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. 2017 Feb 21;7:43046. doi: 10.1038/srep43046

Waist-to-height ratio is an effective indicator for comprehensive cardiovascular health

Shiwei Shen 1,*, Yun Lu 2,*, Huajin Qi 3,*, Feng Li 2,*, Zhenhai Shen 3,a, Liuxin Wu 4,b, Chengjian Yang 1, Ling Wang 2, Kedong Shui 3, Weifeng Yao 1, Dongchang Qiang 4, Jingting Yun 3, Lin Zhou 3
PMCID: PMC5318865  PMID: 28220844

Abstract

The aim of this study was to determine the associations between cardiovascular health and the waist circumference (WC) and waist-to-height ratio (WHtR). A cross-sectional study was performed recruiting 26701 middle-aged Chinese men. Of the seven ideal cardiovascular health metrics, body mass index (BMI), total cholesterol (TC), blood pressure (BP), and fasting blood glucose (FBG) were found to increase with an elevation of the mean WC and WHtR. The mean WC and WHtR were significantly lower in the subjects with intermediate or ideal cardiovascular health than those with poor or intermediate health. After adjustment for age, the mean WC and WHtR decreased by 1.486 cm and 0.009 per 1-point increase in the cardiovascular health score, and 2.242 cm and 0.013 per 1-point increase in the number of ideal cardiovascular health metrics, respectively. The cardiovascular health score was negatively correlated with the WC (r = −0.387) and WHtR (r = −0.400), while the number of ideal cardiovascular health metrics was negatively associated with the WC (r = −0.384) and WHtR (r = −0.395). The cardiovascular health is correlated negatively with the WC and WHtR, and a stronger correlation existed between the cardiovascular health and WHtR than WC.


Cardiovascular disease has become a global public health concern1. The 2013 Report on Cardiovascular Diseases in China estimates that approximately 290 million people have cardiovascular diseases in China2, and obesity has become a major risk factor leading to the increase in the prevalence of cardiovascular diseases3. Notably, abdominal obesity, which is caused by the accumulation of visceral fat, has been identified as an independent risk factor for obesity-related diseases and death4. The waist circumference (WC) and waist-to-height ratio (WHtR) are not only effective indicators of abdominal obesity, but also more effective parameters predicting risk factors for cardiovascular diseases5,6. Ideal cardiovascular health, which was proposed by the American Heart Association (AHA) in 2010, has been shown to be protective against cardiovascular and cerebrovascular diseases7,8,9,10,11,12. In the current study, we determined the associations between cardiovascular health and the WC and WHtR among middle-aged men in southeastern China to provide evidence for the development of preventive and control strategies for cardiovascular diseases.

Results

Baseline cardiovascular health metrics

A total of 26701 subjects were enrolled in this study, and the subjects at 40–49, 50–59, and 60–64 years of age consisted of 45.4%, 41.2%, and 13.4% of the total study subjects, respectively. The percentages of the seven ideal health metrics were as follows: total cholesterol (TC), 69.0%; fasting blood glucose (FBG), 67.4%; body mass index (BMI), 50.6%; physical activity (PA), 45.9%; smoking status, 40.5%; blood pressure (BP), 22.8%; and salt intake, 15.7%. Of the seven cardiovascular health metrics, BMI, TC, BP, and FBG were shown to increase with elevation of the mean WC and WHtR (all P values < 0.05) (Table 1).

Table 1. Ideal cardiovascular health metrics and WC, WhtR.

