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BACKGROUND: With the advancement of world population aging, age-related sarcopenia (SP) imposes enormous clinical burden on hospital. Clinical research of SP in non-geriatric wards has not been appreciated, necessitating further investigation. However, observational studies are susceptible to confounders. Mendelian randomization (MR) can effectively mitigate bias to assess causality. OBJECTIVE: To investigate the correlation between SP and comorbidities in orthopedic wards, and subsequently infer the causality, providing a theoretical basis for developing strategies in SP prevention and treatment. METHODS: Logistic regression models were employed to assess the correlation between SP and comorbidities. The MR analysis was mainly conducted with inverse variance weighted, utilizing data extracted from the UK and FinnGen biobank (Round 9). RESULTS: In the cross-sectional analysis, SP exhibited significant associations with malnutrition (P = 0.013) and some comorbidities, including osteoporosis (P = 0.014), body mass index (BMI) (P = 0.021), Charlson Comorbidity Index (CCI) (P = 0.006). The MR result also provided supporting evidence for the causality between SP and hypertension, osteoporosis and BMI. These results also withstood multiple sensitivity analyses assessing the validity of MR assumptions. CONCLUSION: The result indicated a significant association between SP and BMI, CCI, malnutrition, and osteoporosis. We highlighted that SP and comorbidities deserved more attention in non-geriatric wards, urging further comprehensive investigation.

Asunto(s)

Comorbilidad , Análisis de la Aleatorización Mendeliana , Estado Nutricional , Sarcopenia , Humanos , Análisis de la Aleatorización Mendeliana/métodos , Estudios Transversales , Sarcopenia/epidemiología , Sarcopenia/diagnóstico , Masculino , Femenino , Anciano , Anciano de 80 o más Años , Persona de Mediana Edad , Índice de Masa Corporal , Osteoporosis/epidemiología , Osteoporosis/diagnóstico

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OBJECTIVE: We developed a few-shot learning (FSL) framework for the diagnosis of osteopenia and osteoporosis in knee X-ray images. METHODS: Computer vision models containing deep convolutional neural networks were fine-tuned to enable generalization from natural images (ImageNet) to chest X-ray images (normal vs. pneumonia, base images). Then, a series of automated machine learning classifiers based on the Euclidean distances of base images were developed to make predictions for novel images (normal vs. osteopenia vs. osteoporosis). The performance of the FSL framework was compared with that of junior and senior radiologists. In addition, the gradient-weighted class activation mapping algorithm was used for visual interpretation. RESULTS: In Cohort #1, the mean accuracy (0.728) and sensitivity (0.774) of the FSL models were higher than those of the radiologists (0.512 and 0.448). A diagnostic pipeline of FSL model (first)-radiologists (second) achieved better performance (0.653 accuracy, 0.582 sensitivity, and 0.816 specificity) than radiologists alone. In Cohort #2, the diagnostic pipeline also showed improved performance. CONCLUSIONS: The FSL framework yielded practical performance with respect to the diagnosis of osteopenia and osteoporosis in comparison with radiologists. This retrospective study supports the use of promising FSL methods in computer-aided diagnosis tasks involving limited samples.

Asunto(s)

Enfermedades Óseas Metabólicas , Osteoporosis , Humanos , Osteoporosis/diagnóstico por imagen , Osteoporosis/diagnóstico , Enfermedades Óseas Metabólicas/diagnóstico por imagen , Enfermedades Óseas Metabólicas/diagnóstico , Femenino , Masculino , Persona de Mediana Edad , Anciano , Rodilla/diagnóstico por imagen , Rodilla/patología , Algoritmos , Redes Neurales de la Computación , Aprendizaje Automático , Radiografía/métodos , Adulto , Estudios Retrospectivos , Articulación de la Rodilla/diagnóstico por imagen , Articulación de la Rodilla/patología , Diagnóstico por Computador/métodos

