Dual-energy X-ray Absorptiometry Data Research Articles

Vertebral Fracture Prediction from MRI-based Vertebral Bone Quality Scores in Postmenopausal Women

Study Design. A longitudinal cohort study Objective. The study aims to assess the lumbar MRI-based vertebral bone quality (VBQ) score and dual energy x-ray absorptiometry (DXA) T score in a longitudinal cohort of postmenopausal women and to evaluate the performance of these scores in predicting VF over 5 years of follow-up. Summary of Background Data. VBQ scores can be used for osteoporosis screening and identification of vertebral fractures (VFs). However, due to limitations in prior cross-sectional studies, definitive conclusions about the causal associations between the VBQ score and incident VF have not been drawn. Methods. Postmenopausal women with baseline DXA and lumbar MRI data were enrolled. Follow-up assessments were conducted over 5 years after the baseline examination; both anteroposterior- and lateral-view thoracolumbar spine radiographs were captured to evaluate incident VF. At baseline, correlation analyses between the VBQ score and osteoporosis or preexisting VF were performed. A Cox regression model for multivariate logistic regression was used to analyze the risk factors for incident VF. The hazard ratio (HR) was calculated. Results. 137 postmenopausal women were included. The VBQ score was moderately correlated with lumbar bone mineral density at baseline (r=0.4, P<0.001). Patients with incident VF had significantly lower T scores (-3.4 vs. -1.87, P<0.001) and higher VBQ scores (4.62 vs. 4.03, P<0.001) at baseline than those without incident VF. After adjusting for age, weight, creatinine clearance, and alkaline phosphatase levels, the HRs for the VBQ score and T score were 1.93 and 0.71, respectively. After adjusting for the T score, an increased VBQ score was independently associated with an increased risk of incident VF, with an HR of 1.87. Conclusion. The VBQ score was moderately correlated with DXA T score and was a predictor of incident VF risk independent of bone mineral density in postmenopausal women.

Retrospective analysis of dual-energy x-ray absorptiometry data demonstrates body composition changes with age in dogs and cats.

Use 18 years of dual-energy x-ray absorptiometry (DEXA) scan data to characterize how body composition changes with age in dogs and cats. This was a retrospective observational study using data obtained from DEXA scans performed between 2006 and 2023. A total of 6,973 observations from 1,273 colony-housed dogs ≤ 1 to 16.1 years old and 6,593 observations from 1,096 colony-housed cats ≤ 1 to 16.9 years old were obtained. Animal ages were rounded to the nearest 1/10-year intervals. Means for each interval were calculated and quadratic, cubic, and quartic polynomial models were fit to assess trends over age. Age had an effect on all DEXA measurements. In dogs, lean mass increased early in life before slowing to a peak at age 6.3 and then declined gradually. Fat mass also increased until slowing to a peak at age 9.3 and then decreased. In cats, lean mass increased before slowing to a peak at age 4.5, decreased gradually until age 12.5, and then sharply declined. Fat mass increased until slowing to a peak at age 7.5 and then decreased gradually. This retrospective study provides a baseline for how body composition changes with age. Results suggest that lean mass loss may begin earlier than previously reported in dogs and cats. Sarcopenia and obesity are common conditions in aging pets. Results can be used to improve body composition assessment of patients and investigate the efficacy of nutritional interventions.

