Waist-to-Height Ratio, Waist Circumference, Body Mass Index and Risk of Cardiometabolic Multimorbidity: A National Longitudinal Cohort Study

Abstract

Background: Cardiometabolic multimorbidity is an increasing public health burden. This study aimed to evaluate the association of waist-to-height ratio (WHtR), waist circumference (WC) and body mass index (BMI) with risk of cardiometabolic multimorbidity.

Methods: We used data from the China Health and Retirement Longitudinal Study (CHARLS). 10521 participants aged 45 and over were recruited, including 8807 individuals with 0 cardiometabolic disease at baseline (stage Ⅰ) and 1714 individuals with 1 cardiometabolic disease at baseline (stage Ⅱ). Logistic regression was conducted to estimate the odds ratios (ORs) and confidence intervals (CIs). Net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to evaluate the incremental predictive value beyond conventional factors.

Results: In stage Ⅰ, increased risk of cardiometabolic multimorbidity was observed among participants with WHtR ≥ 0.5 (OR: 1.76, 95% CI: 1.05–2.97), WC ≥ 85 (men) + WC ≥ 75 (women) (OR: 1.77, 95% CI: 1.05–2.97) or BMI ≥ 24 (OR: 1.48, 95% CI: 0.98–2.24). Furthermore, both NRI and IDI of WHtR and WC were higher than those of BMI. In stage Ⅱ, the adjusted ORs (95% CIs) of WHtR ≥ 0.5, WC ≥ 85 (men) + WC ≥ 75 (women), and BMI ≥ 24 were 2.04 (1.24–3.35), 2.07 (1.28–3.34), and 1.47 (1.06–2.04), respectively. In addition, WC exhibited the highest NRI and IDI.

Conclusions: WHtR, WC and BMI are independent predictors of cardiometabolic multimorbidity in middle-aged and elderly Chinese population. WHtR and WC show better abilities in predicting cardiometabolic multimorbidity than BMI.

Introduction

Cardiometabolic diseases, including stroke, diabetes, and heart disease, are the leading cause of deaths worldwide[1, 2]. With the rapid population aging, cardiometabolic multimorbidity, defined as the co-occurrence of 2 or more cardiometabolic diseases, has become a prominent public health concern. Remarkably, the health damages attributed to cardiometabolic multimorbidity are far more significant than a single cardiometabolic disease. It is reported that, when compared with elderly people without cardiometabolic diseases, individuals with any of cardiometabolic diseases or with any two of cardiometabolic diseases were estimated to have reduced life expectancies of 7 years and 12 years, respectively[3]. Furthermore, individuals with one cardiometabolic disease or with cardiometabolic multimorbidity also had 1.41 and 1.89 times the odds of higher mental stress than those without cardiometabolic disease, respectively[4]. However, as a major and persistent problem[5, 6], cardiometabolic multimorbidity has been quite inadequately studied, and early prevention measures need to be taken urgently.

Obesity has been acknowledged as an important risk factor for cardiometabolic diseases. Large amounts of evidence also showed that body mass index (BMI), a common measure of obesity, could predict single cardiometabolic diseases. In contrast, only a few studies reported the prospective association of BMI with cardiometabolic multimorbidity in Europe and America[7, 8]. However, the evidence for this association remains to be further validated in Asian populations. Recently, waist circumference (WC) and waist-to-height ratio (WHtR) have been proposed to be important anthropometric indicators of abdominal obesity[9, 10], and many studies demonstrated that these two indicators were superior to BMI in predicting single cardiometabolic diseases[9–15]. Nevertheless, the relationship between WHtR, WC and cardiometabolic multimorbidity has yet to be assessed.

In this study, we investigated the longitudinal relationship between BMI, WHtR, WC, and cardiometabolic multimorbidity in the middle-aged and elderly Chinese population. Our aims are two-fold: (1) to assess the association of BMI, WHtR and WC with cardiometabolic multimorbidity; (2) to compare the predictive ability of BMI, WHtR, and WC in risk of cardiometabolic multimorbidity.

Methods

Study Design

We used data from the China Health and Retirement Longitudinal Study (CHARLS)[16]. CHARLS is a nationally representative longitudinal survey, which collects data from individuals aged 45 and above in China. A wide range of information concerning the economic standing, physical and psychological health, demographics and social networks has been collected. Anthropometric indicators and cardiometabolic diseases have also been assessed in CHARLS. The first national baseline survey of CHARLS was conducted in 2011-2012, which included 10,257 households and 17,708 individuals from 150 counties/districts and 450 villages/resident communities[17]. Participants were then followed up every 2 years. Details of the study design of CHARLS can be found in previous literature[18]. This study was approved by Biomedical Ethics Review Committee of Peking University. All participants signed informed consents.

