Prediction of subsequent fragility fractures: application of machine learning.

BACKGROUND: Machine learning (ML) has shown exceptional promise in various domains of medical research. However, its application in predicting subsequent fragility fractures is still largely unknown. In this study, we aim to evaluate the predictive power of different ML algorithms in this area and identify key features associated with the risk of subsequent fragility fractures in osteoporotic patients. METHODS: We retrospectively analyzed data from patients presented with fragility fractures at our Fracture Liaison Service, categorizing them into index fragility fracture (n = 905) and subsequent fragility fracture groups (n = 195). We independently trained ML models using 27 features for both male and female cohorts. The algorithms tested include Random Forest, XGBoost, CatBoost, Logistic Regression, LightGBM, AdaBoost, Multi-Layer Perceptron, and Support Vector Machine. Model performance was evaluated through 10-fold cross-validation. RESULTS: The CatBoost model outperformed other models, achieving 87% accuracy and an AUC of 0.951 for females, and 93.4% accuracy with an AUC of 0.990 for males. The most significant predictors for females included age, serum C-reactive protein (CRP), 25(OH)D, creatinine, blood urea nitrogen (BUN), parathyroid hormone (PTH), femoral neck Z-score, menopause age, number of pregnancies, phosphorus, calcium, and body mass index (BMI); for males, the predictors were serum CRP, femoral neck T-score, PTH, hip T-score, BMI, BUN, creatinine, alkaline phosphatase, and spinal Z-score. CONCLUSION: ML models, especially CatBoost, offer a valuable approach for predicting subsequent fragility fractures in osteoporotic patients. These models hold the potential to enhance clinical decision-making by supporting the development of personalized preventative strategies.

Constructing and validating a predictive nomogram for osteoporosis risk among Chinese single-center male population using the systemic immune-inflammation index.

Osteoporosis (OP) is a bone metabolism disease that is associated with inflammatory pathological mechanism. Nonetheless, rare studies have investigated the diagnostic effectiveness of immune-inflammation index in the male population. Therefore, it is interesting to achieve early diagnosis of OP in male population based on the inflammatory makers from blood routine examination. We developed a prediction model based on a training dataset of 826 Chinese male patients through a retrospective study, and the data was collected from January 2022 to May 2023. All participants underwent the dual-energy X-ray absorptiometry (DXEA) and blood routine examination. Inflammatory markers such as systemic immune-inflammation index (SII) and platelet-to-lymphocyte ratio (PLR) was calculated and recorded. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to optimize feature selection. Multivariable logistic regression analysis was applied to construct a predicting model incorporating the feature selected in the LASSO model. This predictive model was displayed as a nomogram. Receiver operating characteristic (ROC) curve, C-index, calibration curve, and clinical decision curve analysis (DCA) to evaluate model performance. Internal validation was test by the bootstrapping method. This study was approved by the Ethic Committee of the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine (Ethic No. JY2023012) and conducted in accordance with the relevant guidelines and regulations. The predictive factors included in the prediction model were age, BMI, cardiovascular diseases, cerebrovascular diseases, neuropathy, thyroid diseases, fracture history, SII, PLR, C-reactive protein (CRP). The model displayed well discrimination with a C-index of 0.822 (95% confidence interval: 0.798-0.846) and good calibration. Internal validation showed a high C-index value of 0.805. Decision curve analysis (DCA) showed that when the threshold probability was between 3 and 76%, the nomogram had a good clinical value. This nomogram can effectively predict the incidence of OP in male population based on SII and PLR, which would help clinicians rapidly and conveniently diagnose OP with men in the future.

Bone metabolism factors in predicting the risk of osteoporosis fracture in the elderly.

