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.

Visceral Adipose Tissue Indices Independently Correlated with Obstructive Sleep Apnea in Patients with Type 2 Diabetes.

AIMS: We aimed to explore whether visceral adiposity indices were significantly associated with obstructive sleep apnea (OSA) in type 2 diabetes (T2DM) patients. METHODS: 100 patients with T2DM who underwent overnight polysomnography were analyzed in this study. Anthropometric data, lipid profiles, and glycemic parameters were recorded. Body fat percentage (BFP) and visceral adipose tissue area (VAT area) were collected from a whole body scan using dual-energy X-ray absorptiometry (DXA). Multivariate logistic regression analysis was performed to explore the associations of AHI with BFP, VAT area, and CVAI. RESULTS: The prevalence rate of OSA was 80%, and the mean (±SD) of age was 47.0 ± 13.6 years. Apnea-hypopnea index (AHI) was significantly and positively associated with either VAT area (r = 0.433, p ≤ 0.001) or Chinese visceral adiposity index (CVAI) (r = 0.355, p ≤ 0.001) but not for BFP (r = 0.107, p = 0.294). Multivariate logistic regression analyses showed that VAT area and CVAI were significantly associated with increased risk of OSA, and the adjusted ORs were (95% CI) 1.025 (1.003-1.047, p = 0.023) and 1.018 (1.002-1.034, p = 0.030), respectively. However, there was no significant association between BFP and increased risk of OSA. CONCLUSIONS: VAT area and CVAI were independent risk factors of OSA in the patients with T2DM.

Long-Term Changes in Sarcopenia and Body Composition in Diabetes Patients with and without Charcot Osteoarthropathy.

BACKGROUND: Charcot osteoarthropathy of the foot (COA) can currently only be treated using prolonged periods of immobilization of the affected extremity. Therefore, the hypothesis is that COA leads to altered body composition and increased sarcopenia. OBJECTIVE: To investigate the changes over several years in sarcopenia, body composition, and fat distribution in diabetes patients with previous COA compared to diabetes patients without previous COA. METHODS: Prospective observational clinical study. Twenty-one subjects were included and had two DXA scans done with mean 8.6-year intervals to compare changes in lean mass and fat distribution. The lean mass of limbs was used as an estimate of appendicular lean mass (aLM). Fat mass and aLM were then used to detect sarcopenic individuals using different methods. Results and Conclusions. As compared to baseline, both groups had significant loss of lean mass, and diabetics without COA had significant gain of total fat percentage. No statistically different prevalence of sarcopenia between the groups could be established. Likewise, no difference was found in total lean and fat mass changes. None of the groups had statistically significant changes of android fat distribution. As compared with published data on sarcopenia, people with diabetes might be more prone to sarcopenia than healthy individuals.

Sarcopenic obesity is associated with telomere shortening: findings from the NHANES 1999-2002.

Sarcopenic obesity (SO) is characterised by the concurrent presence of sarcopenia and excess adiposity. Telomere shortening has been associated with sarcopenia and obesity alone but the association between SO and telomere length (TL) has not been investigated. This study aimed to investigate SO and TL in an adult population. Data were from 5397 individuals (mean age = 44.7 years, 51.3% male) enrolled in the National Health and Nutrition Examination Survey. Body composition (BC) was assessed by Dual Energy X-Ray Absorptiometry. Two models were used to assess SO: a BC model including four phenotypes derived from the combination of high or low adiposity and muscle mass; and, a truncal fat mass to appendicular skeletal mass ratio (TrFM/ASM). TL was assessed using quantitative polymerase chain reaction and expressed as base pairs. The mean TL, relative to the reference DNA, was calculated and expressed as the mean T/S ratio. A General Linear Model was applied to determine associations between TL for SO. In adjusted analysis, only individuals with SO, defined as the presence of high adiposity-low muscle mass (four-phenotype model), had significantly shorter telomeres (p = 0.05) than the reference group (i.e. low adiposity-high muscle mass), with a mean T/S ratio of 1.02 (95%CI: 0.98-1.05) compared to 1.05 (95%CI: 1.01-1.09), respectively. TrFM/ASM was not associated with TL. Preliminary findings suggest that sarcopenia and obesity may act synergistically to shorten telomeres.

