Early Osteoporosis Risks and Associated Factors among Caregivers Working in Disability Institutions: IOF One-Minute Osteoporosis Risk Check

An osteoporosis knowledge assessment instrument - development and validation

INTRODUCTION

The consequences of osteoporotic fractures are extremely detrimental to the individual as well as to society. Adopting effective preventative measures is a top public health priority.

Construction of a predictive model for osteoporosis risk in men: using the IOF 1-min osteoporosis test

OBJECTIVE

To construct a clinical prediction nomogram model using the 1-min IOF osteoporosis risk test as an evaluation tool for male osteoporosis.

METHODS

The 1-min test results and the incidence of osteoporosis were collected from 354 patients in the osteoporotic clinic of our hospital. LASSO regression model and multi-factor logistic regression were used to analyze the risk factors of osteoporosis in patients, and the risk prediction model of osteoporosis was established. Verify with an additional 140 objects.

RESULTS

We used logistic regression to construct a nomogram model. According to the model, the AUC value of the training set was 0.760 (0.704-0.817). The validation set has an AUC value of 0.806 (0.733-0.879). The test set AUC value is 0.714 (0.609-0.818). The calibration curve shows that its advantage is that the deviation correction curve of the nomogram model can maintain a good consistency with the ideal curve. In terms of clinical applicability, compared with the "total intervention" and "no intervention" schemes, the clinical net return rate of the nomogram model showed certain advantages.

CONCLUSION

Using the 1-min osteoporosis risk test provided by IOF, we built a male osteoporosis risk prediction model with good prediction effect, which can provide greater reference and help for clinicians.

Risk of major osteoporotic fractures among ultra-orthodox Jews

The Ultra-Orthodox Jewish population has behaviors that can influence the risk for osteoporotic fractures. We investigated whether this population is more prone to osteoporotic fractures than non-Orthodox Jewish. We did not find a significant difference in osteoporotic fracture rates between the two populations despite major differences in exposure to potential risk factors.

INTRODUCTION

The Ultra-Orthodox Jewish population is a conservative population with unique cultural behaviors such as modest clothing and specific dietary restrictions, which can influence bone density and risk for osteoporotic fractures. The aim of this study is to investigate whether the Ultra-Orthodox Jewish population is more prone to osteoporotic fractures than the non-Orthodox Jewish population.

METHODS

This retrospective cohort study utilized computerized records from Maccabi Health Service. Study population included patients 65 years and older without a history of osteoporotic fracture, who reside in regions of Ultra-Orthodox and non-Orthodox Jews. The primary outcome was the adjusted risk to osteoporotic fracture during 9 years of follow-up. Cox regression included patient characteristics and risk factors for osteoporosis.

RESULTS

A total of 115,134 patients were included in this study: 5397 patients residing in Ultra-Orthodox regions (51.0% female) and 109,737 patients residing in non-Orthodox regions (52.6% female). A total of 16,352 (14.2%) patients had an osteoporotic fracture during the study period. There was no significant difference in fracture rate between Ultra-Orthodox and non-Orthodox (14.3% vs. 14.2%, p = 0.827). Among Ultra-Orthodox and non-Orthodox females and males, there were no significant differences in fracture rates (19.1% vs. 19.1% p = 0.982 and 9.3% vs. 8.8% p = 0.311, respectively). The adjusted hazard risk for the Ultra-Orthodox Jews was 1.026, 95% CI: 0.95-1.11, p = 0.512.

CONCLUSION

We did not find a significant difference in the rate of osteoporotic fractures between Ultra-Orthodox and non-Orthodox populations despite major differences in exposure to potential risk factors. Results suggest that the perception of risk factors relevant for the religious communities should be re-evaluated.

Risk of primary osteoporosis score (RPOPs): an algorithm model for primary osteoporosis risk assessment in grass-roots hospital

BACKGROUND

This study aimed to develop and validate a lasso regression algorithm model which was established by correlation factors of bone mineral density (BMD) and could be accurately predicted a high-risk population of primary osteoporosis (POP). It provides a rapid, economical and acceptable early screening method for osteoporosis in grass-roots hospitals.

METHODS

We collected 120 subjects from primary osteoporosis screening population in Zhejiang Quhua Hospital between May 2021 and November 2021 who were divided into three groups (normal, osteopenia and osteoporosis) according to the BMD T-score. The levels of three micro-RNAs in the plasma of these people were detected and assessed by qRT-PCR. At the same time, the levels of β-CTX and t-P1NP in serum of the three groups were determined. Based on the cluster random sampling method, 84 subjects (84/120, 70%) were selected as the training set and the rest were the test set. Lasso regression was used to screen characteristic variables and establish an algorithm model to evaluate the population at high risk of POP which was evaluated and tested in an independent test cohort. The feature variable screening process was used 10-fold cross validation to find the optimal lambda.

RESULTS

The osteoporosis risk score was established in the training set: Risk of primary osteoporosis score (RPOPs) = -0.1497785 + 2.52Age - 0.19miR21 + 0.35miR182 + 0.17β-CTx. The sensitivity, precision and accuracy of RPOPs in an independent test cohort were 79.17%, 82.61% and 75%, respectively. The AUC in the test set was 0.80. Some risk factors have a significant impact on the abnormal bone mass of the subjects. These risk factors were female (p = 0.00013), older than 55 (p < 2.2e-16) and BMI < 24 (p = 0.0091) who should pay more attention to their bone health.

CONCLUSION

In this study, we successfully constructed and validated an early screening model of osteoporosis that is able to recognize people at high risk for developing osteoporosis and remind them to take preventive measures. But it is necessary to conduct further external and prospective validation research in large sample size for RPOPs prediction models.