The demographic data, the anthropometric measures, lipid profile and cardiometabolic risk factors were measured with standard methods. Waist to hip ratio (WHR), waist to height ratio (WHtR), conicity index(CI), abdominal volume index (AVI) and body mass index(BMI)were calculated. The individual 10-year CVD risk was estimated based on ACC/AHA model. ROC analysis was performed to assess the diagnostic ability of different abdominal obesity measures and body mass index (BMI) in predicting of high risk of CVD events. Results: About 42.5% of men and 15% of women had at least 10% risk of 10-year cardiovascular events and 21.1% of men and 3.0% of women had 20% risk. Except WHR for men, all abdominal obesity measures significant predictors for 10% risk CVD risk in both sexes but not BMI. The greater ability of CVD risk prediction was observed by WHtR and CI in both sexes with higher AUC in females compared with men for 10% risk. Conclusion: WHtR and CI are superior indexes in predicting of high risk of CVD events in both sexes. © 2018 Diabetes India. Published by Elsevier Ltd. All rights reserved.
Keywords: Cardiovascular risk Waist circumference Waist to hip ratio Waist to height ratio Conicity index Abdominal volume index
1. Background Obesity in particular abdominal obesity measures is the central component of metabolic syndrome [1] that has a major contribution in insulin resistance, type 2 diabetes, cardio vascular disease, stroke and cancer [2,3]. The worldwide epidemic of obesity has been progressively extending to both developed and developing countries(3)with high economic cost of the associated morbidities and induced disabilities [4,5] and it has a negative influence on health related quality of life [6]. Iranian population has been experience of epidemiologic transition state where the high prevalence of obesity and abdominal obesity has been reported in adult population in the recent decades [7] and simultaneously, the incidence of cardiovascular disease and its mortality has been increased dramatically [8,9]. An emerging high prevalence of
* Corresponding author. E-mail address: [email protected] (K. Hajian-Tilaki).
obesity, abdominal obesity and metabolic syndrome has been demonstrated in Iranian adult population [7,10–12]. Cardiovascular diseases (CVD) remain the main cause of death in worldwide [13–15].Due to epidemiologic disease transition model in developing countries, the incidence and the death rate due to CVD has been increased significantly during two recent decades in developing counties [8,9,13]. In Islamic Republic of Iran, roughly 20% of total death was attributed to CVD death in decades of 1970’s; it was significantly elevated to 40% in recent decade [9]. Additionally, its incidence was toward increasing in younger adults under 50 years and the pattern of mean age distribution of its incidence became downward [8]. Thus, the knowledge of associated risk factors in screening for high risk individuals provides a basis for population mass or high risk strategy of interventional program. Population living in urban area in the north of Iran, located in the south of Caspian sea, has a rapid experience of life style transition in two recent decades and thus a high prevalence of obesity and abdominal obesity and metabolic syndrome have been reported in this population [7,12].
http://dx.doi.org/10.1016/j.dsx.2018.06.012 1871-4021/© 2018 Diabetes India. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: K. Hajian-Tilaki, B. Heidari, Comparison of abdominal obesity measures in predicting of 10-year cardiovascular risk in an Iranian adult population using ACC/AHA risk model: A population based cross sectional study, Diab Met Syndr: Clin Res Rev (2018), https://doi.org/10.1016/j.dsx.2018.06.012
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Several CVD risk assessment tools have been developed that the 10-years CVD individual risk can be calculated with a given risk factor profiles [16–22]. Among various risk assessment models, American College of Cardiology/American Heart Association (ACC/AHA) is much more popular model that is based on Cox proportional hazard model [17]. The regression coefficients of risk factors and baseline risk were estimated according to sex specific and ethnicity. However, the components of obesity and abdominal obesity indexes were not included directly in this model but cardio metabolic risk factors such as total cholesterol, HDL, systolic blood pressure, treating or not blood pressure, being diabetes and current smoker and some interaction terms were built in the ACC/AHA risk model. A clear association has been established between obesity and abdominal obesity with cardio metabolic risk factors such as lipid profile and FBS and hypertension and thus metabolic syndrome [23]. The body mass index (BMI) as a measure of general adiposity and waist circumference (WC) as a simple measure of abdominal obesity are linked with CVD risk (24–27)whereas no single measure of abdominal obesity was defined but BMI does not account the visceral fat distribution. Although in definition of metabolic syndrome, BMI was used by World Health Organization (WHO) [28], WC by International Diabetes Federation (IDF), National Cholesterol Education Program(NCEP) Adult Treatment Panel III (ATP III) (29) and American Heart Association (AHA)/National Heart, Lung and Blood Institute (30) and Iranian National committee of obesity (INCO) as well but with different cutoff values [31].Several measures of abdominal obesity such as waist to hip ratio(WHR), waist to height ratio (WHtR) and two recent developed measures, conicity index (CI) and abdominal volume index (AVI) have been suggested [1–34].Among these indexes, WC as a simple measure is a greater interesting for assessment of MetS. The choice of abdominal obesity measures, particularly their optimal cut off values depends on sex, age and ethnicity. Therefore, the superiority of these measures and their optimal cutoff value in predicting cardiovascular risk remains controversial. Despite the high prevalence of general adiposity and abdominal obesity in urban population in the north of Iran among aged 40–70 years, the data of 10-year CVD risk and the superiority of abdominal obesity indexes are sparse. Thus, the objective of this study was to compares the predictive ability of different abdominal obesity measures, BMI and to determine their optimal cutoff values in predicting 10-year cardiovascular risk in Iranian adult population. 2. Methods 2.1. Study design and subjects We reanalyzed the data of population based cross sectional study of Babol Lipid and Glucose study that primary focused on cardio metabolic risk factors. This study recruited a representative sample of 1000 subjects aged 20–70 years in Babol urban community, in the north of Iran in 2012. The individual samples were dawn randomly using two stage cluster sampling techniques in a family health survey. The description of sampling procedure and recruitment criteria were explained in details elsewhere [12]. In brief, the population under coverage of urban health centers in Babol composed of several urban parochial that we called as clusters. Twenty five clusters were taken randomly using cumulative population size under coverage of different health centers with a systematic sampling method. Then, the center of each cluster was determined and within each cluster 40 subjects (men and women) age 20–70 years were selected in the study. Since ACC/AHA risk model was established for subjects with aged 40 years or older without history of CVA events, thus in present study, a subset of data of 565 subjects with aged 40–70 years in both genders who had not previous history heart attack,