OBJECTIVE: Osteoporosis (OS) is a systemic bone disease characterized by low bone mass and bone microstructure damage. This study. METHODS: According to the T value, 88 elderly fracture patients were grouped as the control group (without OS, 43 cases) and observation group (with T value <-2.5, which could be diagnosed as OS, 45 cases). The content of boney containing protein (BGP), total type 1 collagen amino terminal extender peptide (TPINP), ß-Crosslaps (ß-CTX), parathyroid hormone (PTH) and insulin-like growth factors-1 (IGF-1) was compared. Multivariate logistic regression was adopted to analyze the correlation between biochemical indexes and the occurrence of senile OS fracture and the related risk factors. The diagnostic value in the elderly was analyzed by receiver operating characteristic (ROC) curve. RESULTS: The levels of BGP, TPINP, ß-CTX, PTH and IGF-1 were elevated, and the level of IGF-1 was decreased in the observation group compared with the control group (P < 0.05). The elevated content of BGP, TPINP, ß-CTX and PTH, and the decreased expression of IGF-1 were influencing factors for OS fractures in the elderly (P < 0.05). The sensitivity and specificity to predict the occurrence of OS fractures in the elderly were 91.70% and 90.50%, respectively. The AUC of combined detection was 0.976 (95% CI: 0.952-1.000), which was memorably higher than single indicator detection (P < 0.05). Among 45 patients, 32 cases had good prognosis and 13 had poor prognosis. In comparison with the good prognosis group, the content of BGP, TPINP, ß-CTX and PTH were sensibly higher, the level of IGF-1 was prominently lower, and the proportion of fracture history was much higher in poor prognosis group (P < 0.05). Fracture history, BGP, TPINP, ß-CTX, PTH and IGF-1 were independent risk factors for poor prognosis of elderly OS fractures (P < 0.05). CONCLUSION: Bone metabolism factors were associated with poor prognosis of OS in the elderly. The combined detection had higher diagnostic value in calculating the risk of OS fracture in the elderly than single indicator detection.

Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning.

BACKGROUND: Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both conditions. There is a strong correlation between fractures in the elderly population, decreased muscle mass, weakened muscle strength, heightened risk of falls, and diminished bone density. This study aimed to pinpoint crucial diagnostic candidate genes for osteoporosis patients with concomitant sarcopenia. METHODS: Two osteoporosis datasets and one sarcopenia dataset were obtained from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) and module genes were identified using Limma and Weighted Gene Co-expression Network Analysis (WGCNA), followed by functional enrichment analysis, construction of protein-protein interaction (PPI) networks, and application of a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to determine candidate hub genes for diagnosing osteoporosis combined with sarcopenia. Receiver operating characteristic (ROC) curves and column line plots were generated. RESULTS: The merged osteoporosis dataset comprised 2067 DEGs, with 424 module genes filtered in sarcopenia. The intersection of DEGs between osteoporosis and sarcopenia module genes consisted of 60 genes, primarily enriched in viral infection. Through construction of the PPI network, 30 node genes were filtered, and after machine learning, 7 candidate hub genes were selected for column line plot construction and diagnostic value assessment. Both the column line plots and all 7 candidate hub genes exhibited high diagnostic value (area under the curve ranging from 1.00 to 0.93). CONCLUSION: We identified 7 candidate hub genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, METTL9) and constructed column line plots for osteoporosis combined with sarcopenia. This study provides reference for potential peripheral blood diagnostic candidate genes for sarcopenia in osteoporosis patients.

Application of machine learning algorithms to identify people with low bone density.

