Computed tomography-based paravertebral muscle density predicts subsequent vertebral fracture risks independently of bone mineral density in postmenopausal women following percutaneous vertebral augmentation

Role of effective atomic number of paraspinal muscles in the prediction of acute vertebral fracture risk assessment: a cross-sectional case-control study

OBJECTIVES

We aim to investigate the relations among effective atomic number (Zeff), density, and area of paraspinal muscles, volumetric bone mineral density (vBMD), and acute vertebral fractures (VF) by using spectral base images (SBIs) and routine CT images.

METHODS

A total of 223 patients (52 men and 171 women) with acute lumber VF and 776 subjects (286 men and 390 women) without VF of at least 60 years were enrolled and underwent dual-layer detector CT scans. We quantified the cross-sectional area, density (paraSMD), and Zeff of paraspinal muscles by CT images and SBIs and measured vBMD of the lumbar spine by quantitative CT.

RESULTS

Higher vBMD was associated with lower VF risk in both sexes (adjusted OR, 0.33 and 0.43). After adjusting for age and body mass index, the associations of paraSMD with VF were not significant in men, and in women the association was borderline significant (OR, 0.80; 95% CI, 0.64-1.00). However, higher Zeff of paraspinal muscles was associated with lower VF risk in men (adjusted OR, 0.59; 0.36-0.96) but not in women. The associations of all muscle indexes with VF were not significant after further adjusting for vBMD.

CONCLUSIONS

A higher Zeff of paraspinal muscles is associated with lower VF risk in older men but not in older women. The density, area, and Zeff of paraspinal muscles were not vBMD independent risk factors for acute VF.

ADVANCES IN KNOWLEDGE

The effective atomic number of paraspinal muscles might be a potential marker for VF risk prediction.

A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study

BACKGROUND

With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations.

OBJECTIVE

The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles.

METHODS

The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles.

RESULTS

The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001).

CONCLUSIONS

The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.

A CT-based Deep Learning Model for Predicting Subsequent Fracture Risk in Patients with Hip Fracture

Background Patients have the highest risk of subsequent fractures in the first few years after an initial fracture, yet models to predict short-term subsequent risk have not been developed. Purpose To develop and validate a deep learning prediction model for subsequent fracture risk using digitally reconstructed radiographs from hip CT in patients with recent hip fractures. Materials and Methods This retrospective study included adult patients who underwent three-dimensional hip CT due to a fracture from January 2004 to December 2020. Two-dimensional frontal, lateral, and axial digitally reconstructed radiographs were generated and assembled to construct an ensemble model. DenseNet modules were used to calculate risk probability based on extracted image features and fracture-free probability plots were output. Model performance was assessed using the C index and area under the receiver operating characteristic curve (AUC) and compared with other models using the paired t test. Results The training and validation set included 1012 patients (mean age, 74.5 years ± 13.3 [SD]; 706 female, 113 subsequent fracture) and the test set included 468 patients (mean age, 75.9 years ± 14.0; 335 female, 22 subsequent fractures). In the test set, the ensemble model had a higher C index (0.73) for predicting subsequent fractures than that of other image-based models (C index range, 0.59-0.70 for five of six models; P value range, < .001 to < .05). The ensemble model achieved AUCs of 0.74, 0.74, and 0.73 at the 2-, 3-, and 5-year follow-ups, respectively; higher than that of most other image-based models at 2 years (AUC range, 0.57-0.71 for five of six models; P value range, < .001 to < .05) and 3 years (AUC range, 0.55-0.72 for four of six models; P value range, < .001 to < .05). Moreover, the AUCs achieved by the ensemble model were higher than that of a clinical model that included known risk factors (2-, 3-, and 5-year AUCs of 0.58, 0.64, and 0.70, respectively; P < .001 for all). Conclusion In patients with recent hip fractures, the ensemble deep learning model using digital reconstructed radiographs from hip CT showed good performance for predicting subsequent fractures in the short term. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Li and Jaremko in this issue.

Self-reported sleep characteristics are linked to type 2 diabetes in middle-aged and elderly individuals: a cross-sectional study based on NHANES

OBJECTIVE

This study was aimed to evaluate the link between sleep characteristics and type 2 diabetes of middle-aged and elderly individuals.

METHODS

Twenty thousand four hundred ninety-seven individuals enrolled in National Health and Nutritional Examination Survey (NHANES) form periods of 2005-2008 were included in this study, and 3965 individuals aged 45 years and older with complete data were detected. Variables related to sleep characteristics were analyzed by univariate analysis to identify the risk factors of type 2 diabetes, the logistic regression model was used to test for the tendency across the sections of sleep duration, and the link between sleep duration and risk of type 2 diabetes was manifested as odds ratio (OR) and 95% confidence interval (CI).

RESULTS

Six hundred ninety-four individuals with type 2 diabetes were identified and enrolled in the type 2 diabetes group, while the remaining individuals (n = 3271) were enrolled in the non-type 2 diabetes group. Individuals in the type 2 diabetes group (63.9 ± 10.2) were older than those in the non-type 2 diabetes group (61.2 ± 11.5, P < 0.001). Factors of taking longer time to fall asleep (P < 0.001), sleeping less (≤ 4 h) or more (≥ 9 h) (P < 0.001), having trouble in falling asleep (P = 0.001), frequent snoring (P < 0.001), frequent sleep apnea (P < 0.001), frequent nighttime awakenings (P = 0.004), and frequent excessive daytime sleepiness (P < 0.001) were linked to the risk of type 2 diabetes.

CONCLUSION

Our study revealed that sleep characteristics were closely linked to type 2 diabetes in middle-aged and elderly individuals, and a longer sleep duration might have protective effects against type 2 diabetes, but it should be constrained within 9 h/night.

The Preventive Effects of Probiotic Prevotella histicola on the Bone Loss of Mice with Ovariectomy-Mediated Osteoporosis

It has been demonstrated that the disturbance of gut microbiota (GM) is closely related to the reduction of bone mass and incidence of osteoporosis (OP). The aim of this study is to investigate whether the supplementation of Prevotella histicola (Ph) can prevent the bone loss in mice with ovariectomy (OVX)-mediated OP, and further explore relevant mechanisms. Regular (once a day for 8 consecutive weeks) and quantitative (200 µL/d) perfusion of Ph (the bacteria that orally gavaged) was conducted starting from 1 week after the construction of mice models. Bone mass and bone microstructure were detected by Micro-computed tomography (Micro-CT). Expressions of intestinal permeability, pro-inflammatory cytokines, and osteogenic and osteoclastic activities of mice were analyzed by histological staining and immunohistochemistry (IHC). 16S rRNA high throughput sequencing technique was applied to analyze the alterations of composition, abundance, and diversity of collected feces. Regular and quantitative perfusion of Ph mitigated the bone loss in mice with OVX-mediated OP. Compared with OVX + PBS group, perfusion of Ph repressed osteoclastogenesis and promoted osteogenesis, reduced release of pro-inflammatory cytokine cytokines (interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α)), and reversed expressions of tight junction proteins (zonula occludens protein 1 (ZO-1) and Occludin). Besides, the perfusion of Ph improved the composition, abundance, and diversity of GM. Collectively, this study revealed that regular and quantitative perfusion of Ph can improve the bone loss in mice with OVX-mediated OP by repairing intestinal mucosal barrier damage, optimizing intestinal permeability, inhibiting release of pro-osteoclastogenic cytokines, and improving disturbance of GM.