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Dysmobility Syndrome (DMS), is a combination, that is analogous to the approach taken with metabolic syndrome, The diagnosis of DMS is complex. So this study aimed to explore the relationship between 25-(OH) Vit D with Dysmobility Syndrome (DMS)in type 2 diabetes mellitus(T2DM) patients. This is a cross-sectional study, including 330 patients (67.0 ± 8.8 years old) with T2DM who were admitted to the Qinhuangdao First Hospital from October 2020 to February 2022. Selected independent variables include grip strength, six-meter gait speed, level of 25-(OH) vitamin D, and bone mineral density (BMD) measured by Dual-energy X-ray (DXA). DMS includes six conditions: osteoporosis, low muscle mass, low muscle strength, slow gait speed, occurrences of falls in the past year ≥ 1, and obesity, having three or more of these conditions were diagnosed with DMS. Patients were classified based on DMS. The detection rate of DMS in patients with T2DM was 25.5%. The proportion of vitamin deficiency is 67.9% in patients with T2DM. The 25-(OH) Vit D deficiency was defined based on the 25th percentile into two groups; < 36.2 nmol/L. The vitamin D levels in Group DMS were significantly lower than that in Group Non-DMS (41.74 ± 14.60 vs. 47.19 ± 13.01, P < 0.05). After adjusting confounder factors including sex, age, vitamin D levels, HbA1c, ALB, HDLC, eGFR, diabetes microvascular complications and macrovascular, there was an independent association between risk of DMS and age (OR value = 1.160, 95% CI 1.091-1.234, P = 0.000), HbA1c(OR value = 1.262, 95% CI 1.046-1.532, P = 0.015), and vitamin D deficiency (< 36.2 nmol/L) (OR value = 2.990, 95% CI 1.284-6.964, P = 0.011). Our findings suggest that low levels of vitamin D are a predictor of DMS in middle-aged and elderly patients with poor control of type 2 diabetes.

Asunto(s)

Diabetes Mellitus Tipo 2 , Deficiencia de Vitamina D , Vitamina D , Humanos , Diabetes Mellitus Tipo 2/complicaciones , Diabetes Mellitus Tipo 2/sangre , Masculino , Femenino , Anciano , Vitamina D/sangre , Vitamina D/análogos & derivados , Estudios Transversales , Persona de Mediana Edad , Deficiencia de Vitamina D/complicaciones , Deficiencia de Vitamina D/sangre , Densidad Ósea , Fuerza de la Mano , Osteoporosis/sangre , Osteoporosis/etiología , Osteoporosis/diagnóstico , Síndrome

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Objective: This systematic review and meta-analysis aimed to investigate the association between circulating irisin levels and osteoporosis in women, exploring irisin's potential role in the pathophysiology and management of osteoporosis. Method: We searched PubMed, Embase, Web of Science, Cochrane Library, CNKI, WanFang, and VIP databases up to January 2023. The inclusion criteria were observational studies reporting on circulating irisin levels in women. The standardized mean difference (SMD) and correlation coefficients with a 95% confidence interval (CI) were used as the main effect measures under a random-effects model. Heterogeneity was evaluated using the Cochrane Q statistic and the I2 statistics. Subgroup analysis and univariate meta-regression analysis were performed to identify the sources of heterogeneity. The quality of the included study was assessed by the Newcastle-Ottawa Score. The quality of evidence was evaluated using the GRADE system. Publication bias was assessed using Begg's and Egger's test, and the trim-and-fill method. Sensitivity analysis was performed to assess the stability of the results. Results: Fifteen studies with a total of 2856 participants met the criteria. The analysis showed significantly lower irisin levels in postmenopausal osteoporotic women compared to non-osteoporotic controls (SMD = -1.66, 95% CI: -2.43 to -0.89, P < 0.0001; I2 = 98%, P < 0.00001) and in postmenopausal individuals with osteoporotic fractures than in non-fractures controls (SMD = -1.25, 95% CI: -2.15 to -0.34, P = 0.007; I2 = 97%, P < 0.00001). Correlation analysis revealed that irisin levels positively correlated with lumbar spine BMD (r = 0.37, 95% CI: 0.18 to 0.54), femoral BMD (r = 0.30, 95% CI: 0.18 to 0.42), and femoral neck BMD (r = 0.31, 95% CI: 0.14 to 0.47) in women. Despite significant heterogeneity, the robustness of the results was supported by using the random effects model and sensitivity analysis. Conclusion: The current evidence suggests that lower irisin levels are significantly associated with osteoporosis and fracture in postmenopausal women, suggesting its utility as a potential biomarker for early detection of osteoporosis and therapeutic target. However, further high-quality prospective research controlling for confounding factors is needed to clarify the relationship between irisin levels and osteoporotic outcomes. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023410264.