Background: Osteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a need to develop a method to identify those at high risk for osteoporosis. Objective: This study aimed to develop a machine learning algorithm to effectively identify people with low bone density, using readily available demographic and blood biochemical data. Methods: Using NHANES 2017-2020 data, participants over 50 years old with complete femoral neck BMD data were selected. This cohort was randomly divided into training (70%) and test (30%) sets. Lasso regression selected variables for inclusion in six machine learning models built on the training data: logistic regression (LR), support vector machine (SVM), gradient boosting machine (GBM), naive Bayes (NB), artificial neural network (ANN) and random forest (RF). NHANES data from the 2013-2014 cycle was used as an external validation set input into the models to verify their generalizability. Model discrimination was assessed via AUC, accuracy, sensitivity, specificity, precision and F1 score. Calibration curves evaluated goodness-of-fit. Decision curves determined clinical utility. The SHAP framework analyzed variable importance. Results: A total of 3,545 participants were included in the internal validation set of this study, of whom 1870 had normal bone density and 1,675 had low bone density Lasso regression selected 19 variables. In the test set, AUC was 0.785 (LR), 0.780 (SVM), 0.775 (GBM), 0.729 (NB), 0.771 (ANN), and 0.768 (RF). The LR model has the best discrimination and a better calibration curve fit, the best clinical net benefit for the decision curve, and it also reflects good predictive power in the external validation dataset The top variables in the LR model were: age, BMI, gender, creatine phosphokinase, total cholesterol and alkaline phosphatase. Conclusion: The machine learning model demonstrated effective classification of low BMD using blood biomarkers. This could aid clinical decision making for osteoporosis prevention and management.

Development and validation of the osteoporosis scale among the system of quality of life instruments for chronic diseases QLICD-OS (V2.0).

BACKGROUND: Quality of life of osteoporosis patients had caused widespread concern, due to high incidence and difficulty to cure. Scale specifics for osteoporosis and suitable for Chinese cultural background lacked. This study aimed to develop an osteoporosis scale in Quality of Life Instruments for Chronic Diseases system, namely QLICD-OS (V2.0). METHODS: Procedural decision-making approach of nominal group, focus group and modular approach were adopted. Our scale was developed based on experience of establishing scales at home and abroad. In this study, Quality of life measurements were performed on 127 osteoporosis patients before and after treatment to evaluate the psychometric properties. Validity was evaluated by qualitative analysis, item-domain correlation analysis, multi-scaling analysis and factor analysis; the SF-36 scale was used as criterion to carry out correlation analysis for criterion-related validity. The reliability was evaluated by the internal consistency coefficients Cronbach's α, test-retest reliability Pearson correlation r. Paired t-tests were performed on data of ​​the scale before and after treatment, with Standardized Response Mean (SRM) being calculated to evaluate the responsiveness. RESULTS: The QLICD-OS, composed of a general module (28 items) and an osteoporosis-specific module (14 items), had good content validity. Correlation analysis and factor analysis confirmed the construct, with the item having a strong correlation (most > 0.40) with its own domains/principle components, and a weak correlation (< 0.40) with other domains/principle components. Correlation coefficient between the similar domains of QLICD-OS and SF-36 showed reasonable criterion-related validity, with all coefficients r being greater than 0.40 exception of physical function of SF-36 and physical domain of QLICD-OS (0.24). Internal consistency reliability of QLICD-OS in all domains was greater than 0.7 except the specific module. The test-retest reliability coefficients (Pearson r) in all domains and overall score are higher than 0.80. Score changes after treatment were statistically significant, with SRM ranging from 0.35 to 0.79, indicating that QLICD-OS could be rated as medium responsiveness. CONCLUSION: As the first osteoporosis-specific quality of life scale developed by the modular approach in China, the QLICD-OS showed good reliability, validity and medium responsiveness, and could be used to measure quality of life in osteoporosis patients.

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.

Guidelines for fracture risk assessment and management of osteoporosis in postmenopausal women and men above the age of 50 in Qatar.

