Changes in waist circumference independent of weight: Implications for population level monitoring of obesity
Introduction
Accurate population monitoring of risk factors for key non-communicable diseases provides the foundation for their effective prevention and management (World Health Organization, 2013). As obesity is a leading contributor to the global burden of disease (Collaborators GBDRF, 2017), it is important to accurately and effectively monitor obesity prevalence at the population level. Internationally, body mass index (BMI, a measure of weight for height) and waist circumference (WC) are the two most common anthropometric classifications of obesity (World Health Organization, 2011). While it is established that there is imperfect overlap in categorisation of obesity according to BMI and WC, as individuals can be obese according to one indicator but not another (Lahti-Koski et al., 2007, Park et al., 2008, Lean et al., 1995), population obesity monitoring primarily relies on BMI alone (NCD Risk Factor Collaboration, 2016). This domination of BMI over other anthropometric markers of obesity has been attributed to recommendation by the World Health Organization (2011), and to a perceived redundancy in measuring both BMI and WC (NCD Risk Factor Collaboration, 2016), given their similar discriminative ability to predict cardio-metabolic risk factors and diseases (Huxley et al., 2010, Cheong et al., 2015, Seo et al., 2017).
However, there is some indication that the nature of obesity is changing to one of greater abdominal adiposity, indicated in part by greater increases in WC over time than would be expected based on increases in body weight (Stern et al., 2014, Janssen et al., 2012, Elobeid et al., 2007, Freedman and Ford, 2015, Albrecht et al., 2015, Walls et al., 2011a). We have previously demonstrated in the Mongolian context that as WC has increased independent of increases in body weight, BMI is detecting an increasingly smaller proportion of the population with obesity, as categorised by BMI or WC (Chimeddamba et al., 2017). However the implications in other countries have not been analysed. Given that current population monitoring initiatives rely on BMI, it is imperative that the extent to which BMI may be underestimating the level of population risk is quantified.
The first aim of this study was to firstly quantify the discordance in changes to WC and weight for urban Australian adults between 1989 and 2011–12. The second aim was to describe trends in classification of obesity according to BMI and WC, between 1989 and 2011–12.
We stratify all analyses by smoking status, body mass index category, highest educational attainment and age to both identify subgroups of interest and assess the potentially modifying effect of these factors.
Section snippets
Data source
Three nationally representative cross-sectional surveys were used: the 1989 National Heart Foundation Risk Factor Prevalence Study (RFPS); the 1999–2000 Australian Diabetes, Obesity and Lifestyle Study (AusDiab); and the 2011–12 National Nutrition and Physical Activity Survey (NNPAS), the details of which have been described previously (National Heart Foundation of Australia, 2001, Dunstan et al., 2002, Australian Bureau of Statistics, 2013). Briefly, the 1989 RFPS was a random selection of
Results
Characteristics of the survey populations are presented in Table 1. The proportion of women and men were similar between surveys. For both sexes, mean age, and the proportion of never smokers was highest in 1999–2000, and the proportion that completed secondary school was highest in 2011–12.
Discussion
Our results demonstrate that between 1989 and 2011–12, WC increased significantly more than would be expected based on increases in weight for urban Australian adults. If changes in weight and WC were in perfect accordance, we would observe a 0 cm change in WC independent of weight over time. In contrast, our results demonstrate that the phenotype of urban Australian adults significantly altered between 1989 and 2011–12, such that on average, compared to women and men in 1989, their counterparts
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Acknowledgements
We wish to thank the National Heart Foundation's Risk Factor Prevalence Study Committee and the Australian Social Science Data Archive for access to the Risk Factor Prevalence Study, the AusDiab Steering Committee for access to the AusDiab study, and the Australian Bureau of Statistics for access to the National Nutrition and Physical Activity Survey.
Financial support
This work was supported by an Australian Research Council (ARC) Linkage grant (LP120100418) and in part by the Victorian Government's Operational Infrastructure Support (OIS) Program. EG was supported by an Australian Government Research Training Program Scholarship, SKT was supported by a National Institutes of Health Visiting Fellow Award, CS was supported by an ARC Discovery Project Grant (DP120103277), VL was supported by the NHMRC Centre for Research Excellence in Healthy, Liveable &
Conflict of interest statement
The authors declare no conflicts of interest.
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