The correlation between osteoporotic vertebrae fracture risk and bone mineral density measured by quantitative computed tomography and dual energy X-ray absorptiometry: a systematic review and meta-analysis

Serum bicarbonate concentration is associated with bone density in adults with type 2 diabetes mellitus: African American-Diabetes Heart Study

BACKGROUND

Osteoporosis is a significant cause of morbidity and mortality in the aging population. Individuals with type 2 diabetes mellitus (T2D) typically have higher bone density yet also a higher rate of fractures. Blacks, meanwhile, have a lower incidence of osteoporosis compared to European Americans. Serum bicarbonate may be a risk factor for bone loss, but studies are conflicting, and little is known about this relationship in T2D or Blacks.

METHODS

We examined the longitudinal relationship between serum bicarbonate and change in bone density in 300 participants with T2D in the African American-Diabetes Heart Study (AA-DHS). Serum bicarbonate was measured at baseline, and bone density was assessed using CT volumetric bone mineral density (vBMD) scans of the thoracic and lumbar vertebrae at baseline and after five years of follow-up. Multivariate linear regression models assessed associations between baseline serum bicarbonate and longitudinal change in vBMD, adjusted for multiple confounders.

RESULTS

The cohort was 50 % female, with mean age and T2D duration 55.1 years and 10.2 years, respectively. The mean baseline serum bicarbonate was 26.6 (SD 3.3) mEq/L; median baseline lumbar spine vBMD 179.3 (IQR 148.2, 208.9) mg/cm3, and median baseline thoracic spine vBMD 204.9 (IQR 171.6, 231.9) mg/cm3. In fully-adjusted analyses, each 1 mEq/L increase in baseline serum bicarbonate was significantly associated with 5-year relative increase in lumbar vBMD (0.94 mg/cm3, p < 0.001) and thoracic vBMD (1.35 mg/cm3, p < 0.001), without a clear threshold effect or differences by sex.

CONCLUSIONS

In this cohort of Blacks with T2D, higher baseline serum bicarbonate levels were associated with improved changes in bone density over time. Further studies are needed to determine if alkali supplementation would ameliorate loss of bone density in this population.

Conventional chest computed tomography-based radiomics for predicting the risk of thoracolumbar osteoporotic vertebral fractures

Our study focused on predicting thoracolumbar osteoporotic vertebral fractures through radiomic analysis of non-fractured thoracic vertebrae using conventional chest CT. Four types of radiomics models were developed and showed acceptable prediction performance. Radiomics models incorporating both cortical-appendicular and trabecular bone may have superior performance compared to those using either feature set individually. The RAD score models based on thoracic vertebral combinations achieved comparable performance with lumbar bone mineral density (BMD) measurements.

PURPOSE

To develop and validate radiomics models based on chest CT for predicting the risk of thoracolumbar osteoporotic vertebral fractures (OVFs).

METHODS

A total of 494 patients (including 198 patients with thoracolumbar OVFs) who underwent conventional chest CT scans were included in this retrospective analysis and were divided into training set 1 (n = 334) and validation set 1 (n = 160). Radiomics features (RFs) were extracted from each thoracic vertebral level on chest CT images. Four types of radiomics models (trabecular RFs, cortical-appendicular RFs, mixed RFs, and RAD score) were constructed and compared. Additionally, RAD score models based on trabecular and cortical-appendicular bone of different vertebral combinations (T1-T6, T7-T12, and top 3 vertebrae) were performed, respectively. A subset of patients with available bone mineral density (BMD) data formed training set 2 (n = 199) and validation set 2 (n = 88). We combined RAD score of different vertebral combinations with lumbar BMD for predicting thoracolumbar OVFs, and further adjusted for age. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

Among the radiomics models, the RAD score model based on trabecular and cortical-appendicular bone achieved highest AUC at the most vertebral levels. The RAD score model of top 3 (T5 + T8 + T10) vertebrae achieved higher AUC (0.813) than T7-T12 (AUC = 0.780) with a statistically significant difference (P = 0.02) and T1-T6 (AUC = 0.772) without a statistically significant difference (P = 0.062). Prior to adjusting for age, both RAD score models (AUCs 0.774-0.807) and RAD score + BMD models (AUCs 0.771-0.800) demonstrated slightly superior performance compared to BMD (AUC = 0.736) alone in predicting OVFs, although the differences were not statistically significant (P > 0.05). Following adjustment for age, our RAD score models, which utilized different vertebral combinations (AUCs 0.784-0.804), were found to be comparable to lumbar BMD (AUC = 0.785) in predicting OVFs (P > 0.05).