Metabolic syndrome is not associated to an increased risk of low bone mineral density in men at risk for osteoporosis.

PURPOSE: We have recently demonstrated a significant association between osteoporosis (Op) and metabolic syndrome (MetS) in Caucasian women examined by Dual-energy X-ray absorptiometry (DXA) for suspected Op. This cross-sectional study was performed to evaluate the association between MetS and Op in Caucasian men enrolled in the same geographical area, with identical criteria and in the same time range. METHODS: Among subjects enrolled in the SIMON study, we selected the medical records of all free-living men who performed a contextual evaluation of both bone mineral density (BMD) by DXA and MetS constitutive elements (arterial blood pressure, waist circumference, serum levels of triglycerides, high-density lipoprotein cholesterol, and fasting glucose). All enrolled subjects refer to "COMEGEN" general practitioners' cooperative operating in Naples, Southern Italy. RESULTS: Overall, the medical records of 880 men were examined. No significant association between MetS and Op was observed. Among MetS constitutive elements, waist circumference was inversely related to Op risk. CONCLUSION: In Caucasian men examined by DXA for suspected Op, no significant association was observed between Op and MetS. The study results contrast to those observed in women enrolled in the same geographical area, with identical criteria and in the same time range and may be related to sexual dimorphism occurring in clinical expressiveness of both MetS and Op.

Screening rates and prevalence of osteoporosis in a real-world, Australian systemic sclerosis cohort.

AIM: Despite reports of decreased bone mineral density in patients with systemic sclerosis (SSc) in international cohorts, the prevalence of osteoporosis in Australian SSc patients remains unknown. We report rates of dual-energy X-ray absorptiometry (DXA) scanning in an SSc cohort at a tertiary hospital specialized outpatient clinic and the prevalence and associations of osteoporosis in screened patients. METHOD: We performed retrospective chart review to determine if patients underwent DXA scanning between 2007 and 2018 and extracted lumbar spine and femoral neck T-scores, fracture history, and osteoporosis therapy. RESULTS: Of 244 patients, 104 (42.6%) underwent DXA scanning and among patients in whom T-scores were available (n = 91), 30 (33.0%) had osteoporosis and 48 (52.7%) had osteopenia. Screened patients were more likely to have longer disease duration (19.9 vs 15.2 years, P = 0.002), calcinosis (50.5% vs 36.4%, P = 0.028), myositis (12.6% vs 0.7%, P < 0.001), synovitis (42.7% vs 28.6%, P = 0.022), ever used prednisolone (76.7% vs 47.1%, P < 0.001) or fractures (23.0% vs 0.0%, P < 0.001). Patients with osteoporosis more commonly had a history of nasogastric feeding, percutaneous endoscopic gastrostomy feeding or intravenous total parenteral nutrition (6.9% vs 0.0%, P = 0.038) and, unexpectedly, less commonly ever used prednisolone (58.6% vs 85.2%, P = 0.005) compared with patients with osteopenia or normal bone density. CONCLUSION: We identified high rates of osteoporosis among screened Australian SSc patients. Further assessment in larger, prospective studies is needed to establish guidelines for formal osteoporosis screening in SSc patients.

Body composition and bone mineral density by Dual Energy X-ray Absorptiometry: Reference values for young children.

BACKGROUND & AIMS: Childhood obesity is a global public health threat, with an alarming rise in incidence. Obesity at young age has short-term and long-term morbidity. It is, therefore, important to accurately assess body composition throughout infancy and childhood to identify excess adiposity. However, reference values for age 2-5 years, needed to interpret measurements and identify young children at risk, are lacking. Our primary objective was to fill the current gap in reference values by constructing sex-specific body composition reference values and charts for fat mass (FM), fat mass percentage (FM%), fat mass index (FMI), lean body mass (LBM), lean body mass index (LBMI) and total body less head bone mineral density (BMDTBLH) for children aged 2-5 years using Dual-Energy X-ray Absorptiometry (DXA). METHODS: We performed 599 accurate DXA-measurements in 340 term-born children aged 2-5 years, using Lunar Prodigy with Encore software (V14.1). Using GAMLSS, sex-specific reference values and charts were created for FM, FM%, FMI, LBM, LBMI and BMDTBLH. RESULTS: Sex-specific body composition reference values and charts for age 2-5 years were constructed. In boys and girls, FM and LBM increased from age 2-5 years (all p ≤ 0.001), but body size-corrected FM% and FMI decreased (all p ≤ 0.023). LBMI remained similar between 2 and 5 years of age. Girls had higher FM, FM% and FMI and lower LBM and LBMI compared to boys. BMC and BMDTBLH increased with age between 2 and 5 years of age (all p < 0.001) and were similar for boys and girls. CONCLUSIONS: We present sex-specific reference values and charts for body composition and total body bone mineral density measured by DXA, based on a large cohort of healthy children aged 2-5 years. These longitudinal references can be used for clinical practice and research purposes to monitor body composition and bone mineral density development and identify children at risk for excess adiposity.

