Patellar tendon biomechanical and morphologic properties and their relationship to serum clinical variables in persons with prediabetes and type 2 diabetes.

Tendon biomechanical properties and fibril organization are altered in patients with diabetes compared to healthy individuals, yet few biomarkers have been associated with in vivo tendon properties. We investigated the relationships between in vivo imaging-based tendon properties, serum variables, and patient characteristics across healthy controls (n = 14, age: 45 ± 5 years, body mass index [BMI]: 24 ± 1, hemoglobin A1c [HbA1c]: 5.3 ± 0.1%), prediabetes (n = 14, age: 54 ± 5 years, BMI: 29 ± 2; HbA1c: 5.7 ± 0.1), and type 2 diabetes (n = 13, age: 55 ± 3 years, BMI: 33 ± 2, HbA1c: 6.7 ± 0.3). We used ultrasound speckle-tracking and measurements from magnetic resonance imaging (MRI) to estimate the patellar tendon in vivo tangent modulus. Analysis of plasma c-peptide, interleukin-1ß (IL-1ß), IL-6, IL-8, tumor necrosis factor-α (TNF-α), adiponectin, leptin, insulin-like growth factor 1 (IGF-1), and C-reactive protein (CRP) was completed. We built regression models incorporating statistically significant covariates and indicators for the clinically defined groups. We found that tendon cross-sectional area normalized to body weight (BWN CSA) and modulus were lower in patients with type 2 diabetes than in healthy controls (p < 0.05). Our regression analysis revealed that a model that included BMI, leptin, high-density lipoprotein (HDL), low-density lipoprotein (LDL), age, and group explained ~70% of the variability in BWN CSA (R2 = 0.70, p < 0.001). For modulus, including the main effects LDL, groups, HbA1c, age, BMI, cholesterol, IGF-1, c-peptide, leptin, and IL-6, accounted for ~54% of the variability in modulus (R2 = 0.54, p < 0.05). While BWN CSA and modulus were lower in those with diabetes, group was a poor predicter of tendon properties when considering the selected covariates. These data highlight the multifactorial nature of tendon changes with diabetes and suggest that blood variables could be reliable predictors of tendon properties.

Differentiation of Low- and High-Grade Pediatric Brain Tumors with High b-Value Diffusion-weighted MR Imaging and a Fractional Order Calculus Model.

PURPOSE: To demonstrate that a new set of parameters (D, ß, and µ) from a fractional order calculus (FROC) diffusion model can be used to improve the accuracy of MR imaging for differentiating among low- and high-grade pediatric brain tumors. MATERIALS AND METHODS: The institutional review board of the performing hospital approved this study, and written informed consent was obtained from the legal guardians of pediatric patients. Multi-b-value diffusion-weighted magnetic resonance (MR) imaging was performed in 67 pediatric patients with brain tumors. Diffusion coefficient D, fractional order parameter ß (which correlates with tissue heterogeneity), and a microstructural quantity µ were calculated by fitting the multi-b-value diffusion-weighted images to an FROC model. D, ß, and µ values were measured in solid tumor regions, as well as in normal-appearing gray matter as a control. These values were compared between the low- and high-grade tumor groups by using the Mann-Whitney U test. The performance of FROC parameters for differentiating among patient groups was evaluated with receiver operating characteristic (ROC) analysis. RESULTS: None of the FROC parameters exhibited significant differences in normal-appearing gray matter (P ≥ .24), but all showed a significant difference (P < .002) between low- (D, 1.53 µm(2)/msec ± 0.47; ß, 0.87 ± 0.06; µ, 8.67 µm ± 0.95) and high-grade (D, 0.86 µm(2)/msec ± 0.23; ß, 0.73 ± 0.06; µ, 7.8 µm ± 0.70) brain tumor groups. The combination of D and ß produced the largest area under the ROC curve (0.962) in the ROC analysis compared with individual parameters (ß, 0.943; D,0.910; and µ, 0.763), indicating an improved performance for tumor differentiation. CONCLUSION: The FROC parameters can be used to differentiate between low- and high-grade pediatric brain tumor groups. The combination of FROC parameters or individual parameters may serve as in vivo, noninvasive, and quantitative imaging markers for classifying pediatric brain tumors.