Asunto(s)

Fibronectinas , Osteoporosis , Femenino , Humanos , Biomarcadores/sangre , Densidad Ósea , Fibronectinas/sangre , Estudios Observacionales como Asunto , Osteoporosis/sangre , Osteoporosis/diagnóstico , Osteoporosis Posmenopáusica/sangre

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Non-coding RNAs (ncRNAs) belong to a class of untranslated nucleic acids involved in regulation of gene expression. ncRNAs are categorized as small (<200 ribonucleotides in length), i.e., microRNAs (miRNAs), and long ncRNAs (lncRNAs) (200 to thousands of ribonucleotides in length) and circular RNAs (circRNAs). In contrast to miRNAs, the roles of lncRNAs in general and circRNAs in bone metabolism specifically are not well understood. As such, a comprehensive understanding of these RNA species in bone turnover could be of great value in the development of new diagnostic tools and therapeutic targets. Unfortunately, measurement of these unique RNAs lacks standardization, a component critical to clinical translation. This review examines the potential role of lncRNA and circRNA as bone biomarkers, the need for validated and standardized measurement and challenges thereof.

Asunto(s)

Osteoporosis , ARN Circular , ARN Largo no Codificante , Humanos , ARN Circular/genética , Osteoporosis/genética , Osteoporosis/metabolismo , Osteoporosis/diagnóstico , ARN Largo no Codificante/genética , Biomarcadores/metabolismo , Biomarcadores/análisis

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The primary aim of this study was to evaluate computed tomography (CT)-based bone density analysis at the level of thoracic vertebra 12 (Th12) as a screening method for decreased bone density in patients admitted to the intensive care unit (ICU). Interobserver variability was analyzed. Secondary aims were to assess the prevalence of CT-based low bone density upon ICU admission in a cohort of COVID-19 patients and to assess the potential effect of long-term ICU stay on bone density in these patients. Retrospective single-center cohort study. ICU of the Leiden University Medical Center (LUMC), the Netherlands. Patients admitted to the ICU of the LUMC between March 1st, 2020 and February 1st, 2022 with a diagnosis of COVID-19, and a length of ICU stay of ≥ 21 days. In the included patients both baseline chest CT scans (obtained upon ICU admission) and follow-up chest CT scans (obtained ≥ 21 days after ICU admission) were available for analysis. A total of 118 CT scans in 38 patients were analyzed. There was a good interobserver variability, with an overall mean absolute difference (between measurements of three observers) of 9.7 Hounsfield Units (HU) and an intraclass correlation coefficient (ICC) of 0.93 (95% CI 0.88-0.96). The effect of intravenous contrast administration on bone density measurements was small (+ 7.5 HU (95% CI 3.4-11.5 HU)) higher in contrast enhanced CT images compared to non contrast enhanced CT images). Thirty-seven percent of patients had a bone density < 140 HU, suggestive of osteoporosis. No significant difference was found between bone density upon ICU admission and bone density at follow-up (≥ 21 days after ICU admission). Vertebral CT-based bone density analysis using routine CT scans is an easily applicable method to identify ICU patients with decreased bone density, which could enable enrollment in osteoporosis prevention programs. A high prevalence of low bone density was found in our cohort of ICU patients. There were no changes observed in bone density between baseline and follow-up measurements.

Asunto(s)

Densidad Ósea , COVID-19 , Osteoporosis , Tomografía Computarizada por Rayos X , Humanos , Osteoporosis/diagnóstico por imagen , Osteoporosis/diagnóstico , Femenino , Tomografía Computarizada por Rayos X/métodos , Masculino , Anciano , Estudios Retrospectivos , Persona de Mediana Edad , COVID-19/diagnóstico por imagen , COVID-19/epidemiología , Unidades de Cuidados Intensivos , Países Bajos/epidemiología , Tamizaje Masivo/métodos , Vértebras Torácicas/diagnóstico por imagen , SARS-CoV-2/aislamiento & purificación , Anciano de 80 o más Años

RESUMEN

BACKGROUND: Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. METHODS AND FINDINGS: We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. CONCLUSIONS: In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.

