DXA-Derived Adiposity and Lean Indices for Management of Cardiometabolic and Musculoskeletal Frailty: Data Interpretation Tricks and Reporting Tips

Pre-Transplant Dual-Energy X-ray Absorptiometry (DXA)-Derived Body Composition Measures as Predictors of Treatment Outcomes and Early Post-Transplant Complications in Patients with Multiple Myeloma (MM) Treated with Autologous Hematopoietic Stem Cell Transplantation (AutoHSCT)

Background/Objectives: Changes in muscle mass and bone density are common in multiple myeloma (MM) patients. Dual-energy X-ray absorptiometry (DXA) offers precise, non-invasive insights into a patient's physical condition before autologous stem cell transplantation (autoHSCT). This study examines how pre-transplant body composition impacts treatment outcomes and early complications in MM patients undergoing autoHSCT. Methods: This study is a single-center, retrospective analysis of patients with MM who were treated with first or second autoHSCT and underwent DXA pre-transplant between 11 August 2019 and 12 June 2024. Results: We conducted a study of pre-transplant body composition in 127 patients with MM. Among them, 108 (85%) qualified for first autoHSCT, while 19 (15%) qualified for a second. The median age of the patients was 64 years (range 50-73). In the Cox proportional hazards regression conducted in the group of women, Total Body %Fat was a statistically significant predictor for progression-free survival (PFS) (HR = 0.07, 95% CI = 0.01,0.6, p = 0.0157). In the Mann-Whitney U test conducted on males, Lean Mass/Height2 and Appen. Lean Height2 were statistically significant predictors of early infections after autoHSCT (Z = 1.98, p = 0.0473 and Z = 2.32, p = 0.0204, respectively). In males, Fat Mass/Height2 was a significant predictor of non-infectious toxicity related to treatment (Z = -1.98, p = 0.0476). Conclusions: In women, higher levels of adipose tissue initially appear to exert a protective effect; however, this benefit diminishes over time, with greater fat mass eventually correlating with an increased risk of disease progression. In men, muscle mass has been identified as a significant predictor of early infection risk post-autoHSCT. Furthermore, our findings indicate that an increased amount of adipose tissue in men is statistically associated with a higher risk of non-infectious treatment-related toxicity. These conclusions highlight the critical need for further investigation into the role of body composition.

Machine learning allows robust classification of visceral fat in women with obesity using common laboratory metrics

The excessive accumulation and malfunctioning of visceral adipose tissue (VAT) is a major determinant of increased risk of obesity-related comorbidities. Thus, risk stratification of people living with obesity according to their amount of VAT is of clinical interest. Currently, the most common VAT measurement methods include mathematical formulae based on anthropometric dimensions, often biased by human measurement errors, bio-impedance, and image techniques such as X-ray absorptiometry (DXA) analysis, which requires specialized equipment. However, previous studies showed the possibility of classifying people living with obesity according to their VAT through blood chemical concentrations by applying machine learning techniques. In addition, most of the efforts were spent on men living with obesity while little was done for women. Therefore, this study aims to compare the performance of the multilinear regression model (MLR) in estimating VAT and six different supervised machine learning classifiers, including logistic regression (LR), support vector machine and decision tree-based models, to categorize 149 women living with obesity. For clustering, the study population was categorized into classes 0, 1, and 2 according to their VAT and the accuracy of each MLR and classification model was evaluated using DXA-data (DXAdata), blood chemical concentrations (BLDdata), and both DXAdata and BLDdata together (ALLdata). Estimation error and R 2 were computed for MLR, while receiver operating characteristic (ROC) and precision-recall curves (PR) area under the curve (AUC) were used to assess the performance of every classification model. MLR models showed a poor ability to estimate VAT with mean absolute error ≥ 401.40 andR 2 ≤ 0.62 in all the datasets. The highest accuracy was found for LR with values of 0.57, 0.63, and 0.53 for ALLdata, DXAdata, and BLDdata, respectively. The ROC AUC showed a poor ability of both ALLdata and DXAdata to distinguish class 1 from classes 0 and 2 (AUC = 0.31, 0.71, and 0.85, respectively) as also confirmed by PR (AUC = 0.24, 0.57, and 0.73, respectively). However, improved performances were obtained when applying LR model to BLDdata (ROC AUC ≥ 0.61 and PR AUC ≥ 0.42), especially for class 1. These results seem to suggest that, while a direct and reliable estimation of VAT was not possible in our cohort, blood sample-derived information can robustly classify women living with obesity by machine learning-based classifiers, a fact that could benefit the clinical practice, especially in those health centres where medical imaging devices are not available. Nonetheless, these promising findings should be further validated over a larger population.

