Association between advanced glycation end-products and fall risk in older adults: The Yakumo Study.

AIM: Advanced glycation end-products (AGEs) are irreversibly and heterogeneously formed compounds during the non-enzymatic modification of macromolecules, such as proteins. Aging and lifestyle habits, such as high-fat and high-protein diets, and smoking, promote AGEs accumulation. This study aimed to investigate the relationship between fall risk and AGEs in community-dwelling older adults. METHODS: This cross-sectional study included patients from the 2022 Yakumo Study who were evaluated for fall risk index 5-items version, locomotive syndrome stage and AGEs. AGEs were evaluated using Skin autofluorescence (SAF) measured by the AGE reader (DiagnOptics Technologies BV, Groningen, the Netherlands). We divided the participants into two groups according to the presence or absence of fall risk (fall risk index 5-items version ≥6 or not), and investigated the factors associated with fall risk. RESULTS: The fall risk group had a higher age and SAF, and a higher proportion of locomotive syndrome stage >2 than the without fall risk group in patients aged ≥65 years (P < 0.01). The multivariate logistic regression analysis after adjustment of age, sex and body mass index showed that locomotive syndrome stage ≥2 and SAF were independent associators of fall risk in older adults (odds ratio 3.26, P < 0.01, odds ratio 2.96, P < 0.05, respectively). The optimal cutoff value of the SAF for fall risk was 2.4 (area under the curve 0.631; 95% CI 0.53-0.733; sensitivity 0.415; specificity 0.814; P < 0.05). CONCLUSION: The accumulation of AGEs in skin tissues can be used to screen for fall risk comprehensively. Geriatr Gerontol Int 2024; 24: 517-522.

Association of mid-thigh quadriceps muscle quality with postoperative outcomes in patients with hip osteoarthritis undergoing total hip arthroplasty: a retrospective cohort study.

PURPOSE: Muscle quality is more important than muscle mass for assessing physical function. Computed tomography (CT) is used to evaluate intramuscular fatty infiltration. The mid-thigh quadriceps CT attenuation values (CTV) expressed in Hounsfield units (HU) negatively correlate with physical function. Patients with hip osteoarthritis (HOA) have lower extremity muscle atrophy, including decreased cross-sectional area (CSA), CTV, and muscle strength. Using preoperative CT images, we investigated the association between mid-thigh quadriceps CSA, CTV, and postoperative outcomes in patients with HOA. METHODS: This study included 62 patients who had unilateral HOA (62 hips) and underwent total hip arthroplasty (THA). We investigated the association between preoperative and postoperative Japanese Orthopaedic Association (JOA) hip scores, 12-item Short Form survey (SF-12), mid-thigh quadriceps CSA, and CTV. RESULTS: The mean age was 64.7 ± 10.1 years, with 15 men (24.2%), and the mean body mass index was 24.3 ± 4.3 kg/m2. Secondary HOA was present in 79.0% of patients. The mean CSA and CTV of the mid-thigh quadriceps on the operative side were 38.8 ± 9.8 cm2 and 40.3 ± 7.8 HU, respectively. Multiple regression analyses adjusted for age and sex showed that preoperative mid-thigh quadriceps CSA was not associated with preoperative and postoperative JOA hip scores or SF-12. The preoperative mid-thigh quadriceps CTV was associated with the postoperative JOA hip score in the gait ability domain and SF-12 in the physical component summary domain. CONCLUSION: Preoperative muscle quality is associated with postoperative outcomes in patients who have HOA regardless of age and sex.

External validation of a deep learning model for predicting bone mineral density on chest radiographs.

We developed a new model for predicting bone mineral density on chest radiographs and externally validated it using images captured at facilities other than the development environment. The model performed well and showed potential for clinical use. PURPOSE: In this study, we performed external validation (EV) of a developed deep learning model for predicting bone mineral density (BMD) of femoral neck on chest radiographs to verify the usefulness of this model in clinical practice. METHODS: This study included patients who visited any of the collaborating facilities from 2010 to 2020 and underwent chest radiography and dual-energy X-ray absorptiometry (DXA) at the femoral neck in the year before and after their visit. A total of 50,114 chest radiographs were obtained, and BMD was measured using DXA. We developed the model with 47,150 images from 17 facilities and performed EV with 2914 images from three other facilities (EV dataset). We trained the deep learning model via ensemble learning based on chest radiographs, age, and sex to predict BMD using regression. The outcomes were the correlation of the predicted BMD and measured BMD with diagnoses of osteoporosis and osteopenia using the T-score estimated from the predicted BMD. RESULTS: The mean BMD was 0.64±0.14 g/cm2 in the EV dataset. The BMD predicted by the model averaged 0.61±0.08 g/cm2, with a correlation coefficient of 0.68 (p<0.01) when compared with the BMD measured using DXA. The accuracy, sensitivity, and specificity of the model were 79.0%, 96.6%, and 34.1% for T-score < -1 and 79.7%, 77.1%, and 80.4% for T-score ≤ -2.5, respectively. CONCLUSION: Our model, which was externally validated using data obtained at facilities other than the development environment, predicted BMD of femoral neck on chest radiographs. The model performed well and showed potential for clinical use.