Asunto(s)

Absorciometría de Fotón , Densidad Ósea , Osteoporosis , Humanos , Masculino , Densidad Ósea/genética , Femenino , Persona de Mediana Edad , Anciano , Osteoporosis/genética , Osteoporosis/diagnóstico , Medición de Riesgo/métodos , Polimorfismo de Nucleótido Simple , Cuello Femoral/diagnóstico por imagen , Reino Unido , Fracturas Osteoporóticas/genética , Vértebras Lumbares/diagnóstico por imagen , Factores de Riesgo , Adulto , Población Blanca/genética , Etnicidad/genética

RESUMEN

To assess the diagnostic utility of bone turnover markers (BTMs) and demographic variables for identifying individuals with osteoporosis. A cross-sectional study involving 280 participants was conducted. Serum BTM values were obtained from 88 patients with osteoporosis and 192 controls without osteoporosis. Six machine learning models, including extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), CatBoost, random forest, support vector machine, and k-nearest neighbors, were employed to evaluate osteoporosis diagnosis. The performance measures included the area under the receiver operating characteristic curve (AUROC), F1-score, and accuracy. After AUROC optimization, LGBM exhibited the highest AUROC of 0.706. Post F1-score optimization, LGBM's F1-score was improved from 0.50 to 0.65. Combining the top three optimized models (LGBM, XGBoost, and CatBoost) resulted in an AUROC of 0.706, an F1-score of 0.65, and an accuracy of 0.73. BTMs, along with age and sex, were found to contribute significantly to osteoporosis diagnosis. This study demonstrates the potential of machine learning models utilizing BTMs and demographic variables for diagnosing preexisting osteoporosis. The findings highlight the clinical relevance of accessible clinical data in osteoporosis assessment, providing a promising tool for early diagnosis and management.

Asunto(s)

Biomarcadores , Aprendizaje Automático , Osteoporosis , Humanos , Osteoporosis/diagnóstico , Femenino , Masculino , Estudios Transversales , Biomarcadores/sangre , Persona de Mediana Edad , Anciano , Remodelación Ósea/fisiología , Curva ROC

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Osteoporosis is under-diagnosed while detectable by measuring bone mineral density (BMD) using quantitative computer tomography (QCT). Opportunistic screening for low BMD has previously been suggested using lumbar QCT. However, thoracic QCT also possesses this potential to develop upper and lower cut-off values for low thoracic BMD, corresponding to the current cut-offs for lumbar BMD. In participants referred with chest pain, lumbar and thoracic BMD were measured using non-contrast lumbar- and cardiac CT scans. Lumbar BMD cut-off values for very low (< 80 mg/cm3), low (80-120 mg/cm3), and normal BMD (> 120 mg/cm3) were used to assess the corresponding thoracic values. A linear regression enabled identification of new diagnostic thoracic BMD cut-off values. The 177 participants (mean age 61 [range 31-74] years, 51% women) had a lumbar BMD of 121.6 mg/cm3 (95% CI 115.9-127.3) and a thoracic BMD of 137.0 mg/cm3 (95% CI: 131.5-142.5), p < 0.001. Categorization of lumbar BMD revealed 14%, 35%, and 45% in each BMD category. When applied for the thoracic BMD measurements, 25% of participants were reclassified into a lower group. Linear regression predicted a relationship of Thoracic BMD = 0.85 * Lumbar BMD + 33.5, yielding adjusted thoracic cut-off values of < 102 and > 136 mg/cm3. Significant differences in BMD between lumbar and thoracic regions were found, but a linear relationship enabled the development of thoracic upper and lower cut-off values for low BMD in the thoracic spine. As Thoracic CT scans are frequent, these findings will strengthen the utilization of CT images for opportunistic detection of osteoporosis.

Asunto(s)

Densidad Ósea , Vértebras Lumbares , Osteoporosis , Vértebras Torácicas , Tomografía Computarizada por Rayos X , Humanos , Densidad Ósea/fisiología , Femenino , Masculino , Persona de Mediana Edad , Anciano , Vértebras Torácicas/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Adulto , Osteoporosis/diagnóstico por imagen , Osteoporosis/diagnóstico , Vértebras Lumbares/diagnóstico por imagen

