J W Bea1,2,3,4, C-H Hsu5, R M Blew3,4, A P Irving1,3, B J Caan6, M L Kwan6, I Abraham1,7, S B Going3,4. 1. University of Arizona Cancer Center, Tucson, Arizona 85724-5024. 2. Department of Medicine, College of Medicine, University of Arizona, Tucson, Arizona 85724-5024. 3. Department of Nutritional Sciences, University of Arizona, Tucson, Arizona 85721. 4. The Collaboratory for Metabolic Disease Prevention and Treatment, University of Arizona, Tucson, Arizona 85714. 5. Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona 85724. 6. Division of Research, Kaiser Permanente Northern California, Oakland, California 94612. 7. Center for Health Outcomes and PharmacoEconomic Research, University of Arizona, Tucson, Arizona 85721.
Abstract
OBJECTIVES: Abdominal fat may be a better predictor than body mass index (BMI) for risk of metabolically-related diseases, such as diabetes, cardiovascular disease, and some cancers. We sought to validate the percent fat reported on dual energy X-ray absorptiometry (DXA) regional spine scans (spine fat fraction, SFF) against abdominal fat obtained from total body scans using the iDXA machine (General Electric, Madison, WI), as previously done on the Prodigy model. METHODS: Total body scans and regional spine scans were completed on the same day (N = 50). In alignment with the Prodigy-based study, the following regions of interest (ROI) were assessed from total body scans and compared to the SFF from regional spine scans: total abdominal fat at (1) lumbar vertebrae L2-L4 and (2) L2-Iliac Crest (L2-IC); (3) total trunk fat; and (4) visceral fat in the android region. Separate linear regression models were used to predict each total body scan ROI from SFF; models were validated by bootstrapping. RESULTS: The sample was 84% female, a mean age of 38.5 ± 17.4 years, and mean BMI of 23.0 ± 3.8 kg/m2 . The SFF, adjusted for BMI, predicted L2-L4 and L2-IC total abdominal fat (%; Adj. R2 : 0.90) and total trunk fat (%; Adj. R2 : 0.88) well; visceral fat (%) adjusted R2 was 0.83. Linear regression models adjusted for additional participant characteristics resulted in similar adjusted R2 values. CONCLUSIONS: This replication of the strong correlation between SFF and abdominal fat measures on the iDXA in a new population confirms the previous Prodigy model findings and improves generalizability.
OBJECTIVES: Abdominal fat may be a better predictor than body mass index (BMI) for risk of metabolically-related diseases, such as diabetes, cardiovascular disease, and some cancers. We sought to validate the percent fat reported on dual energy X-ray absorptiometry (DXA) regional spine scans (spine fat fraction, SFF) against abdominal fat obtained from total body scans using the iDXA machine (General Electric, Madison, WI), as previously done on the Prodigy model. METHODS: Total body scans and regional spine scans were completed on the same day (N = 50). In alignment with the Prodigy-based study, the following regions of interest (ROI) were assessed from total body scans and compared to the SFF from regional spine scans: total abdominal fat at (1) lumbar vertebrae L2-L4 and (2) L2-Iliac Crest (L2-IC); (3) total trunk fat; and (4) visceral fat in the android region. Separate linear regression models were used to predict each total body scan ROI from SFF; models were validated by bootstrapping. RESULTS: The sample was 84% female, a mean age of 38.5 ± 17.4 years, and mean BMI of 23.0 ± 3.8 kg/m2 . The SFF, adjusted for BMI, predicted L2-L4 and L2-IC total abdominal fat (%; Adj. R2 : 0.90) and total trunk fat (%; Adj. R2 : 0.88) well; visceral fat (%) adjusted R2 was 0.83. Linear regression models adjusted for additional participant characteristics resulted in similar adjusted R2 values. CONCLUSIONS: This replication of the strong correlation between SFF and abdominal fat measures on the iDXA in a new population confirms the previous Prodigy model findings and improves generalizability.
Authors: Barbara J Nicklas; Brenda W J H Penninx; Matteo Cesari; Stephen B Kritchevsky; Anne B Newman; Alka M Kanaya; Marco Pahor; Ding Jingzhong; Tamara B Harris Journal: Am J Epidemiol Date: 2004-10-15 Impact factor: 4.897
Authors: Sanjiv Kaul; Megan P Rothney; Dawn M Peters; Wynn K Wacker; Cynthia E Davis; Michael D Shapiro; David L Ergun Journal: Obesity (Silver Spring) Date: 2012-01-26 Impact factor: 5.002