myocardial infarction, stroke and any CVA events were extracted for analysis and the pregnant women were excluded. All participants had given a written consent for participation into the study. The study protocol had been approved by Ethical counsel of Babol university of medical sciences. 2.2. Measurements and data collection The demographic and life style data, history of treating hypertension and diabetes was collected using a design questionnaire by interview. The weight and height were measured to nearest 0.1 kg and 0.1 cm using a digital portable scale and a portable stadiometer respectively by trained staff with standard method, light clothes and without shoes. The body mass index (BMI) was calculated as weight in kg divided by square of height in m2. The WC was determined at level of midpoint between the lowest coastal ridge and the upper border of the iliac crest while participants keep their breathing and hip circumference (HC) was measured at the largest circumference between waist and knee. Both WC and HC were measured using a non-stretchable type meter with precision of 0.1 cm. The WHtR was calculated as the ratio of WC (cm) to height (cm) and WHR was determined as the ratio of waist (cm) to hip circumference (cm). Also, the two other more recent indexes, conicity index (CI) and abdominal volume index (AVI) was calculated based on weight, height, waist and hip as follows CI ¼
waist ðmÞ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi;
0:109
weight ðkgÞ height ðmÞ
h i AVI ¼ f2 wiast2 ðcmÞ2 þ 0:7 waistðcmÞ hipðcmÞ2 g=1000 Systolic and diastolic blood pressures (SBP, DBP) were also measured by trained staff two times consecutively in an interval of 10 min rest where the subjects were in sitting position using a digital psygmonameter (Model BP101 A named Dr. Axon, made in China). The cuff was placed on over meddle of right arm at head level. The average of two measures of blood pressure was taken in analysis. The reliability of anthropometric and blood pressures measures were assessed in a pilot study with repeated measures on the same subjects using a statistic of coefficient of variation the almost produced less than 1% and 3% for anthropometric and blood pressure measures respectively. All participants were invited to go overnight fasting to 10–12 h they were referred to central lab of Ayatollah Rohani hospital, affiliated to Babol university of medical sciences. A venous blood sample was taken from all participants at sitting position in the morning according to standard protocol for measuring fasting blood sugar (FBS) and lipid profiles. It was immediately centrifuged and transferred under cold chain conditions. Total cholesterol, high density lipoprotein (HDL) cholesterol, low density lipoprotein (LDL) cholesterol and triglyceride and fasting blood sugar (FBS) were measured by auto analyzer using enzymatic method. Subjects with FBS > 126 and/or treating for lower blood sugar were classified as diabetes. 2.3. Statistical analysis We used SPSS software for data analysis using receiver operator characteristic (ROC) curve analysis. First, the 10-years CVD risk (first severe atherosclerotic cardiovascular disease (ASCVD) event, including coronary heart diseases (CHD), death, fatal myocardial infarction and nonfatal or nonfatal stroke) was calculated at individual level for each participant using Cox proportional hazard model based on the coefficients of ACC/ AHA models and its baseline risk [17]. Since the original ethnicity of our population is
Please cite this article in press as: K. Hajian-Tilaki, B. Heidari, Comparison of abdominal obesity measures in predicting of 10-year cardiovascular risk in an Iranian adult population using ACC/AHA risk model: A population based cross sectional study, Diab Met Syndr: Clin Res Rev (2018), https://doi.org/10.1016/j.dsx.2018.06.012
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near to Caucasian ethnicity, we used coefficients of risk factors and baseline hazard from sex specific, white non-Hispanic ethnic group. The predictive risk factors in profile of study subjects were age (year), treated/untreated systolic (mm/Hg), total cholesterol (mg/dl), HDL (mg/dl), diabetic (diabetic vs. non-diabetic) and current smoker (current smoker vs. not current smoker) and their interaction terms that their coefficients was determined in the ACC/AHA model. After calculating the individual 10-year risk, the participants were classified based on cutoff values of 10% as high risk group versus low risk. We used ROC analysis to determine the predictive ability of each indicator of abdominal obesity and also BMI as an indicator of general adiposity. The stratified ROC curve was constructed according to gender status. Then, the area under the curve and their 95% confidence interval were calculated for all anthropometric measures (WC, WHR, WHtR, CI, AVI and BMI) and the p-value less than 0.05 were considered as significant level. Additionally, we used the criterion of Youden index that minimizes the total false positive and false negative fraction or equivalently maximizes the sum of sensitivity and specificity in ROC space in order to calculate the sex-specific optimal cut off values for calculating sensitivity and specificity of 10-year risk for each obesity index. 3. Results A total of 567 participants, 267(47%) subjects were male and 300 (53%) female. The prospective mean age (SD) of men and women was 53.4 8.9 and 51 8.3 years respectively. The educational level of the majority of women at primary level or illiterate (55%) but this figure was 28% for men and only 15.4% of men and 8.9% of women’s education were at university level (P = 0.001). About 29.6% of men and 1.6% of women were either exsmoker or current smoker (P = 0.001) and the remainder was nonsmoker. Table 1 compares the mean of anthropometric indexes and cardio metabolic risk factors between sexes. The mean of conicity index, AVI and WC were not significant between men and women but women had significantly higher level of BMI and WHtR and lower of WHR (P = 0.001). In addition, women had significantly higher level of cardio metabolic risk factors such as total cholesterol, LDL and HDL (P = 0.001). No significant difference was observed on FBS, systolic blood pressure between genders. Table 2 shows that 42.5% of men and 15% of women had at least 10% risk of 10-year cardiovascular events and 21.1% of men and 3.0% of women had 20% risk and only 12.4% of men and 42.5 of women at low risk (<2.5%) and the rest was at intermediate level of risk (2.5– 9.99%) (P = 0.001). Table 3 demonstrates that among women the mean of abdominal obesity index not BMI are significantly Table 1 The mean SD of abdominal obesity measures, general adiposity, and cardio metabolic risk factors in men and women. Cardio metabolic risk factors
Men (n = 267) Mean SD
Women (n = 300) Mean SD
P-value
Conicity index Abdominal volume index Waist circumference (cm) Waist to hip ratio Waist to height ratio Body mass index (kg/m2) Cholesterol (mg/dl) Triglyceride (mg/dl) Low density lipoprotein (mg/dl) High density lipoprotein (mg/dl) Fasting blood sugar (mg/dl) Systolic blood pressure (mg/dl) Diastolic blood pressure (mmHg)
1.29 0.16 18.47 6.25 94.71 14.54 0.93 0.09 0.56 0.09 26.84 5.23 199.12 59.63 195.50 124.48 122.56 39.79 36.22 9.34 114.85 41.80 130.3 16.07 84.17 13.38
1.28 0.16 19.21 6.61 96.15 15.44 0.86 0.09 0.61 0.10 30.13 5.76 213.14 43.62 161.56 129.10 137.66 41.76 39.95 14.93 120.16 54.44 130.89 19.89 85.34 16.07
