Left-right differences in the proximal femur's strength of post-menopausal women: a multicentric finite element study.

UNLABELLED: The strength of both femurs was estimated in 198 post-menopausal women through subject-specific finite element models. Important random differences between contralateral femurs were found in a significant number of subjects, pointing to the usefulness of further studies to understand if strength-based classification of patients at risk of fracture can be affected by laterality issues. INTRODUCTION: Significant, although small, differences exist in mineral density and anatomy of contralateral proximal femurs. These differences, and their combined effect, may result in a side difference in femurs' strength. However, this has never been tested on a large sample of a homogenous population. METHODS: The strength of both femurs was estimated in 198 post-menopausal women through CT-derived finite element models, built using a validated procedure, in sideways fall conditions. The impact of the resulting asymmetry on the classification of subjects at risk of fracture was analysed. RESULTS: The small difference observed between sides (the right femur on average 4 % stronger than the left) was statistically significant but mechanically negligible. In contrast, higher random differences (absolute difference between sides with respect to mean value) were found: on average close to 15 % (compared to 9.2 % for areal bone mineral density (aBMD) alone), with high scatter among the subjects. When using a threshold-based classification, the right and left femurs were discordant up to over 20 % of cases (K always lower than 0.60) but the left femur was concordant (mean K = 0.84) with the minimum strength between right and left. CONCLUSION: Considering both femurs may be important when trying to classify subjects at risk of failure with strength estimates. Future studies including fracture assessment would be necessary to quantify the real impact.

Discriminatory accuracy and potential clinical utility of genomic profiling for breast cancer risk in BRCA-negative women.

Several single nucleotide polymorphisms (SNPs) are associated with an increased risk of breast cancer. The clinical utility of genotyping individuals at these loci is not known. Subjects were 519 unaffected women without BRCA mutations. Gail, Claus, and IBIS models were used to estimate absolute breast cancer risks. Subjects were then genotyped at 15 independent risk loci. Published per-allele and genotype-specific odds ratios were used to calculate the composite cumulative genomic risk (CGR) for each subject. Affected age- and ethnicity-matched BRCA mutation-negative women were also genotyped as a comparison group for the calculation of discriminatory accuracy. The CGR was used to adjust absolute breast cancer risks calculated by Gail, Claus and IBIS models to determine the proportion of subjects whose recommendations for chemoprevention or MRI screening might be altered (reclassified) by such adjustment. Mean lifetime breast cancer risks calculated using the Gail, Claus, and IBIS models were 19.4, 13.0, and 17.7%, respectively. CGR did not correlate with breast cancer risk as calculated using any model. CGR was significantly higher in affected women (mean 3.35 vs. 3.12, P = 0.009). The discriminatory accuracy of the CGR alone was 0.55 (SE 0.019; P = 0.006). CGR adjustment of model-derived absolute risk estimates would have altered clinical recommendations for chemoprevention in 11-19% of subjects and for MRI screening in 8-32%. CGR has limited discriminatory accuracy. However, the use of a genomic risk term to adjust model-derived estimates has the potential to alter individual recommendations. These observations warrant investigation to evaluate the calibration of adjusted risk estimates.