Metrics   n % WC WHtR
Mean S.D. Mean S.D.
Ageabcdef 40–49 12124 45.4 86.75 8.197 0.5034 0.04737
  50–59 11008 41.2 87.59 8.250 0.5123 0.04756
  60–64 3569 13.4 87.26 8.496 0.5148 0.04985
Smoking Statusabcdef Ideal 10801 40.5 86.97 8.020 0.5078 0.04645
  Intermediate 829 3.1 85.51 8.399 0.4985 0.04799
  Poor 15071 56.4 87.39 8.422 0.5098 0.04906
Body Mass Indexabcdef Ideal 13519 50.6 82.12 6.472 0.4786 0.03697
  Intermediate 12038 45.1 91.45 5.902 0.5341 0.03356
  Poor 1144 4.3 101.62 5.599 0.5941 0.03236
Physical Activitybcdef Ideal 12268 45.9 87.12 8.066 0.5086 0.04684
  Intermediate 11914 44.6 86.98 8.366 0.5070 0.04859
  Poor 2519 9.4 88.27 8.689 0.5161 0.05031
Salt Intakeabcdef Ideal 4201 15.7 87.93 7.555 0.5135 0.04366
  Intermediate 17660 66.1 86.23 8.364 0.5030 0.04850
  Poor 4840 18.1 89.90 7.824 0.5248 0.04569
Total Cholesterolacdf Ideal 18425 69.0 86.64 8.306 0.5053 0.04825
  Intermediate 6856 25.7 88.31 8.026 0.5157 0.04650
  Poor 1420 5.3 88.36 8.260 0.5166 0.04776
Blood Pressureabcdef Ideal 6082 22.8 83.61 7.969 0.4871 0.04629
  Intermediate 11883 44.5 87.17 7.893 0.5082 0.04583
  Poor 8736 32.7 89.63 8.069 0.5241 0.04626
Fasting Blood Glucoseabcdef Ideal 17990 67.4 86.30 8.188 0.5032 0.04748
  Intermediate 5108 19.1 88.30 7.873 0.5158 0.04546
  Poor 3603 13.5 89.85 8.445 0.5255 0.04905

Note: a indicate that the difference of WC between Ideal and Intermediate (40–49 y and 50–59 y) is statistically significant; b, the difference of WC between Intermediate and Poor (50–59 y and 60–64 y) is statistically significant; c, the difference of WC between Ideal and Poor (40–49 y and 50–59 y) is statistically significant; d, the difference of WhtR between Ideal and Intermediate (40–49 y and 50–59 y) is statistically significant; e, the difference of WhtR between Intermediate and Poor (50–59 y and 60–64 y) is statistically significant; f, the difference of WhtR between Ideal and Poor (40–49 y and 50–59 y) is statistically significant.

Number of cardiovascular health metrics and the WC and WHtR

There were only 132 subjects (0.5%) with seven ideal health metrics, 595 subjects (2.2%) with 0 ideal health metrics, and 7383 (27.7%), 6126 (22.9%), and 5702 (21.4%) subjects with 3, 4, and 2 ideal health metrics, respectively. The WC and WHtR were shown to have a clear-cut decreasing trend with the increase in the number of ideal cardiovascular health metrics (Table 2).

Table 2. The number of ideal cardiovascular health metrics and WC, WHtR.

The number of ideal metrics n % WC WHtR
Mean S.D. Mean S.D.
0 595 2.2 94.04 6.978 0.5499 0.04031
1 2621 9.8 92.13 7.388 0.5377 0.04232
2 5702 21.4 90.08 7.602 0.5262 0.04419
3 7383 27.7 87.26 7.970 0.5092 0.04618
4 6126 22.9 84.56 7.859 0.4932 0.04536
5 3168 11.9 83.09 7.238 0.4841 0.04153
6 974 3.6 82.16 6.622 0.4784 0.03827
7 132 0.5 81.56 5.780 0.4773 0.03463

Ideal cardiovascular health score and the WC and WHtR

The ideal cardiovascular health score predominantly ranged between 7 and 11, and there were 3432 (12.9%), 4470 (16.7%), 4847 (18.2%), 4422 (16.6%), and 2924 (11.0%) subjects with ideal cardiovascular health scores of 7, 8, 9, 10 and 11, respectively. Overall, the WC and WHtR had a remarkable decreasing trend with the increase in ideal cardiovascular health score (Table 3).

Table 3. The ideal cardiovascular health score and WC, WHtR.

