BACKGROUND: It is unknown whether the ability of waist circumference (WC) to predict health risk beyond that predicted by body mass index (BMI) alone is explained in part by the ability of WC to identify those with elevated concentrations of total or abdominal fat. OBJECTIVE: We sought to determine whether BMI and WC independently contribute to the prediction of non-abdominal (total fat - abdominal fat), abdominal subcutaneous, and visceral fat. DESIGN: Fat distribution was measured by magnetic resonance imaging in 341 white men and women. Multiple regression analysis was performed to measure whether the combination of BMI and WC explained a greater variance in non-abdominal, abdominal subcutaneous, and visceral fat than did BMI or WC alone. These fat depots were also compared after a subdivision of the cohort into 3 BMI (normal, overweight, and class I obese) and 3 WC (low, intermediate, and high) categories according to the classification system used to identify associations between BMI, WC, and health risk. RESULTS: Independent of age and sex, the combination of BMI and WC explained a greater variance in non-abdominal, abdominal subcutaneous, and visceral fat than did either BMI or WC alone (P < 0.05). For non-abdominal and abdominal subcutaneous fat, BMI was the strongest correlate; thus, by adding BMI to WC, the variance accrued was greater than when WC was added to BMI. However, when WC was added to BMI, the added variance explained for visceral fat was greater than when BMI was added to WC. Furthermore, within each of the 3 BMI categories studied, an increase in the WC category was associated with an increase in visceral fat (P < 0.05). CONCLUSIONS: BMI and WC independently contribute to the prediction of non-abdominal, abdominal subcutaneous, and visceral fat in white men and women. These observations reinforce the importance of using both BMI and WC in clinical practice.
BACKGROUND: It is unknown whether the ability of waist circumference (WC) to predict health risk beyond that predicted by body mass index (BMI) alone is explained in part by the ability of WC to identify those with elevated concentrations of total or abdominal fat. OBJECTIVE: We sought to determine whether BMI and WC independently contribute to the prediction of non-abdominal (total fat - abdominal fat), abdominal subcutaneous, and visceral fat. DESIGN: Fat distribution was measured by magnetic resonance imaging in 341 white men and women. Multiple regression analysis was performed to measure whether the combination of BMI and WC explained a greater variance in non-abdominal, abdominal subcutaneous, and visceral fat than did BMI or WC alone. These fat depots were also compared after a subdivision of the cohort into 3 BMI (normal, overweight, and class I obese) and 3 WC (low, intermediate, and high) categories according to the classification system used to identify associations between BMI, WC, and health risk. RESULTS: Independent of age and sex, the combination of BMI and WC explained a greater variance in non-abdominal, abdominal subcutaneous, and visceral fat than did either BMI or WC alone (P < 0.05). For non-abdominal and abdominal subcutaneous fat, BMI was the strongest correlate; thus, by adding BMI to WC, the variance accrued was greater than when WC was added to BMI. However, when WC was added to BMI, the added variance explained for visceral fat was greater than when BMI was added to WC. Furthermore, within each of the 3 BMI categories studied, an increase in the WC category was associated with an increase in visceral fat (P < 0.05). CONCLUSIONS: BMI and WC independently contribute to the prediction of non-abdominal, abdominal subcutaneous, and visceral fat in white men and women. These observations reinforce the importance of using both BMI and WC in clinical practice.
Authors: Mingxia Yuan; Fang-Chi Hsu; Donald W Bowden; Jianzhao Xu; S Carrie Smith; Lynne E Wagenknecht; Mary E Comeau; Jasmin Divers; Thomas C Register; J Jeffrey Carr; Carl D Langefeld; Barry I Freedman Journal: Obesity (Silver Spring) Date: 2016-06-29 Impact factor: 5.002
Authors: E DeVallance; S B Fournier; D A Donley; D E Bonner; K Lee; J C Frisbee; P D Chantler Journal: Int J Obes (Lond) Date: 2014-06-24 Impact factor: 5.095
Authors: James R Cerhan; Steven C Moore; Eric J Jacobs; Cari M Kitahara; Philip S Rosenberg; Hans-Olov Adami; Jon O Ebbert; Dallas R English; Susan M Gapstur; Graham G Giles; Pamela L Horn-Ross; Yikyung Park; Alpa V Patel; Kim Robien; Elisabete Weiderpass; Walter C Willett; Alicja Wolk; Anne Zeleniuch-Jacquotte; Patricia Hartge; Leslie Bernstein; Amy Berrington de Gonzalez Journal: Mayo Clin Proc Date: 2014-03 Impact factor: 7.616
Authors: Timo E Saaristo; Noël C Barengo; Eeva Korpi-Hyövälti; Heikki Oksa; Hannu Puolijoki; Juha T Saltevo; Mauno Vanhala; Jouko Sundvall; Liisa Saarikoski; Markku Peltonen; Jaakko Tuomilehto Journal: BMC Public Health Date: 2008-12-29 Impact factor: 3.295