12480 REMS Technology For The Bone Health Assessment After Hormonal Therapy In Transgender Subjects

Abstract Disclosure: F.A. Lombardi*: None. A. Russo: None. P. Pisani*: None. C. Stomaci*: None. F. Conversano*: None. R. Franchini*: None. M. Di Paola*: None. V. Racanelli*: None. S. Casciaro: None. Introduction: Sex steroid hormones play crucial roles in bone metabolism and the musculoskeletal system. These hormones interact with specific nuclear receptors, such as estrogen and androgen receptors, which are expressed in bone tissue. This interaction regulates the ongoing process of bone remodeling and induces positive effects on bone health.In this regard, it is crucial to examine the long-term impact of gender affirming hormone therapy (GAHT) on bone metabolism. Radiofrequency Echographic Multi-spectrometry (REMS) is a radiation-free technology for assessing bone mineral density (BMD) and fracture risk prediction by the analysis of the reference axial sites: lumbar spine (LS) and femoral neck (FN). REMS has been widely validated in comparison with Dual Energy X-ray Absorptiometry (DXA), showing good accuracy and precision (>90%), thereby representing a valid technology to monitor bone health status (BHS) in a short-term period. In this context, the goal of this preliminary clinical study was to track the bone health of transgender subjects for one year after starting treatment, using REMS, in order to comprehend the effects of GAHT on BHS. Methods: 7 transgender patients were recruited at Endocrinology Unit of Internal Medicine Department of Santa Chiara Hospital Trento (Italy). Among them were 4 individuals assigned male at birth (AMAB) undergoing therapy with oestradiol valerate 2 g/day and cyproterone acetate 25 mg/day, as well as 3 individuals assigned female at birth (AFAB) undergoing therapy with testosterone 25 mg/day. Before starting GAHT, patients underwent LS-DXA scan, followed by monitoring of BHS by REMS technology after 12 months from the start of treatment. Results: The results show an increase of T-score values in AMAB (-0,3 ± 0,3 REMS vs -1,8 ± 1,8 DXA) after estrogen treatment starting, and no effect on bone in AFAB subjects after GAHT (0.5 ± 0.4 REMS vs 0.9 ± 0.4 DXA); this is due to:-the protective effect of estrogen therapy in AMAB subjects against bone loss;-the combined effect of the decrease of estrogen together with the increase of testosterone in AFAB ones. Conclusions In summary, these initial findings indicate that REMS could be appropriate for monitoring the bone health status of these individuals, confirming the anticipated beneficial effects of estrogen therapy on bone loss in AMAB individuals. The limitation of the study is the lack of REMS data at the beginning of the treatment, but the possibility of carrying out the technique in subsequent evaluation times will allow us to evaluate the change in bone mass during hormonal reaffirmation therapy and compare it with the progress of the measurements obtained with DXA data ones. Presentation: 6/2/2024

Association between physical function and parameters of hip structural analysis in patients with hip fracture.

The current study sought to investigate whether physical function and activity were associated with hip structural analysis (HSA) parameters on the non-fracture side of patients with hip fractures. Participants were patients with unilateral hip fracture treated by surgery. HSA of the proximal femur was conducted based on dual-energy x-ray absorptiometry data. HSA parameters in the narrow neck region included cross-sectional area (CSA), cross-sectional moment of inertia (CSMI), section modulus (SM), and buckling ratio (BR). Hierarchical multiple regression analysis was conducted to identify predictors of HSA. Except for the adjustment variables, age, gender and BMI, other variables were extracted. Hierarchical multiple regression analysis (standardised partial regression coefficients) identified movement control during one-leg standing on the non-fractured side (0.15) as factors associated with CSA. Hierarchical multiple regression analysis (standardised partial regression coefficients) identified hand grip (0.12, 0.23) as factors associated with CSMI and SM, respectively. Hierarchical multiple regression analysis (standardised partial regression coefficients) identified presence of steroid (0.23) and cerebrovascular disease (0.19) as factors associated with BR. The coefficients of determination adjusted for degrees of freedom (R2) were 0.545, 0.331, 0.401, and 0.148 for CSA, CSMI, SM, and BR, respectively. Our results indicate that movement control during 1-leg standing and muscle strength may be important for maintaining and improving bone strength.

Bone mineral density assessment in patients with cystinuria.

Cystinuria is a rare genetic disease characterized by impaired tubular transport of cystine. Clinical features of cystinuria mainlyinclude nephrolithiasis and its complications, although cystinuric patients may present with other comorbidities. There are currently no data on bone features of patients with cystinuria. Our aim is to characterize bone mineral density (BMD) in cystinuria. Our study included adult cystinuric patients with estimated glomerular filtration rate (eGFR) ≥ 60mL/min/1.73m2 followed at 3 specialized outpatient clinics in Italy (Rome, Naples and Verona). Markers of bone turnover were analyzed in a centralized laboratory. Clinical, biochemical and dual-energy X-ray absorptiometry (DEXA) data were collected from September 2021 to December 2022. Linear regression models were used to evaluate statistically significant deviations from zero of Z-scores. Twenty-seven patients were included in the study. Mean (SD) age was 37 (15) years, 41% were women. Mean estimated glomerular filtration rate was 99mL/min/1.73m2. Serum parameters associated with bone turnover (parathyroid hormone, FGF23, calcium and phosphate) were all in the normal range, with only 4 patients showing mild hypophosphatemia. Prevalence of low bone mineral density, defined as Z-score ≤ - 2 at any site, was 15%. Average Z-scores were negative across most sites. Our study suggests that cystinuric patients have lower bone mineral density compared with individuals of the same sex and age, even when theirkidney functionis normal.