Anti-sclerostin antibody therapy prevents post-ischemic osteonecrosis bone collapse via interleukin-6 association.

Osteonecrosis of the femoral head (ONFH) is a debilitating condition characterized by subchondral bone necrosis, which frequently culminates in joint destruction. Although total hip arthroplasty is conventionally practiced to remediate ONFH, for patients under the age of 60, the outcomes can be suboptimal. Chronic inflammation, particularly that mediated by interleukin-6 (IL-6), has been conjectured to be a potential mechanism underlying the etiology of ONFH. This study aimed at exploring the interplay between IL-6, the canonical Wnt signaling pathway, and ONFH to provide insights for potential therapeutic interventions. Human ONFH specimens depicted an elevation in ß-catenin expression in the transitional layer, while IL-6 levels were pronounced in the same region. Subsequently, mouse models of ischemic osteonecrosis were treated with an anti-sclerostin antibody to assess its effects on bone metabolism and cellular processes. Histological analysis revealed that the administration of anti-sclerostin antibodies effectuated early recovery from bone necrosis, reduced empty lacunae, and suppressed IL-6 expression. The treatment evidently initiated the activation of the Wnt/ß-catenin signaling pathway, presenting a potential mechanism associated with IL-6-mediated inflammation. Furthermore, the antibody upregulated osteoblast formation, downregulated osteoclast formation, and increased bone volume. Micro-CT imaging demonstrated increased bone volume, prevented epiphyseal deformity, and improved compression strength. Therefore, this study yields significant findings, indicating the potency of anti-sclerostin antibodies in effectively modulating the Wnt/ß-catenin pathway, associating with IL-6 expression, and preventing post-ONFH bone collapse. Additionally, this preclinical investigation in mouse models offers an avenue for prospective research on potential therapeutic interventions against human ONFH.

Fall risk in patient with dysplastic hip osteoarthritis.

PURPOSE: Patients with dysplastic hip osteo arthritis (DHOA) often have a spinopelvic imbalance, and they are more likely to experience falls. This study aimed to evaluate the risk factors for falls in patients with DHOA, including spinopelvic parameters. METHODS: In this cross-sectional study, a total of 103 patients with DHOA were included from 2019 to 2021. Fall risk was evaluated using the Fall Risk Index 5 items version (FRI-5). Demographics, functional outcomes, and spinopelvic parameters were compared between the high-risk group (FRI-5 ≥ 6) and the low-risk group (FRI-5 < 6). Multivariate analysis was performed using factors with significant differences in univariate analysis. RESULTS: High-risk and low-risk groups comprised 54 and 49 patients, respectively. Females were significantly more common in the high-risk group than in the low-risk group. The Harris Hip Score was significantly lower in the high-risk group than in the low-risk group (p = 0.02). Pelvic incidence, tilt, and obliquity were significantly higher in the high-risk group than in the low-risk group (p < 0.01). In multivariate analysis, female sex (odds ratio [OR]: 3.76, 95% confidence interval [CI]: 1.11-12.64, p = 0.03), pelvic obliquity (OR: 1.36, 95% CI: 1.09-1.71, p < 0.01), and Harris hip score (OR: 0.96, 95% CI: 0.93-0.99, p = 0.02) were identified as risk factors. CONCLUSION: Female sex, pelvic obliquity, and low Harris hip score were associated with an increased risk of falls among patients with DHOA.

The survey of bone allograft transplantation in a Japanese regional bone bank.