Score n % WC WHtR
Mean S.D. Mean S.D.
0 2 0.0 100.00 5.657 0.5837 0.01496
1 6 0.0 101.67 6.683 0.5830 0.03687
2 62 0.2 99.95 6.624 0.5857 0.04448
3 174 0.7 96.53 7.139 0.5666 0.04008
4 554 2.1 94.81 7.842 0.5545 0.04456
5 1142 4.3 93.15 7.686 0.5443 0.04433
6 2073 7.8 91.18 7.678 0.5336 0.04396
7 3432 12.9 90.00 7.710 0.5255 0.04428
8 4470 16.7 88.05 7.690 0.5135 0.04447
9 4847 18.2 86.33 7.743 0.5037 0.04475
10 4422 16.6 84.79 7.613 0.4944 0.04445
11 2924 11.0 84.05 7.736 0.4897 0.04426
12 1788 6.7 82.92 6.913 0.4837 0.03980
13 673 2.5 81.67 6.883 0.4747 0.03930
14 132 0.5 81.56 5.780 0.4773 0.03463

Cardiovascular health status and the WC and WHtR

There were 798 (3.0%), 15964 (59.8%), and 9939 (37.2%) subjects with inadequate, average, and optimum cardiovascular health, respectively. The WC and WHtR were significantly lower in the subjects with average cardiovascular health than subjects with inadequate cardiovascular health, while a lower WC and WHtR were found in the subjects with optimum cardiovascular health relative to subjects with average cardiovascular health (Table 4).

Table 4. Cardiovascular health status and the WC and WHtR.

Cardiovascular health status n % WC WHtR
Mean S.D. Mean S.D.
Inadequate 798 3.0 95.65 7.735 0.5599 0.04443
Average 15964 59.8 88.72 7.988 0.5179 0.04621
Optimum 9939 37.2 83.98 7.521 0.4895 0.04351

Association of cardiovascular health with WC and WHtR

Correlation analyses showed that cardiovascular health score was negatively correlated with the WC (r = −0.387) and WHtR (r = −0.400), and the number of ideal cardiovascular health metrics was negatively associated with the WC (r = −0.384) and WHtR (r = −0.395), while cardiovascular health was also negatively correlated with the WC (r = −0.319) and WHtR (r = −0.330). Stronger associations between the cardiovascular health score, number of ideal cardiovascular health metrics, and cardiovascular health were detected with the WHtR than the WC (Table 5). 10 as the cut-off point of cardiovascular health score, i.e. cardiovascular health score greater than or equal to 10 was defined as ideal cardiovascular health and cardiovascular health score less than 10 was defined as non-ideal cardiovascular health. The result of ROC analysis showed that the area under the curve (AUC) of WC was 0.678 and AUC of WHtR was 0.684.

Table 5. Correlation coefficient between cardiovascular health and WC, WHtR.

  The cardiovascular health score The number of ideal cardiovascular health metrics Cardiovascular health status
WC −0.387* −0.384** −0.319**
WHtR −0.400* −0.395** −0.330**

*Pearson correlation coefficient; **Spearman correlation coefficient.

Discussion

Since ideal cardiovascular health was first proposed and defined by the AHA in 2010, the prevalence of ideal cardiovascular health has been reported worldwide; however, the cardiovascular health metrics and scores vary as a function of country, race, region, economy, and lifestyle8,9,13,14,15,16.

In the current study, we found that 132 of 26701 middle-aged Chinese men (0.5%) exhibited ideal levels of all seven cardiovascular health metrics, and 595 subjects (2.2%) had 0 ideal health metrics. The results of this study validate a low prevalence of ideal cardiovascular health in Chinese adults. The TC (69.0%) and FBG (67.4%) had the highest proportion of ideal levels, while salt intake (15.7%) and BP (22.8%) showed the lowest percentage of ideal levels, which was similar to the previous studies reporting a daily salt intake of >12 g per person in most areas of China17,18. High-salt diet is considered one of the major risk factors for developing hypertension in China, therefore BP control and salt intake reduction are one of the top priorities for the prevention and control of cardiovascular diseases.

Our findings showed that among the seven cardiovascular health metrics, BMI, TC, BP, and FBG correlated positively with WC and WHtR (all P values < 0.05). In addition, the WC and WHtR had a remarkable decreasing trend with an increase in the number of ideal cardiovascular health metrics (both P values < 0.05), and the WC and WHtR were significantly lower in the subjects with intermediate or ideal cardiovascular health than subjects with poor or intermediate health (both P values < 0.05), demonstrating close associations between ideal cardiovascular health, number of ideal cardiovascular health metrics, and cardiovascular health score with the WC and WHtR.