0.50 0.17 0.25 0.001 0.001 0.001 0.001 0.19 0.001 0.001 0.20 0.62 0.35
3
different between low, moderate and high CD risk but among men only WC and WHtR were significantly higher with higher level of CVD risk (P = 0.001). In Table 4, the results of ROC analysis shows that except WHR for men, all abdominal obesity measures not BMI are significant predictors for 10% of 10-year CVD risk in both sexes. The greater ability of CVD risk prediction was observed by WHtR and CI in both sexes with higher level of AUC in females compared with males. Table 4 shows that the optimal cutoff values for WHtR and CI produced a higher sensitivity of 78% and 67% respectively but in women the optimal cutoff for WC and AVI yielded a greater sensitivity. The derived cutoff values of WC and WHtR is greater in men compared with women (95.5 vs. 93.5 cm and 0.92 vs. 0.87 respectively) but the cutoff value of WHtR is slightly greater in women and for CI is rather similar between sexes. Fig. 1 (in panels of a and b) shows the ROC curves of all abdominal obesity measures and also BMI for predicting 10% CVD risk in men and women respectively. 4. Discussion The findings of this study indicate variations in distribution of CVD risk factors between men and women with higher risk in men than women at 10% risk of 10-year cardiovascular events (42.5%vs. 15% respectively), in spite of significantly higher levels of total cholesterol, LDL and HDL in women. In this study the mean values of CI, AVI, and WC did not differ between men and women but BMI and WHtR were higher and WHR was lower in women. All measures of abdominal obesity except BMI were positively associated with 10-year CVD risk in women whereas in men only WC and WHtR were a significant predictors CVD risk. Based on ROC curve analysis, all measures of abdominal obesity were significant predictors for 10% of 10-year CVD risk in both sexes, except WHR in men and BMI in both men and women. The highest AUC value was exhibited by WHtR and CI in both genders especially in women. The results of this study regarding higher ability of measures of abdominal obesity in estimating of CVD risk in women as well as WC and WHtR in men are consistent with other studies [32–41]. Superiority of the measures of central obesity in predicting CVD risk has been also observed in the study of Turkish Chronic Diseases and Risk factors Survey that WHtR yielded highest AUC value for estimating coronary heart disease [42]. Meanwhile, measures of abdominal obesity were not associated with risk of CVD in community dwelling Chinese people aged > 65 year in Shanghai [35]. In a study of Iranian people age > 40 years old without cardiovascular diseases at baseline, the ability of all measures of abdominals obesity for predicting CVD was similar over a 7.6-years follow-up period in men, whereas in females WHR and WHtR were better predictors of CVD risk [42]. In contras in a study from Taiwan, none of the obesity related measures predicted CVD risk, only WHR and WHtR in men were predictors of moderate and high 10- year risk of CVD [43] but WHtR yielded the greatest predictive ability for CVD in a population based study of men and women aged> 60 years without CVD at baseline. In this study, WHtR showed the highest ability in predicting CVD [41] whose aged> 60 years over 11-year follow-up [41]. Also in another study, amongst the all measures of obesity, the WHtR yielded the highest predictive ability for CVD [44]. Nonetheless, the superiority of each measure varies according to ethnicity, age and sex of the study population and type of study across diverse studies. In a population-based study of women with various ethnicities, measures of central obesity like WC and WHR were better predictors of CVD risk in Caucasion women, BMI for Northern European women and WC for Asian [40]. In a crosssectional study of Iranian urban population aged 15–64 years old, WHR and WHtR were the best predictors of CVD risk factors in
Please cite this article in press as: K. Hajian-Tilaki, B. Heidari, Comparison of abdominal obesity measures in predicting of 10-year cardiovascular risk in an Iranian adult population using ACC/AHA risk model: A population based cross sectional study, Diab Met Syndr: Clin Res Rev (2018), https://doi.org/10.1016/j.dsx.2018.06.012
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Table 2 The distribution of 10-year CVD risk according to gender. Gender
Men Women All
10-year CVD risk <2.5% n (%)
2.5-4.99% n (%)
5-7.49% n (%)
7.5-9.99% n (%)
10-19.99%
20%
All
33(12.4) 127(42.5) 160(28.3)
52(19.5) 82(27.4) 134(27.7)
39(14.7) 28(9.4) 67(11.9)
29(10.9) 17(5.7) 46(8.1)
57(21.4) 36(12.0) 93(16.5)
56(21.0) 9(3.0) 65(11.5)
266(100) 299(100) 565(100)
Table 3 The mean SD of abdominal obesity and general adiposity indexes according to low, moderate and high 10-year CVD risk with respect to gender status and P-value. Abdominal obesity indexes
Men Conicity index Abdominal volume index Waist circumference (cm) Waist to hip ratio Waist to height ratio Body mass index (kg/m2) Women Conicity index Abdominal volume index Waist circumference Waist to hip ratio Waist to height ratio Body mass index (kg/m2)
10-year CVD risk Low <2.5% Mean SD
Moderate 2.5-9.99% Mean SD
High 10% Mean SD
P-value
1.25 1.36 16.77 5.35 90.12 14.00 0.90 0.08 0.52 0.08 25.13 3.68
1.28 5.04 18.02 5.05 93.18 13.08 0.93 0.07 0.55 0.08 26.57 3.53
1.31 0.17 19.42 7.45 96.93 15.87 0.94 0.12 0.58 0.10 27.63 6.79
0.12 0.05 0.04 0.10 0.004 0.04
1.25 0.13 17.83 5.27 92.70 13.65 0.84 0.07 0.59 0.04 29.25 5.71
1.30 0.17 20.17 7.68 98.42 16.56 0.87 0.11 0.62 0.11 30.59 5.69
1.32 0.17 20.27 6.15 99.09 15.43 0.88 0.09 0.64 0.10 30.97 5.66
0.01 0.009 0.004 0.003 0.002 0.09
Table 4 The discriminant ability of abdominal obesity indexes and BMI for prediction of 10-year CVD risk of 10%, the optimal cutoffs for sensitivity and specificity according to gender. Abdominal obesity indexes& body mass index
AUC (95% CI)
P-value
Optimal cutoff
Sen
Sp
Men Conicity index Abdominal volume index Waist circumference (cm) Waist to hip ratio Waist to height ratio Body mass index (kg/m2) Women Conicity index Abdominal volume index Waist circumference (cm) Waist to hip ratio Waist to height ratio Body mass index (kg/m2)
0.591(0.521, 0.680) 0.586(0.516, 0.655) 0.585(0.515, 0.655) 0.566(0.495, 0.637) 0.615(0.546, 0.684) 0.550(0.479, 0.621)
0.011 0.017 0.018 0.067 0.001 0.160
1.28 18.08 95.5 0.92 0.53 25.8
0.67 0.59 0.56 0.57 0.78 0.57
0.50 0.54 0.61 0.52 0.51 0.50
0.624(0.538, 0.710) 0.588(0.497, 0.687) 0.590(0.500, 0.680) 0.606(0.517, 0.695) 0.628(0.540, 0.716) 0.568(0.475, 0.661)
0.008 0.061 0.055 0.023 0.006 0.146
1.30 18.44 93.5 0.87 0.61 26.9
0.62 0.73 0.78 0.62 0.67 0.80
0.63 0.51 0.47 0.55 0.53 0.31
AUC area under the curve, Sen sensitivity, Sp specificity.
men, whereas in women WHR and WC yielded higher predictive ability [37].In another cross-sectional study of Australians aged > 25 years, WHR was the most useful measure of obesity to identify high risk individuals for CVD [41]. In a cross-sectional study of Iranian urban population aged 15– 64 years old, the WHR and WHtR were the best predictors of CVD risk factors in men versus WHR and WC in women [34].In female participants of the Tehran Lipid and Glucose Study aged 18–74 years WC was the best screening measure of CVD risk factors as compared with BMI, WHR, and WHtR [45]. A study of Chinese people aged 20 years and more WHtR yielded the largest AUC value for all CVD risk factors such as diabetes, hypertension, dyslipidemia and the metabolic syndrome in both sexes followed by WC and BMI [47].