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Bone health is crucial at all stages of life. Several medical conditions and changes in lifestyle affect the growth, structure, and functions of bones. This may lead to the development of bone degenerative disorders, such as osteoporosis, osteoarthritis, rheumatoid arthritis, etc., which are major public health concerns worldwide. Accurate and reliable measurement and monitoring of bone health are important aspects for early diagnosis and interventions to prevent such disorders. Significant progress has recently been made in developing new sensing technologies that offer non-invasive, low-cost, and accurate measurements of bone health. In this review, we have described bone remodeling processes and common bone disorders. We have also compiled information on the bone turnover markers for their use as biomarkers in biosensing devices to monitor bone health. Second, this review details biosensing technology for bone health assessment, including the latest developments in various non-invasive techniques, including dual-energy X-ray absorptiometry, magnetic resonance imaging, computed tomography, and biosensors. Further, we have also discussed the potential of emerging technologies, such as biosensors based on nano- and micro-electromechanical systems and application of artificial intelligence in non-invasive techniques for improving bone health assessment. Finally, we have summarized the advantages and limitations of each technology and described clinical applications for detecting bone disorders and monitoring treatment outcomes. Overall, this review highlights the potential of emerging technologies for improving bone health assessment with the potential to revolutionize clinical practice and improve patient outcomes. The review highlights key challenges and future directions for biosensor research that pave the way for continued innovations to improve diagnosis, monitoring, and treatment of bone-related diseases.

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Absorciometría de Fotón , Técnicas Biosensibles , Huesos , Humanos , Técnicas Biosensibles/instrumentación , Técnicas Biosensibles/métodos , Huesos/diagnóstico por imagen , Absorciometría de Fotón/métodos , Enfermedades Óseas/diagnóstico , Remodelación Ósea/fisiología , Biomarcadores/análisis , Imagen por Resonancia Magnética/métodos , Tomografía Computarizada por Rayos X/métodos , Osteoporosis/diagnóstico , Osteoporosis/diagnóstico por imagen , Animales

RESUMEN

BACKGROUND: T-score measurement via dual-energy X-ray absorptiometry (DXA) is the gold standard for assessing and classifying the bone mineral density status of patients as normal, osteopenic, or osteoporotic according to the World Health Organization criteria. However, the diagnostic accuracy may be affected by the skeletal site selected for DXA. OBJECTIVES: Estimate the prevalence of femoral and lumbar BMD discordance in a community-based setting in Riyadh, Saudi Arabia. DESIGN: Cross-sectional. SETTING: Polyclinics at a tertiary care center. PATIENTS AND METHODS: This study included all patients aged ≥60 years who visited the Department of Family Medicine and underwent DXA screening between 2016 and 2022. MAIN OUTCOME MEASURES: Discordance was defined as a difference in BMD status between two skeletal sites. Minor discordance occurs when adjacent sites have different diagnoses; i.e., one site exhibits osteoporosis and the other exhibits osteopenia. In contrast, major discordance occurs when one site exhibits osteoporosis and the other exhibits normal BMD. SAMPLE SIZE: 1429 older adults. RESULTS: The study patients had a median age of 66 years (60-99, minimum-maximum). The prevalence of discordance was 41.6%, with major discordance present in 2.2% of patients and minor discordance in 39.4%. The distribution of discordance did not differ significantly among the sociodemographic factors. CONCLUSION: Discordance is prevalent among the Saudi geriatric population. During the analysis of DXA results, physicians should account for discordance when diagnosing and ruling out osteoporosis in high-risk patients. LIMITATIONS: All factors influencing discordance were not explored thoroughly; this study mainly focused on older adults. Furthermore, diverse age groups need to be investigated for a more comprehensive understanding of the analyzed factors.

Asunto(s)

Absorciometría de Fotón , Densidad Ósea , Enfermedades Óseas Metabólicas , Fémur , Vértebras Lumbares , Osteoporosis , Humanos , Femenino , Anciano , Masculino , Estudios Transversales , Arabia Saudita/epidemiología , Absorciometría de Fotón/métodos , Osteoporosis/epidemiología , Osteoporosis/diagnóstico , Osteoporosis/diagnóstico por imagen , Persona de Mediana Edad , Prevalencia , Vértebras Lumbares/diagnóstico por imagen , Anciano de 80 o más Años , Enfermedades Óseas Metabólicas/epidemiología , Enfermedades Óseas Metabólicas/diagnóstico , Enfermedades Óseas Metabólicas/diagnóstico por imagen , Fémur/diagnóstico por imagen