We present comprehensive guidelines for osteoporosis management in Qatar. Formulated by the Qatar Osteoporosis Association, the guidelines recommend the age-dependent Qatar fracture risk assessment tool for screening, emphasizing risk-based treatment strategies and discouraging routine dual-energy X-ray scans. They offer a vital resource for physicians managing osteoporosis and fragility fractures nationwide. PURPOSE: Osteoporosis and related fragility fractures are a growing public health issue with an impact on individuals and the healthcare system. We aimed to present guidelines providing unified guidance to all healthcare professionals in Qatar regarding the management of osteoporosis. METHODS: The Qatar Osteoporosis Association formulated guidelines for the diagnosis and management of osteoporosis in postmenopausal women and men above the age of 50. A panel of six local rheumatologists who are experts in the field of osteoporosis met together and conducted an extensive review of published articles and local and international guidelines to formulate guidance for the screening and management of postmenopausal women and men older than 50 years in Qatar. RESULTS: The guidelines emphasize the use of the age-dependent hybrid model of the Qatar fracture risk assessment tool for screening osteoporosis and risk categorization. The guidelines include screening, risk stratification, investigations, treatment, and monitoring of patients with osteoporosis. The use of a dual-energy X-ray absorptiometry scan without any risk factors is discouraged. Treatment options are recommended based on risk stratification. CONCLUSION: Guidance is provided to all physicians across the country who are involved in the care of patients with osteoporosis and fragility fractures.

Perspectives on Fracture Liaison Service in Austria: clinical and economic considerations.

Osteoporosis is a widespread disease and affects over 500,000 people in Austria. Fragility fractures are associated with it and represent not only an individual problem for the patients, but also an enormous burden for the healthcare system. While trauma surgery care is well provided in Vienna, there is an enormous treatment gap in secondary prevention after osteoporotic fracture. Systematic approaches such as the Fracture Liaison Service (FLS) aim to identify patients with osteoporosis after fracture, to clarify diagnostically, to initiate specific therapy, and to check therapy adherence. The aim of this article is to describe the practical implementation and operational flow of an already established FLS in Vienna. This includes the identification of potential FLS inpatients, the diagnostic workup, and recommendations for an IT solution for baseline assessment and follow-up of FLS patients. We summarize the concept, benefits, and limitations of FLS and provide prospective as well as clinical and economic considerations for a city-wide FLS, managed from a central location. Future concepts of FLS should include artificial intelligence for vertebral fracture detection and simple IT tools for the implementation of FLS in the outpatient sector.

[Osteoporosis - implications of the new guidelines in practice]. / Osteoporose ­ Implikation der neuen Leitlinie in die Praxis.

In September 2023, the guideline on the prophylaxis, diagnosis, and treatment of osteoporosis in postmenopausal women and men was published as a completely revised guideline. The implications for practice include a change in the justifying indication for performing a bone density measurement, the time interval over which the fracture risk is determined, the level and number of therapy thresholds, and the recommendations for the therapeutic approach that are adapted to the individual fracture risk present. Risk assessment for the prediction of spine and hip fractures is essential in the context of osteoporosis diagnostics. In addition to age and gender, there are a total of 33 risk factors to determine the individual risk of fracture. Much more attention is paid to the assessment of the risk of falls and, depending on the result, combined with recommendations for muscle training and protein intake from the age of 65. Risk indicators must also be taken into account when determining the indication for osteoporosis diagnosis, as well as the risk factors of the imminent risk of fracture. The indication for baseline diagnostics has changed from the >20% 10-year fracture risk to diagnostics in postmenopausal women and in men aged 50 years and older, depending on the fracture risk factor profile. This eliminates a specific fracture risk threshold for basic diagnostics. Thus, in the young patient group (50-60 years), the risk factors considered medically relevant for the indication for osteoporosis diagnosis must be taken into account. New thresholds as an indication for initiating therapy is the determination of fracture risk using a risk calculator over 3 years instead of 10 years. The indication for drug therapy should be based on the threshold values of the DVO risk model. The data clearly suggests a significantly faster and more effective fracture risk-reducing effect of anabolic therapy. This is recommended in the first sequence in cases of a very high risk of fracture from 10%/3 years with osteoanabolic active substances (teriparatide or romosozumab). Such a therapy sequence should be initiated directly and not delayed due to upcoming dental procedures. Follow-up therapy to consolidate the reduction of fracture risk should be chosen individually.

Proteomic Insights into Osteoporosis: Unraveling Diagnostic Markers of and Therapeutic Targets for the Metabolic Bone Disease.