In the present study predictive ability of BMI was lower than measures of abdominal obesity. Similar results were observed in other studies [43,45,46]. Nevertheless, in a study of Chinese population with median age of 45 (range 18–93) years old in Hong Kong the WC at optimal cutoff value of 88 cm in men and 80 cm in women and respective BMI cutoffs of 25 and 23 kg/m2 in men and women were predictors of 15% to 30% 10- years CVD risk [47]. While, in a prospective cohort study of Australian community, WC and BMI yielded similar predictive ability for 10-year CVD risk. In this study WC values from 91 to 93 cm and 99–103 cm were equivalent for predictive ability of overweight and obesity respectively [48]. The results of a systematic review of 31 studies showed higher predictive ability of WHtR as compared with WC and BMI [38]. Additionally, meta-analysis of 15 studies showed
Please cite this article in press as: K. Hajian-Tilaki, B. Heidari, Comparison of abdominal obesity measures in predicting of 10-year cardiovascular risk in an Iranian adult population using ACC/AHA risk model: A population based cross sectional study, Diab Met Syndr: Clin Res Rev (2018), https://doi.org/10.1016/j.dsx.2018.06.012
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Fig. 1. ROC curves for abdominal obesity and obesity indexes in predicting high CVD risk for men and women (panel a for men and b for women in predicting of 10% risk).
WHR and WC as significant predictors of CVD risk, and for 1 cm increase in WC, the relative risk of CVD increased by 2% and for each 0.01 unit increase in WHR by 5% in both men and women [49]. Regarding high prevalence of cardiometabolic risk factors in particular obesity even in children and young adults [50,51] in the geographic region of the present study [7,8,12] identification of an appropriate measure of obesity with high predictive ability is of particular importance. Although, in most studies a number of conventional measures of obesity were significantly associated with CVD risk, however there are discrepancies across different study populations. This study has limitations regarding study design which is cross-sectional and the association does not indicate causality and also the baseline risk of study population might be different from non-Hispanic Caucasian the was assumed
in AHA/ACC model. In addition, the low AUC derived for some measures of obesity in particular for BMI in our ROC analysis significantly restrict the reproducibility of calculation of its cut-off point and it might be not relevant for low AUC. Nevertheless, the derived cut-off value for BMI in our results is closed to those suggested by WHO for overweight. However, the population based recruitment of study subjects with similar ethnicity and culture and standard sampling procedure and data collection overweigh the advantages. In conclusion, the results of the present study indicate that WC and WHtR are more appropriate predictive measures in cardiovascular events in men whereas all measures of abdominal obesity exhibit similar predictive ability in women. However, the future prospective study provides more information in recognizing high risk individuals.
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Ethics approval and consent to participate The study protocol had been approved by Ethical counsel of Babol university of medical sciences and all subjects had given a written consent for participation in the study. Competing interests The authors declare that there is no competing interest. Funding This study was funded by Babol University of Medical Sciences, Babol, Iran. Acknowledgements The authors acknowledge the Deputy of Research of Babol University of Medical Sciences for their supports. References [1] Eckel R.H., Krauss RM. American heart association call to action: obesity as a major risk factor for coronary heart disease. Circulation 1998;97:2099–100. [2] Sidney CS. Multiple risk factors for cardiovascular disease and diabetes mellitus. Am J Med 2007;120(3A):S3–S11. [3] James PT, Leach R, Kalamara E, Shayeghi M. The worldwide obesity epidemic. Obes Res 2001;9(Supl 4):228S–33S. [4] Wolf AM, Colditz GA. Current estimates of the economic cost of obesity in the United States. Obes Res 1998;6(2):9701–6. [5] Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the global burden of disease study 2010. Lancet 2012;380(9859):2197–223. [6] Hajian-Tilaki K, Heidari B, Hajian-Tilaki A. Solitary and combined negative influences of diabetes, obesity and hypertension on health-related quality of life of elderly individuals: a population-based cross-sectional study. Diabetes Metab Syndr: Clin Res Rev 2016;1092(Suppl):S37–42. [7] Hajian-Tilaki KO, Heidari B. Prevalence of obesity, central obesity and the associated factors in urban population aged 20-7 years, in the north of Iran: a population-based study and regression approach. Obs Rev 2007;8(1):3–10. [8] Hajian-Tilaki KO, Jalali F. Changing patterns of cardiovascular risk factors in hospitalized patients with acute myocardial infarction in Babol, Iran. Kuwait Med. J. 2007;39:243–7. [9] Ministry of Health and Medical Education of Iran, a report of survey of Iranian health, Tehran, 1997, p234. [10] Azizi F, Salehi P, Etemadi A, Zahedi-Asl S. Prevalence of metabolic syndrome in urban population: Tehran lipid and glucose study. Diabetes Res Clin Pract 2003;61:29–37. [11] Hajian-Tilaki K. Metabolic syndrome and the associated risk factors in Iranian adults: a systematic review. Caspian J Intern Med 2015;6(2):51–61. [12] Hajian-Tilaki K, Heidari B, Firozjahi A, Bagherzadeh M, Hajian-Tilaki A. Prevalence of metabolic syndrome and the associated socio-demographic characteristics and physical activity in urban population of Iranian adults: a population-based study. Diabetes Metab Syndr 2014;8(3):170–6. [13] Ford ES. The metabolic syndrome and mortality from cardiovascular disease and all causes: findings from the national health and nutrition examination survey II mortality study. Atherosclerosis 2004;173:309–14. [14] Church TS, Thompson AM, Katzmarzyk PT, et al. Metabolic syndrome and diabetes, alone and in combination as predictors of cardiovascular disease mortality among men. Diabetes Care 2009;32:1289–376. [15] Lakka HM, Laakasonen DF, Laaka TA, et al. The metabolic syndrome and total cardiovascular disease mortality in middle age men. JAMA 2002;288:2709–16. [16] JAAG Damen, HooffL Schuit E, Debray TPA, Collins G, Tzoulaki H, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ 2016;353:i2416. [17] Goff Jr. DC, Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R. 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. [18] D’Agostine RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM. General cardiovascular risk profile for use in primary care, the Framingham heart study. Circulation 2008;117:743–53. [19] Lloyd-Jones DM. Cardiovascular risk prediction, basic concept, current status and future direction. Circulation 2010;121:1768–77. [20] Berry JD, Lloyd DM, Garside DB, Greenland P. Framingham risk score and prediction of coronary heart disease death in young men. Am Heart J 2007;154 (1):80–6.