RESUMEN

AIM: Patients with rheumatoid arthritis (RA) are at a higher risk of osteoporotic fractures. Studies have shown that patients with Sjogren's syndrome (SS) and systemic lupus erythematosus (SLE) experienced an increase in bone mineral density (BMD) after receiving hydroxychloroquine (HCQ) treatment, indicating a potential protective effect against osteoporosis. Therefore, this study is to examine the relationship between HCQ usage and the risk of osteoporosis in patients diagnosed with RA. METHODS: The retrospective cohort study used data from Taiwan's National Health Insurance Research Database (NHIRD) covering the period from January 2010 to December 2018, which included 14 050 newly diagnosed RA patients, subsequently divided into two groups: HCQ users and non-users. Propensity score matching (PSM) based on sex, age, urbanization, insured unit type, insured area, and comorbidities was conducted to match the groups. The primary outcome assessed was the evaluation of the risk of osteoporosis by employing a multivariable Cox proportional hazard regression model to calculate the adjusted hazard ratio (aHR). RESULTS: After PSM, a total of 6408 RA patients were included in the analysis (3204 HCQ users and 3204 non-users). There was no significantly higher risk of osteoporosis in HCQ users compared with non-users, aHR = 0.99 (95% CI: 0.82-1.196). Additionally, different durations of HCQ usage demonstrated a neutral effect on the risk of osteoporosis [HCQ <90 days, aHR = 0.88 (95% CI: 0.585-1.324); HCQ 90-180 days, aHR = 0.941 (95% CI: 0.625-1.418); HCQ >180 days, aHR = 1.019 (95% CI: 0.832-1.249)]. CONCLUSIONS: The study indicates that there is no significant association between the use of HCQ and the risk of osteoporosis in patients with RA.

Asunto(s)

Antirreumáticos , Artritis Reumatoide , Bases de Datos Factuales , Hidroxicloroquina , Osteoporosis , Humanos , Artritis Reumatoide/tratamiento farmacológico , Artritis Reumatoide/epidemiología , Artritis Reumatoide/diagnóstico , Hidroxicloroquina/efectos adversos , Hidroxicloroquina/uso terapéutico , Estudios Retrospectivos , Osteoporosis/epidemiología , Osteoporosis/inducido químicamente , Osteoporosis/diagnóstico , Femenino , Masculino , Persona de Mediana Edad , Antirreumáticos/efectos adversos , Taiwán/epidemiología , Factores de Riesgo , Adulto , Anciano , Medición de Riesgo , Densidad Ósea/efectos de los fármacos , Resultado del Tratamiento , Factores de Tiempo , Factores Protectores

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Background: Osteoporosis (OP) associated with aging exerts substantial clinical and fiscal strains on societal structures. An increasing number of research studies have suggested a bidirectional relationship between circulating inflammatory markers (CIMs) and OP. However, observational studies are susceptible to perturbations in confounding variables. In contrast, Mendelian randomization (MR) offers a robust methodological framework to circumvent such confounders, facilitating a more accurate assessment of causality. Our study aimed to evaluate the causal relationships between CIMs and OP, identifying new approaches and strategies for the prevention, diagnosis and treatment of OP. Methods: We analyzed publicly available GWAS summary statistics to investigate the causal relationships between CIMs and OP. Causal estimates were calculated via a systematic analytical framework, including bidirectional MR analysis and Bayesian colocalization analysis. Results: Genetically determined levels of CXCL11 (OR = 0.91, 95% CI = 0.85-0.98, P = 0.008, PFDR = 0.119), IL-18 (OR = 0.88, 95% CI = 0.83-0.94, P = 8.66×10-5, PFDR = 0.008), and LIF (OR = 0.86, 95% CI = 0.76-0.96, P = 0.008, PFDR = 0.119) were linked to a reduced risk of OP. Conversely, higher levels of ARTN (OR = 1.11, 95% CI = 1.02-1.20, P = 0.012, PFDR = 0.119) and IFNG (OR = 1.16, 95% CI = 1.03-1.30, P = 0.013, PFDR = 0.119) were associated with an increased risk of OP. Bayesian colocalization analysis revealed no evidence of shared causal variants. Conclusion: Despite finding no overall association between CIMs and OP, five CIMs demonstrated a potentially significant association with OP. These findings could pave the way for future mechanistic studies aimed at discovering new treatments for this disease. Additionally, we are the first to suggest a unidirectional causal relationship between ARTN and OP. This novel insight introduces new avenues for research into diagnostic and therapeutic strategies for OP.