Osteoporosis (OP), a prevalent skeletal disorder characterized by compromised bone strength and increased susceptibility to fractures, poses a significant public health concern. This review aims to provide a comprehensive analysis of the current state of research in the field, focusing on the application of proteomic techniques to elucidate diagnostic markers and therapeutic targets for OP. The integration of cutting-edge proteomic technologies has enabled the identification and quantification of proteins associated with bone metabolism, leading to a deeper understanding of the molecular mechanisms underlying OP. In this review, we systematically examine recent advancements in proteomic studies related to OP, emphasizing the identification of potential biomarkers for OP diagnosis and the discovery of novel therapeutic targets. Additionally, we discuss the challenges and future directions in the field, highlighting the potential impact of proteomic research in transforming the landscape of OP diagnosis and treatment.

A novel case-finding strategy based on artificial intelligence for the systematic identification and management of individuals with osteoporosis or at varying risk of fragility fracture.

An artificial intelligence-based case-finding strategy has been developed to systematically identify individuals with osteoporosis or at varying risk of fragility fracture. This strategy has the potential to close the critical care gap in osteoporosis treatment in primary care, thereby lessening the societal burden imposed by fragility fractures. BACKGROUND: Osteoporotic fractures represent a major cause of morbidity and, in older adults, a precursor of disability, loss of independence, poor quality of life and premature death. Despite the detrimental health impact, osteoporosis remains largely underdiagnosed and undertreated worldwide. Subjects at risk for osteoporosis-related fractures are identified either via organised screening or case finding. In the absence of a population-based screening policy, subjects at high risk of fragility fractures are opportunistically identified when a fracture occurs or because of other clinical risk factors (CRFs) for osteoporotic fracture and areal bone mineral density (aBMD) measured by dual-energy X-ray absorptiometry (DXA). PURPOSE: This paper describes the development of a novel case-finding strategy, named Osteoporosis Diagnostic and Therapeutic Pathway (ODTP), which enables to identify subjects with osteoporosis or at varying risk of fragility fracture. This strategy is based on a specifically designed software tool, named "Bone Fragility Query" (BFQ), which analyses the electronic health record (EHR) databases of General Practitioners (GPs) to systematically identify individuals who should be prescribed DXA-BMD measurement, vertebral fracture assessment (VFA) and anti-osteoporosis medications (AOM). CONCLUSIONS: The ODTP through BFQ tool is a feasible, convenient and time-saving osteoporosis model of care for GPs during routine clinical practice. It enables GPs to shift their focus from what to do (clinical guidelines) to how to do it in the primary health care setting. It also allows a systematic approach to primary and secondary prevention of fragility fractures, thereby overcoming clinical inertia and contributing to closing the gap between evidence and practice for the management of osteoporosis in primary care.

The efficacy of nutritional screening indexes in predicting the incidence of osteosarcopenia and major osteoporotic fracture in the elderly.

INTRODUCTION: The effect of nutritional status on osteosarcopenia (OS) and major osteoporotic fracture (MOF) among the elderly is still unclear. So we aimed to compare the efficacy of the Mini-Nutrition Assessment-Short Form (MNA-sf), the Geriatric Nutritional Risk Index (GNRI) and Controlling Nutritional Status (CONUT) for predicting OS and MOF among the elderly. MATERIALS AND METHODS: A total of 409 participants were enrolled in this prospective study. Blood biochemical indexes, nutritional status, and bone- and muscle-related examinations were assessed at initial visit to the outpatient. Participants were divided into 4 groups: (1) control; (2) osteopenia/osteoporosis; (3) sarcopenia; (4) osteosarcopenia, and then followed for 5 years, recording the occurrence time of MOF. RESULTS: The frequency values of osteopenia/osteoporosis, sarcopenia, and OS, at baseline, were respectively 13.4, 16.1, and 12% among the study samples. Correlation analysis showed that nutritional status scores were associated with body mass index, handgrip strength, albumin, bone mineral density, and physical functions. According to multivariate models, poor nutritional status was significantly associated with a higher risk of OS and MOF (P < 0.05). Survival analysis showed that the MOF rate in malnutrition group was significantly higher than normal nutrition group (P < 0.05). The receiver operator characteristic curve shows that the value of MNA-sf to diagnose OS and MOF is greater (P < 0.05). CONCLUSION: The poor nutritional status was associated with a higher risk of both OS and MOF. MNA-sf showed a superior diagnostic power for OS and MOF among the elderly. Early nutrition assessments and interventions may be key strategies to prevent OS and fractures.

