Body Composition Assessment Research Articles

Resting energy expenditure during spinal cord injury rehabilitation and utility of fat-free mass-based energy prediction equations: a pilot study

Study designLongitudinal observational study. Measurements were undertaken between weeks 4–6 post-spinal cord injury (SCI), repeated at week 8 and every 4 weeks thereafter until week 20 or rehabilitation discharge, whichever occurred first.ObjectivesObserve variation in measured resting energy expenditure (REE) and body composition in males undergoing SCI rehabilitation, compare REE with SCI-specific prediction equations incorporating fat-free mass (FFM), and explore the prevalence of clinical factors that may influence individual REE.SettingSpinal Injuries Unit, Brisbane, Queensland, Australia.MethodsIndirect calorimetry was used to measure REE and bioimpedance spectroscopy to assess body composition. Four SCI-specific FFM-based REE and basal metabolic rate (BMR) prediction equations were compared to measured REE. A clinically significant change in REE was defined as +/− 10% difference from the week 4–6 measurement. Clinical factors that may affect REE variations were collected including infection, pressure injuries, autonomic dysreflexia, spasticity, and medications.ResultsFifteen people participated (mean age 35 ± 13 years, 67% paraplegic). There was no statistically significant change in mean REE, weight, or body composition, and the Chun and Nightingale BMR prediction equations performed best (rc > 0.8 at all time points). One-third of participants had >10% change in REE on 11 occasions, with clinical factors not consistently associated with the observed changes.ConclusionDuring SCI rehabilitation, mean REE, weight, and body composition remain unchanged, and FFM-based BMR prediction equations may be an acceptable alternative to indirect calorimetry. Future research designs should avoid single indirect calorimetry measures as snapshot data may not represent typical REE in this population.

Assessment of Body Composition by Whole-Body Bioimpedance and Body Cardio Scale in Patients on Peritoneal Dialysis

Assessment of Body Composition by Whole-Body Bioimpedance and Body Cardio Scale in Patients on Peritoneal Dialysis

Bioimpedance Analysis for the Assessment of Body Composition and Relation with Muscle Strength in Patients with CKD Stage G3 to G5 and Healthy Controls

Bioimpedance Analysis for the Assessment of Body Composition and Relation with Muscle Strength in Patients with CKD Stage G3 to G5 and Healthy Controls

Feasibility of implementing body composition assessment into clinical practice in patients with acute spinal cord injury

Feasibility of implementing body composition assessment into clinical practice in patients with acute spinal cord injury

Enhancing body composition assessment tools: Validation of dietetic-led ultrasound versus magnetic resonance imaging for muscle measurement

Enhancing body composition assessment tools: Validation of dietetic-led ultrasound versus magnetic resonance imaging for muscle measurement

Comparing The Accuracy Of Body Composition Assessment Information Produced By ChatGpt 3.5 And Claude2 AI Platforms

Comparing The Accuracy Of Body Composition Assessment Information Produced By ChatGpt 3.5 And Claude2 AI Platforms

Imaging of Body Composition.

Body composition is now recognized to have a major impact on health and disease. Imaging enables its analysis in an objective and quantitative way through diverse techniques such as dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging, and ultrasonography. This review article first surveys the methodological aspects underpinning the use of these modalities to assess body composition, highlighting their strengths and limitations as well as the set of parameters that they measure and their clinical relevance. It then provides an update on the main applications of body composition imaging in current practice, with a focus on sarcopenia, obesity, lipodystrophies, cancer, and critical care. We conclude by considering the emerging role of artificial intelligence in the analysis of body composition, enabling the extraction of numerous metrics with the potential to refine prognostication and management across a number of pathologies, paving the way toward personalized medicine.

Usefulness of the new bioelectrical impedance vector references in for qualitatively assessing body composition among hospitalized patients with malnutrition

Usefulness of the new bioelectrical impedance vector references in for qualitatively assessing body composition among hospitalized patients with malnutrition

Quantitative Assessment of Body Composition in Cirrhosis.

Changes in body composition often accompany the progression of liver disease and seem to be an aggravating pathophysiological factor. Specifically, accelerated loss of skeletal muscle mass, lower muscle quality, and changes in body fat distribution have been shown to be associated with poor clinical outcomes. The aim of the present narrative review was to discuss the current status and relevance of commonly applied, advanced, non-invasive methods to quantify skeletal muscle mass, muscle fat infiltration-i.e., myosteatosis-and fat distribution. This review focuses in particular on Computed Tomography (CT), Dual-energy X-ray Absorptiometry (DXA), Bioelectrical Impedance Analysis (BIA), Magnetic Resonance Imaging (MRI), and Ultrasonography (US). We propose future directions to enhance the diagnostic and prognostic relevance of using these methods for quantitative body composition assessment in patients with cirrhosis.

