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• W2890969701 abstract "Visceral adiposity measured by computed tomography (CT) as intra-abdominal fat area (IAFA) predicts metabolic diseases. Existing adiposity surrogates have not been systematically compared to a regression-based model derived in individuals of Japanese ancestry. We developed and validated a method to estimate IAFA in individuals of Japanese ancestry and compared it to existing adiposity surrogates. We assessed age, BMI, waist circumference (WC), fasting lipids, glucose, smoking status, grip strength, mid-thigh circumference (MTC), humeral length, leg length, and IAFA by single-slice CT at the umbilicus for 622 Japanese Americans. We used stepwise linear regression to predict IAFA and termed the predicted value the Estimate of Visceral Adipose Tissue Area (EVA). For men, the final model included age, BMI, WC, high-density lipoprotein cholesterol (HDLc), glucose, and MTC; for women, age, BMI, WC, HDLc, low-density lipoprotein cholesterol, glucose, and MTC. We compared goodness-of-fit (R2) from linear regression models and mean-squared errors (MSE) from k-fold cross-validation to compare the ability of EVA to estimate IAFA compared to an estimate by Després et al., waist-to-height ratio, WC, deep abdominal adipose tissue index, BMI, lipid accumulation product, and visceral adiposity index (VAI). We classified low/high IAFA using area under receiver-operating characteristic curves (AUROC) for IAFA dichotomized at the 75th percentile. EVA gave the least MSE and greatest R2 (men: 1244, 0.61; women: 581, 0.72). VAI gave the greatest MSE and smallest R2 (mean 2888, 0.08; women 1734, 0.14). EVA better predicts IAFA in Japanese-American men and women compared to existing surrogates for adiposity." @default.
• W2890969701 created "2018-09-27" @default.
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• W2890969701 date "2018-11-01" @default.
• W2890969701 modified "2023-10-14" @default.
• W2890969701 title "Design and validation of a novel estimator of visceral adipose tissue area and comparison to existing adiposity surrogates" @default.
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• W2890969701 doi "https://doi.org/10.1016/j.jdiacomp.2018.09.004" @default.
• W2890969701 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6174110" @default.
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