Correlation of serum matrix metalloproteinase 3 with osteoporosis in patients of postmenopausal rheumatoid arthritis.

Osteoporosis (OP) is a common complication of postmenopausal women with rheumatoid arthritis (RA). Herein, the objective of our study was to explore the correlation between serum matrix metalloproteinase 3 (MMP3) and OP among postmenopausal women with RA to foster better diagnosis and treatment. A total of 208 elderly postmenopausal women with RA were included in this study, with 83 patients diagnosed with OP after RA diagnosis and 125 patients without OP. Serum MMP3 levels and bone mineral density (BMD) were measured and compared. The predictive value of serum MMP3 for OP in this population was also analyzed using receiver operating curve (ROC) analysis. Postmenopausal women with RA and OP diagnosis had markedly higher serum MMP3 levels, compared to those without OP. ROC analysis showed that serum MMP3 had predictive value for OP. Additionally, a negative correlation was observed between serum MMP3 levels and BMD. High serum MMP3 levels were also found to be associated with high abnormal bone metabolism. We found that serum MMP3 levels are strongly correlated with OP in postmenopausal women with RA and that elevated levels of serum MMP3 are linked to low BMD and high abnormal bone metabolism. Serum MMP3 may be a useful biomarker for predicting OP in this population, and could potentially aid in the development of targeted prevention and treatment strategies.

The role of bone turnover markers in diagnosis, monitoring, and pathological fractures of osteoporosis.

BACKGROUND: We investigated the utility of specific biomarkers-namely, c-terminal telopeptide (CTX), n-telopeptide (NTX), deoxypyridinoline (DPD), and tartrate-resistant acid phosphatase (TRAP)-compared to conventional diagnostic methods. We hy-pothesized that these novel biomarkers could hold substantial value in the diagnosis, treatment, and monitoring of osteoporosis. METHODS: The study was conducted over a three-year period, from January 1, 2020, to January 1, 2023. We enrolled a total of 520 patients aged 50 years or older who had been diagnosed with osteoporosis. Patients undergoing steroid treatments, which are known to contribute to osteoporosis, were excluded from the study. Additionally, we carefully selected and matched a control group consisting of 500 patients based on demographic characteristics relevant to the diagnosis of osteoporosis. This meticulous selection process resulted in a comprehensive cohort comprising 1,020 patients. Throughout the study, patients were closely monitored for a duration of one year to track the occurrence of pathological fractures and assess their overall prognosis. RESULTS: As a result of our rigorous investigation, we identified CTX, NTX, DPD, and TRAP as pivotal biomarkers that play a crucial role in evaluating bone health, monitoring treatment effectiveness, and detecting pathological fractures in the context of osteoporosis. CONCLUSION: Our study underscores the significance of these biomarkers in advancing the diagnosis and management of osteo-porosis, offering valuable insights into the disease's progression and treatment outcomes.

Age-adjusted Charlson comorbidity index is associated with the risk of osteoporosis in older fall-prone men: a retrospective cohort study.

