Estimating the proportion of metabolic health outcomes attributable to obesity: a cross-sectional exploration of body mass index and waist circumference combinationsReportar como inadecuado




Estimating the proportion of metabolic health outcomes attributable to obesity: a cross-sectional exploration of body mass index and waist circumference combinations - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

BMC Obesity

, 3:4

Epidemiology and ethnicity

Abstract

BackgroundRecent evidence suggests that a substantial subgroup of the population who have a high-risk waist circumference WC do not have an obese body mass index BMI. This study aimed to explore whether including those with a non-obese BMI but high risk WC as ‘obese’ improves prediction of adiposity-related metabolic outcomes.

MethodsEleven thousand, two hundred forty-seven participants were recruited. Height, weight and WC were measured. Ten thousand, six hundred fifty-nine participants with complete data were included. Adiposity categories were defined as: BMI-WC, BMI-WC, BMI-WC, and BMI-WC N = non-obese and O = obese. Population attributable fraction, area under the receiver operating characteristic curve AUC, and odds ratios OR were calculated.

ResultsParticipants were on average 48 years old and 50 % were men. The proportions of BMI-WC, BMI-WC, BMI-WC and BMI-WC were 68, 12, 2 and 18 %, respectively. A lower proportion of diabetes was attributable to obesity defined using BMI alone compared to BMI and WC combined 32 % vs 47 %. AUC for diabetes was also lower when obesity was defined using BMI alone 0.62 vs 0.66. Similar results were observed for all outcomes. The odds for hypertension, dyslipidaemia, diabetes and CVD were increased for those with BMI-WC OR range 1.8–2.7 and BMI-WC OR 1.9–4.9 compared to those with BMI-WC.

ConclusionsCurrent population monitoring, assessing obesity by BMI only, misses a proportion of the population who are at increased health risk through excess adiposity. Improved identification of those at increased health risk needs to be considered for better prioritisation of policy and resources.

AbbreviationsAUCarea under the receiver operating characteristic curve

AusDiabAustralian Diabetes, Obesity and Lifestyle

BMIbody mass index

CIconfidence interval

CVDcardiovascular disease

HDLhigh density lipoprotein

ORodds ratio

PAFpopulation attributable fraction

SDstandard deviation

WCwaist circumference

Electronic supplementary materialThe online version of this article doi:10.1186-s40608-016-0085-5 contains supplementary material, which is available to authorized users.

Download fulltext PDF



Autor: Stephanie K. Tanamas - Viandini Permatahati - Winda L. Ng - Kathryn Backholer - Rory Wolfe - Jonathan E. Shaw - Anna Pee

Fuente: https://link.springer.com/







Documentos relacionados