Application of Medical Imaging Based on Deep Learning in the Treatment of Lumbar Degenerative Diseases and Osteoporosis with Bone Cement Screws.

OBJECTIVE: To explore the application value of magnetic resonance spectroscopy (MRS) and GSI-energy spectrum electronic computed tomography (CT) medical imaging based on the deep convolutional neural network (CNN) in the treatment of lumbar degenerative disease and osteoporosis. METHODS: There were 56 cases of suspected lumbar degenerative disease and osteoporosis. A group of 56 subjects were examined using 1.5 TMR spectrum (MRS) and dual-energy X-ray absorptiometry (DXA) to collect the lumbar L3 vertebral body fat ratio (FF) and L1~4 vertebral bone mineral density (BMD) value. We divided the subjects into 2 groups with T value -2.5 as the critical point. Set T value > -2.5 as the negative group and T value ≤ -2.5 as the positive group. Pearson's method is used for FF-MRS and BMD correlation analyses. A group of all patients underwent GSI-energy spectrum CT scan, and X-ray bone mineral density (DXA) test results (bone density per unit area) were used as the gold standard to analyze the diagnosis of osteoporosis by the GSI-energy spectrum CT scan method value. RESULTS: The differences in FF and BMD between the negative group and the positive group were statistically significant (P < 0.01), and there was a highly negative correlation between the average value of FF and BMD. 30 cases were diagnosed as osteoporosis by DXA. The accuracy of GSI-energy spectrum CT medical imaging in diagnosing osteoporosis is 89.30%. The GSI-energy spectrum CT diagnosis of osteoporosis and DXA examination results have good consistency. CONCLUSION: Based on the deep convolutional neural network (CNN) MRS technology, GSI-energy spectrum CT medical imaging is used in the clinical diagnosis and treatment of lumbar degenerative lesions and osteoporosis. It has a good advantage in assessing bone quality and has good consistency with DXA examination and has better application value high.

The Role of the Trabecular Bone Score in the Assessment of Osteoarticular Disorders in Patients with HFE-Hemochromatosis: A Single-Center Study from Poland.

Type 1 hereditary hemochromatosis (HH) is an autosomal, recessive genetic entity with systemic iron overload. Iron homeostasis disorders develop as a result of HFE gene mutations, which are associated with hepcidin arthropathy or osteoporosis and may cause permanent disability in HH patients despite a properly conducted treatment with phlebotomies. In this study, selected parameters of calcium and phosphate metabolism were analyzed in combination with the assessment of bone mineral density (BMD) disorders in patients from northern Poland with clinically overt HFE-HH. BMD was determined by a dual-energy X-ray absorptiometry (DXA) test with the use of the trabecular bone score (TBS) function. The study included 29 HH patients (mean age = 53.14 years) who were compared with 20 healthy volunteers. A significantly lower TBS parameter and serum 25-OH-D3 concentration, a higher concentration of intact parathormone and more a frequent occurrence of joint pain were found in HH patients compared with the control group. In HH patients, the diagnosis of liver cirrhosis was associated with lower serum 25-OH-D3 and osteocalcin concentrations. In HH, DXA with the TBS option is a valuable tool in the early assessment of the bone microarchitecture and fracture risk. A supplementation of vitamin D, monitoring its concentration, should be considered especially in HH patients with liver damage and liver cirrhosis.