BACKGROUND: There is growing evidence linking the age-adjusted Charlson comorbidity index (aCCI), an assessment tool for multimorbidity, to fragility fracture and fracture-related postoperative complications. However, the role of multimorbidity in osteoporosis has not yet been thoroughly evaluated. We aimed to investigate the association between aCCI and the risk of osteoporosis in older adults at moderate to high risk of falling. METHODS: A total of 947 men were included from January 2015 to August 2022 in a hospital in Beijing, China. The aCCI was calculated by counting age and each comorbidity according to their weighted scores, and the participants were stratified into two groups by aCCI: low (aCCI < 5), and high (aCCI ≥5). The Kaplan Meier method was used to assess the cumulative incidence of osteoporosis by different levels of aCCI. The Cox proportional hazards regression model was used to estimate the association of aCCI with the risk of osteoporosis. Receiver operating characteristic (ROC) curve was adapted to assess the performance for aCCI in osteoporosis screening. RESULTS: At baseline, the mean age of all patients was 75.7 years, the mean BMI was 24.8 kg/m2, and 531 (56.1%) patients had high aCCI while 416 (43.9%) were having low aCCI. During a median follow-up of 6.6 years, 296 participants developed osteoporosis. Kaplan-Meier survival curves showed that participants with high aCCI had significantly higher cumulative incidence of osteoporosis compared with those had low aCCI (log-rank test: P < 0.001). When aCCI was examined as a continuous variable, the multivariable-adjusted model showed that the osteoporosis risk increased by 12.1% (HR = 1.121, 95% CI 1.041-1.206, P = 0.002) as aCCI increased by one unit. When aCCI was changed to a categorical variable, the multivariable-adjusted hazard ratios associated with different levels of aCCI [low (reference group) and high] were 1.00 and 1.557 (95% CI 1.223-1.983) for osteoporosis (P <  0.001), respectively. The aCCI (cutoff ≥5) revealed an area under ROC curve (AUC) of 0.566 (95%CI 0.527-0.605, P = 0.001) in identifying osteoporosis in older fall-prone men, with sensitivity of 64.9% and specificity of 47.9%. CONCLUSIONS: The current study indicated an association of higher aCCI with an increased risk of osteoporosis among older fall-prone men, supporting the possibility of aCCI as a marker of long-term skeletal-related adverse clinical outcomes.

Development and experimental validation of an energy metabolism-related gene signature for diagnosing of osteoporosis.

Osteoporosis is usually caused by excessive bone resorption and energy metabolism plays a critical role in the development of osteoporosis. However, little is known about the role of energy metabolism-related genes in osteoporosis. This study aimed to explore the important energy metabolism-related genes involved in the development of osteoporosis and develop a diagnosis signature for osteoporosis. The GSE56814, GSE62402, and GSE7158 datasets were downloaded from the NCBI Gene Expression Omnibus. The intersection of differentially expressed genes between high and low levels of body mineral density (BMD) and genes related to energy metabolism were screened as differentially expressed energy metabolism genes (DE-EMGs). Subsequently, a DE-EMG-based diagnostic model was constructed and differential expression of genes in the model was validated by RT-qPCR. Furthermore, a receiver operating characteristic curve and nomogram model were constructed to evaluate the predictive ability of the diagnostic model. Finally, the immune cell types in the merged samples and networks associated with the selected optimal DE-EMGs were constructed. A total of 72 overlapped genes were selected as DE-EMGs, and a five DE-EMG based diagnostic model consisting B4GALT4, ADH4, ACAD11, B4GALT2, and PPP1R3C was established. The areas under the curve of the five genes in the merged training dataset and B4GALT2 in the validation dataset were 0.784 and 0.790, respectively. Moreover, good prognostic prediction ability was observed using the nomogram model (C index = 0.9201; P = 5.507e-14). Significant differences were observed in five immune cell types between the high- and low-BMD groups. These included central memory, effector memory, and activated CD8 T cells, as well as regulatory T cells and activated B cells. A network related to DE-EMGs was constructed, including hsa-miR-23b-3p, DANCR, 17 small-molecule drugs, and two Kyoto Encyclopedia of Genes and Genomes pathways, including metabolic pathways and pyruvate metabolism. Our findings highlighted the important roles of DE-EMGs in the development of osteoporosis. Furthermore, the DANCR/hsa-miR-23b-3p/B4GALT4 axis might provide novel molecular insights into the process of osteoporosis development.