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Arq Bras Cardiol 2005;85(1):26–31. [26] Lee CM, Huxley RR, Wildman RP, et al. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol 2008;61:646–53. [27] Hajian-Tilaki K, Heidari B. Is waist circumference a better predictor of diabetes than body mass index or waist-to-height ratio in Iranian adults? Int J Prev Med 2015;6:5. [28] Alberti KG, Zimmet PZ. Definition, diagnosis and classification diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabetes Med. 1998;15:539–53. [29] Executive summary of the third report of the national cholesterol education program expert panel on detection, evaluation, and treatment of High blood cholesterol (NCEP) in adults (adult treatment panel III). JAMA 2001;285:2486– 97. [30] Grundy SM, Brewer HB, Cleeman JI, Smith SC, Lenfant C. 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A comparison between BMI and conicity index on predicting coronary heart disease. The Framingham Heart Study. Am Epidemiol 2000;10:24–31. [35] Fan H, Li X, Zheng L, Chen X, Lan Q, Wu H, et al. Abdominal obesity is strongly associated with cardiovascular disease and its risk factors in elderly and very elderly community-dwelling. Chin Sci Rep 2016;6:21521. [36] Goh LG, Dhaliwal SS, Welborn TA, Lee AH, Della PR. Anthropometric measurements of general and central obesity and the prediction of cardiovascular disease risk in women: a cross-sectional study? BMJ Open 2014;4:e004138. [37] Gharakhanlou R, Farzad B, Agha-Alinejad H, Steffen LM, Bayati M. Anthropometric measures as predictors of cardiovascular disease risk factors in the urban population of Iran. Int J Obes 2013;46:, doi:http://dx.doi. org/10.1038/ijo. [38] Ashwell M1, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev 2012;l13(3):275–86. [39] Sozmen K, Unal B, Kalaca S, Dinc G, Yard N, Buzgan T, et al. Association of anthropometric measurementmethods with cardiovascular disease risk; Findings from Turkish chronic diseases and risk factors survey 22 November. 7th European Public Health Conference 2014;13:10–4 10. [40] Goh LG, Dhaliwal SS, Welborn TA, Lee AH, Della PR. Ethnicity and the association between anthropometric indices of obesity and cardiovascular risk in women: a cross-sectional study. BMJ Open 2014;4(5):e004702. [41] Goh LG, Dhaliwal SS, Welborn TA, Lee AH, Della PR. Anthropometric measurements of general and central obesity and the prediction of cardiovascular disease risk in women: a cross-sectional study. BMJ Open 2014;4(2):e004138. [42] Hadaegh F, Zabetian A, Sarbakhsh P, Khalili D, James WP, Azizi F. 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Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2018) xxx–xxx
Contents lists available at ScienceDirect
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Comparison of abdominal obesity measures in predicting of 10-year cardiovascular risk in an Iranian adult population using ACC/AHA risk model: A population based cross sectional study K. Hajian-Tilakia,* , B. Heidarib a b
Dept of Biostatistics and Epidemiology, Babol University of Medical Sciences, Babol, Iran Dept of Internal Medicine, Ayatollah Rohani hospital, Babol University of Medical Sciences, Babol, Iran
A R T I C L E I N F O
A B S T R A C T
Article history: Available online xxx
Background: Several abdominal obesity measures have been used for prediction of 10-year cardiovascular disease (CVD) risk but the superiority of these measures remains controversial. The objective of this study was to assess the predictive ability of abdominal obesity measures for risk of CVD events in an Iranian adult population. Methods: We analyzed the data of population based cross-section study of 567 representative samples of adult population aged 40–70 years in Babol, the north of Iran. The demographic data, the anthropometric measures, lipid profile and cardiometabolic risk factors were measured with standard methods. Waist to hip ratio (WHR), waist to height ratio (WHtR), conicity index(CI), abdominal volume index (AVI) and body mass index(BMI)were calculated. The individual 10-year CVD risk was estimated based on ACC/AHA model. ROC analysis was performed to assess the diagnostic ability of different abdominal obesity measures and body mass index (BMI) in predicting of high risk of CVD events. Results: About 42.5% of men and 15% of women had at least 10% risk of 10-year cardiovascular events and 21.1% of men and 3.0% of women had 20% risk. Except WHR for men, all abdominal obesity measures significant predictors for 10% risk CVD risk in both sexes but not BMI. The greater ability of CVD risk prediction was observed by WHtR and CI in both sexes with higher AUC in females compared with men for 10% risk. Conclusion: WHtR and CI are superior indexes in predicting of high risk of CVD events in both sexes. © 2018 Diabetes India. Published by Elsevier Ltd. All rights reserved.
Keywords: Cardiovascular risk Waist circumference Waist to hip ratio Waist to height ratio Conicity index Abdominal volume index
1. Background Obesity in particular abdominal obesity measures is the central component of metabolic syndrome [1] that has a major contribution in insulin resistance, type 2 diabetes, cardio vascular disease, stroke and cancer [2,3]. The worldwide epidemic of obesity has been progressively extending to both developed and developing countries(3)with high economic cost of the associated morbidities and induced disabilities [4,5] and it has a negative influence on health related quality of life [6]. Iranian population has been experience of epidemiologic transition state where the high prevalence of obesity and abdominal obesity has been reported in adult population in the recent decades [7] and simultaneously, the incidence of cardiovascular disease and its mortality has been increased dramatically [8,9]. An emerging high prevalence of
* Corresponding author. E-mail address: [email protected] (K. Hajian-Tilaki).
obesity, abdominal obesity and metabolic syndrome has been demonstrated in Iranian adult population [7,10–12]. Cardiovascular diseases (CVD) remain the main cause of death in worldwide [13–15].Due to epidemiologic disease transition model in developing countries, the incidence and the death rate due to CVD has been increased significantly during two recent decades in developing counties [8,9,13]. In Islamic Republic of Iran, roughly 20% of total death was attributed to CVD death in decades of 1970’s; it was significantly elevated to 40% in recent decade [9]. Additionally, its incidence was toward increasing in younger adults under 50 years and the pattern of mean age distribution of its incidence became downward [8]. Thus, the knowledge of associated risk factors in screening for high risk individuals provides a basis for population mass or high risk strategy of interventional program. Population living in urban area in the north of Iran, located in the south of Caspian sea, has a rapid experience of life style transition in two recent decades and thus a high prevalence of obesity and abdominal obesity and metabolic syndrome have been reported in this population [7,12].