Study Population

All participants recruited in the national baseline survey were included if they met the following criteria: (1) aged at least 45 years; (2) no history of cardiometabolic multimorbidity at baseline; (3) collected anthropometric indicators successfully; (4) successfully followed up. Finally, we included 8807 individuals without any cardiometabolic disease at baseline and 1714 individuals with one of cardiometabolic diseases at baseline (Figure 1).

WHtR, WC and BMI Measurements

BMI was calculated as weight (kg) divided by the square of height (m). WHtR was defined as WC (cm) divided by height (cm). Height and weight were measured by vertical height measuring instrument and weighing scale with bare feet and light clothes. When measuring WC, the measured personnel used a soft ruler to circle the waist horizontally at the level of the navel.

Cardiometabolic multimorbidity and outcomes

Cardiometabolic multimorbidity is defined as having two or more of the following three diseases: diabetes, stroke, and heart problems[3]. The diagnosis of cardiometabolic diseases was determined by participants’ self-reported information. This study evaluated the development of cardiometabolic multimorbidity from two situations: 1) From 0 cardiometabolic disease at baseline to cardiometabolic multimorbidity (stage Ⅰ); 2) From 1 cardiometabolic disease at baseline to cardiometabolic multimorbidity (Stage Ⅱ). For the first situation, subjects who did not suffer from any of the three diseases in 2011 were included. Subsequently, people who eventually developed two or more cardiometabolic diseases during follow-up were deemed as individuals with cardiometabolic multimorbidity. For the second situation, participants having only one of the three diseases in 2011 were included, and then developing one or more new-onset cardiometabolic diseases during follow-up was defined as cardiometabolic multimorbidity.

Covariates

Covariates included sociodemographic characteristics, lifestyle factors and current disease status[19]. Sociodemographic characteristics included age (years), gender (male/female), education level (less than lower secondary education/upper secondary & vocational training/tertiary education), and residence (rural/urban). Lifestyle factors included smoking status (ever smoking/never smoking) and drinking status (ever drinking/never drinking). Current diseases (yes/no) included hypertension, cancer, lung disease, psychological problems, arthritis, dyslipidemia, liver disease, kidney disease, stomach/digestive system diseases, and asthma.

Statistical Analysis

Categorical variables were presented by frequency (percentage) and compared by the Chi-square test. Continuous data were described as median (inter-quartile range) and compared by the Wilcoxon rank sum test[20, 21].

For subsequent analyses, we converted the continuous variables into binary variables, using the cut-off values of 24kg/m² for BMI[20], 0.5 for WHtR[13], and 75 cm (women) and 85cm (men) for WC[22], respectively. Binary logistic regression model was used to access the associations of BMI, WHtR and WC with cardiometabolic multimorbidity, and the odds ratios (ORs) and 95% confidence intervals (CIs) were estimated. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to compare the predictive utilities of BMI, WHtR and WC for cardiometabolic multimorbidity beyond other conventional factors[17, 23].

All analyses were performed using SAS 9.4 (SAS Institute, Cary, NC, USA). A two-sided P-value less than 0.05 was deemed statistically significant.

Results

Stage Ⅱ: From 1 Cardiometabolic Disease To Cardiometabolic Multimorbidity

Among 1714 participants in stage Ⅱ, 213 (12.43%) participants were classed as having cardiometabolic multimorbidity and 1501 (87.57%) participants were defined as non-cardiometabolic multimorbidity. Likewise, people with cardiometabolic multimorbidity had higher BMI, WHtR and WC, and possessed higher prevalence of hypertension and dyslipidemia (all P < 0.05) (Table 1).

During follow-up, incidence rates of cardiometabolic multimorbidity in the high BMI, WHtR and WC groups were 15.41% (137), 14.08% (193) and 14.21% (189), respectively, while incidence rates in the low BMI, WHtR and WC groups were 9.21% (76), 5.83% (20) and 6.25% (24), respectively. Furthermore, the risk of having cardiometabolic multimorbidity was most pronounced for WC (OR: 2.07, 95% CI: 1.28–3.34), followed by WHtR (OR: 2.04, 95% CI: 1.24–3.35) and BMI (OR: 1.47, 95% CI: 1.06–2.04) (Fig. 2).