Association between A/G ratio and arterial stiffness among Chinese type 2 diabetics: A cross-sectional study

BACKGROUND

The android-to-gynoid fat ratio (A/G ratio), an emerging indicator of obesity independent of body mass index (BMI), has yet to be conclusively associated with arterial stiffness in type 2 diabetes mellitus (T2DM). This study aimed to construct a nomogram to estimate arterial stiffness risk in diabetics and explore the interaction effect between A/G ratio and traditional obesity indicators on arterial stiffness.

METHODS

1313 diabetics were divided into 2 groups based on arterial stiffness identified by brachial ankle pulse wave velocity (baPWV), and demographic and clinical features were measured. The LASSO and multivariate logistics regression were used to develop the nomogram. Calibration curve, decision curve analysis (DCA) and receiver operating characteristic (ROC) were applied to assess calibration and clinical usefulness. Interaction effect analysis was performed to quantify the interactive relationship of A/G ratio and obesity indicators on arterial stiffness.

RESULTS

6 independent predictors (age, gender, A/G ratio, SBP, LDL-C and HbA1C) were screened to construct a nomogram prediction model. The calibration curve demonstrated satisfactory agreement between predicted and actual probability, and the nomogram exhibited clinical beneficial at the threshold between 8 % and 95 % indicated by DCA. The area under curve (AUC) was 0.918 and 0.833 for training and external set, respectively. Further investigation revealed A/G ratio and BMI acted positively synergistically towards arterial stiffness, and in BMI-based subgroup analysis, elevated A/G ratio was a significant risk factor for arterial stiffness, especially in normal BMI.

CONCLUSIONS

A/G ratio showed a substantial association with arterial stiffness, and the nomogram, incorporating age, gender, A/G ratio, SBP, LDL-C, and HbA1c, exhibited high predictive value. A/G ratio measurement in BMI-normal individuals assisted in identifying cardiovascular diseases early.

Exploring the correlation of skin temperature and body composition in athletes undergoing exhaustive physical exercise

During strenuous exercise, skin temperature (Tsk) plays an essential role in thermoregulatory processes. As indicated in the literature, its response might be influenced by body composition, among other factors. Hence, the objectives of this investigation were to determine whether there is a correlation between selected body components, specifically fat tissue and muscle tissue, and Tsk during graded exercise and recovery in athletes, and to identify which body component exhibits the strongest correlation with Tsk. Participants were grouped according to their aerobic capacity (VO2max/kg). A significant main effect was observed for the test stages (p < .001, η2 = 0.71), with Tsk decreasing from the start of the exercise, significantly decreasing at 12 km/h-1 (p < .001), and then increasing after exercise, especially within the first 5 min of recovery. Weak and non-significant effect for group/stage interaction was detected (p = .374, η2 = 0.03). A significant negative correlation was found between Tsk and both total tissue fat [%] (-0.51 < r < -0.63, p < .001) and lower limb tissue fat [%] (-0.50 < r < -0.71, p < .001) across all test stages. The correlation between Tsk and BMI was inconsistent, appearing only during the first stage of exercise and throughout recovery. No correlation was observed between Tsk and skeletal muscle mass, appendicular lean soft tissue, or relative skeletal muscle index. Endurance running to exhaustion leads to a progressive decrease in the Tsk of the lower extremity, followed by rewarming during recovery. The observed inverse correlation between adipose tissue and Tsk, along with the distinct temperature trends in groups with varying levels of fat tissue, could imply that the skin and subcutaneous tissue complex may play a more intricate role in thermal energy exchange beyond its insulating function. This implies a multifaceted involvement of these tissues in thermoregulation.