BACKGROUND: In reconstructive surgery for large bone defects, the demand for bone allografts has increased over the years; however, it is unclear how the supply and demand in Japanese regional bone banks have evolved over time. This study investigated the 15-year supply and demand of bone allografts stored in a regional bone bank, along with assessing the screening process's effectiveness. METHODS: The target period was 15 years from April 2005 to March 2020. The period was subdivided into three 5-year periods: first, second, and third. The study items included the number of bone allografts donated, the number of bone allografts used, donor and user facilities, surgical methods using bone allografts, and the number of bone allografts discarded. We used the Cochran-Armitage test for statistical analysis. RESULTS: A total of 1852 bone allografts were donated to the bone bank, and a total of 1721 were used. A total of 677 bone allografts grafts were provided in the first period, 738 in the second period, and 525 in the third period, indicating a decreasing trend. The average number of allografts per surgery was 2.8 in the first, 3.1 in the second, and 1.7 in the third, showing a decreasing trend. Concerning the percentage of each surgery using bone allografts, spine fusion decreased in the third period but not significantly, whereas primary hip arthroplasty increased significantly in the third period. The total number of discarded bone allografts was 4.8% of the total number of donated bone allografts, largely because of a lack of screening tests. CONCLUSION: Although the number of allogeneic bone surgeries has been increasing over time, the number of allogeneic bone donations has shown a decreasing trend, and there is a need to develop a system that can provide surgeons with sufficient bone allografts.

Addressing osteoporosis treatment after hemiarthroplasty for a femoral neck fracture: impact on survival rates after a subsequent periprosthetic femoral fracture - a multicenter (TRON group) retrospective study.

This study investigated the impact of osteoporosis treatment on the prognosis of patients with periprosthetic femoral fracture (PPF) following femoral neck fracture (FNF). Our results suggested an association between osteoporosis treatment and potentially improved survival prognosis in patients who underwent surgery for PPF. These results imply that osteoporosis treatment may have a beneficial effect on patient outcomes. PURPOSE: This study aimed to investigate the effect of osteoporosis treatment on the prognosis of periprosthetic femoral fracture (PPF) patients after femoral neck fracture. METHODS: A multicenter retrospective study named as TRON was conducted. The study population included 156 PPF patients who had undergone hemiarthroplasty for femoral neck fracture between January 2010 and December 2019. Patients were divided based on whether they had received osteoporosis treatment before PPF injury. A log-rank test was used to compare survival rates. We conducted a Cox proportional hazards analysis to identify factors associated with the survival rate after PFF injury. RESULTS: Twenty-seven of the 156 patients had received osteoporosis treatment prior to PPF injury. The 1-year and 2-year overall survival rates after PPF were 80.9% and 75.3%, respectively. The log-rank test revealed that the 1-year survival rate with and without osteoporosis treatment was 89.5% and 78.1%, respectively (P=0.012). In the Cox proportional hazards analysis, age, BMI, presence or absence of surgery, and presence or absence of osteoporosis treatment showed independent associations with the survival rate after PFF injury. The hazard ratio for the presence of osteoporosis treatment was 0.22 (95% confidence interval 0.07-0.75, P=0.015). CONCLUSION: The findings of this study suggest an association between osteoporosis treatment and potentially improved survival prognosis in patients who underwent surgery for PPF. These results imply that osteoporosis treatment may have a beneficial effect on patient outcomes. It is important to consider that osteoporosis treatment could be significant not only in preventing secondary fractures but also in potentially improving prognosis in the rare event of PPF.

Deep Learning for Bone Mineral Density and T-Score Prediction from Chest X-rays: A Multicenter Study.

Although the number of patients with osteoporosis is increasing worldwide, diagnosis and treatment are presently inadequate. In this study, we developed a deep learning model to predict bone mineral density (BMD) and T-score from chest X-rays, which are one of the most common, easily accessible, and low-cost medical imaging examination methods. The dataset used in this study contained patients who underwent dual-energy X-ray absorptiometry (DXA) and chest radiography at six hospitals between 2010 and 2021. We trained the deep learning model through ensemble learning of chest X-rays, age, and sex to predict BMD using regression and T-score for multiclass classification. We assessed the following two metrics to evaluate the performance of the deep learning model: (1) correlation between the predicted and true BMDs and (2) consistency in the T-score between the predicted class and true class. The correlation coefficients for BMD prediction were hip = 0.75 and lumbar spine = 0.63. The areas under the curves for the T-score predictions of normal, osteopenia, and osteoporosis diagnoses were 0.89, 0.70, and 0.84, respectively. These results suggest that the proposed deep learning model may be suitable for screening patients with osteoporosis by predicting BMD and T-score from chest X-rays.