An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health

BackgroundDiabetes is a chronic condition that can result in many long-term physiological, metabolic, and neurological complications. Therefore, early detection of diabetes would help to determine a proper diagnosis and treatment plan.MethodsIn this study, we employed machine learning (ML) based case-control study on a diabetic cohort size of 1000 participants form Qatar Biobank to predict diabetes using clinical and bone health indicators from Dual Energy X-ray Absorptiometry (DXA) machines. ML models were utilized to distinguish diabetes groups from non-diabetes controls. Recursive feature elimination (RFE) was leveraged to identify a subset of features to improve the performance of model. SHAP based analysis was used for the importance of features and support the explainability of the proposed model.ResultsEnsemble based models XGboost and RF achieved over 84% accuracy for detecting diabetes. After applying RFE, we selected only 20 features which improved the model accuracy to 87.2%. From a clinical standpoint, higher HDL-Cholesterol and Neutrophil levels were observed in the diabetic group, along with lower vitamin B12 and testosterone levels. Lower sodium levels were found in diabetics, potentially stemming from clinical factors including specific medications, hormonal imbalances, unmanaged diabetes. We believe Dapagliflozin prescriptions in Qatar were associated with decreased Gamma Glutamyltransferase and Aspartate Aminotransferase enzyme levels, confirming prior research. We observed that bone area, bone mineral content, and bone mineral density were slightly lower in the Diabetes group across almost all body parts, but the difference against the control group was not statistically significant except in T12, troch and trunk area. No significant negative impact of diabetes progression on bone health was observed over a period of 5-15 yrs in the cohort.ConclusionThis study recommends the inclusion of ML model which combines both DXA and clinical data for the early diagnosis of diabetes.

Evaluation of the modified MRI vertebral bone quality score for bone quality in lumbar degenerative disorders.

The traditional VBQ scoring method may lead to overestimation due to the concentration of intravertebral fat and vascular structures in the posterior half of vertebral bodies, potentially resulting in false-positive outcomes. This study aims to modify the measurement method of VBQ score (Modified-VBQ) and evaluate its effectiveness in evaluating bone quality of lumbar degenerative diseases. Retrospective analysis was conducted on clinical data from patients undergoing lumbar surgery for degenerative diseases between September 2022 and September 2023. Preoperative lumbar t1-weighted Magnetic resonance imaging was used for both modified and traditional VBQ scoring. Computed tomography (CT) images and dual-energy X-ray absorptiometry (DEXA) data were collected through the picture archiving and communication system. The effectiveness of the modified VBQ score was evaluated, considering P < 0.05 as statistically significant. The study included 212 patients, revealing a significant difference between the modified VBQ and VBQ scores (P < 0.0001). Notably, patients with a history of hyperlipidemia exhibited a significant difference between the two scores (P = 0.0037). The area under the ROC curve (AUC) for the modified VBQ was 0.86, surpassing the VBQ score (AUC = 0.74). Linear regression analysis demonstrated a moderate to strong correlation between the modified VBQ and DEXA T-score (r = - 0.49, P < 0.0001) and a high correlation with CT Hounsfield units (HU) values (r = - 0.60, P < 0.0001). The modified VBQ score provides a simple, effective, and relatively accurate means of assessing bone quality in lumbar degenerative diseases. Preoperative implementation of the modified VBQ score facilitates rapid screening for patients with abnormal bone quality.

GBA1 as a risk gene for osteoporosis in the specific populations and its role in the development of Gaucher disease

