Premium Use of iDXA spine scans to evaluate total and visceral abdominal fatPremium
Bea J. W.,
Blew R. M.,
Irving A. P.,
Caan B. J.,
Kwan M. L.,
Going S. B.
american journal of human biology
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/m 2 . The SFF, adjusted for BMI, predicted L2‐L4 and L2‐IC total abdominal fat (%; Adj. R 2 : 0.90) and total trunk fat (%; Adj. R 2 : 0.88) well; visceral fat (%) adjusted R 2 was 0.83. Linear regression models adjusted for additional participant characteristics resulted in similar adjusted R 2 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.
Subject(s)abdominal fat , anatomy , biology , body mass index , bone mineral , classification of obesity , dual energy x ray absorptiometry , ecology , environmental health , fat mass , iliac crest , insulin resistance , intra abdominal fat , linear regression , mathematics , medicine , nuclear medicine , obesity , osteoporosis , population , radiology , statistics , trunk , visceral fat
SCImago Journal Rank0.559
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