Development and clinical application of bioelectrical impedance analysis method for body composition assessment.

Obesity, which is characterized by excessive body fat, increases the risk of chronic diseases, such as type 2 diabetes, cardiovascular diseases, and certain cancers. Sarcopenia, a decline in muscle mass, is also associated with many chronic disorders and is therefore a major concern in aging populations. Body composition analysis is important in the evaluation of obesity and sarcopenia because it provides information about the distribution of body fat and muscle mass. It is also useful for monitoring nutritional status, disease severity, and the effectiveness of interventions, such as exercise, diet, and drugs, and thus helps assess overall health and longevity. Computed tomography, magnetic resonance imaging, and dual-energy X-ray absorptiometry are commonly used for this purpose. However, they have limitations, such as high cost, long measurement time, and radiation exposure. Instead, bioelectrical impedance analysis (BIA), which was introduced several decades ago and has undergone significant technological advancements, can be used. It is easily accessible, affordable, and importantly, poses no radiation risk, making it suitable for use in hospitals, fitness centers, and even at home. Herein, we review the recent technological developments and clinical applications of BIA to provide an updated understanding of BIA technology and its strengths and limitations.

Effects of an 8-Week Active Play Intervention on Body Composition and Fundamental Motor Skills in Preschool Children.

Objective: Examine the effect of an 8-week teacher-guided active play intervention on preschoolers' body composition and fundamental motor skills. Methods: Participants were from two local preschool centers randomly assigned to either the intervention (n = 25, 3.91 ± 0.53 years) or the control group (n = 25, 3.69 ± 0.81 years). All measures were assessed at baseline (week 0), post-intervention (weeks 9-11), and follow-up (weeks 30-33). Bioelectrical Impedance assessed body composition (fat mass (FM) and fat-free mass (FFM)). The Peabody Developmental Motor Scales, Second Edition (PDMS-2) assessed fundamental motor skills (gross motor quartile (GMQ)). Results: A significant Group × Time interaction for GMQ at post-intervention (p = 0.03), with the intervention group scoring significantly higher on GMQ. A significant main effect of Time (p < 0.001) indicated that GMQ increased in both groups across the 33-week period. For FM, a significant main effect of Time at both post-intervention (p < 0.05) and follow-up testing (p < 0.001) indicated that participants increased FM over the 33-week period. Lastly, there was a significant main effect of Time for FFM at post-intervention (p = 0.003) and follow-up (p < 0.001). Interestingly, there was a significant Group × Time interaction (p < 0.05) at follow-up testing showing that FFM increased over time but significantly more for the control group. Conclusions: Results indicate that active play interventions might be a successful pathway to improve gross motor skills in young children. Further research is needed to understand the effect that active play interventions have on body composition in preschoolers.

Assessing body composition through anthropometry: Implications for diagnosing and managing polycystic ovary syndrome (PCOS).

Polycystic ovary syndrome (PCOS) is a multifaceted endocrine disorder with profound implications for the reproductive and metabolic health of women. The utilization of anthropometric measures in the diagnosis and management of PCOS has gained increasing attention due to their practicality and predictive capacity for associated conditions such as obesity and insulin resistance. This review rigorously explores the application of various anthropometric indices, including body mass index, waist-to-hip ratio, and advanced metrics such as the body shape index and body roundness index, wrist circumference, neck circumference. These indices offer critical insights into body fat distribution and its association with the metabolic and hormonal perturbations characteristic of PCOS. The review underscores the necessity of addressing obesity, a prevalent comorbidity in PCOS, through lifestyle modifications and personalized therapeutic approaches. By incorporating anthropometric evaluations into routine clinical practice, healthcare professionals can enhance diagnostic precision, optimize treatment strategies, and ultimately improve patient outcomes. This integrative approach not only facilitates the management of the metabolic challenges inherent in PCOS but also contributes to the development of more individualized therapeutic interventions, thereby enhancing the overall quality of life for women affected by PCOS.