Development of a program theory for osteoporosis patient education in Denmark: a qualitative study based on realist evaluation.

BACKGROUND: Osteoporosis patient education is offered in many countries worldwide. When evaluating complex interventions like these, it is important to understand how and why the intervention leads to effects. This study aimed to develop a program theory of osteoporosis patient education in Danish municipalities with a focus on examining the mechanisms of change i.e. what is about the programs that generate change. METHODS: The program theory was developed in an iterative process. The initial draft was based on a previous published systematic review, and subsequently the draft was continually refined based on findings from observations (10 h during osteoporosis patient education) and interviews (individual interviews with six employees in municipalities and three health professionals at hospitals, as well as four focus group interviews with participants in patient education (in total 27 informants)). The transcribed interviews were analyzed using thematic analysis and with inspiration from realist evaluation the mechanisms as well as the contextual factors and outcomes were examined. RESULTS: Based on this qualitative study we developed a program theory of osteoporosis patient education and identified four mechanisms: motivation, recognizability, reassurance, and peer reflection. For each mechanism we examined how contextual factors activated the mechanism as well as which outcomes were achieved. For instance, the participants' motivation is activated when they meet in groups, and thereafter outcomes such as more physical activity may be achieved. Recognizability is activated by the participants' course of disease, which may lead to better ergonomic habits. Reassurance may result in more physical activity, and this mechanism is activated in newly diagnosed participants without previous fractures. Peer reflection is activated when the participants meet in groups, and the outcome healthier diet may be achieved. CONCLUSIONS: We developed a program theory and examined how and why osteoporosis patient education is likely to be effective. Understanding these prerequisites is important for future implementation and evaluation of osteoporosis patient education.

Understanding primary care providers' attitudes towards preventive screenings to patients with inflammatory bowel disease.

BACKGROUND: Preventive care is important for managing inflammatory bowel disease (IBD), yet primary care providers (PCPs) often face challenges in delivering such care due to discomfort and unfamiliarity with IBD-specific guidelines. This study aims to assess PCPs' attitudes towards, and practices in, providing preventive screenings for IBD patients, highlighting areas for improvement in guideline dissemination and education. METHODS: Using a web-based opt-in panel of PCPs (DocStyles survey, spring 2022), we assessed PCPs' comfort level with providing/recommending screenings and the reasons PCPs felt uncomfortable (n = 1,503). Being likely to provide/recommend screenings for depression/anxiety, skin cancer, osteoporosis, and cervical cancer were compared by PCPs' comfort level and frequency of seeing patients with IBD. We estimated adjusted odd ratios (AORs) of being likely to recommend screenings and selecting responses aligned with IBD-specific guidelines by use of clinical practice methods. RESULTS: About 72% of PCPs reported being comfortable recommending screenings to patients with IBD. The top reason identified for not feeling comfortable was unfamiliarity with IBD-specific screening guidelines (55%). Being comfortable was significantly associated with being likely to provide/recommend depression/anxiety (AOR = 3.99) and skin cancer screenings (AOR = 3.19) compared to being uncomfortable or unsure. Percentages of responses aligned with IBD-specific guidelines were lower than those aligned with general population guidelines for osteoporosis (21.7% vs. 27.8%) and cervical cancer screenings (34.9% vs. 43.9%), and responses aligned with IBD-specific guidelines did not differ by comfort level for both screenings. Timely review of guidelines specific to immunosuppressed patients was associated with being likely to provide/recommend screenings and selecting responses aligned with IBD-specific guidelines. CONCLUSIONS: Despite a general comfort among PCPs in recommending preventive screenings for IBD patients, gaps in knowledge regarding IBD-specific screening guidelines persist. Enhancing awareness and understanding of these guidelines through targeted education and resource provision may bridge this gap.