scholarly journals Reference indices of hip structural analysis in Ukrainian women

2018 ◽  
Vol 7 (4) ◽  
pp. 152-160
Author(s):  
N.V. Grygorieva ◽  
V.V. Povoroznyuk ◽  
Vas.V. Povoroznjuk ◽  
O.B. Zubach
2008 ◽  
Vol 11 (2) ◽  
pp. 232-236 ◽  
Author(s):  
Sven Prevrhal ◽  
John A. Shepherd ◽  
Kenneth G. Faulkner ◽  
Ken W. Gaither ◽  
Dennis M. Black ◽  
...  

2020 ◽  
Vol 16 (12) ◽  
pp. 2022-2030 ◽  
Author(s):  
Madhusmita Misra ◽  
Abisayo Animashaun ◽  
Amita Bose ◽  
Vibha Singhal ◽  
Fatima Cody Stanford ◽  
...  

2020 ◽  
Vol 105 (12) ◽  
pp. e4848-e4856
Author(s):  
Taïsha V Joseph ◽  
Signe Caksa ◽  
Madhusmita Misra ◽  
Deborah M Mitchell

Abstract Context Among patients with type 1 diabetes (T1D), the risk of hip fracture is up to 6-fold greater than that of the general population. However, the cause of this skeletal fragility remains poorly understood. Objective To assess differences in hip geometry and imaging-based estimates of bone strength between youth with and without T1D using dual-energy x-ray absorptiometry (DXA)-based hip structural analysis. Design Cross-sectional comparison. Participants Girls ages 10 to 16 years, including n = 62 with T1D and n = 61 controls. Results The groups had similar age, bone age, pubertal stage, height, lean mass, and physical activity. Bone mineral density at the femoral neck and total hip did not differ in univariate comparisons but was lower at the femoral neck in T1D after adjusting for bone age, height, and lean mass. Subjects with T1D had significantly lower cross-sectional area, cross-sectional moment of inertia, section modulus, and cortical thickness at the narrow neck, with deficits of 5.7% to 10.3%. Cross-sectional area was also lower at the intertrochanteric region in girls with T1D. Among those T1D subjects with HbA1c greater than the cohort median of 8.5%, deficits in hip geometry and strength estimates were more pronounced. Conclusions DXA-based hip structural analysis revealed that girls with T1D have unfavorable geometry and lower estimates of bone strength at the hip, which may contribute to skeletal fragility and excess hip fracture risk in adulthood. Higher average glycemia may exacerbate effects of T1D on hip geometry.


2019 ◽  
Vol 22 (2) ◽  
pp. 257-265 ◽  
Author(s):  
Sanne K.C. Buitendijk ◽  
Denise M. van de Laarschot ◽  
Alexandra A.A. Smits ◽  
Fjorda Koromani ◽  
Fernando Rivadeneira ◽  
...  

2013 ◽  
Vol 98 (7) ◽  
pp. 2952-2958 ◽  
Author(s):  
Madhusmita Misra ◽  
Debra K. Katzman ◽  
Hannah Clarke ◽  
Deirdre Snelgrove ◽  
Kathryn Brigham ◽  
...  

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Alessandra Aldieri ◽  
Mara Terzini ◽  
Giangiacomo Osella ◽  
Adriano M. Priola ◽  
Alberto Angeli ◽  
...  

At present, the current gold-standard for osteoporosis diagnosis is based on bone mineral density (BMD) measurement, which, however, has been demonstrated to poorly estimate fracture risk. Further parameters in the hands of the clinicians are represented by the hip structural analysis (HSA) variables, which include geometric information of the proximal femur cross section. The purpose of this study was to investigate the suitability of HSA parameters as additional hip fracture risk predictors. With this aim, twenty-eight three-dimensional patient-specific models of the proximal femur were built from computed tomography (CT) images and a sideways fall condition was reproduced by finite element (FE) analyses. A tensile or compressive predominance based on minimum and maximum principal strains was determined at each volume element and a risk factor (RF) was calculated. The power of HSA variables combinations to predict the maximum superficial RF values was assessed by multivariate linear regression analysis. The optimal regression model, identified through the Akaike information criterion (AIC), only comprises two variables: the buckling ratio (BR) and the neck-shaft angle (NSA). In order to validate the study, the model was tested on two additional patients who suffered a hip fracture after a fall. The results classified the patients in the high risk level, confirming the prediction power of the adopted model.


2008 ◽  
Vol 20 (6) ◽  
pp. 911-921 ◽  
Author(s):  
S. L. Bonnick ◽  
T. J. Beck ◽  
F. Cosman ◽  
M. C. Hochberg ◽  
H. Wang ◽  
...  

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