Analyzing the effects of competitive fatigue on body composition and functional capacities of youth elite handball players

Handball demands intense movements like rapid direction changes and physical contact. Consecutive competitions expose player fatigue and weaknesses due to the sport’s exhaustive nature. Therefore, the aims of this study are twofold: (i) to investigate the impact of competitive fatigue on the body composition of youth elite handball players, and (ii) to analyze the impact of concentrated competition on their functional capacity performance. Seventeen young male handball players (age: 16.2 years, height; 177.8 cm, body mass: 73.0 kg) were assessed twenty-four hours before (TM1) and after the competitive period (TM2) in body composition, static strength, lower- and upper-body explosive strength, speed, agility, balance, and flexibility indicators. A Wilcoxon Signed Rank Test was conducted to assess differences in body composition and functional capacities assessment between TM1 and TM2. Regarding results, body mass significantly increased in TM2 (p &lt; 0.01), while increases in waist circumference and body fat percentage were not statistically significant. Functional capacity assessments revealed a significant decline in jumping performance (CMJ and SJ; p ≤ 0.01). Speed and agility assessments showed worse performance in TM2, significantly increasing 30 m sprint time (p ≤ 0.01). Balance indicators showed no significant differences, with mixed results in performance across different conditions. In sum, evaluating vertical jumps and long-distance maximum speed (i.e., 30 meters) could be valid tests for measuring and controlling fatigue in young elite handball players. Future research should regularly monitor young handballers after the competition to analyze the entire fatigue recovery process.

Application of Bioelectrical Impedance Analysis in Weight Management of Children with Spina Bifida.

Children with spina bifida (SB) face an elevated risk of obesity, which necessitates precise methods for assessing body composition to ensure effective weight management. Conventional measures like BMI are inadequate for this population because of variations in growth patterns and skeletal structure. Bioelectrical impedance analysis (BIA) is a method that offers a clearer picture of body composition, yet its use in children with SB remains underexplored. Conducted on 57 children with SB and 28 healthy controls, with a median age of 11 years, this study evaluated anthropometrics, including BMI and BIA-derived metrics. The Hoffer's scale to assess physical activity was applied in SB children. Results showed that while 32% of SB patients were classified as overweight or obese based on BMI, 62% exhibited high body fat percentage via BIA. Fat-free mass, muscle and fat mass, and fat-to-muscle ratio (FMR) differed significantly compared to the reference group. Non-ambulators showed a higher median body fat mass percentage (25.9% vs. 17.8%, p = 0.01) and FMR (0.92 vs. 0.44, p = 0.003) in comparison to the community walkers. In SB children, BIA-measured fat mass is a better obesity indicator than BMI. Non-ambulatory, SB patients with obesity had the highest FMR values, indicating a higher risk for metabolic syndrome.

Phase angle as an indicator of depression in maintenance hemodialysis patients

Introduction: Depression is a common psychiatric problem in maintenance hemodialysis (MHD) patients. Recent studies have begun to explore the relationship between body composition and depression. Phase angle (PhA), a core parameter for assessing body composition, has been observed to be associated with frailty and cognitive dysfunction. The aim of this study was to investigate the association between PhA and depression in MHD patients. Methods: This multicenter, cross-sectional study included 843 MHD patients from seven dialysis centers in Shanghai, China. Depressive symptoms were evaluated using the Patient Health Questionnaire (PHQ-9), with a score of ≥10 indicating depression. PhA was measured by bioelectrical impedance analysis. Nutritional status was assessed by Malnutrition inflammation score (MIS) . Multivariable logistic regression models were used to investigate the association between PhA and depression. Restricted cubic spline (RCS) analysis was utilized to examine the association. Receiver operating characteristic (ROC) curve was used to identify the cut-off value of PhA for depression. Results: 15.2% of patients (62.8% male, median age 66 years) had depression. Median PhA level (interquartile range, IQR) of depressed patients was 4.4° (3.9-4.9°) for males and 3.9° (3.2-4.7°) for females. There was a significant decrease in the prevalence of depression with increasing quartiles of PhA levels. In multivariable logistic regression analyses, after adjusting for age, sex, education level, spkt/v, dialysis vintage, Charlson comorbidity index, hemoglobin and serum albumin, lower PhA levels (lowest quartile group) were significantly associated with depressive symptoms (adjusted odds ratio, 2.19; 95% confidence interval, 1.07 to 4.48), compared to higher PhA levels (highest quartile group). RCS analysis showed a relatively inverse linear association between PhA and depression. The optimal cut-off value of PhA for depression was 4.9° for males and 3.5° for females. Subgroup analyses validated the findings across patient characteristics, including age, sex, diabetes, education and malnutrition. Conclusion: Our findings indicated an inverse association between PhA and depressive symptoms in Chinese MHD patients, suggesting that PhA could serve as a valuable indicator for assessing the risk of depression in this population. Further studies are needed to explore the potential of PhA as a prognostic tool and its implications for intervention strategies.