Asunto(s)

Biomarcadores , Estudio de Asociación del Genoma Completo , Análisis de la Aleatorización Mendeliana , Osteoporosis , Humanos , Osteoporosis/sangre , Osteoporosis/genética , Osteoporosis/etiología , Osteoporosis/diagnóstico , Biomarcadores/sangre , Teorema de Bayes , Polimorfismo de Nucleótido Simple , Predisposición Genética a la Enfermedad , Inflamación/sangre , Inflamación/genética , Femenino

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Machine learning (ML) models have been increasingly employed to predict osteoporosis. However, the incorporation of hair minerals into ML models remains unexplored. This study aimed to develop ML models for predicting low bone mass (LBM) using health checkup data and hair mineral analysis. A total of 1206 postmenopausal women and 820 men aged 50 years or older at a health promotion center were included in this study. LBM was defined as a T-score below - 1 at the lumbar, femur neck, or total hip area. The proportion of individuals with LBM was 59.4% (n = 1205). The features used in the models comprised 50 health checkup items and 22 hair minerals. The ML algorithms employed were Extreme Gradient Boosting (XGB), Random Forest (RF), Gradient Boosting (GB), and Adaptive Boosting (AdaBoost). The subjects were divided into training and test datasets with an 80:20 ratio. The area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and an F1 score were evaluated to measure the performances of the models. Through 50 repetitions, the mean (standard deviation) AUROC for LBM was 0.744 (± 0.021) for XGB, the highest among the models, followed by 0.737 (± 0.023) for AdaBoost, and 0.733 (± 0.023) for GB, and 0.732 (± 0.021) for RF. The XGB model had an accuracy of 68.7%, sensitivity of 80.7%, specificity of 51.1%, PPV of 70.9%, NPV of 64.3%, and an F1 score of 0.754. However, these performance metrics did not demonstrate notable differences among the models. The XGB model identified sulfur, sodium, mercury, copper, magnesium, arsenic, and phosphate as crucial hair mineral features. The study findings emphasize the significance of employing ML algorithms for predicting LBM. Integrating health checkup data and hair mineral analysis into these models may provide valuable insights into identifying individuals at risk of LBM.

Asunto(s)

Densidad Ósea , Cabello , Aprendizaje Automático , Humanos , Persona de Mediana Edad , Femenino , Cabello/química , Cabello/metabolismo , Masculino , Anciano , Osteoporosis/diagnóstico , Osteoporosis/metabolismo , Curva ROC , Algoritmos , Minerales/análisis , Minerales/metabolismo

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Absorciometría de Fotón , Densidad Ósea , Osteoporosis , Humanos , Absorciometría de Fotón/métodos , Osteoporosis/diagnóstico

RESUMEN

Osteoporotic fractures, also known as fragility fractures, are reflective of compromised bone strength and are associated with significant morbidity and mortality. Such fractures may be clinically silent, and others may present clinically with pain and deformity at the time of the injury. Unfortunately, and even at the time of detection, most individuals sustaining fragility fractures are not identified as having underlying metabolic bone disease and are not evaluated or treated to reduce the incidence of future fractures. A multidisciplinary international working group with representation from international societies dedicated to advancing the care of patients with metabolic bone disease has developed best practice recommendations for the diagnosis and evaluation of individuals with fragility fractures. A comprehensive narrative review was conducted to identify key articles on fragility fractures and their impact on the incidence of further fractures, morbidity, and mortality. This document represents consensus among the supporting societies and harmonizes best practice recommendations consistent with advances in research. A fragility fracture in an adult is an important predictor of future fractures and requires further evaluation and treatment of the underlying osteoporosis. It is important to recognize that most fragility fractures occur in patients with bone mineral density T scores higher than -2.5, and these fractures confirm the presence of skeletal fragility even in the presence of a well-maintained bone mineral density. Fragility fractures require further evaluation with exclusion of contributing factors for osteoporosis and assessment of clinical risk factors for fracture followed by appropriate pharmacological intervention designed to reduce the risk of future fracture. Because most low-trauma vertebral fractures do not present with pain, dedicated vertebral imaging and review of past imaging is useful in identifying fractures in patients at high risk for vertebral fractures. Given the importance of fractures in confirming skeletal fragility and predicting future events, it is recommended that an established classification system be used for fracture identification and reporting.