In the current study, both the WC and WHtR exhibited a remarkable decreasing trend with the increase in ideal cardiovascular health score. After adjustment for age, a 1-point increase in the cardiovascular health score was associated with a 1.486 cm reduction in the mean WC and a 0.009 reduction in the mean WHtR, and a 1-point increase in the number of ideal cardiovascular health metrics was associated with a 2.242 cm reduction in the mean WC and a 0.013 reduction in the mean WHtR. Ambar Kulshreshtha, et al., found that individuals with intermediate or ideal cardiovascular health had a significantly lower risk of stroke than those with poor health7. In addition, a 1-point higher cardiovascular health score was associated with an 8% lower risk of stroke (hazard ratio, 0.92; 95% CI, 0.88–0.95)7. It is therefore suggested that the following control strategy should be implemented to reduce the prevalence of cardiovascular diseases: (1) The four cardiovascular health behaviors (smoking, body mass index, physical activity and salt intake) and three health factors (total cholesterol, blood pressure and fasting plasma glucose) should be improved to increase the cardiovascular health score and/or the number of ideal cardiovascular health metrics. (2) WC and/or WHtR should be maintained within the normal range for abdominal obesity control. Although the seven cardiovascular health metrics include BMI, but the WC and/or WHtR are effective parameters in measuring the accumulation of abdominal fat.

Excessive body fat accumulation may lead to an increase in the risk factors for cardiovascular diseases, such as hyperinsulinemia, insulin resistance, hypertension, and blood lipid abnormalities, thereby resulting in the development of cardiovascular diseases19,20. The WC and WHtR are effective parameters for measuring abdominal obesity and predicting the risk factors for cardiovascular diseases5,6; however, the predictive value of the WC versus WHtR remains controversial. It has been widely reported that the WHtR is superior to the WC and BMI in predicting the risk for cardiovascular diseases21,22,23,24,25,26,27,28,29. A follow-up study conducted by Gelber which recruiting 16000 men and 32000 women showed the strongest correlation between the WHtR, one of the parameters measuring obesity, and cardiovascular diseases21. And the results from another 11-year prospective study involving 45,000 women <60 years of age revealed that the WHtR was superior to the WC, and the WC was superior to waist-to-hip-ratio (WHpR) in predicting the risk of stroke22. Lucy and colleagues proposed that the WHtR is a more ideal tool (a 0.5 cut-off value) to predict cardiovascular diseases and diabetes30, while Ashwel et al. reported that the WHtR is superior to the WC and BMI in predicting the risk for cardiovascular diseases29. Mannucci, et al., consider that the WHtR was shown to be superior to the WC and WHpR for predicting hypertension and hyperlipidemia in a United States population31. Most China researches revealed that the WHtR is better than the WC and BMI in predicting blood lipid abnormalities in a Chinese population32,33,34,35. In addition, a recent study conducted in Korea showed that the WHtR is better than the WC, while the WC is better than the BMI in predicting the risk for coronary heart disease, thus suggesting that the WHtR is an indicator measuring abdominal obesity in clinical practice36. It has been widely reported that the WHtR has a satisfactory predictive value, which may be explained by the following reasons. The WC cannot be used to quantify or differentiate visceral fat and subcutaneous fat, and the WC may be affected by many factors, such as gender, height, age, race, region, economy, environment, and lifestyle, while the BMI can only be used to measure total body fat and cannot represent fat distribution, the use of BMI alone may overestimate the risk for developing cardiovascular diseases in the population with a high weight and many muscular tissues37. The WHtR, which comprehensively considers the impact of height and WC, varies little as a function of race, age, and gender, and is relatively stable38. Our findings showed stronger associations between the cardiovascular health score, number of ideal cardiovascular health metrics, and cardiovascular health status with the WHtR than the WC. It is therefore suggested that the WC should be replaced by the WHtR as a simple tool to measure abdominal obesity and predict cardiovascular risk factors in primary health care.