BackgroundOsteoporosis and its primary complication, fragility fractures, contribute to substantial global morbidity and mortality. Gaucher disease (GD) is caused by glucocerebrosidase (GBA1) deficiency, leading to skeletal complications. This study aimed to investigate the impact of the GBA1 gene on osteoporosis progression in GD patients and the specific populations.MethodsWe selected 8115 patients with osteoporosis (T-score ≤ − 2.5) and 55,942 healthy individuals (T-score > − 1) from a clinical database (N = 95,223). Monocytes from GD patients were evaluated in relation to endoplasmic reticulum (ER) stress, inflammasome activation, and osteoclastogenesis. An in vitro model of GD patient’s cells treated with adeno-associated virus 9 (AAV9)-GBA1 to assess GBA1 enzyme activity, chitotriosidase activity, ER stress, and osteoclast differentiation. Longitudinal dual-energy X-ray absorptiometry (DXA) data tracking bone density in patients with Gaucher disease (GD) undergoing enzyme replacement therapy (ERT) over an extended period.ResultsThe GBA1 gene variant rs11264345 was significantly associated [P < 0.002, Odds Ratio (OR) = 1.06] with an increased risk of bone disease. Upregulation of Calnexin, NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) and Apoptosis-associated speck-like protein containing a C-terminal caspase recruitment domain (ASC) was positively associated with osteoclastogenesis in patients with GD. In vitro AAV9-GBA1 treatment of GD patient cells led to enhanced GBA1 enzyme activity, reduced chitotriosidase activity, diminished ER stress, and decreased osteoclast differentiation. Long-term bone density data suggests that initiating ERT earlier in GD leads to greater improvements in bone density.ConclusionsElevated ER stress and inflammasome activation are indicative of osteoporosis development, suggesting the need for clinical monitoring of patients with GD. Furthermore, disease-associated variant in the GBA1 gene may constitute a risk factor predisposing specific populations to osteoporosis.

Visceral adipose tissue reference data computed for GE HealthCare DXA from the National Health and Nutrition Examination Survey data set.

The National Health and Nutrition Examination Survey (NHANES) characterizes body composition representative of the US population using dual-energy x-ray absorptiometry (DXA) scans. These population-level trends of abdominal subcutaneous and visceral adipose tissue (SAT and VAT) are useful for identifying measures associated with increased disease risk. Recently, VAT and SAT data collected by Hologic DXA in NHANES were published online; however, there are known differences in the absolute calibration of DXA systems by make. The purpose of this study was to create reference tables suitable for calculating z scores and percentile values for GE HealthCare (GEHC) DXA systems. DXA scans were acquired on participants aged 8 to 59 years using Hologic systems. DXA measures were converted to GEHC and described using the least median squares curve fitting method in pediatrics (aged <20 years) and adults (aged 20-59 years). A total of 11,972 adults and 7298 pediatrics were included for this analysis. Adult and pediatric curves were generated by sex and by ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian, Other) and were derived as a function of age. These results show the ability to generate VAT and SAT reference data for GEHC systems using Hologic DXA data representative of the US youth and adult population.

Body Composition, Physical Function and Exercise Capacity in Chronic Obstructive Pulmonary Disease

Current literature yields unequivocal results regarding the effect of body composition on physical function in patients with chronic obstructive pulmonary disease and disproportionately includes a majority of males. The purpose of this study was to determine whether specific body composition measures are significantly associated with physical function and exercise capacity in patients with chronic obstructive pulmonary disease with equal representation of males and females. Independent variables included sex, total body mass, total body lean and fat mass, appendicular total mass, and appendicular lean and fat mass. Dependent variables included peak oxygen consumption, 6-minute walk distance and self-reported physical function. Patients (n = 170) with dual-energy X-ray absorptiometry data, 6-minute walk distance, and self-reported physical function were used for these analyses. A sub-set of 145 of these patients with peak oxygen consumption data were also analysed. Hierarchical multiple regression analysis was used to determine if sex and body composition measures correlated with physical function and exercise capacity and if they explained additional variance after controlling for disease severity. After controlling for disease severity, appendicular lean mass, total body fat mass, and sex explained an additional 16.5% of the variance in peak oxygen consumption (p < 0.001). Appendicular lean mass explained an additional 8.9% of the variance in 6-minute walk distance (p < 0.001) and an additional 2.5% of the variance in self-reported physical function (p = 0.057). Body composition measures may further predict exercise capacity, 6-minute walk distance, and self-reported physical function in patients with chronic obstructive pulmonary disease.

279-LB: The Role of Body Composition Phenotypes on Insulin Resistance in Nondiabetic Asians—The Korea National Health and Nutrition Examination Survey (KNHANES) 2008–2011