DXA Versus Clinical Measures of Adiposity as Predictors of Cardiometabolic Diseases and All-Cause Mortality in Postmenopausal Women.

OBJECTIVE: To investigate whether dual-energy x-ray absorptiometry (DXA) estimates of adiposity improve risk prediction for cardiometabolic diseases over traditional surrogates, body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) in older women. PATIENTS AND METHODS: We analyzed up to 9744 postmenopausal women aged 50 to 79 years participating in the Women's Health Initiative who underwent a DXA scan and were free of cardiovascular disease and diabetes at baseline (October 1993 to December 1998) and followed through September 2015. Baseline BMI, WC, WHR, and DXA-derived percent total-body and trunk fat (%TrF) were incorporated into multivariable Cox proportional hazards models to estimate the risk of incident diabetes, atherosclerosis-related cardiovascular diseases (ASCVDs), heart failure, and death. Concordance probability estimates assessed the relative discriminatory value between pairs of adiposity measures. RESULTS: A total of 1327 diabetes cases, 1266 atherosclerotic cardiovascular disease (ASCVD) cases, 292 heart failure cases, and 1811 deaths from any cause accrued during a median follow-up of up to 17.2 years. The largest hazard ratio observed per 1 standard deviation increase of an adiposity measure was for %TrF and diabetes (1.77; 95% CI, 1.66-1.88) followed by %TrF and broadly defined ASCVD (1.22; 95% CI, 1.15-1.30). These hazard ratios remained significant for both diabetes (1.47; 95% CI, 1.37-1.57) and ASCVD (1.22; 95% CI, 1.14-1.31) even after adjusting for the best traditional surrogate measure of adiposity, WC. Percentage of trunk fat was also the only adiposity measure to demonstrate statistically significant improved concordance probability estimates over BMI, WC, and WHR for diabetes and ASCVD (all P<0.05). CONCLUSION: DXA-derived estimates of abdominal adiposity in postmenopausal women may allow for substantially improved risk prediction of diabetes over standard clinical risk models. Larger DXA studies with complete lipid biomarker profiles and clinical trials are needed before firm conclusions can be made.

Patterns of surveillance for late effects of BCR-ABL tyrosine kinase inhibitors in survivors of pediatric Philadelphia chromosome positive leukemias.

BACKGROUND: Targeted anticancer therapies such as BCR-ABL tyrosine kinase inhibitors (TKIs) have improved outcomes for chronic myeloid leukemia (CML) and Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph + ALL). However, little is known about long-term risks of TKIs in children. Exposure-based survivorship guidelines do not include TKIs, thus surveillance practices may be variable. METHODS: We retrospectively examined surveillance for cardiac and endocrine late effects in children receiving TKIs for Ph + leukemias, diagnosed at < 21 years between 2000 and 2018. Frequency of echocardiogram (ECHO), electrocardiogram (EKG), thyroid stimulating hormone (TSH), dual-energy x-ray absorptiometry (DXA), and bone age testing were abstracted. Descriptive statistics were stratified by leukemia type. RESULTS: 66 patients (CML n = 44; Ph + ALL n = 22) met inclusion criteria. Among patients with CML, ≥1 evaluation was done: ECHO (50.0%), EKG (48.8%), TSH (43.9%), DXA (2.6%), bone age (7.4%). Among patients with Ph + ALL, ≥1 evaluation was done: ECHO (86.4%), EKG (68.2%), TSH (59.1%), DXA (63.6%), bone age (44.4%). Over a median 6.3 and 5.7 years of observation, respectively, 2% of patients with CML and 57% with Ph + ALL attended a survivorship clinic. CONCLUSIONS: Despite common exposure to TKIs in survivors of Ph + leukemias, patterns of surveillance for late effects differed in CML and Ph + ALL, with the latter receiving more surveillance likely due to concomitant chemotherapy exposures. Targeted therapies such as TKIs are revolutionizing cancer treatment, but surveillance for late effects and referral to survivorship clinics are variable despite the chronicity of exposure. Evidence based guidelines and longer follow-up are needed.

Association between serum uric acid and bone mineral density in patients with type 2 diabetes: A 6-year longitudinal study in China.