Usefulness of nutritional assessment using Geriatric Nutritional Risk Index as an independent predictor of 30-day mortality after hip fracture surgery.

INTRODUCTION: The Geriatric Nutritional Risk Index (GNRI) is an objective nutritional status assessment tool used for predicting mortality risk in hospitalized patients. However, it is unclear whether GNRI reflects short-term mortality for hip fracture patients after surgery. We examined the usefulness of the nutritional status assessed by the GNRI and identified cutoff scores that predict mortality risk. Does GNRI on admission predict the mortality after surgery for hip fracture? HYPOTHESIS: Evaluation of GNRI could help identify patients at higher risk of 30-day mortality after hip fracture surgery. MATERIALS AND METHODS: This retrospective study used data from 1040 patients who underwent hip fracture surgery. Fatalities within 30 days after hip fracture surgery were investigated. The GNRI was calculated on admission in all patients as follows: 14.89×serum albumin (g/dL)+41.7×body mass index/22. Receiver operating characteristic (ROC) curves were used to calculate the area under the curve (AUC) and the optimal cutoff score that could predict 30-day mortality after hip fracture surgery. This cutoff score was used for comparing the mortality rates between patient groups with a GNRI higher and lower than the cutoff score using Fisher's exact test. Logistic regression analysis was used to determine risk factors of 30-day mortality. RESULTS: There were 17 fatalities (1.6%) in the cohort. The ROC-AUC value was 0.811, and the cutoff GNRI was 75.4. Mortality was significantly higher in the group with a GNRI<75.4 compared with the group with a GNRI≥75.4 (odds ratio [OR], 22.99; 95% confidence interval [95% CI], 7.55-78.05; p=0.00000004). A GNRI<75.4 was a significant predictor of mortality within 30-days after hip fracture surgery (OR, 27.1; 95% CI, 8.57-85.9; p≤0.0001). DISCUSSION: Our results show that nutritional status assessment using GNRI can help predict 30-day mortality among geriatric patients undergoing surgery for hip fracture. The GNRI is a simple and accurate tool for predicting the risk of mortality after hip fracture surgery. LEVEL OF EVIDENCE: IV; case series study.

Artificial intelligence improves the accuracy of residents in the diagnosis of hip fractures: a multicenter study.

BACKGROUND: Less experienced clinicians sometimes make misdiagnosis of hip fractures. We developed computer-aided diagnosis (CAD) system for hip fractures on plain X-rays using a deep learning model trained on a large dataset. In this study, we examined whether the accuracy of the diagnosis of hip fracture of the residents could be improved by using this system. METHODS: A deep convolutional neural network approach was used for machine learning. Pytorch 1.3 and Fast.ai 1.0 were applied as frameworks, and an EfficientNet-B4 model (a pre-trained ImageNet model) was used. We handled the 5295 X-rays from the patients with femoral neck fracture or femoral trochanteric fracture from 2009 to 2019. We excluded cases in which the bilateral hips were not included within an image range, and cases of femoral shaft fracture and periprosthetic fracture. Finally, we included 5242 AP pelvic X-rays from 4851 cases. We divided these 5242 images into two images per image, and prepared 5242 images including fracture site and 5242 images without fracture site. Thus, a total of 10,484 images were used for machine learning. The accuracy, sensitivity, specificity, F-value, and area under the curve (AUC) were assessed. Gradient-weighted class activation mapping (Grad-CAM) was used to conceptualize the basis for the diagnosis of the fracture by the deep learning algorithm. Secondly, we conducted a controlled experiment with clinicians. Thirty-one residents;young doctors within 2 years of graduation from medical school who rotate through various specialties, were tested using 300 hip fracture images that were randomly extracted from the dataset. We evaluated the diagnostic accuracy with and without the use of the CAD system for each of the 300 images. RESULTS: The accuracy, sensitivity, specificity, F-value, and AUC were 96.1, 95.2, 96.9%, 0.961, and 0.99, respectively, with the correct diagnostic basis generated by Grad-CAM. In the controlled experiment, the diagnostic accuracy of the residents significantly improved when they used the CAD system. CONCLUSIONS: We developed a newly CAD system with a deep learning algorithm from a relatively large dataset from multiple institutions. Our system achieved high diagnostic performance. Our system improved the diagnostic accuracy of residents for hip fractures. LEVEL OF EVIDENCE: Level III, Foundational evidence, before-after study. CLINICAL RELEVANCE: high.