It is increasingly recognized that obesity as defined by body fat distribution as well as body mass index (BMI) contributes to metabolic disturbances. The relationship between body fat percent (%BF) and insulin resistance in the Asian population remains uncertain. We investigated the association between insulin dynamics and the four body composition phenotypes using BMI and %BF; normal with normal-fat percent (NN), normal with high-fat percent (NH), obese (overweight/obesity) with normal-fat percent (ON), and obese with high-fat percent (OH). We performed a cross-sectional study with 16,578 adult participants aged between 19 and 80 in the fourth and fifth KNHANES with Dual Energy X-ray Absorptiometry (DEXA) data. The subjects with diabetes are excluded. The mean age was 42.72 (standard error (SE) 0.30) years old. The prevalence of impaired fasting glucose, hypertension, and dyslipidemia was higher depending on the body composition phenotypes (p for trend &amp;lt;0.001). Homeostatic model assessment of insulin resistance (HOMA-IR) increased and the quantitative insulin check index (QUICKI) decreased significantly according to the body composition phenotypes (p for trend &amp;lt;0.001). The OH group exhibited significantly the highest HOMA-IR (mean 2.69, SE 0.03) and the lowest QUICKI (mean 0.335, SE 0.001) values. The odds ratio (OR) for high HOMA-IR (men &amp;gt;2.5, women &amp;gt;2.0) and low QUICKI (men &amp;lt;0.347, women &amp;lt;0.343) was increased according to the body composition phenotypes. NH group showed significantly higher OR than ON group, compared to NN group (OR 1.25, 95%CI 1.02-1.53 vs OR 0.91, 95% CI 0.70-1.19, respectively). In conclusion, using body fat percent with BMI to categorize obesity is useful for estimating HOMA-IR and QUICKI. Although subjects have normal BMI, the amount of their fat mass plays an important role in insulin dynamics. The subjects with high-fat percent need to detect and intervene earlier for future prognosis. Disclosure A. Khang: None.

Different correlation of body mass index with body fatness and obesity-related biomarker according to age, sex and race-ethnicity

The relationship between body mass index (BMI) and body fatness could differ according to age, sex, and race-ethnicity. We aimed to evaluate in which contexts BMI could be a good measure for body fatness compared to dual-energy X-ray absorptiometry (DXA) derived measures. The study population included 18,061 participants (9141 men and 8920 women) aged 18 and older who tested DXA from the National Health and Nutrition Examination Survey (NHANES) database from 1999 to 2006, and 8107 men and 10,754 women with DXA data from Korea NHANES from 2008 to 2011 to represent the Asian population. We calculated Pearson correlation coefficients between BMI and DXA derived fat mass index (FMI) and percentage body fat (PBF) depending on age, sex, and race-ethnicity. The correlation between BMI, FMI and PBF and obesity-related biomarkers was also estimated among the subgroup with both DXA and information on each biomarker. BMI was strongly correlated with FMI (r = 0.944 in men and 0.976 in women), PBF (r = 0.735 in men and 0.799 in women), and truncal fat mass (r = 0.914 in men and 0.941 in women) with correlations stronger in women than in men except for with waist-height ratio (r = 0.921 in men and 0.911 in women). The correlation between BMI and DXA derived adiposity weakened with age in both sexes. BMI was less correlated with FMI (r = 0.840 in men and 0.912 in women), PBF (r = 0.645 in men and 0.681 in women), and truncal fat mass (r = 0.836 in men and 0.884 in women) in Korean compared to other race-ethnicities. Among obesity-related biomarkers, insulin was the most strongly correlated to body adiposity indices in both sexes and strength of these correlations generally decreased with age. BMI predicted obesity-related biomarkers as well as FMI and truncal fat mass and superior to PBF. BMI could be a good measure for body fatness, particularly among young age groups, women, the US population, but less so in Korean populations. The lower correlation between BMI and body fatness in older compared to younger age groups could be related to increasing PBF and decreasing lean body mass.

CT required to perform robotic-assisted total hip arthroplasty can identify previously undiagnosed osteoporosis and guide femoral fixation strategy

Osteoporosis can determine surgical strategy for total hip arthroplasty (THA), and perioperative fracture risk. The aims of this study were to use hip CT to measure femoral bone mineral density (BMD) using CT X-ray absorptiometry (CTXA), determine if systematic evaluation of preoperative femoral BMD with CTXA would improve identification of osteopenia and osteoporosis compared with available preoperative dual-energy X-ray absorptiometry (DXA) analysis, and determine if improved recognition of low BMD would affect the use of cemented stem fixation. Retrospective chart review of a single-surgeon database identified 78 patients with CTXA performed prior to robotic-assisted THA (raTHA) (Group 1). Group 1 was age- and sex-matched to 78 raTHAs that had a preoperative hip CT but did not have CTXA analysis (Group 2). Clinical demographics, femoral fixation method, CTXA, and DXA data were recorded. Demographic data were similar for both groups. Preoperative femoral BMD was available for 100% of Group 1 patients (CTXA) and 43.6% of Group 2 patients (DXA). CTXA analysis for all Group 1 patients preoperatively identified 13 osteopenic and eight osteoporotic patients for whom there were no available preoperative DXA data. Cemented stem fixation was used with higher frequency in Group 1 versus Group 2 (28.2% vs 14.3%, respectively; p = 0.030), and in all cases where osteoporosis was diagnosed, irrespective of technique (DXA or CTXA). Preoperative hip CT scans which are routinely obtained prior to raTHA can determine bone health, and thus guide femoral fixation strategy. Systematic preoperative evaluation with CTXA resulted in increased recognition of osteopenia and osteoporosis, and contributed to increased use of cemented femoral fixation compared with routine clinical care; in this small study, however, it did not impact short-term periprosthetic fracture risk.