ABSTRACT: The relationship between serum uric acid (UA) and bone mineral density (BMD) has been proposed by several researchers. However, there has been no consensus regarding the relationships among serum UA, diabetes, and BMD. The aim of this study is to investigate the association between UA, BMD, and at least osteopenia in type 2 diabetes patients.This research was a longitudinal study performed at Xiao-Tang-Shan Hospital in Beijing. Type 2 diabetes diagnosis was consistent with the WHO standard classification. Participants with osteopenia or osteoporosis documented by dual-energy X-ray absorptiometry were defined as having "at least osteopenia." A generalized additive model and multivariable logistic regressions were performed to explore the relationship between serum UA and at least osteopenia. Receiver operating characteristic analysis was conducted. Propensity score matching was used to verify the correctness of the cutoff point.In total, 3476 type 2 diabetes patients free of any osteopenia-related diseases were recruited in 2012 and followed up to 2018. The general proportions of patients with at least osteopenia in 2018 was 16.46% (572/3476). Serum UA was negatively associated with BMD stratified by sex, age group, and BMI level. Setting the first quartile as the reference, the risk of at least osteopenia in the fourth quartile was significant among all patients (odds ratio [OR]: 0.75; 95% confidence interval [CI]: 0.57, 0.98) and specifically in females (OR: 0.79; 95% CI: 0.43, 0.97), patients aged over 50 years (OR: 0.79; 95% CI: 0.60, 0.97) and patients with a BMI greater than 25 (OR: 0.74; 95% CI: 0.47, 0.97). The optimal cutoff point for the serum UA level to distinguish at least osteopenia in diabetic patients was 395 µmol/L.Serum UA concentration is negatively associated with the occurrence of at least osteopenia in Chinese patients with type 2 diabetes.

An Updated Reference for Calculating Bone Mineral Density T-Scores.

CONTEXT: Bone mineral density (BMD) T-score references may be updated when the peak BMD of the population is unclear and warrants reevaluation. OBJECTIVE: To update BMD T-score references using the peak BMD from the most recent National Health and Nutrition Examination Survey (NHANES) data. METHODS: This cross-sectional study used NHANES data from 2005 to 2014. Non-Hispanic White females between the ages of 10 and 40 years (N = 1549) were our target population to estimate peak BMD (SD). Individuals aged ≥ 50 years (N = 5523) were used to compare the percentages of osteoporosis and low bone mass based on existing and updated BMD T-score references. BMD data within the age at attainment of peak BMD ± 5 years were used to calculate updated BMD T-score references. RESULTS: The updated average of BMD (SD) for diagnosing osteoporosis at the femoral neck and lumbar spine were 0.888 g/cm2 (0.121 g/cm2) and 1.065 g/cm2 (0.122 g/cm2), respectively. The percentages of individuals with osteoporosis at the femoral neck and low bone mass at the femoral neck and lumbar spine based on the updated BMD T-score references were higher than the percentages of people designated with these outcomes under the existing guidelines (P < 0.001). However, we observed the opposite pattern for lumbar spine osteoporosis (P < 0.001). CONCLUSIONS: We calculated new BMD T-score references at the femoral neck and lumbar spine. We found significant differences in the percentages of individuals classified as having osteoporosis and low bone mass between the updated and existing BMD T-score references.

Can a Deep-learning Model for the Automated Detection of Vertebral Fractures Approach the Performance Level of Human Subspecialists?