Assessment and Rehabilitation in Sarcopenic Patients

Sarcopenia is a degenerative disorder that particularly affects older people and is defined by a pathological decrease in muscle strength. This disease represents one of the topics of great interest in the medical world of the last two decades. In our study, we tried to underline the importance of an adapted recovery program based on physical exercise for regaining clinical and functional status in patients with age-related sarcopenia. No nutritional intervention was applied. We performed our rehabilitation program in accordance with present international recommendations for sarcopenia. After complete assessment, our patients were randomised into two groups: G1 (Lot 1=25 patients) and G2 (Lot 2=15 patients). G1 patients were compliant with kinetic training, and performed all rehabilitation measures, and G2 patients accepted rehabilitation program without kinetic exercises. Patients assessment (lab tests, gait analysis, VAS and the Clinical Frailty Scale) was made on two levels-first (T1-inpatient assessment), and after 6 months (T2-outpatient assessment). The rehabilitation program based on the kinetic program brought positive improvements in physical performance and locomotion (gait speed and walking cadence) in sarcopenic patients.

No Differences in Urine Bisphenol A Concentrations between Subjects Categorized with Normal Cognitive Function and Mild Cognitive Impairment Based on Montreal Cognitive Assessment Scores

A link between bisphenol A (BPA) exposure and cognitive disorders has been suggested. However, the differences in BPA concentrations between subjects with and without cognitive impairment have not been analysed. Therefore, this observational study aimed to compare urine BPA levels in subjects with normal cognitive function (NCF) and mild cognitive impairment (MCI). A total of 89 MCI subjects and 89 well-matched NCF individuals were included in this study. Cognitive functions were assessed using the Montreal Cognitive Assessment (MOCA) scale. Urine BPA concentrations were evaluated by gas chromatography-mass spectrometry and adjusted for creatinine levels. Moreover, anthropometric parameters, body composition, sociodemographic factors, and physical activity were also assessed. Creatinine-adjusted urine BPA levels did not differ between the NCF and MCI groups (1.8 (1.4-2.7) vs. 2.2 (1.4-3.6) µg/g creatinine, p = 0.1528). However, there were significant differences in MOCA results between groups when the study population was divided into tertiles according to BPA concentrations (p = 0.0325). Nevertheless, multivariate logistic regression demonstrated that only education levels were independently associated with MCI. In conclusion, urine BPA levels are not significantly different between subjects with MCI and NCF, but these findings need to be confirmed in further studies.

DXA-Based Detection of Low Muscle Mass Using the Total Body Muscularity Assessment Index (TB-MAXI): A New Index with Cutoff Values from the NHANES 1999-2004

The aims of this study were to investigate age-related changes in total body skeletal muscle mass (TBSMM) and the between-limb asymmetry in lean mass in a large sample of adults. Demographic, anthropometric, and DXA-derived data of National Health and Nutrition Examination Survey participants were considered. The sample included 10,014 participants of two ethnic groups (Caucasians and African Americans). The age-related decline of TBSMM absolute values was between 5% and 6% per decade in males and between 4.5% and 5.0% per decade in females. The adjustment of TBSMM for body surface area (TB-MAXI) showed that muscle mass peaked in the second decade and decreased progressively during the subsequent decades. The following thresholds were identified to distinguish between low and normal TB-MAXI: (i) 10.0 kg/m2 and 11.0 kg/m2 in Caucasian and African American females; and (ii) 12.5 kg/m2 and 14.5 kg/m2 in Caucasian and African American males. The lean asymmetry indices were higher for the lower limbs compared with the upper limbs and were higher for males compared with females. In conclusion, the present study proposes the TB-MAXI and lean asymmetry index, which can be used (and included in DXA reports) as clinically relevant markers for muscle amount and lean distribution.