CONCLUSION

Radiomics analysis based on conventional chest CT can provide valuable information for predicting thoracolumbar OVFs. Radiomics models incorporating both cortical-appendicular and trabecular bone may have superior performance compared to those using either feature set alone. RAD score models based on thoracic vertebral combinations comparable performance compared to lumbar BMD highlights its clinical utility.

Undetected low bone mineral density in patients undergoing lumbar fusion surgery-prevalence and risk factors

Background

Sufficient bone quality is a prerequisite for low complication rates and satisfactory outcomes in lumbar fusion surgery (LFS). Low bone mineral density (BMD), including osteoporosis and osteopenia, is linked to adverse postoperative outcomes. Despite reports of a high prevalence of undiagnosed osteoporosis, it is uncertain which risk factors should guide preoperative BMD screening in LFS.

Methods

This secondary cross-sectional analysis of a prospective institutional database at an academic spine center included adult patients undergoing LFS for degenerative conditions between 2014 and 2023. Opportunistic quantitative CT (qCT) at the L1/2 level was performed before surgery, and demographic and medical history data were extracted. Descriptive and comparative statistics, univariable and multivariable logistic regression were performed to determine risk factors for present and undiagnosed osteoporosis.

Results

Of the 675 patients screened, 578 (54% female) were included after excluding those with preoperative lumbar CT scans not suitable for qCT. The median age was 65 years (IQR 58-72), and the median BMI of 28.9 kg/m2 (IQR 25.2-32.9). Osteoporosis was identified in 182 patients (31%), with 114 previously diagnosed and 68 newly detected via preoperative qCT. Undiagnosed osteoporosis was found in 12% of all patients and 37% of those with osteoporosis. Osteopenia was present in 199 patients (34%), leading to an overall impaired bone quality prevalence of 66%. Multivariable analysis revealed that age and female sex were independent risk factors for osteoporosis, while undiagnosed cases were more common in males, patients with higher BMI, and older individuals.

Conclusions

This study found a high prevalence of abnormal BMD in LFS patients, with a significant proportion of undiagnosed osteoporosis. While osteoporosis was more common in females, male patients with osteoporosis were more frequently undiagnosed. Spine surgeons must remain vigilant about metabolic bone disease in LFS patients to ensure preoperative optimization and prevent complications.

Predicting Proximal Humerus Fracture Mechanical Complications: Are Computed Tomography Hounsfield Units the Answer?

INTRODUCTION

The purpose was to determine whether computed tomography (CT) Hounsfield units (HU) as a proxy for bone quality can predict postoperative complications following surgical treatment of proximal humerus fractures.

METHODS

Sixty-six patients with 2-, 3-, or 4-part proximal humerus fractures who underwent surgical fixation at single institution and had complete radiographic data available were included. Radiographic measurements included the deltoid tuberosity index (DTI) on preoperative anterior-posterior shoulder radiographs, and the HU value from the surgical proximal humerus was determined by measuring the humeral head at the midaxial/coronal/sagittal CT image using a circle-type region of interest (≥35 mm 2 ). Postoperative complications recorded were implant failure, development of osteonecrosis, nonunion, and acute periprosthetic fracture. Patients with and without complications were statistically compared, and binary logistic regression was performed to determine whether preoperative proximal humerus CT HU were predictive of complications.

RESULTS

Eight patients (12.1%) developed 11 overall complications, with three patients experiencing multiple complications each. Complications included osteonecrosis (4), implant failure (5), nonunion (1), and acute periprosthetic fracture (1). No difference was observed in demographics or Neer or AO/OTA classification between those with and without complications. Patients with complications had markedly lower DTI and overall HU as well as HU in the coronal and sagittal planes. Regression analysis for average DTI demonstrated a higher DTI and had a 10 times decreased risk of complication ( P = 0.040, odds ratio = -10.5, 95% confidence interval, 0.000 to 0.616). Regression analysis for average total HU also found a higher HU associated with a decreased risk of complications ( P = 0.034, odds ratio = -0.020, 95% confidence interval, 0.980 to 0.962). Logistic regression analysis, including age, age-adjusted Charlson Comorbidity Index, mean DTI, and mean total HU, only found mean total HU to be notable within the model.

DISCUSSION

CT HU may identify patients with poorer bone quality and thus help predict postoperative complications.

LEVEL OF EVIDENCE

Diagnostic Level III.