The WC and WHtR cut-offs for measuring adult abdominal obesity has been controversial until now. The AHA recommends a 102 cm WC for men and 88 cm for women39, and the World Health Organization (WHO) and International Diabetes Federation (IDF) recommend a 90 cm WC for men and 80 cm for women in Asian-Pacific populations40, while the Working Group on Obesity in China recommends an 85 cm WC for men and 80 cm for women41. A study by the Japan Society for the Study of Obesity defined an 85 cm WC for men and 90 cm for women, which was similar to the visceral fat mass, and a Korean study reported an 83.2 cm WC for men and 79.7 cm for women42. He and colleagues recommended a 0.5 WHtR in both mainland Chinese men and women32, while a 0.45–0.48 WHtR cut-off was recommended for Taiwanese populations33,34 and a 0.48 cut-off in both men and women living in Hong Kong37. Lucy et al. reported a 0.5 WHtR cut-off in both men and women, and proposed a health initiative that WC does not exceed one-half of the height30. In addition, a recent Korean study defined a 0.5 WHtR in men and 0.52 in women36. Our findings showed that a 90 cm WC and 0.5255 WHtR at a 7 cardiovascular health score, and a 84.79 cm WC and 0.4944 WHtR at a 10 cardiovascular health score, which is similar to previous studies30,32,36. We consider that different regions should develop a reasonable WC and WHtR cut-off point based on the local epidemiological study and an 85 cm WC cut-off and a 0.5 WHtR cut-off may reasonable to Jiangsu resident.

In summary, the results of this study demonstrate that the cardiovascular health score correlates negatively with the WC and WHtR, and a stronger association between the cardiovascular health score was detected with the WHtR than the WC. In addition, the WHtR is of great value in screening populations at high risk for abdominal obesity and cardiovascular diseases and predicting the risk for cardiovascular diseases.

Methods

Subjects

A cross-sectional study was performed. The men between 40 and 64 years of age receiving health examinations in our hospital from 1 January 2014 through 30 June 2015 were recruited, and all recruited subjects resided in the Suzhou, Wuxi, and Changzhou regions of southeastern China. The study exclusion criteria included the following: use of lipid-regulating drugs; a history of myocardial infarction or stroke; severe hepatic or renal insufficiency; or incomplete medical records. A total of 26701 patients met the appropriate criteria.

The study protocol was approved by the Ethics Review Committee of the Taihu Rehabilitation Hospital of Jiangsu Province, and the study was performed in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants following a detailed description of the purpose of this study.

Questionnaire survey

Demographic and clinical characteristics were captured using a self-designed questionnaire, including age, residency, profession, smoking status, alcohol consumption, salt consumption, living habits, physical activity status, medical history of chronic diseases (hypertension, diabetes, coronary heart disease, stroke, and other cardiovascular diseases), and medications. The questionnaire was administered by well-trained medical professionals.

Measurement of cardiovascular risk factors

All subjects had measurements of height, weight, waist circumstance (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), and body mass index (BMI). In addition, all participants fasted for 8–12 h, and 5 mL of venous blood was collected from the cubital vein the following morning. The serum levels of TG, total cholesterol (TC), HDL-C, and LDL-C were determined using the glycerol phosphate oxidase method, the oxidase method, an antibody-based homogeneous assay, and the homogeneous assay on a fully automatically biochemical analyzer (Hitachi 7600; Hitachi, Ltd., Tokyo, Japan), respectively.

Definition of cardiovascular health

Based on the definition of cardiovascular health proposed by the AHA in 201013, vegetable intakes were changed to salt intake in this study. Physical activity was defined as moderate-intensity aerobic exercise, including fast walking, running, bicycle riding, rope skipping, and swimming and the classification criterion of physical activity was adjusted.