http://dx.doi.org/10.1016/j.dsx.2018.06.012 1871-4021/© 2018 Diabetes India. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: K. Hajian-Tilaki, B. Heidari, Comparison of abdominal obesity measures in predicting of 10-year cardiovascular risk in an Iranian adult population using ACC/AHA risk model: A population based cross sectional study, Diab Met Syndr: Clin Res Rev (2018), https://doi.org/10.1016/j.dsx.2018.06.012
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Several CVD risk assessment tools have been developed that the 10-years CVD individual risk can be calculated with a given risk factor profiles [16–22]. Among various risk assessment models, American College of Cardiology/American Heart Association (ACC/AHA) is much more popular model that is based on Cox proportional hazard model [17]. The regression coefficients of risk factors and baseline risk were estimated according to sex specific and ethnicity. However, the components of obesity and abdominal obesity indexes were not included directly in this model but cardio metabolic risk factors such as total cholesterol, HDL, systolic blood pressure, treating or not blood pressure, being diabetes and current smoker and some interaction terms were built in the ACC/AHA risk model. A clear association has been established between obesity and abdominal obesity with cardio metabolic risk factors such as lipid profile and FBS and hypertension and thus metabolic syndrome [23]. The body mass index (BMI) as a measure of general adiposity and waist circumference (WC) as a simple measure of abdominal obesity are linked with CVD risk (24–27)whereas no single measure of abdominal obesity was defined but BMI does not account the visceral fat distribution. Although in definition of metabolic syndrome, BMI was used by World Health Organization (WHO) [28], WC by International Diabetes Federation (IDF), National Cholesterol Education Program(NCEP) Adult Treatment Panel III (ATP III) (29) and American Heart Association (AHA)/National Heart, Lung and Blood Institute (30) and Iranian National committee of obesity (INCO) as well but with different cutoff values [31].Several measures of abdominal obesity such as waist to hip ratio(WHR), waist to height ratio (WHtR) and two recent developed measures, conicity index (CI) and abdominal volume index (AVI) have been suggested [1–34].Among these indexes, WC as a simple measure is a greater interesting for assessment of MetS. The choice of abdominal obesity measures, particularly their optimal cut off values depends on sex, age and ethnicity. Therefore, the superiority of these measures and their optimal cutoff value in predicting cardiovascular risk remains controversial. Despite the high prevalence of general adiposity and abdominal obesity in urban population in the north of Iran among aged 40–70 years, the data of 10-year CVD risk and the superiority of abdominal obesity indexes are sparse. Thus, the objective of this study was to compares the predictive ability of different abdominal obesity measures, BMI and to determine their optimal cutoff values in predicting 10-year cardiovascular risk in Iranian adult population. 2. Methods 2.1. Study design and subjects We reanalyzed the data of population based cross sectional study of Babol Lipid and Glucose study that primary focused on cardio metabolic risk factors. This study recruited a representative sample of 1000 subjects aged 20–70 years in Babol urban community, in the north of Iran in 2012. The individual samples were dawn randomly using two stage cluster sampling techniques in a family health survey. The description of sampling procedure and recruitment criteria were explained in details elsewhere [12]. In brief, the population under coverage of urban health centers in Babol composed of several urban parochial that we called as clusters. Twenty five clusters were taken randomly using cumulative population size under coverage of different health centers with a systematic sampling method. Then, the center of each cluster was determined and within each cluster 40 subjects (men and women) age 20–70 years were selected in the study. Since ACC/AHA risk model was established for subjects with aged 40 years or older without history of CVA events, thus in present study, a subset of data of 565 subjects with aged 40–70 years in both genders who had not previous history heart attack,
myocardial infarction, stroke and any CVA events were extracted for analysis and the pregnant women were excluded. All participants had given a written consent for participation into the study. The study protocol had been approved by Ethical counsel of Babol university of medical sciences. 2.2. Measurements and data collection The demographic and life style data, history of treating hypertension and diabetes was collected using a design questionnaire by interview. The weight and height were measured to nearest 0.1 kg and 0.1 cm using a digital portable scale and a portable stadiometer respectively by trained staff with standard method, light clothes and without shoes. The body mass index (BMI) was calculated as weight in kg divided by square of height in m2. The WC was determined at level of midpoint between the lowest coastal ridge and the upper border of the iliac crest while participants keep their breathing and hip circumference (HC) was measured at the largest circumference between waist and knee. Both WC and HC were measured using a non-stretchable type meter with precision of 0.1 cm. The WHtR was calculated as the ratio of WC (cm) to height (cm) and WHR was determined as the ratio of waist (cm) to hip circumference (cm). Also, the two other more recent indexes, conicity index (CI) and abdominal volume index (AVI) was calculated based on weight, height, waist and hip as follows CI ¼
waist ðmÞ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi;
0:109
weight ðkgÞ height ðmÞ
h i AVI ¼ f2 wiast2 ðcmÞ2 þ 0:7 waistðcmÞ hipðcmÞ2 g=1000 Systolic and diastolic blood pressures (SBP, DBP) were also measured by trained staff two times consecutively in an interval of 10 min rest where the subjects were in sitting position using a digital psygmonameter (Model BP101 A named Dr. Axon, made in China). The cuff was placed on over meddle of right arm at head level. The average of two measures of blood pressure was taken in analysis. The reliability of anthropometric and blood pressures measures were assessed in a pilot study with repeated measures on the same subjects using a statistic of coefficient of variation the almost produced less than 1% and 3% for anthropometric and blood pressure measures respectively. All participants were invited to go overnight fasting to 10–12 h they were referred to central lab of Ayatollah Rohani hospital, affiliated to Babol university of medical sciences. A venous blood sample was taken from all participants at sitting position in the morning according to standard protocol for measuring fasting blood sugar (FBS) and lipid profiles. It was immediately centrifuged and transferred under cold chain conditions. Total cholesterol, high density lipoprotein (HDL) cholesterol, low density lipoprotein (LDL) cholesterol and triglyceride and fasting blood sugar (FBS) were measured by auto analyzer using enzymatic method. Subjects with FBS > 126 and/or treating for lower blood sugar were classified as diabetes. 2.3. Statistical analysis We used SPSS software for data analysis using receiver operator characteristic (ROC) curve analysis. First, the 10-years CVD risk (first severe atherosclerotic cardiovascular disease (ASCVD) event, including coronary heart diseases (CHD), death, fatal myocardial infarction and nonfatal or nonfatal stroke) was calculated at individual level for each participant using Cox proportional hazard model based on the coefficients of ACC/ AHA models and its baseline risk [17]. Since the original ethnicity of our population is