NRI and IDI values were applied to compare the predictive abilities of BMI, WHtR and WC on risk of cardiometabolic multimorbidity. The NRI and IDI values showed that WC was advantageous over BMI and WHtR in predicting cardiometabolic multimorbidity among individulas with only one cardiometabolic disease at baseline. Adding BMI, WHtR and WC to model 1 improved patient classification by 26.43% (12.15–40.70%), 25.22% (10.94–39.59%) and 31.52% (17.33–45.72%), respectively, and probabilitic difference of suffering cardiometabolic multimorbidity between cases and controls by 0.92% (0.42–1.42%), 1.26% (0.68–1.84%), and 1.87% (1.16–2.59%), respectively. Likewise, in model 2, the NRI and IDI (95% CI) were 0.2261 (0.0813–0.3708) and 0.006 (0.0017–0.0102) for BMI, 0.1875 (0.0426–0.3324) and 0.0089 (0.0039–0.0138) for WHtR, 0.2893 (0.1454–0.4333) and 0.014 (0.0078–0.0202), respectively (Table 2).

Discussion

The present study investigated the prospective association of BMI, WHtR and WC with cardiometabolic multimorbidity in a nationally representative cohort. Our results showed that BMI, WHtR and WC were all independently associated with increased risk of cardiometabolic multimorbidity among the middle-aged and elderly Chinese population. Moreover, compared with BMI, WHtR and WC exhibited better predictive utilities in future cardiometabolic multimorbidity.

Cardiometabolic multimorbidity, as a growing problem, poses a major challenge to health care systems throughout the world. Previous studies have suggested that cardiometabolic multimorbidity is much more harmful than a single cardiometabolic disease. For example, compared to the absence of any of the three cardiometabolic diseases, the hazard ratio (HR) for all-cause mortality was about twice in any one of these diseases, 4 times in any two of these diseases, and 7 times in the presence of all three diseases[3]. In view of the serious harm of cardiometabolic multimorbidity, early predictive indicators need to be discovered urgently. However, quite limited studies have investigated the associations of easy-to access anthropometric indicators, such as BMI, WHtR and WC, with risk of cardiometabolic multimorbidity. To the best of our knowledge, there are only two studies exploring the link between BMI and cardiometabolic multimorbidity[7, 8]. The study by Kivimäki et al. involving 16 longitudinal research databases and 120,813 subjects suggested that the risk of cardiometabolic multimorbidity increased as BMI increased[8]. This comprehensive analysis indicated that compared with healthy-weight individuals, overweight and obesity individuals (BMI ≥ 24) had twice the risk of developing cardiometabolic multimorbidity. The OR values between this study and ours are similar, indicating the predictive power of BMI. However, the Asian population has not been included in this study, and our study can be a supplement in this regard. Another study recruiting 8270 subjects showed that the hazard ratio for overweight/obesity was 1.19 times (95% CI: 1.00–1.43) higher for developing cardiometabolic multimorbidity from 1 baseline cardiometabolic disease than individuals with healthy weight.

WC, as an abdominal obesity measurement indicator, has been supported as an obesity-related health risk indicator for both Western and Asian populations[24–26]. Previous studies have suggested that WC has greater predictive power for risk of cardiometabolic diseases than BMI[9, 27]. Furthermore, a large amount of evidence has also supported that WHtR was more effective than BMI in predicting coronary heart disease, stroke and diabetes[9, 12–14, 21, 28, 29]. Remarkably, our study also found that WHtR and WC were independent predictors of cardiometabolic multimorbidity. Moreover, WHtR and WC were demonstrated to possess higher predictive abilities on risk of cardiometabolic multimorbidity than BMI in the current study. This phenomenon may be explained by the following reasons. First of all, BMI can only be used to measure the total body fat and cannot represent the body fat distribution[30]. The susceptibility of cardiometabolic diseases may depend on the difference of regional body fat distribution and the ability of subcutaneous adipose tissue[31]. Moreover, WC reflects body fat ratio more accurately than BMI, and it may play an important role in the early development of metabolic syndrome[32, 33]. A recent systematic review demonstrated that compared with BMI, WC increased the ability to discriminate adverse cardiometabolic risk outcomes by 3%[29]. In addition, WHtR has been suggested to be less affected by race, age and gender and be relatively more stable[9, 11, 34]. People with the same BMI might have different risks of cardiometabolic diseases[31]. Even among people with normal BMI, those with high WHtR are more likely to suffer from cardiometabolic diseases. Notably, about 35% of men and 14% of women with high WHtR would be missed if screened by BMI only, which could bring serious consequences for cardiometabolic disease prevention[35]. Considering that WHtR and WC have better predictive power in cardiometabolic multimorbidity than BMI, screening by WHtR and WC might be applicable in future practice.