In accordance with AHA definitions, 7 CVH metrics were classified into ideal, intermediate, and poor: (1) smoking: ideal (never or quit >1 year), intermediate (quit <1 year), and poor (current); (2) body mass index (BMI): ideal (<25 kg/m2), intermediate (25 to <30 kg/m2), and poor (≥30 kg/m2); (3) physical activity: ideal (physical activity ≥3 times a week, with >30 min each time or physical activity >90 min per week), intermediate (physical activity of <3 times a week, with <30 min each time or ≤ 89 min of physical activity per week), and poor (no extra physical activity except daily life and work activities); (4) salt intake: ideal(<6 g/d), intermediate (6–12 g/d), and poor (>12 g/d) based on responses to questions related to salt preferences; (5) total cholesterol (TC): ideal (untreated and <5.2 mmol/L [200 mg/dL]), intermediate (treated to <5.2 mmol/L or 5.2–6.2 mmol/L), and poor (>6.2 mmol/L [240 mg/dL]); (6) blood pressure (BP): ideal (untreated and <120/<80 mm Hg), intermediate (treated to <120/<80 mm Hg or 120–139/80–89 mm Hg), and poor (≥140/90 mm Hg); and (7) fasting plasma glucose (FPG): ideal (untreated and <5.6 mmol/L [100 mg/dL]), intermediate (treated to <5.6 mmol/L or 5.6–7.0 mmol/L), and poor (≥7.0 mmol/L [125 mg/dL]).

For each subject, the seven cardiovascular health metrics were scored as follows: 0, poor; 1, general; and 2, ideal. The sum of the scores of the seven cardiovascular health metrics was defined as the total cardiovascular health score, and cardiovascular health status was classified according to the total score, as follows: 0–4, inadequate; 5–9, average; and 10–14, optimum14.

Statistics

The WC and WHtR were described as the mean ± standard deviation (SD), while the distribution of ideal cardiovascular health components and number of ideal cardiovascular health metrics were expressed as a number (proportion).

The associations between WC, WHtR and the cardiovascular health score were calculated using Pearson correlation analysis. The associations between WC, WHtR and the number of ideal cardiovascular health metrics, cardiovascular health status were calculated using Spearman correlation analysis. The receiver operating characteristic curve (ROC) was used to compare the predictive value of WC and WHtR in ideal cardiovascular health. All statistical analyses were conducted using SPSS version 16.0 (SPSS, Inc., Chicago, IL, USA), with a two-tailed P-value < 0.05 considered statistically significant.

Additional Information

How to cite this article: Shen, S. et al. Waist-to-height ratio is an effective indicator for comprehensive cardiovascular health. Sci. Rep. 7, 43046; doi: 10.1038/srep43046 (2017).

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Acknowledgments

The research reported in this article was supported by the China’s Ministry of Science and Technology (Grant No.: 2013BAI04B00), the National Natural Science Foundation of China (Grant No.: 81600346), Natural Science Foundation of Jiangsu Province, China (Grant No.: BK20131096, BK20151115), the R & D Fund of Wuxi Municipal Science & Technology Bureau, China (Grant No.: CMB21S1301), and Jiangsu Provincial Commission of Health and Family Planning, China (Grant Nos: BJ13021, BJ14023, Y2015073, BJ15032, BJ15033 and Z201519).

Footnotes

The authors declare no competing financial interests.

Author Contributions S.Z.H., S.S.W. and W.L.X. designed research, L.Y., Q.H.J., and L.F. performed experiments, S.S.W. and Q.H.J. analyzed the data, Y.C.J., W.L., S.K.D., Y.W.F., Q.D.C., Y.J.T. and Z.L. provided critical reagents, S.S.W. and Q.H.J. wrote the manuscript.