Please cite this article in press as: K. Hajian-Tilaki, B. Heidari, Comparison of abdominal obesity measures in predicting of 10-year cardiovascular risk in an Iranian adult population using ACC/AHA risk model: A population based cross sectional study, Diab Met Syndr: Clin Res Rev (2018), https://doi.org/10.1016/j.dsx.2018.06.012
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near to Caucasian ethnicity, we used coefficients of risk factors and baseline hazard from sex specific, white non-Hispanic ethnic group. The predictive risk factors in profile of study subjects were age (year), treated/untreated systolic (mm/Hg), total cholesterol (mg/dl), HDL (mg/dl), diabetic (diabetic vs. non-diabetic) and current smoker (current smoker vs. not current smoker) and their interaction terms that their coefficients was determined in the ACC/AHA model. After calculating the individual 10-year risk, the participants were classified based on cutoff values of 10% as high risk group versus low risk. We used ROC analysis to determine the predictive ability of each indicator of abdominal obesity and also BMI as an indicator of general adiposity. The stratified ROC curve was constructed according to gender status. Then, the area under the curve and their 95% confidence interval were calculated for all anthropometric measures (WC, WHR, WHtR, CI, AVI and BMI) and the p-value less than 0.05 were considered as significant level. Additionally, we used the criterion of Youden index that minimizes the total false positive and false negative fraction or equivalently maximizes the sum of sensitivity and specificity in ROC space in order to calculate the sex-specific optimal cut off values for calculating sensitivity and specificity of 10-year risk for each obesity index. 3. Results A total of 567 participants, 267(47%) subjects were male and 300 (53%) female. The prospective mean age (SD) of men and women was 53.4 8.9 and 51 8.3 years respectively. The educational level of the majority of women at primary level or illiterate (55%) but this figure was 28% for men and only 15.4% of men and 8.9% of women’s education were at university level (P = 0.001). About 29.6% of men and 1.6% of women were either exsmoker or current smoker (P = 0.001) and the remainder was nonsmoker. Table 1 compares the mean of anthropometric indexes and cardio metabolic risk factors between sexes. The mean of conicity index, AVI and WC were not significant between men and women but women had significantly higher level of BMI and WHtR and lower of WHR (P = 0.001). In addition, women had significantly higher level of cardio metabolic risk factors such as total cholesterol, LDL and HDL (P = 0.001). No significant difference was observed on FBS, systolic blood pressure between genders. Table 2 shows that 42.5% of men and 15% of women had at least 10% risk of 10-year cardiovascular events and 21.1% of men and 3.0% of women had 20% risk and only 12.4% of men and 42.5 of women at low risk (<2.5%) and the rest was at intermediate level of risk (2.5– 9.99%) (P = 0.001). Table 3 demonstrates that among women the mean of abdominal obesity index not BMI are significantly Table 1 The mean SD of abdominal obesity measures, general adiposity, and cardio metabolic risk factors in men and women. Cardio metabolic risk factors
Men (n = 267) Mean SD
Women (n = 300) Mean SD
P-value
Conicity index Abdominal volume index Waist circumference (cm) Waist to hip ratio Waist to height ratio Body mass index (kg/m2) Cholesterol (mg/dl) Triglyceride (mg/dl) Low density lipoprotein (mg/dl) High density lipoprotein (mg/dl) Fasting blood sugar (mg/dl) Systolic blood pressure (mg/dl) Diastolic blood pressure (mmHg)
1.29 0.16 18.47 6.25 94.71 14.54 0.93 0.09 0.56 0.09 26.84 5.23 199.12 59.63 195.50 124.48 122.56 39.79 36.22 9.34 114.85 41.80 130.3 16.07 84.17 13.38
1.28 0.16 19.21 6.61 96.15 15.44 0.86 0.09 0.61 0.10 30.13 5.76 213.14 43.62 161.56 129.10 137.66 41.76 39.95 14.93 120.16 54.44 130.89 19.89 85.34 16.07
0.50 0.17 0.25 0.001 0.001 0.001 0.001 0.19 0.001 0.001 0.20 0.62 0.35
3
different between low, moderate and high CD risk but among men only WC and WHtR were significantly higher with higher level of CVD risk (P = 0.001). In Table 4, the results of ROC analysis shows that except WHR for men, all abdominal obesity measures not BMI are significant predictors for 10% of 10-year CVD risk in both sexes. The greater ability of CVD risk prediction was observed by WHtR and CI in both sexes with higher level of AUC in females compared with males. Table 4 shows that the optimal cutoff values for WHtR and CI produced a higher sensitivity of 78% and 67% respectively but in women the optimal cutoff for WC and AVI yielded a greater sensitivity. The derived cutoff values of WC and WHtR is greater in men compared with women (95.5 vs. 93.5 cm and 0.92 vs. 0.87 respectively) but the cutoff value of WHtR is slightly greater in women and for CI is rather similar between sexes. Fig. 1 (in panels of a and b) shows the ROC curves of all abdominal obesity measures and also BMI for predicting 10% CVD risk in men and women respectively. 4. Discussion The findings of this study indicate variations in distribution of CVD risk factors between men and women with higher risk in men than women at 10% risk of 10-year cardiovascular events (42.5%vs. 15% respectively), in spite of significantly higher levels of total cholesterol, LDL and HDL in women. In this study the mean values of CI, AVI, and WC did not differ between men and women but BMI and WHtR were higher and WHR was lower in women. All measures of abdominal obesity except BMI were positively associated with 10-year CVD risk in women whereas in men only WC and WHtR were a significant predictors CVD risk. Based on ROC curve analysis, all measures of abdominal obesity were significant predictors for 10% of 10-year CVD risk in both sexes, except WHR in men and BMI in both men and women. The highest AUC value was exhibited by WHtR and CI in both genders especially in women. The results of this study regarding higher ability of measures of abdominal obesity in estimating of CVD risk in women as well as WC and WHtR in men are consistent with other studies [32–41]. Superiority of the measures of central obesity in predicting CVD risk has been also observed in the study of Turkish Chronic Diseases and Risk factors Survey that WHtR yielded highest AUC value for estimating coronary heart disease [42]. Meanwhile, measures of abdominal obesity were not associated with risk of CVD in community dwelling Chinese people aged > 65 year in Shanghai [35]. In a study of Iranian people age > 40 years old without cardiovascular diseases at baseline, the ability of all measures of abdominals obesity for predicting CVD was similar over a 7.6-years follow-up period in men, whereas in females WHR and WHtR were better predictors of CVD risk [42]. In contras in a study from Taiwan, none of the obesity related measures predicted CVD risk, only WHR and WHtR in men were predictors of moderate and high 10- year risk of CVD [43] but WHtR yielded the greatest predictive ability for CVD in a population based study of men and women aged> 60 years without CVD at baseline. In this study, WHtR showed the highest ability in predicting CVD [41] whose aged> 60 years over 11-year follow-up [41]. Also in another study, amongst the all measures of obesity, the WHtR yielded the highest predictive ability for CVD [44]. Nonetheless, the superiority of each measure varies according to ethnicity, age and sex of the study population and type of study across diverse studies. In a population-based study of women with various ethnicities, measures of central obesity like WC and WHR were better predictors of CVD risk in Caucasion women, BMI for Northern European women and WC for Asian [40]. In a crosssectional study of Iranian urban population aged 15–64 years old, WHR and WHtR were the best predictors of CVD risk factors in