The exact mechanisms underlying the association between BMI, WHtR, WC, and cardiometabolic multimorbidity remain to be illuminated, but insulin resistance and ectopic fat deposition may be the main contributors. Adipose tissue produces a large amount of bioactive mediators, which leads to insulin resistance. Insulin resistance may cause cardiometabolic diseases in the following ways. First, in a state of insulin resistance, inflammation occurs in the body, which eventually leads to atherosclerosis[36]. Second, insulin resistance affects the production of apolipoprotein A1 (apoA-Ⅰ) or the liver secretion of high-density lipoprotein (HDL), which could be a trigger for metabolic syndrome[37]. Third, insulin resistance would obstruct normal heart function through inhibiting metabolic pathways and over-stimulating growth factors[38]. In addition, ectopic fat deposition triggers a pathological metabolic response, increasing the risk of metabolic diseases[31]. Excess free fatty acids are produced outside the fat storage tissue, and transferred to ectopic sites, including the viscera, heart, and vasculature, ultimately leading to cardiometabolic diseases[39].

There are several merits in this study. Our study is the first to investigate the association of WHtR and WC with risk of cardiometabolic multimorbidity. Moreover, WHtR and WC were shown to be advantageous in predicting cardiometabolic multimorbidity than BMI. In addition, the two-stage strategy to assess cardiometabolic multimorbidity could provide mutual validation and increase the reliability of the associations. Limitations should be noted as well. First, our research was based on the four-year follow-up data, preventing the assessments of a long-term association. Second, the cardiometabolic diseases included in this study were self-reported. However, self-reported cardiometabolic diseases have been proved to be highly reliable in large-scale epidemiological studies[40].

Conclusions

In the middle-aged and elderly Chinese population, WHtR, WC and BMI were found to be independent predictors of cardiometabolic multimorbidity. In addition, WHtR and WC exhibited better predictive power in future cardiometabolic multimorbidity than BMI. Our findings highlight the significance of screening and interventions of high-risk individuals through easy-to-access and cost-effective tools, such as WHtR and WC, for the prevention of cardiometabolic multimorbidity.

Abbreviations

WHtR, waist-to-height ratio; WC, waist circumference; BMI, body mass index; CHARLS, China Health and Retirement Longitudinal Study; ORs, odds ratios; CIs, confidence intervals; NRI, net reclassification index; IDI, integrated discrimination improvement.

Declarations

Ethics approval and consent to participate

CHARLS was approved by Biomedical Ethics Review Committee of Peking University, and all participants signed informed consents.

Consent for publication

Not applicable.

Availability of data and materials

The data used and analyzed in this study are publicly available from the China Health and Retirement Longitudinal Study (http://charls.pku.edu.cn/zh-CN).

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by National Natural Science Foundation of China (81703316, 81703322); Natural Science Foundation of Jiangsu Province (project number BK20170350) and A Project Funded by Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Authors’ contributions

CK conceived and designed the research; YL and SL wrote the manuscript; and GL, YQ and YW performed the data analysis. All authors contributed to the interpretations of the findings. All authors reviewed the manuscript.

Acknowledgments

This analysis uses data or information from the Harmonized CHARLS dataset and Codebook, Version C as of April 2018 developed by the Gateway to Global Aging Data. The development of the Harmonized CHARLS was funded by the National Institute on Ageing (R01 AG030153, RC2 AG036619, R03 AG043052). For more information, please refer to www.g2aging.org.