BACKGROUND: Vertebral fractures are the most common osteoporotic fractures in older individuals. Recent studies suggest that the performance of artificial intelligence is equal to humans in detecting osteoporotic fractures, such as fractures of the hip, distal radius, and proximal humerus. However, whether artificial intelligence performs as well in the detection of vertebral fractures on plain lateral spine radiographs has not yet been reported. QUESTIONS/PURPOSES: (1) What is the accuracy, sensitivity, specificity, and interobserver reliability (kappa value) of an artificial intelligence model in detecting vertebral fractures, based on Genant fracture grades, using plain lateral spine radiographs compared with values obtained by human observers? (2) Do patients' clinical data, including the anatomic location of the fracture (thoracic or lumbar spine), T-score on dual-energy x-ray absorptiometry, or fracture grade severity, affect the performance of an artificial intelligence model? (3) How does the artificial intelligence model perform on external validation? METHODS: Between 2016 and 2018, 1019 patients older than 60 years were treated for vertebral fractures in our institution. Seventy-eight patients were excluded because of missing CT or MRI scans (24% [19]), poor image quality in plain lateral radiographs of spines (54% [42]), multiple myeloma (5% [4]), and prior spine instrumentation (17% [13]). The plain lateral radiographs of 941 patients (one radiograph per person), with a mean age of 76 ± 12 years, and 1101 vertebral fractures between T7 and L5 were retrospectively evaluated for training (n = 565), validating (n = 188), and testing (n = 188) of an artificial intelligence deep-learning model. The gold standard for diagnosis (ground truth) of a vertebral fracture is the interpretation of the CT or MRI reports by a spine surgeon and a radiologist independently. If there were any disagreements between human observers, the corresponding CT or MRI images would be rechecked by them together to reach a consensus. For the Genant classification, the injured vertebral body height was measured in the anterior, middle, and posterior third. Fractures were classified as Grade 1 (< 25%), Grade 2 (26% to 40%), or Grade 3 (> 40%). The framework of the artificial intelligence deep-learning model included object detection, data preprocessing of radiographs, and classification to detect vertebral fractures. Approximately 90 seconds was needed to complete the procedure and obtain the artificial intelligence model results when applied clinically. The accuracy, sensitivity, specificity, interobserver reliability (kappa value), receiver operating characteristic curve, and area under the curve (AUC) were analyzed. The bootstrapping method was applied to our testing dataset and external validation dataset. The accuracy, sensitivity, and specificity were used to investigate whether fracture anatomic location or T-score in dual-energy x-ray absorptiometry report affected the performance of the artificial intelligence model. The receiver operating characteristic curve and AUC were used to investigate the relationship between the performance of the artificial intelligence model and fracture grade. External validation with a similar age population and plain lateral radiographs from another medical institute was also performed to investigate the performance of the artificial intelligence model. RESULTS: The artificial intelligence model with ensemble method demonstrated excellent accuracy (93% [773 of 830] of vertebrae), sensitivity (91% [129 of 141]), and specificity (93% [644 of 689]) for detecting vertebral fractures of the lumbar spine. The interobserver reliability (kappa value) of the artificial intelligence performance and human observers for thoracic and lumbar vertebrae were 0.72 (95% CI 0.65 to 0.80; p < 0.001) and 0.77 (95% CI 0.72 to 0.83; p < 0.001), respectively. The AUCs for Grades 1, 2, and 3 vertebral fractures were 0.919, 0.989, and 0.990, respectively. The artificial intelligence model with ensemble method demonstrated poorer performance for discriminating normal osteoporotic lumbar vertebrae, with a specificity of 91% (260 of 285) compared with nonosteoporotic lumbar vertebrae, with a specificity of 95% (222 of 234). There was a higher sensitivity 97% (60 of 62) for detecting osteoporotic (dual-energy x-ray absorptiometry T-score ≤ -2.5) lumbar vertebral fractures, implying easier detection, than for nonosteoporotic vertebral fractures (83% [39 of 47]). The artificial intelligence model also demonstrated better detection of lumbar vertebral fractures compared with detection of thoracic vertebral fractures based on the external dataset using various radiographic techniques. Based on the dataset for external validation, the overall accuracy, sensitivity, and specificity on bootstrapping method were 89%, 83%, and 95%, respectively. CONCLUSION: The artificial intelligence model detected vertebral fractures on plain lateral radiographs with high accuracy, sensitivity, and specificity, especially for osteoporotic lumbar vertebral fractures (Genant Grades 2 and 3). The rapid reporting of results using this artificial intelligence model may improve the efficiency of diagnosing vertebral fractures. The testing model is available at http://140.113.114.104/vght_demo/corr/. One or multiple plain lateral radiographs of the spine in the Digital Imaging and Communications in Medicine format can be uploaded to see the performance of the artificial intelligence model. LEVEL OF EVIDENCE: Level II, diagnostic study.

Circumference Method Estimates Percent Body Fat in Male US Service Members with Lower Limb Loss.