Enhancing fracture risk indication: The impact of bone load index and muscle fat infiltration on vertebral compression fracture

PURPOSE

This study aims to identify risk factors for vertebral compression fracture and enhance the ability to indicate fracture risk.

METHODS

A retrospective collection of clinical and imaging data was conducted for patients with vertebral compression fractures and control subjects who underwent quantitative computed tomography scans. Stepwise logistic regression analysis was employed to identify variables associated with fractures, constructing both unadjusted model and adjusted model.

RESULTS

Compared with the non-fracture group, the fracture group showed significant differences in weight, body mass index (BMI), bone mineral density (BMD), vertebral cross-sectional area, paraspinal muscle area and right psoas major muscle fat area (all P < 0.05). Adjusted characteristics analyzed by stepwise logistic regression indicated that bone load index (BLI) (OR = 3.19, P = 0.041), paraspinal muscle fat infiltration (PMFI) (OR = 2.27, P = 0.039), and right psoas major muscle fat infiltration (RPMFI) (OR = 1.08, P = 0.005) were independent risk factors for vertebral fractures. Interaction analysis revealed a positive interaction between BLI and PMFI (OR = 1.95, P = 0.008) as well as RPMFI (OR = 1.53, P = 0.045). Compared with the unadjusted model, the diagnostic performance of the adjusted model was significantly improved (training set IDI: 19.5 %, validation set IDI: 18.4 %, P < 0.001). Correlation analysis demonstrated significant associations between BMD (r = -0.353, P = 0.002), BLI (r = 0.631, P < 0.001), PMFI (r = 0.412, P < 0.001), RPMFI (r = 0.513, P < 0.001), and the degree of vertebral compression.

CONCLUSION

Under conditions of bone maladaptive loading and muscle degeneration, vertebral bodies may become more susceptible to external forces, increasing the risk of vertebral compression fracture.

Association of Osteoporosis with Tooth Loss and Dental Radiomorphometric Indices

Background/Objectives: Osteoporosis is a systemic disease associated with reduced bone mass, impaired bone microarchitecture, and thus an increased risk of bone fractures. Moreover, patients with osteoporosis are more likely to experience periodontal diseases and tooth loss. Some indices have been proposed to detect osteoporosis on dental panoramic radiographs. The aim of our retrospective study was to investigate the association between osteoporosis and the loss of alveolar bone and teeth and to evaluate the validity of several dental radiomorphometric indices for assessing osteoporosis. Methods: In patients with and without osteoporosis, tooth loss, alveolar bone loss, the panoramic mandibular index (PMI), mental index (MI), and mandibular cortical index (MCI) were determined. Results: Compared with the non-osteoporotic group, patients with osteoporosis showed more tooth loss and more severe alveolar bone loss. PMI and MI were lower in patients with osteoporosis than in the non-osteoporotic group. Analysis of MCI showed that category C3 (cortical layer forms strong endosteal cortical residues and is clearly porous) was significantly more common in patients with osteoporosis. Conclusions: Osteoporosis is associated with more tooth and alveolar bone loss. Furthermore, various dental radiomorphometric indices are altered in osteoporosis and could thus help to better assess osteoporosis of the jaw.

The quantification of bone mineral density using photon counting computed tomography and its implications for detecting bone remodeling

HR-pQCT has become standard practice when quantifying volumetric BMD (vBMD) in vivo. Yet, it is only accessible to peripheral sites, with small fields of view and lengthy scanning times. This limits general applicability in clinical workflows. The goal of this study was to assess the potential of photon counting CT (PCCT) in quantitative bone imaging. Using the European Forearm Phantom, PCCT was calibrated to hydroxyapatite (HA) density. Eight cadaveric forearms were scanned twice with PCCT and once with HR-pQCT. The dominant forearm of two volunteers was scanned twice with PCCT. In each scan, the carpals were delineated. At bone level, accuracy was assessed with a paired measurement of total vBMD (Tt.vBMD) calculated with PCCT and HR-pQCT. At voxel-level, repeatability was assessed by image registration and voxel-wise subtraction of the ex vivo PCCT scans. In an ideal scenario, this difference would be zero; any deviation was interpreted as falsely detected remodeling. For clinical usage, the least detectable remodeling was determined by finding a threshold in the PCCT difference image that resulted in a classification of bone formation and resorption below acceptable noise levels (<0.5%). The paired measurement of Tt.vBMD had a Pearson correlation of 0.986. Compared to HR-pQCT, PCCT showed a bias of 7.46 mgHA/cm3. At voxel-level, the repeated PCCT scans showed a bias of 17.66 mgHA/cm3 and a standard error of 96.23 mgHA/cm3. Least detectable remodeling was found to be 250 mgHA/cm3, for which 0.37% of the voxels was incorrectly classified as newly added or resorbed bone. In vivo, this volume increased to 0.97%. Based on the cadaver data, we conclude that PCCT can be used to quantify vBMD and bone turnover. We provided proof of principle that this technique is also accurate in vivo, hence, that it has high potential for clinical applications.