References

1. Ralston J, Nugent R. Toward a broader response to cardiometabolic disease. Nature medicine. 2019;25(11):1644–6.
2. Yuan S, Li X, Jin Y, Lu J. Chocolate Consumption and Risk of Coronary Heart Disease, Stroke, and Diabetes: A Meta-Analysis of Prospective Studies. Nutrients. 2017;9(7).
3. Di Angelantonio E, Kaptoge S, Wormser D, Willeit P, Butterworth AS, Bansal N, et al. Association of Cardiometabolic Multimorbidity With Mortality. Jama. 2015;314(1):52–60.
4. Sakakibara BM, Obembe AO, Eng JJ. The prevalence of cardiometabolic multimorbidity and its association with physical activity, diet, and stress in Canada: evidence from a population-based cross-sectional study. BMC Public Health. 2019;19(1):1361.
5. Jovic D, Vukovic D, Marinkovic J. Prevalence and Patterns of Multi-Morbidity in Serbian Adults: A Cross-Sectional Study. PloS one. 2016;11(2):e0148646.
6. Zhang D, Tang X, Shen P, Si Y, Liu X, Xu Z, et al. Multimorbidity of cardiometabolic diseases: prevalence and risk for mortality from one million Chinese adults in a longitudinal cohort study. BMJ open. 2019;9(3):e024476.
7. Singh-Manoux A, Fayosse A, Sabia S, Tabak A, Shipley M, Dugravot A, et al. Clinical, socioeconomic, and behavioural factors at age 50 years and risk of cardiometabolic multimorbidity and mortality: A cohort study. PLoS Med. 2018;15(5):e1002571.
8. Kivimäki M, Kuosma E, Ferrie JE, Luukkonen R, Nyberg ST, Alfredsson L, et al. Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120†࿽813 adults from 16 cohort studies from the USA and Europe. The Lancet Public health. 2017;2(6):e277-e85.
9. Castanheira M, Chor D, Braga JU, Cardoso LO, Griep RH, Molina M, et al. Predicting cardiometabolic disturbances from waist-to-height ratio: findings from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) baseline. Public Health Nutr. 2018;21(6):1028–35.
10. Gu Z, Li D, He H, Wang J, Hu X, Zhang P, et al. Body mass index, waist circumference, and waist-to-height ratio for prediction of multiple metabolic risk factors in Chinese elderly population. Scientific reports. 2018;8(1):385.
11. Meseri R, Ucku R, Unal B. Waist:height ratio: a superior index in estimating cardiovascular risks in Turkish adults. Public Health Nutr. 2014;17(10):2246–52.
12. Zhu Q, Shen F, Ye T, Zhou Q, Deng H, Gu X. Waist-to-height ratio is an appropriate index for identifying cardiometabolic risk in Chinese individuals with normal body mass index and waist circumference. Journal of diabetes. 2014;6(6):527–34.
13. Ashwell M, Gibson S. A proposal for a primary screening tool: 'Keep your waist circumference to less than half your height'. BMC Med. 2014;12:207.
14. Li WC, Chen IC, Chang YC, Loke SS, Wang SH, Hsiao KY. Waist-to-height ratio, waist circumference, and body mass index as indices of cardiometabolic risk among 36,642 Taiwanese adults. Eur J Nutr. 2013;52(1):57–65.
15. Liu J, Tse LA, Liu Z, Rangarajan S, Hu B, Yin L, et al. Predictive Values of Anthropometric Measurements for Cardiometabolic Risk Factors and Cardiovascular Diseases Among 44 048 Chinese. Journal of the American Heart Association. 2019;8(16):e010870.
16. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43(1):61–8.
17. Shen Y, Zhang Y, Xiong S, Zhu X, Ke C. High-sensitivity C-reactive protein and cystatin C independently and jointly predict all-cause mortality among the middle-aged and elderly Chinese population. Clinical biochemistry. 2019;65:7–14.
18. Li H, Qian F, Hou C, Li X, Gao Q, Luo Y, et al. Longitudinal Changes in Depressive Symptoms and Risks of Cardiovascular Disease and All-Cause Mortality: A Nationwide Population-Based Cohort Study. The journals of gerontology Series A, Biological sciences and medical sciences. 2020;75(11):2200–6.
19. Li W, Taskin T, Gautam P, Gamber M, Sun W. Is there an association among sleep duration, nap, and stroke? Findings from the China Health and Retirement Longitudinal Study. Sleep & breathing = Schlaf & Atmung. 2020.
20. Li Q, Zhang D, Guo C, Zhou Q, Tian G, Liu D, et al. Association of hypertriglyceridemic waist-to-height ratio and its dynamic status with incident hypertension: the Rural Chinese Cohort Study. Journal of hypertension. 2019;37(12):2354–60.