References

  1. Writing Group Members et al. Heart disease and stroke statistics–2010 update: a report from the American Heart Association. Circulation 121, e46–e215 (2010). [DOI] [PubMed] [Google Scholar]
  2. National Center for Cardiovascular Disease, China. Report on Cardiovascular disease in China (2013) Beijing, Encyclopedia of China Publishing House (2014). [Google Scholar]
  3. Zhu Z. Obesity-related cardiovascular risk and its appropriate intervention. Chin J Endocrinol Metab 27, 707–710 (2011). [Google Scholar]
  4. Matsuzawa Y. Establishment of a concept of visceral fat syndrome and discovery of adiponectin. Proc Jpn Acad Ser B Phys Biol Sci 86, 131–141 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Zhu S. et al. Waist circumference and obesity-associated risk factors among whites in the third National Health and Nutrition Examination Survey: clinical action thresholds. Am J Clin Nutr 76, 743–9 (2002). [DOI] [PubMed] [Google Scholar]
  6. Hsieh S. D., Yoshinaga H. & Muto T. Waist-to-height ratio, a simple and practical index for assessing central fat distribution and metabolic risk in Japanese men and women. Int J Obes Relat Metab Disord 27, 610–6 (2003). [DOI] [PubMed] [Google Scholar]
  7. Kulshreshtha A. et al. Life’s Simple 7 and risk of incident stroke: the reasons for geographic and racial differences in stroke study. Stroke 44, 1909–14 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bi Y. et al. Status of cardiovascular health in Chinese adults. J Am Coll Cardiol 65, 1013–25 (2015). [DOI] [PubMed] [Google Scholar]
  9. Ford E. S., Greenlund K. J. & Hong Y. Ideal cardiovascular health and mortality from all causes and diseases of the circulatory system among adults in the United States. Circulation 125, 987–995 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Zeng Q. et al. Ideal cardiovascular health in Chinese urban population. Int J Cardiol 167, 2311–7 (2013). [DOI] [PubMed] [Google Scholar]
  11. Shay C. M. et al. Status of cardiovascular health in US adults: prevalence estimates from the National Health and Nutrition Examination Surveys (NHANES) 2003-2008. Circulation 125, 45–56 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Lu Y. et al. Prevalence of ideal cardiovascular health in southeast Chinese adults. Int J Cardio 184, 385–387 (2015). [DOI] [PubMed] [Google Scholar]
  13. Lloyd-Jones D. M. et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation 121, 586–613 (2010). [DOI] [PubMed] [Google Scholar]
  14. Dong C. et al. Ideal cardiovascular health predicts lower risks of myocardial infarction, stroke, and vascular death across whites, blacks, and hispanics: the northern Manhattan study. Circulation 125, 2975–84 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Wu S. et al. Prevalence of ideal cardiovascular health and its relationship with the 4-year cardiovascular events in a northern Chinese industrial city. Circ Cardiovasc Qual Outcomes 5, 487–93 (2012). [DOI] [PubMed] [Google Scholar]
  16. Folsom A. R. et al. Community prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular disease incidence. J Am Coll Cardiol 57, 1690–6 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Chen C. M. et al. The role of dietary factors in chronic disease control in China. Obes Rev 9, Suppl 1, 100–3 (2008). [DOI] [PubMed] [Google Scholar]
  18. Bi Z. et al. Hypertension Prevalence, Awareness, Treatment, and Control and Sodium Intake in Shandong Province, China: Baseline Results From Shandong–Ministry of Health Action on Salt Reduction and Hypertension (SMASH), 2011. Prev Chronic Dis 11, 130423 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Carr M. C. & Brunzell J. D. Abdominal obesity and dyslipidemia in the metabolic syndrome: importance of type 2 diabetes and familial combined hyperlipidemia in coronary artery disease risk. J Clin Endocrinol Metab 89, 2601–7 (2004). [DOI] [PubMed] [Google Scholar]
  20. Chinese Society of Endocrinology. The expert consensus on Chinese adult obesity prevention and control. Chin J Endocrinol Metab 27, 711–717 (2011). [Google Scholar]
  21. Gelber R. P. et al. Measures of obesity and cardiovascular risk among men and women. J Am Coll Cardiol 52, 605–615 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Lu M., Ye W., Adami H. O. & Weiderpass E. Prospective study of body size and risk for stroke amongst women below age 60. J Intern Med 260, 442–450 (2006). [DOI] [PubMed] [Google Scholar]