Analysis of guide wire displacement in robot-assisted spinal pedicle screw implantation

Robot-assisted pedicle screw placement is prone to guide wire migration, and the related influencing factors have not yet been discussed. Therefore, this study aimed to investigate and analyze the causes of robot-assisted spinal pedicle guide wire displacement and summarize the relevant treatment strategies. The surgical outcomes of 82 patients who underwent robotic-assisted pedicle screw spinal placement at our hospital between July 2022 and June 2023 were retrospectively analyzed. A total of 342 screws were placed in 82 patients; 47 guide wires were offset, 47 guide wires were replaced, and 295 guide wires were not significantly offset, with a first guide wire offset rate of 13.7% and a total guide wire offset rate of 12.1%. Univariate analysis showed that Screw placement level, whether respiration was controlled during guide wire placement, Hu value of CT, the position of needle insertion point, and operation time had a significant effect on guide wire deviation (P < 0.05). Multivariate logistic regression analysis showed that the inclusion of screw placement segments, whether breathing was controlled during guide wire placement, and Hu value of CT had a significant effect on guide wire offset (P < 0.05). Whether the guide wire was offset had no significant effect on the accuracy of subsequent pedicle screw implantation (P > 0.05). The level of screw placement, whether breathing was controlled during guide wire placement, and Hu value of CT were independent risk factors for guide wire deviation. When causing an excursion, screw orientation can be adjusted during intraoperative screw placement, and guide wire excursion has no significant impact on the accuracy of subsequent pedicle screw placement.

Identification of mitophagy-related biomarkers in human osteoporosis based on a machine learning model

Background: Osteoporosis (OP) is a chronic bone metabolic disease and a serious global public health problem. Several studies have shown that mitophagy plays an important role in bone metabolism disorders; however, its role in osteoporosis remains unclear. Methods: The Gene Expression Omnibus (GEO) database was used to download GSE56815, a dataset containing low and high BMD, and differentially expressed genes (DEGs) were analyzed. Mitochondrial autophagy-related genes (MRG) were downloaded from the existing literature, and highly correlated MRG were screened by bioinformatics methods. The results from both were taken as differentially expressed (DE)-MRG, and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Protein-protein interaction network (PPI) analysis, support vector machine recursive feature elimination (SVM-RFE), and Boruta method were used to identify DE-MRG. A receiver operating characteristic curve (ROC) was drawn, a nomogram model was constructed to determine its diagnostic value, and a variety of bioinformatics methods were used to verify the relationship between these related genes and OP, including GO and KEGG analysis, IP pathway analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA). In addition, a hub gene-related network was constructed and potential drugs for the treatment of OP were predicted. Finally, the specific genes were verified by real-time quantitative polymerase chain reaction (RT-qPCR). Results: In total, 548 DEGs were identified in the GSE56815 dataset. The weighted gene co-expression network analysis(WGCNA) identified 2291 key module genes, and 91 DE-MRG were obtained by combining the two. The PPI network revealed that the target gene for AKT1 interacted with most proteins. Three MRG (NELFB, SFSWAP, and MAP3K3) were identified as hub genes, with areas under the curve (AUC) 0.75, 0.71, and 0.70, respectively. The nomogram model has high diagnostic value. GO and KEGG analysis showed that ribosome pathway and cellular ribosome pathway may be the pathways regulating the progression of OP. IPA showed that MAP3K3 was associated with six pathways, including GNRH Signaling. The ssGSEA indicated that NELFB was highly correlated with iDCs (cor = -0.390, p < 0.001). The regulatory network showed a complex relationship between miRNA, transcription factor(TF) and hub genes. In addition, 4 drugs such as vinclozolin were predicted to be potential therapeutic drugs for OP. In RT-qPCR verification, the hub gene NELFB was consistent with the results of bioinformatics analysis. Conclusion: Mitophagy plays an important role in the development of osteoporosis. The identification of three mitophagy-related genes may contribute to the early diagnosis, mechanism research and treatment of OP.