Asunto(s)

Absorciometría de Fotón , Fracturas Osteoporóticas , Humanos , Fracturas Osteoporóticas/prevención & control , Fracturas Osteoporóticas/diagnóstico por imagen , Fracturas Osteoporóticas/diagnóstico , Absorciometría de Fotón/métodos , Densidad Ósea , Guías de Práctica Clínica como Asunto , Osteoporosis/diagnóstico , Osteoporosis/diagnóstico por imagen , Femenino , Factores de Riesgo

RESUMEN

Transient osteoporosis of the hip (TOH) is an important but often neglected cause of hip pain, which can gradually lead to debilitating mobility and carries risks such as fracture or avascular necrosis. A 39-year-old woman presented to the Rheumatology department two weeks post-cesarean delivery, reporting the onset of left mechanical hip pain since the 33rd week of pregnancy. After delivery, similar complaints emerged on the right side. Hip X-ray showed a decrease in bone density in the left hip. Later, Magnetic Resonance Imaging revealed bilateral bone marrow edema in both proximal femurs. The diagnosis of TOH was established, and the patient was treated with conservative measures. Seven months later, she was asymptomatic. Pregnancy is a recognized risk factor for TOH, especially in the last trimester. It is an important differential diagnosis to consider in cases of hip pain in pregnant or newly breastfeeding women.

Asunto(s)

Articulación de la Cadera , Osteoporosis , Complicaciones del Embarazo , Humanos , Femenino , Embarazo , Adulto , Osteoporosis/diagnóstico por imagen , Osteoporosis/diagnóstico , Osteoporosis/complicaciones , Articulación de la Cadera/diagnóstico por imagen , Articulación de la Cadera/patología , Artralgia/etiología , Artralgia/diagnóstico por imagen , Imagen por Resonancia Magnética

Asunto(s)

Tamizaje Masivo , Osteoporosis , Humanos , Osteoporosis/diagnóstico , Osteoporosis/epidemiología , Portugal/epidemiología , Femenino , Anciano , Persona de Mediana Edad , Masculino , Absorciometría de Fotón

RESUMEN

Osteoporosis is a globally relevant public health issue. Our study aimed to summarize the knowledge on the proteomic biomarkers for low bone mineral density over the last years. We conducted a systematic review following the PRISMA guidelines; the scoured databases were PubMed, Web of Sciences, Scopus, and EBSCO, from inception to 2 June 2023. A total of 610 relevant studies were identified and 33 were assessed for eligibility. Finally, 29 studies met the criteria for this systematic review. The risk of bias was evaluated using the Joanna Briggs Institute Critical Appraisal Checklist tool. From the studies selected, 154 proteins were associated with changes of bone mineral density, from which only 10 were reported in at least two articles. The protein-protein network analysis indicated potential biomarkers involved in the skeletal system, immune system process, regulation of protein metabolic process, regulation of signaling, transport, cellular component assembly, cell differentiation, hemostasis, and extracellular matrix organization. Mass spectrometry-based proteomic profiling has allowed the discovery of new biomarkers with diagnostic potential. However, it is necessary to compare and validate the potential biomarkers in different populations to determine their association with bone metabolism and evaluate their translation to the clinical management of osteoporosis.

Asunto(s)

Biomarcadores , Densidad Ósea , Osteoporosis , Proteómica , Humanos , Biomarcadores/metabolismo , Proteómica/métodos , Osteoporosis/metabolismo , Osteoporosis/diagnóstico , Proteoma/metabolismo , Proteoma/análisis , Mapas de Interacción de Proteínas

RESUMEN

Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional Neural Network to automatically learn an optimal combination of pre-processing strategies, for the classification of Raman spectra of superficial and deep layers of cartilage harvested from 45 Osteoarthritis and 19 Osteoporosis (Healthy controls) patients. Using 6-fold cross-validation, the Multi-Convolutional Neural Network achieves comparable or improved classification accuracy against the best-performing Convolutional Neural Network applied to either the raw or pre-processed spectra. We utilised Integrated Gradients to identify the contributing features (Raman signatures) in the network decision process, showing they are biologically relevant. Using these features, we compared Artificial Neural Networks, Decision Trees and Support Vector Machines for the feature selection task. Results show that training on fewer than 3 and 300 features, respectively, for the disease classification and layer assignment task provide performance comparable to the best-performing CNN-based network applied to the full dataset. Our approach, incorporating multi-channel input and Integrated Gradients, can potentially facilitate the clinical translation of Raman spectroscopy-based diagnosis without the need for laborious manual pre-processing and feature selection.

Asunto(s)

Aprendizaje Profundo , Redes Neurales de la Computación , Osteoartritis , Espectrometría Raman , Humanos , Espectrometría Raman/métodos , Osteoartritis/clasificación , Osteoartritis/diagnóstico , Femenino , Masculino , Cartílago Articular/patología , Persona de Mediana Edad , Anciano , Osteoporosis/diagnóstico , Máquina de Vectores de Soporte