Literature DB >> 18695656

Selection of measures in epidemiologic studies of the consequences of obesity.

J Stevens1, J E McClain, K P Truesdale.   

Abstract

The most popular measure for conducting analyses in studies of adiposity is body mass index (BMI); however, BMI does not discriminate between muscle and adipose tissue and does not directly assess regional adiposity. In this article, we address the question of whether alternatives to BMI should be used in epidemiologic analyses of the consequences of obesity. In general, measures of fat distribution such as waist circumference and sagittal abdominal diameter are more highly correlated with cardiovascular disease risk factors and diabetes than BMI; however, differences are usually small. Precise measures of adiposity from methods such as dual-energy x-ray absorptiometry may provide more specific and larger associations with disease, but published studies show that this is not always true. Further, practical considerations such as cost and feasibility must influence the choice of measure in many studies of large populations. Measures of adiposity are highly correlated with each other, and the additional cost of a more precise measure may not be justified in many circumstances. Validated prediction equations that include multiple anthropometric measures, along with demographic variables, may offer a practical means of obtaining assessments of total adiposity in large populations, whereas waist circumference can provide a feasible assessment of abdominal adiposity. Finally, public health messages to the public must be simple to be effective. Therefore, investigators may need to consider the ease of translation of results to the public when choosing a measure.

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Year:  2008        PMID: 18695656     DOI: 10.1038/ijo.2008.88

Source DB:  PubMed          Journal:  Int J Obes (Lond)        ISSN: 0307-0565            Impact factor:   5.095


  33 in total

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2.  Obesity and risk of monoclonal gammopathy of undetermined significance and progression to multiple myeloma: a population-based study.

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3.  Overeating styles and adiposity among multiethnic youth.

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4.  Association between PPAR-γ2 Pro12Ala polymorphism and obesity: a meta-analysis.

Authors:  Ying-Shui Yao; Jie Li; Yue-Long Jin; Yan Chen; Lian-Ping He
Journal:  Mol Biol Rep       Date:  2014-12-13       Impact factor: 2.316

5.  Lean NASH: distinctiveness and clinical implication.

Authors:  Kausik Das; Abhijit Chowdhury
Journal:  Hepatol Int       Date:  2013-10-17       Impact factor: 6.047

6.  Anthropometrics to Identify Overweight Children at Most Risk for the Development of Cardiometabolic Disease.

Authors:  Corinne A Labyak; David M Janicke; Crystal S Lim; James Colee; Anne E Mathews
Journal:  Infant Child Adolesc Nutr       Date:  2013-12

7.  The metabolic syndrome: useful concept or clinical tool? Report of a WHO Expert Consultation.

Authors:  R K Simmons; K G M M Alberti; E A M Gale; S Colagiuri; J Tuomilehto; Q Qiao; A Ramachandran; N Tajima; I Brajkovich Mirchov; A Ben-Nakhi; G Reaven; B Hama Sambo; S Mendis; G Roglic
Journal:  Diabetologia       Date:  2009-12-11       Impact factor: 10.122

8.  BMI, all-cause and cause-specific mortality in Chinese Singaporean men and women: the Singapore Chinese health study.

Authors:  Andrew O Odegaard; Mark A Pereira; Woon-Puay Koh; Myron D Gross; Sue Duval; Mimi C Yu; Jian-Min Yuan
Journal:  PLoS One       Date:  2010-11-15       Impact factor: 3.240

9.  Impact of overweight and obesity on hospitalization: race and gender differences.

Authors:  E Han; K P Truesdale; D R Taber; J Cai; J Juhaeri; J Stevens
Journal:  Int J Obes (Lond)       Date:  2009-01-20       Impact factor: 5.095

10.  Accuracy and usefulness of BMI measures based on self-reported weight and height: findings from the NHANES & NHIS 2001-2006.

Authors:  Manfred Stommel; Charlotte A Schoenborn
Journal:  BMC Public Health       Date:  2009-11-19       Impact factor: 3.295

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