BACKGROUND: The Department of Defense circumference method (CM) is used to estimate percent body fat (%BF) in evaluation of health, physical fitness, appearance, and military readiness; however, the CM has not been validated in individuals with lower limb loss. OBJECTIVE: To evaluate the agreement between CM and dual-energy X-ray absorptiometry (DXA) for measuring %BF in individuals with lower limb loss. DESIGN: This study is part of a larger cross-sectional comparison study, and this analysis was included as a secondary objective. Two methods of measuring %BF included CM and DXA, with DXA as the reference standard for this study. PARTICIPANTS/SETTING: This study was conducted at Walter Reed Army Medical Center. Data were collected from summer 2010 to summer 2011. One hundred individuals, 50 with and 50 without lower limb loss, were screened for this study; three individuals with limb loss and two without limb loss had incomplete data, and one individual (female, without limb loss) lacked a comparison participant. All participants were recruited from a military medical center, and data were collected in a clinic research laboratory. MAIN OUTCOME MEASURES: Measurements of %BF were compared between methods for each group. STATISTICAL ANALYSES PERFORMED: Measurements of %BF were compared using paired t-tests and intraclass correlation coefficient. Agreement and bias were assessed with Bland-Altman analysis. Receiver operating characteristic analysis was used to determine the diagnostic accuracy of the CM to identify participants with %BF levels in the obese category (≥25%). RESULTS: A statistically significant difference was found between %BF methods in the group with limb loss (1.7%; P = 0.001) and the group without limb loss (1.4%; P = 0.005), with DXA consistently higher than CM. However, the intraclass correlation coefficient estimates for the agreement between %BF by CM and DXA were 0.848 (95% confidence interval [CI]: 0.683-0.922; P < 0.001) and 0.828 (CI: 0.679-0.906; P < 0.001), for the groups with and without limb loss, respectively, suggesting that CM has good to near excellent agreement with DXA for estimating %BF in these groups. Receiver operating characteristic analysis indicated that the area under the curve supported predictive ability to detect obesity-based %BF in males with and without limb loss. CONCLUSIONS: Although a statistically significant difference was found between methods for individuals with limb loss, there was also good agreement between the methods, suggesting that CM may be a useful tool for estimating %BF in individuals with lower limb loss. The CM may be a useful and field expedient method for assessing %BF in a clinical setting when DXA is not available.

Assessment of non-traumatic vertebral fractures in Cushing's syndrome patients.

PURPOSE: Hypercortisolism has detrimental effects on bone metabolism with the consequences of bone loss and bone fractures. We aimed to evaluate the frequency of vertebral fragility fractures and to determine the factors associated with Cushing's syndrome (CS). METHODS: A total of 135 patients diagnosed with Cushing's syndrome [108 patients with Cushing's disease and 27 patients with adrenocortical adenoma] and 107 healthy controls were included in this cross-sectional study. The available clinical, laboratory, and radiologic data of patients with CS were recorded, retrospectively. Lateral vertebral radiograms were evaluated for vertebral fragility fractures according to Genant's semi-quantitative method. Bone mineral density (BMD) was determined using a Dual-energy X-ray absorptiometry (DEXA). RESULTS: Vertebral fragility fractures (VFs) were observed in 75.3% (n = 61) of the patients. The median number of VFs was six (min-max: 2-12). All patients with vertebral fractures had thoracic VF, and 50.7% of the patients had lumbar fragility fractures. Thirty-three (40.7%) patients with vertebral fractures had normal bone densitometry values. Osteoporosis and osteopenia were observed in 16.2% and 40.7% of the patients, respectively. The duration of active disease, the presence of ACTH-secreting pituitary adenoma, and 24-h urinary cortisol did not influence the presence of vertebral fractures. Vertebral fractures were independently associated with age, FSH, LH levels, and lumbar BMD (R2 = 68.18%, p = 0.028). The femoral neck BMD (but not lumbar BMD) was independently associated with age, BMI, and PTH levels (R2 = 48.48%, p < 0.001). CONCLUSION: Vertebral fracture frequency was higher in CS patients. Most of the patients with vertebral fractures had multiple fractures. Although low lumbar BMD was associated with VF, patients with CS with normal bone densitometry could experience VF. Vertebral radiograph evaluations as a part of routine evaluation for silent vertebral fractures may help to prevent further fractures in patients with CS.