  23. Han T. S., van, Leer E. M., Seidell J. C. & Lean M. E. Waist circumference action levels in the identification of cardiovascular risk factors: prevalence study in a random sample. BMJ 311, 1401–1405 (1995). [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Lee J. S., Aoki K., Kawakubo K. & Gunji A. A study on indices of body fat distribution for screening for obesity. J Occup Health 37, 9–18 (1995). [DOI] [PubMed] [Google Scholar]
  25. Hsieh S. D. & Yoshinaga H. Waist/height ratio as a simple and useful predictor of coronary heart disease risk factors in women. Inter Med 34, 1147–1152 (1995). [DOI] [PubMed] [Google Scholar]
  26. Hsieh S. D. & Yoshinaga H. Abdominal fat distribution and coronary heart disease risk factors in men-waist/height ratio as a simple and useful predictor. Int J Obes Relat Metab Disord 9, 585–9 (1995). [PubMed] [Google Scholar]
  27. Taylor R. W., Keil D., Gold E. J. & Goulding A. Body mass index, waist circumference girth, and waist-to-hip circumference ratio as indexes of total and regional adiposity in woman: evaluation using receiver operating characteristic curve. Am J Clin Nutr 67, 44–49 (1998). [DOI] [PubMed] [Google Scholar]
  28. Murphy N. F. et al. Long-term cardiovascular consequences of obesity: 20-years follow-up of more than 15000 middle-aged men and women (the Renfrew-Paisley study). Eur Heart J 27, 96–106 (2006). [DOI] [PubMed] [Google Scholar]
  29. Ashwell M., 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 13, 275–86 (2012). [DOI] [PubMed] [Google Scholar]
  30. Browning L. M., Hsieh S. D. & Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0·5 could be a suitable global boundary value. Nutr Res Rev 23, 247–69 (2010). [DOI] [PubMed] [Google Scholar]
  31. Mannucci E. et al. Indexes of abdominal adiposity in patients with type 2 diabetes. J Endocrinol Invest 27, 535–40 (2004). [DOI] [PubMed] [Google Scholar]
  32. He Y., Zeng Q., Tian J., Chen Z. & Zhao X. Waist-to-height ratio as a predictor of dyslipidemia for Chinese adults. Chin J Health Manage 7, 9–13 (2013). [Google Scholar]
  33. Tseng C. H. et al. Optimal anthropometric factor cutoffs for hyperglycemia, hypertension and dyslipidemia for the Taiwanese population. Atherosclerosis 210, 585–9 (2010). [DOI] [PubMed] [Google Scholar]
  34. Lin W. Y. et al. Optimal cut-off values for obesity: using simple anthropometric indices to predict cardiovascular risk factors in Taiwan. Int J Obes Relat Metab Disord 26, 1232–8 (2002). [DOI] [PubMed] [Google Scholar]
  35. Ho S. Y., Lam T. H. & Janus E. D. & Hong Kong Cardiovascular Risk Factor Prevalence Study Steering Committee. Waist to stature ratio is more strongly associated with cardiovascular risk factors than other simple anthropometric indices. Ann Epidemiol 13, 683–91 (2003). [DOI] [PubMed] [Google Scholar]
  36. Kim S. H., Choi H., Won C. W. & Kim B. S. Optimal Cutoff Points of Anthropometric Parameters to Identify High Coronary Heart Disease Risk in Korean Adults. J Korean Med Sci 31, 61–6 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Guasch-Ferré M. et al. Waist-to-height ratio and cardiovascular risk factors in elderly individuals at high cardiovascular risk. PLoS One 7, e43275 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Ashwell M. & Hsieh S. D. 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 56, 303–7 (2005). [DOI] [PubMed] [Google Scholar]
  39. Grundy S. M. et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Curr Opin Cardiol 21, 1–6 (2006). [DOI] [PubMed] [Google Scholar]
  40. Zimmet P., Magliano D., Matsuzawa Y., Alberti G. & Shaw J. The metabolic syndrome: a global public health problem and a new definition. J Atheroscler Thromb 12, 295–300 (2005). [DOI] [PubMed] [Google Scholar]
  41. Cooperative Meta-analysis Group of China Obesity Task Force. Predictive values of body mass index and waist circumference to risk factors of related diseases in Chinese adult population. Chin J Epidemiol 23, 5–10 (2002). [PubMed] [Google Scholar]
  42. Examination Committee of Criteria for ‘Obesity Disease’ in Japan & Japan Society for the Study of Obesity. New criteria for ‘obesity disease’ in Japan. Circ J 66, 987–992 (2002). [DOI] [PubMed] [Google Scholar]

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