An Observational Study on Response to Growth Hormone Therapy in Indian Patients of Short Stature with Special Emphasis on Biochemical Parameters and Bone Biomarkers.

There is a lack of Indian data on short stature treatment using recombinant human growth hormone (rhGH). We explored the effects of such treatment in eastern Indian patients, with emphasis on biochemical parameters and bone biomarkers in addition to basic anthropometry. Our descriptive study covered 50 short stature patients of varied aetiology attending endocrine outpatient department (OPD) of a tertiary care teaching hospital. Patients were followed up for 1 year after the index visit, and prospective data were reconciled with past medical records. A dose of rhGH used was 0.18-0.375 mg/kg as standard, starting dose mostly being 0.2 mg/kg. Dosing was adjusted if the physician judged the clinical outcome to be less favourable than expected. Anthropometric parameters (height, weight, body mass index (BMI) and skeletal age) were recorded clinically, and various biochemical parameters and bone biomarkers were estimated from blood. Among 50 subjects, 60% had idiopathic growth hormone (GH) deficiency and 26% had Turner's syndrome. The median age at treatment start was 10 years, and the median treatment duration was 25.5 months. The height increased more in the first year of therapy. In the last 6 months, the height velocity was approximately 0.5 cm/month. Although the weight increased significantly, the increment slowed down in the last 6 months. Both remained less than age- and gender-matched references throughout. The skeletal age was on average 2 years behind chronological age (CA)-being 8.7, 9.6 and 11.3 years, respectively, at therapy start, after one year and at study end. Fasting blood glucose (FBG), total cholesterol and calcium level changes were not statistically significant. Serum cortisol and phosphate showed a modest but statistically significant rise, while thyroid-stimulating hormone (TSH) level declined. Insulin-like growth factor 1 (IGF-1) increase was relatively pronounced. Among bone biomarkers, a decrease in CTx and an increase in vitamin D were significant. Dual-energy X-ray absorptiometry (DEXA) data indicated that bone mineral density was less than that of age-matched controls despite treatment. The therapy was well tolerated. rhGH treatment leads to significant improvement in anthropometry in Indian children comparable with Western data. Bone biomarker changes indicate decreased bone resorption and increased bone formation although bone mineral density still lags behind age-matched controls.

Arterial stiffness in postmenopausal women with arterial hypertension

Background. It was established that the prevalence of reduced bone mineral density is significantly higher among patients with arterial hypertension compared to healthy subjects. The purpose of the research was to study arterial stiffness and central aortic pressure in women with arterial hypertension combined with osteoporosis. Materials and methods. 88 postmenopausal women with arterial hypertension (average age 62.7±3.8 years) were included in the study, of which 35 women with osteoporosis (OP) according to dual-energy X-ray absorptiometry (DXA) data (group 1) and 33 without it (group 2). The control group consisted of 20practically healthy women without arterial hypertension and OP. Before the start of the study, the patients did not regularly receive antihypertensive drugs. In addition to conventional studies, daily blood pressure monitoring, applanation tonometry, and DXA were performed. Statistical processing was performed using the Statistica 10.0 software package using standard variational statistics algorithms. Results. It was found that in group 1, the levels of office systolic (SBP) and diastolic blood pressure (DBP) were by 3.7 and 4.9% (p &lt; 0.05) higher than in group 2. According to daily blood pressure monitoring, a more pronounced increase in average daily SBP and DBP by 4.1 and 6.6% (p &lt; 0.05) was observed in group 1 compared to group 2. It was found that in group 1, compared to the control group and group 2, an increase in pulse wave velocity was detected by 37.2 and 26.4%, respectively. Conclusions. In women with postmenopausal osteoporosis in combination with arterial hypertension according to data of office measurement, daily monitoring blood pressure and data of applanation tonometry, blood pressure levels and parameters of arterial stiffness were significantly higher than in patients with arterial hypertension without osteoporosis.