21. Rådholm K, Chalmers J, Ohkuma T, Peters S, Poulter N, Hamet P, et al. Use of the waist-to-height ratio to predict cardiovascular risk in patients with diabetes: Results from the ADVANCE-ON study. Diabetes Obes Metab. 2018;20(8):1903–10.
22. Zeng Q, He Y, Dong S, Zhao X, Chen Z, Song Z, et al. Optimal cut-off values of BMI, waist circumference and waist:height ratio for defining obesity in Chinese adults. Br J Nutr. 2014;112(10):1735–44.
23. Martens FK, Tonk ECM, Janssens A. Evaluation of polygenic risk models using multiple performance measures: a critical assessment of discordant results. Genetics in medicine: official journal of the American College of Medical Genetics. 2019;21(2):391–7.
24. Janssen I, Katzmarzyk PT, Ross R. Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr. 2004;79(3):379–84.
25. Leitzmann MF, Moore SC, Koster A, Harris TB, Park Y, Hollenbeck A, et al. Waist circumference as compared with body-mass index in predicting mortality from specific causes. PloS one. 2011;6(4):e18582.
26. Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J. Body mass index, waist circumference and waist:hip ratio as predictors of cardiovascular risk–a review of the literature. Eur J Clin Nutr. 2010;64(1):16–22.
27. Staiano AE, Reeder BA, Elliott S, Joffres MR, Pahwa P, Kirkland SA, et al Body mass index versus waist circumference as predictors of mortality in Canadian adults. International journal of obesity (2005). 2012;36(11):1450-4.
28. Liu PJ, Ma F, Lou HP, Zhu YN. Comparison of the ability to identify cardiometabolic risk factors between two new body indices and waist-to-height ratio among Chinese adults with normal BMI and waist circumference. Public Health Nutr. 2017;20(6):984–91.
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. Obesity reviews: an official journal of the International Association for the Study of Obesity. 2012;13(3):275–86.
30. Shen S, Lu Y, Qi H, Li F, Shen Z, Wu L, et al. Waist-to-height ratio is an effective indicator for comprehensive cardiovascular health. Scientific reports. 2017;7:43046.
31. Piché ME, Poirier P, Lemieux I, Després JP. Overview of Epidemiology and Contribution of Obesity and Body Fat Distribution to Cardiovascular Disease: An Update. Prog Cardiovasc Dis. 2018;61(2):103–13.
32. Cibičková Ľ, Langová K, Vaverková H, Lukeš J, Cibiček N, Karásek D. Superior Role of Waist Circumference to Body-Mass Index in the Prediction of Cardiometabolic Risk in Dyslipidemic Patients. Physiological research. 2019;68(6):931–8.
33. Alberti KG, Zimmet P, Shaw J. The metabolic syndrome–a new worldwide definition. Lancet. 2005;366(9491):1059–62.
34. Kazlauskaite R, Avery-Mamer EF, Li H, Chataut CP, Janssen I, Powell LH, et al. Race/ethnic comparisons of waist-to-height ratio for cardiometabolic screening: The study of women's health across the nation. American journal of human biology: the official journal of the Human Biology Council. 2017;29(1).
35. Ashwell M, Gibson S. Waist to height ratio is a simple and effective obesity screening tool for cardiovascular risk factors: Analysis of data from the British National Diet And Nutrition Survey of adults aged 19–64 years. Obes Facts. 2009;2(2):97–103.
36. Kwiterovich PO Jr. Clinical relevance of the biochemical, metabolic, and genetic factors that influence low-density lipoprotein heterogeneity. The American journal of cardiology. 2002;90(8a):30i–47i.
37. Mertens I, Van Gaal LF. New International Diabetes Federation (IDF) and National Cholesterol Education Program Adult Treatment panel III (NCEP-ATPIII) criteria and the involvement of hemostasis and fibrinolysis in the metabolic syndrome. Journal of thrombosis haemostasis: JTH. 2006;4(5):1164–6.
38. Van Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease. Nature. 2006;444(7121):875–80.
39. Britton KA, Fox CS. Ectopic fat depots and cardiovascular disease. Circulation. 2011;124(24):e837-41.
40. Barr EL, Tonkin AM, Welborn TA, Shaw JE. Validity of self-reported cardiovascular disease events in comparison to medical record adjudication and a statewide hospital morbidity database: the AusDiab study. Intern Med J. 2009;39(1):49–53.