WHAT'S KNOWN ON THIS SUBJECT: Obesity is associated with cardiometabolic risk factors and chronic conditions, such as type 2 diabetes. However, a proportion of overweight and obese youth remain free from cardiometabolic risk factors and are considered metabolically healthy. WHAT THIS STUDY ADDS:This study provides insight into the determinants of cardiometabolic risk factors and the concept in health promotion of "fitness versus fatness." Hepatic lipid accumulation and not fitness level appears to drive cardiometabolic risk factor clustering among overweight and obese youth. abstract OBJECTIVE: Controversy exists surrounding the contribution of fitness and adiposity as determinants of the Metabolically Healthy Overweight (MHO) phenotype in youth. This study investigated the independent contribution of cardiorespiratory fitness and adiposity to the MHO phenotype among overweight and obese youth. METHODS:This cross-sectional study included 108 overweight and obese youth classified as MHO (no cardiometabolic risk factors) or non-MHO ($1 cardiometabolic risk factor), based on age-and genderspecific cut-points for fasting glucose, triglycerides, high-density lipoprotein cholesterol, systolic and diastolic blood pressure, and hepatic steatosis.RESULTS: Twenty-five percent of overweight and obese youth were classified as MHO. This phenotype was associated with lower BMI z-score (BMI z-score: 1.8 6 0.3 vs 2.1 6 0.4, P = .02) and waist circumference (99.7 6 13.2 vs 106.1 6 13.7 cm, P = .04) compared with non-MHO youth. When matched for fitness level and stratified by BMI z-score (1.6 6 0.3 vs 2.4 6 0.2), the prevalence of MHO was fourfold higher in the low BMI z-score group (27% vs 7%; P = .03). Multiple logistic regression analyses revealed that the best predictor of MHO was the absence of hepatic steatosis even after adjusting for waist circumference (odds ratio 0.57, 95% confidence interval 0.40-0.80) or BMI z-score (odds ratio 0.59, 95% confidence interval 0.43-0.80). CONCLUSIONS:The MHO phenotype was present in 25% of overweight and obese youth and is strongly associated with lower levels of adiposity, and the absence of hepatic steatosis, but not with cardiorespiratory fitness. Pediatrics 2013;132:e85-e92 Obesity is associated with a clustering of cardiometabolic risk factors, including hypertension, insulin resistance, inflammation, and dyslipidemia. 1 However, cardiometabolic risk factor clustering is not an obligatory consequence of obesity. In fact, 18% to 44% of obese individuals are free from cardiometabolic risk factors. 2,3 The absence of cardiometabolic risk factor clustering in obese individuals is associated with lower measures of adiposity, including lower total fat mass, 4 abdominal obesity, 5,6 visceral fat mass, 3,7,8 and the absence of ectopic lipid accumulation. 9 Surprisingly, little attention has been paid to the modifiable factors as determinants of this "Metabolically Healthy Overweight" (MHO) phenotype. 10,11 This is particularly important, as the identification of modifiable behaviors associa...

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Image registration is an important step in the radiotherapy treatment planning process. It provides a method of fusing different types of diagnostic imaging information. One such application is to combine magnetic resonance spectroscopic images (MRSI) of the prostate with anatomical MRI and/or computed tomography images that are routinely used in the radiation treatment planning of prostate cancer. MRSI provides in vivo information related to the underlying metabolic activity of tissues, and can be related to the presence of cancer. However, the inflated endorectal coil required during MRS imaging poses a potential problem by deforming the prostate when it is filled with approximately 100 cm3 of air during image acquisition. This pushes the prostate superiorly/anteriorly, deforming the prostate and consequently the spectroscopic imaging data in a nonlinear manner. In this application, the coil-deformed MRS images are warped back to a non-deformed state, using a single data set. A nonlinear warping algorithm is presented to achieve this. Results indicate that the algorithm attains an accuracy of 97% (4 cm3 difference) when reproducing the total prostate volume compared to a Radiation Oncologist defined prostate volume. This difference is slightly smaller than the measured intra-operator variance of +/-1.5 cm3 (deflated coil) and the measured algorithm variance of +/-1.0 cm3. Additionally, intraprostatic nodules were used to assess the accuracy of the warping algorithm in regions inside the prostate. While choosing anatomical tie points along the external prostate surface, analysis of the nodules revealed the algorithm accuracy reduced to 63-93%.