Body-composition phenotypes and their associations with cardiometabolic risks and health behaviours in a representative general US sample

Body mass index is poor at distinguishing between adiposity and muscle. Based on dual energy X-ray absorptiometry data, a diagnostic framework to analyze body composition by categorizing fat- and muscle-mass body composition into four phenotypes has been proposed. The objective of this study was to assess the association between body-composition phenotypes with adiposity measures, health behaviours and cardiometabolic risks in a representative U.S. adult population. Data were from NHANES (1999–2006: n = 9867; 2011–2018: n = 10,454). Four phenotypes based on being above/below the 50th percentile of age- and sex- adjusted reference curves of fat-mass and muscle-mass were identified. Multiple linear and logistic regressions were used to assess phenotypes (high [H] or low [L] adiposity [A] or muscle mass [M]) against adiposity measures, health behaviours, cardiometabolic risk, and dietary intake. Low-adiposity/high-muscle (LA-HM) was the referent. Analyses incorporated the complex sampling design and survey weights, and were adjusted for age, sex, race, and education. Compared to the LA-HM reference group, the HA-LM phenotype was less physically active, had higher total and lower high-density lipoprotein cholesterol, and had lower intake of all examined nutrients (all p < 0.01). For the HA-HM phenotype, unfavourable values were detected for all adiposity and cardiometabolic measures compared to the LA-HM phenotype (all p < 0.01). The two high adiposity phenotypes were associated with poorer health behaviours and cardiovascular risk factors, regardless of muscle-mass, but associations differed across the phenotypes. Results further underscores the importance of accounting for both adiposity and muscle mass in measurement and analysis. Further longitudinal investigation is needed.

Issue microcirculation around extended implant-supported prostheses with few abutment sites in patients with terminal mandibular dentition

Relevance. The success of implant-supported prostheses depends on the quality of the jawbone. Traditionally, it is assessed radiographically, but this method is not only invasive but also unreliable and inaccurate for predicting the outcome of treatment.Material and methods. The study included 80 patients (49 women and 31 men) with a mean age of 71 ± 7 years, which formed four groups. Group A (control group, n = 20) consisted of patients with healthy periodontium; comparison group B, n = 20, comprised patients with terminal dentition; the main group C (n = 20) included patients with extended rehabilitation, fixed 7-10 days before; group G (n = 20) was composed of patients with “Trefoil” implant-supported prostheses, fixed three years earlier. The blood flow of peri-implant tissues was assessed using ultrasound Doppler flowmetry (UDF). All patients (n = 20) underwent dual-energy X-ray absorptiometry (DXA) before the prosthetic treatment.Results. The analysis of pre-prosthetic-treatment ultrasound Doppler flowmetry results showed low values of microcirculation in the alveolar ridge mucous membrane in patients with terminal dentition compared with the control group. On the 7th day after implant-supported prosthetic treatment, group C demonstrated an increase in microcirculation by 11.42% compared to the control group and by 147.36% compared to group B. Three years after implant-supported prosthetic treatment, the ultrasound data revealed a statistically significant increase in blood flow velocity 0.342 ± 0.04 (cm/s) (p &lt; 0.01). The Pearson coefficient determined a high correlation between T-scores of DXA and ultrasound Doppler flowmetry data (r = 0.829, p = 0.0001).Conclusion. Ultrasound Doppler flowmetry (UDF) can be the main method for studying the peri-implant tissue condition at various stages of implant-supported prosthetic treatment.

Development Of A Method For Evaluating Body Composition And Perimeters Using Cell Phone Photos.

