title: Comparison of three-dimensional anthropometric body surface scanning to waist-hip ratio and body mass index in correlation with metabolic risk factors.
abstract: In this study, we used a three-dimensional (3D) body scanner to evaluate subjects with central obesity. These measurements were correlated with blood pressure, blood glucose, lipid, and uric acid levels. We randomly selected 1,204 subjects who visited the Department of Health Management at Chang Gung Medical Center in Linkou. The health index (HI) was calculated as HI = (body weight x 2 x waist profile area)/[body height(2) x (breast profile area + hip profile area)]. Among the 1,204 subjects, body mass index (BMI) equal to or greater than 30 kg/m(2) was found in 8.3% of the subjects. Pearson correlation coefficient studies revealed that HI was a better index for correlating metabolic parameters than BMI and waist-to-hips girth ratio. Of the 1,204 subjects, 9.5% (114) had diabetes mellitus, according to American Diabetes Association criteria. The percentages of men and women with total cholesterol greater than or equal to 245 mg/dL were almost the same (11.0% vs. 11.1%). Elevated low-density lipoprotein (> or =130 mg/dL) was found in 36.9% (444) of subjects. In conclusion, HI as determined by 3D scanning, is another method to predict these metabolic abnormalities. Further prospective long-term follow-up study is needed to determine the relationships of 3D body scanning data and HI to blood pressure, blood glucose, lipid, and uric acid levels.
authors.affiliation: Division of Endocrinology and Metabolism, Department of Internal Medicine, Chang Gung Memorial Hospital, Kweishan, Taoyuan Hsien, Taiwan, R.O.C. einjd@adm.cgmh.org.tw
authors.forename: Jen-Der
authors.initials: JD
authors.lastname: Lin
authors.forename: Wen-Ko
authors.initials: WK
authors.lastname: Chiou
authors.forename: Hsiao-Fen
authors.initials: HF
authors.lastname: Weng
authors.forename: Ying-Huang
authors.initials: YH
authors.lastname: Tsai
authors.forename: Thu-Hua
authors.initials: TH
authors.lastname: Liu
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D001786:Blood Glucose / Q000032:analysis; D001794:Blood Pressure; D001823:Body Composition; D001824:Body Constitution; D001827:Body Height; D015992:Body Mass Index; D001835:Body Weight; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D007328:Insulin / Q000097:blood; D008055:Lipids / Q000097:blood; D008297:Male; D008659:Metabolic Diseases / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology / Q000209:etiology*; D008875:Middle Aged; D009765:Obesity / Q000097:blood* / Q000150:complications / Q000453:epidemiology*; D012307:Risk Factors; D012709:Serum Albumin / Q000032:analysis; D017678:Sex Distribution; D013624:Taiwan / Q000453:epidemiology; D014527:Uric Acid / Q000097:blood
chemical_list: D001786:Blood Glucose; D007328:Insulin; D008055:Lipids; D012709:Serum Albumin; D014527:Uric Acid