Please cite this article in press as: K. Hajian-Tilaki, B. Heidari, Comparison of abdominal obesity measures in predicting of 10-year cardiovascular risk in an Iranian adult population using ACC/AHA risk model: A population based cross sectional study, Diab Met Syndr: Clin Res Rev (2018), https://doi.org/10.1016/j.dsx.2018.06.012
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Table 2 The distribution of 10-year CVD risk according to gender. Gender
Men Women All
10-year CVD risk <2.5% n (%)
2.5-4.99% n (%)
5-7.49% n (%)
7.5-9.99% n (%)
10-19.99%
20%
All
33(12.4) 127(42.5) 160(28.3)
52(19.5) 82(27.4) 134(27.7)
39(14.7) 28(9.4) 67(11.9)
29(10.9) 17(5.7) 46(8.1)
57(21.4) 36(12.0) 93(16.5)
56(21.0) 9(3.0) 65(11.5)
266(100) 299(100) 565(100)
Table 3 The mean SD of abdominal obesity and general adiposity indexes according to low, moderate and high 10-year CVD risk with respect to gender status and P-value. Abdominal obesity indexes
Men Conicity index Abdominal volume index Waist circumference (cm) Waist to hip ratio Waist to height ratio Body mass index (kg/m2) Women Conicity index Abdominal volume index Waist circumference Waist to hip ratio Waist to height ratio Body mass index (kg/m2)
10-year CVD risk Low <2.5% Mean SD
Moderate 2.5-9.99% Mean SD
High 10% Mean SD
P-value
1.25 1.36 16.77 5.35 90.12 14.00 0.90 0.08 0.52 0.08 25.13 3.68
1.28 5.04 18.02 5.05 93.18 13.08 0.93 0.07 0.55 0.08 26.57 3.53
1.31 0.17 19.42 7.45 96.93 15.87 0.94 0.12 0.58 0.10 27.63 6.79
0.12 0.05 0.04 0.10 0.004 0.04
1.25 0.13 17.83 5.27 92.70 13.65 0.84 0.07 0.59 0.04 29.25 5.71
1.30 0.17 20.17 7.68 98.42 16.56 0.87 0.11 0.62 0.11 30.59 5.69
1.32 0.17 20.27 6.15 99.09 15.43 0.88 0.09 0.64 0.10 30.97 5.66
0.01 0.009 0.004 0.003 0.002 0.09
Table 4 The discriminant ability of abdominal obesity indexes and BMI for prediction of 10-year CVD risk of 10%, the optimal cutoffs for sensitivity and specificity according to gender. Abdominal obesity indexes& body mass index
AUC (95% CI)
P-value
Optimal cutoff
Sen
Sp
Men Conicity index Abdominal volume index Waist circumference (cm) Waist to hip ratio Waist to height ratio Body mass index (kg/m2) Women Conicity index Abdominal volume index Waist circumference (cm) Waist to hip ratio Waist to height ratio Body mass index (kg/m2)
0.591(0.521, 0.680) 0.586(0.516, 0.655) 0.585(0.515, 0.655) 0.566(0.495, 0.637) 0.615(0.546, 0.684) 0.550(0.479, 0.621)
0.011 0.017 0.018 0.067 0.001 0.160
1.28 18.08 95.5 0.92 0.53 25.8
0.67 0.59 0.56 0.57 0.78 0.57
0.50 0.54 0.61 0.52 0.51 0.50
0.624(0.538, 0.710) 0.588(0.497, 0.687) 0.590(0.500, 0.680) 0.606(0.517, 0.695) 0.628(0.540, 0.716) 0.568(0.475, 0.661)
0.008 0.061 0.055 0.023 0.006 0.146
1.30 18.44 93.5 0.87 0.61 26.9
0.62 0.73 0.78 0.62 0.67 0.80
0.63 0.51 0.47 0.55 0.53 0.31
AUC area under the curve, Sen sensitivity, Sp specificity.
men, whereas in women WHR and WC yielded higher predictive ability [37].In another cross-sectional study of Australians aged > 25 years, WHR was the most useful measure of obesity to identify high risk individuals for CVD [41]. In a cross-sectional study of Iranian urban population aged 15– 64 years old, the WHR and WHtR were the best predictors of CVD risk factors in men versus WHR and WC in women [34].In female participants of the Tehran Lipid and Glucose Study aged 18–74 years WC was the best screening measure of CVD risk factors as compared with BMI, WHR, and WHtR [45]. A study of Chinese people aged 20 years and more WHtR yielded the largest AUC value for all CVD risk factors such as diabetes, hypertension, dyslipidemia and the metabolic syndrome in both sexes followed by WC and BMI [47].
In the present study predictive ability of BMI was lower than measures of abdominal obesity. Similar results were observed in other studies [43,45,46]. Nevertheless, in a study of Chinese population with median age of 45 (range 18–93) years old in Hong Kong the WC at optimal cutoff value of 88 cm in men and 80 cm in women and respective BMI cutoffs of 25 and 23 kg/m2 in men and women were predictors of 15% to 30% 10- years CVD risk [47]. While, in a prospective cohort study of Australian community, WC and BMI yielded similar predictive ability for 10-year CVD risk. In this study WC values from 91 to 93 cm and 99–103 cm were equivalent for predictive ability of overweight and obesity respectively [48]. The results of a systematic review of 31 studies showed higher predictive ability of WHtR as compared with WC and BMI [38]. Additionally, meta-analysis of 15 studies showed
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Fig. 1. ROC curves for abdominal obesity and obesity indexes in predicting high CVD risk for men and women (panel a for men and b for women in predicting of 10% risk).
WHR and WC as significant predictors of CVD risk, and for 1 cm increase in WC, the relative risk of CVD increased by 2% and for each 0.01 unit increase in WHR by 5% in both men and women [49]. Regarding high prevalence of cardiometabolic risk factors in particular obesity even in children and young adults [50,51] in the geographic region of the present study [7,8,12] identification of an appropriate measure of obesity with high predictive ability is of particular importance. Although, in most studies a number of conventional measures of obesity were significantly associated with CVD risk, however there are discrepancies across different study populations. This study has limitations regarding study design which is cross-sectional and the association does not indicate causality and also the baseline risk of study population might be different from non-Hispanic Caucasian the was assumed
in AHA/ACC model. In addition, the low AUC derived for some measures of obesity in particular for BMI in our ROC analysis significantly restrict the reproducibility of calculation of its cut-off point and it might be not relevant for low AUC. Nevertheless, the derived cut-off value for BMI in our results is closed to those suggested by WHO for overweight. However, the population based recruitment of study subjects with similar ethnicity and culture and standard sampling procedure and data collection overweigh the advantages. In conclusion, the results of the present study indicate that WC and WHtR are more appropriate predictive measures in cardiovascular events in men whereas all measures of abdominal obesity exhibit similar predictive ability in women. However, the future prospective study provides more information in recognizing high risk individuals.
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