Quantitative peripheral computed tomography to measure muscle area and assess lean soft tissue mass in children.

BACKGROUND: Skeletal muscle is one of the main components of lean soft tissue mass (LSTM). Low levels in children affect locomotion, posture, and increase the risk of metabolic syndrome. AIM: (1) To evaluate the association between muscle cross-sectional area (MCSA) of the lower left leg measured by peripheral quantitative computed tomography (pQCT) and total LSTM; namely, total left leg and left lower leg LSTM assessed by dual-energy X-ray absorptiometry (DXA) in a group of children, (2) to examine if MCSA is a predictor of LSTM, (3) to determine the ability of pQCT to identify children with low LSTM. SUBJECTS AND METHODS: Lower left leg MCSA and LSTM were measured using pQCT and DXA, respectively, in 396 children. RESULTS: Statistically significant positive correlations were found between the lower leg MCSA - total LSTM (r² = 0.789), total leg LSTM (r² = 0.79), and lower leg LSTM (r² = 0.791) (p < .01). MCSA explained 64-68% of the variance in LSTM. Receiver operator characteristic (ROC) curves determined the capacity of the lower left leg MCSA to identify low LSTM in girls (AUC 0.95) and boys (AUC 0.87). CONCLUSIONS: Our results showed that lower left leg MCSA, measured using pQCT, could be a tool to predict low LSTM in children.

Comparing the bone mineral density among male patients with latent autoimmune diabetes and classical type 1 and type 2 diabetes, and exploring risk factors for osteoporosis.

AIMS: This study aimed to compare the bone mineral densities (BMDs) among male patients with latent autoimmune diabetes in adults (LADA), classical type 1 diabetes (T1DM), and type 2 diabetes (T2DM), and to examine the risk factors for developing low BMD in these patients. PATIENTS AND METHODS: Between January 2017 and October 2020, a total of 57, 67, and 223 male patients with LADA, classical T1DM, and T2DM, respectively, were recruited from the endocrinology department of Shanghai Tenth People's Hospital. Hormonal markers of bone metabolism, lipid profiles, uric acid, glycosylated hemoglobin A1c (HbA1c), and beta-cell function were measured using blood samples. BMD was measured at the lumbar spine, femoral neck, and right hip by dual-energy X-ray absorptiometry. RESULTS: The mean BMD values from all three skeletal sites in male patients with LADA were comparable to those with classical T1DM but were much lower than those with T2DM. After adjusting for confounding factors, multiple linear regression analysis demonstrated that in all male patients with diabetes, body mass index (BMI), uric acid, and fasting C-peptide showed significant positive associations with BMD at all three skeletal sites; however, osteocalcin showed a negative association at all three sites. CONCLUSIONS: Compared with male patients with T2DM, lower BMDs were observed in patients with LADA and T1DM. Low BMI, uric acid, C-peptide levels, and high osteocalcin levels are risk factors for developing low BMD in male patients with diabetes.

Evaluation of the body adiposity index against dual-energy X-ray absorptiometry for assessing body composition in children and adolescents.

BACKGROUND: It is important to clarify the association of lean and fat mass between children and adolescents considering the rising prevalence of overweight and obesity in this age group. The aim of this study was to verify the association between the body adiposity index (BAI) and dual-energy X-ray absorptiometry (DXA) methods for analyzing body composition, as well as analyzing the validity of BAI to verify the percentage of fat in children and adolescents. METHODS: The sample was composed of 106 children and adolescents, 44 females (age: 11.5 ± 1.8 years) and 62 evils (13.6 ± 2.6 years). The body fat (%F) was measured using DXA and the doubly indirect BAI body fat estimation technique. RESULTS: The BAI and DXA estimates of %F were strongly correlated (boys: r = .71, P < .0001; girls: r = .72, P < .0001). The linear regression analyses showed that BAI is significant to estimate the %F in total sample (P < .0001). For boys, the %F analyzes performed by BAI and DXA did not show any differences when compared (P = .2). In addition, BAI pointed out a significant proportion bias for both sexes (P < .0001), which suggests its inefficiency in the analysis of %F. CONCLUSIONS: BAI and DXA correlate; however, there is low reliability and a high proportion bias for the analysis of %F by BAI.