Use of quantitative ultrasound of the hand phalanges in the diagnosis of two different osteoporotic syndromes: Cushing's syndrome and postmenopausal osteoporosis

Rate of fracture in patients with glucocorticoid replacement therapy: a systematic review and meta-analysis

PURPOSE

The association between glucocorticoid replacement therapy for adrenal insufficiency (AI) and osteoporosis is unclear. Fracture is a major cause of morbidity in patients with osteoporosis. This study aims to determine if patients on glucocorticoid replacement therapy for AI have an increased rate of fractures compared to the general population.

METHODS

We included all studies with adult patients receiving glucocorticoid replacement therapy for either congenital adrenal hyperplasia (CAH), primary adrenal insufficiency (PAI), or secondary adrenal insufficiency (SAI). Studies without fracture data were excluded, as well as meeting abstracts. Studies with fractures but without a control group were eligible to be included in the systematic review but not in the meta-analysis. The primary outcome was the number of fractures, which was further differentiated into osteoporotic fractures. In addition, the glucocorticoid dose equivalents used were noted whenever possible.

RESULTS

Seventeen studies were included in the systematic review. Seven were used in the meta-analysis of any fracture and six were used for osteoporotic fracture. The reported fracture rate ranged between no fracture to 60.8% in the patient group and no fracture to 43.8% in the control group. The odds ratio (OR) for any fracture was 2.71 (95%CI: 1.36-5.43, P = 0.005) and for osteoporotic fracture 2.76 (95%CI: 2.39-3.19 P < 0.00001), favoring the control group.

CONCLUSIONS

Patients with AI on glucocorticoid replacement therapy have a higher rate of fractures compared to the control population.

Comparison of classification algorithms with wrapper-based feature selection for predicting osteoporosis outcome based on genetic factors in a taiwanese women population

An essential task in a genomic analysis of a human disease is limiting the number of strongly associated genes when studying susceptibility to the disease. The goal of this study was to compare computational tools with and without feature selection for predicting osteoporosis outcome in Taiwanese women based on genetic factors such as single nucleotide polymorphisms (SNPs). To elucidate relationships between osteoporosis and SNPs in this population, three classification algorithms were applied: multilayer feedforward neural network (MFNN), naive Bayes, and logistic regression. A wrapper-based feature selection method was also used to identify a subset of major SNPs. Experimental results showed that the MFNN model with the wrapper-based approach was the best predictive model for inferring disease susceptibility based on the complex relationship between osteoporosis and SNPs in Taiwanese women. The findings suggest that patients and doctors can use the proposed tool to enhance decision making based on clinical factors such as SNP genotyping data.