Accurate assessment of body composition requires expensive methods that are inaccessible to most of the population, such as dual-energy X-ray absorptiometry (DXA). Technological advances allowed the creation of an application that uses artificial intelligence and computer vision to assess body measurements (BM) and body fat (%BF) through photos, making it a non-invasive, practical, and affordable method. PURPOSE: Develop and validate a method for predicting body measurements and body fat from 2D images. METHODS: To predict BM and BF, two groups of volunteers were evaluated. The variables weight, height and perimeters were evaluated by an Isak level 2 anthropometrist. Four images of the same patient were captured in frontal, posterior, right and left side perspectives. The %BF was measured by DXA (GE, Madison, USA). For prediction of BM, data from perimeters and photos from group 1 were collected (493 adults (327 females) with age 35.5 ± 8.8 years, height 165.6 ± 17.3 cm, weight 67.7 ± 14.0 kg). The data set from the second group (287 adults (157 females) with age 30.8 ± 9.6 years, height 168.8 ± 9.6 cm, weight 71.4 ± 16.0 kg) was used to predict %BF using DXA and real perimeter data as reference. To create the prediction model, 50 independent executions were performed with the shuffled dataset. Each dataset was separated into 80% for training and 20% for final validation. Of these 80%, the sample was separated into 70% for training, and 30% for testing on each independent execution. Bland-Altman analysis was used to assess the agreement between predicted and measured values in the final validation sample. RESULTS: Our gender-based approach successfully estimates the %BF achieving a MAE = 3.33 on the male set and MAE = 3.41 on the female set. The prediction of the perimeters of relaxed arm, forearm, waist, hip, middle thigh, and calf obtained MAE value of 1,2; 0.9; 2.8; 2; 2.3; 0.9, respectively, for male and 1.3; 1; 4.8; 4.2; 4.5; 1.4 for female. The BIAS of %error was 0.7, 0.1; 1.3; 0.1; 2.6; 0.6, between predicted and measured values for perimeters, respectively, and 1.21 for %BF, without dividing the sample by sex. CONCLUSION: The model was able to predict body measurements and %BF with an acceptable margin of error, making it a possible method to be used in clinical practice, especially in distance assessments.

Validation of Non-empirical Fat-free Mass Estimation Model for a Wrist-worn Device.

Fat-free mass (FFM) estimation has dramatic importance for body composition evaluation, often providing a basis for treatment of obesity and muscular dystrophy. However, current methods of FFM estimation have several drawbacks, usually related to either cost-effectiveness and equipment size (dual-energy X-ray absorptiometry (DEXA) scan) or model limitations. In this study, we present and validate a new FFM estimation model based on hand-to-hand bioimpedance analysis (BIA) and arm volume. Forty-two participants underwent a full-body DEXA scan, a series of anthropometric measurements, and upper-body BIA measurements with the custom-designed wearable wrist-worn impedance meter. A new two truncated cones (TTC) model was trained on DEXA data and achieved the best performance metrics of 0.886 ± 0.051 r2, 0.052 ± 0.009 % mean average error, and 6.884 ± 1.283 kg maximal residual error in FFM estimation. The model further demonstrated its effectiveness in Bland-Altman comparisons with the skinfold thickness-based FFM estimation method, achieving the least mean bias (0.007 kg). The novel TTC model can provide an alternative to full-body BIA measurements, demonstrating an accurate FFM estimation independently of population variables.

Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning.

Cardiovascular diseases (CVD) are the leading cause of death worldwide. People affected by CVDs may go undiagnosed until the occurrence of a serious heart failure event such as stroke, heart attack, and myocardial infraction. In Qatar, there is a lack of studies focusing on CVD diagnosis based on non-invasive methods such as retinal image or dual-energy X-ray absorptiometry (DXA). In this study, we aimed at diagnosing CVD using a novel approach integrating information from retinal images and DXA data. We considered an adult Qatari cohort of 500 participants from Qatar Biobank (QBB) with an equal number of participants from the CVD and the control groups. We designed a case-control study with a novel multi-modal (combining data from multiple modalities—DXA and retinal images)—to propose a deep learning (DL)-based technique to distinguish the CVD group from the control group. Uni-modal models based on retinal images and DXA data achieved 75.6% and 77.4% accuracy, respectively. The multi-modal model showed an improved accuracy of 78.3% in classifying CVD group and the control group. We used gradient class activation map (GradCAM) to highlight the areas of interest in the retinal images that influenced the decisions of the proposed DL model most. It was observed that the model focused mostly on the centre of the retinal images where signs of CVD such as hemorrhages were present. This indicates that our model can identify and make use of certain prognosis markers for hypertension and ischemic heart disease. From DXA data, we found higher values for bone mineral density, fat content, muscle mass and bone area across majority of the body parts in CVD group compared to the control group indicating better bone health in the Qatari CVD cohort. This seminal method based on DXA scans and retinal images demonstrate major potentials for the early detection of CVD in a fast and relatively non-invasive manner.