Comparison of Four Body Composition Methods: Circumference Measurements, Eight-Point Bioelectrical Impedance Analysis up to 500 and 1000 kHz to Dual-Energy X-Ray Absorptiometry to Measure Body Fat Percentage

ABSTRACT Introduction A comparison of body composition assessments using military circumferences to bioelectrical impedance analysis (BIA) and the reference standard dual-energy X-ray absorptiometry (DEXA) can gauge effectiveness of assessments. High-frequency (500 KHz) direct segmental multifrequency bioelectrical impedance analysis (DSM-BIA) accurately calculates total water mass and body fat% (BF%), but it is unknown whether higher frequencies (1,000 KHz) increase measurement accuracy. The purpose was to compare DSM-BIA 500, DSM-BIA 1000, the DoD Circumference Method (CM), and the reference-standard DEXA. Materials and Methods Design: Cross sectional, observational study. Participants/Setting: A total of 62 participants from the military healthcare system (n = 25 males, 38.8 ± 11.4 years, n = 37 females 43.7 ± 15.95 years) were measured in an outpatient clinic setting. Statistical Analysis: BF% was estimated via DEXA, DSM-BIA 500, DSM-BIA 1000, and CM to identify the relationship between methods using Pearson correlation, intraclass correlation coefficients (ICCs), and Bland-Altman plots. The study was approved by the IRB from Walter Reed National Military Medical Center at Bethesda and Concordia University Chicago. Results Circumference Method BF% was moderately correlated with DSM-BIA 500 (males r = 0.63, ICC = 0.76; females r = 0.77, ICC = 0.85), DSM-BIA 1000 (males r = 0.59, ICC = 0.74; females r = 0.77, ICC = 0.85), and DEXA (males r = 0.62, ICC = 0.62; females r = 0.73, ICC = 0.82). DSM-BIA 500 BF% was strongly correlated with DSM-BIA 1000 (males r = 0.99, ICC = 0.99; females r = 0.99, ICC = 0.99) and DEXA (males r = 0.93, ICC = 0.94; females r = 0.89, ICC = 0.89). Lastly, DSM-BIA 1000 BF% was also strongly correlated with DEXA (males r = 0.93, ICC = 0.94; females r = 0.84, ICC = 0.90) (P for each reported r &amp;lt; 0.01). Bland-Altman analysis confirmed an overall mean bias of −1.72% CM vs. DEXA in females, indicating the tendency of CM to underestimate BF% compared to DEXA limits of agreement from −14.24 to 10.8. There was an upward slope of the linear relationship between the bias and mean of the measures (Beta = 0.34, P = 0.01). In the full cohort, there was an overall mean bias of 1.14% of CM vs. DSM BIA 1000, with CM tending to overestimate BF% compared to DSM BIA 1000 with limits of agreement −11.13 to 13.41%. There is an upward slope line of the linear relationship between the bias and the mean of the measures (Beta = 0.17, P = .03). Conclusion This study found that CM BF% was moderately correlated with DSM-BIA 500 kHz, DSM-BIA 1,000 kHz BIA, and DEXA. Both DSM-BIA 500 and DSM-BIA 1,000 kHz strongly correlated well with DEXA implying that there was no further increase in correlation with increased frequency. Additionally, there was proportional bias in BF% in the female group between CM and DEXA and in the total group between CM and DSM BIA 1000.

Ultrasound for assessing paediatric body composition and nutritional status: Scoping review and future directions.

This scoping review aims to assess the utility of ultrasound as a prospective tool in measuring body composition and nutritional status in the paediatric population. We provide a comprehensive summary of the existing literature, identify gaps, and propose future research directions. We conducted a systematic scoping review following the PRISMA Extension for Scoping Reviews guidelines. This involved screening titles and abstracts of relevant studies, followed by a detailed full-text review and extraction of pertinent data. We identified and synthesised 34 articles. The review revealed that while ultrasound has been used to assess body composition and bone properties in children, significant gaps remain in the literature. These include limited studies on ultrasound performance, insufficient attention to relevant sample characteristics, reliance on manual image measurements, and limited sample diversity. Point-of-care ultrasound shows significant promise for assessing paediatric body composition and nutritional status. To validate and enhance its effectiveness, further research is needed. Future studies should include larger and more diverse patient cohorts and conduct longitudinal investigations to evaluate nutritional interventions. Additionally, developing artificial intelligence (AI) for standardising and automating data interpretation will be crucial in improving the accuracy and efficiency of ultrasound assessments.

External validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on abdominal computed tomography images.

Body composition assessment using computed tomography (CT) images at the L3-level is increasingly applied in cancer research. Robust high-throughput automated segmentation is key to assess large patient cohorts and to support implementation of body composition analysis into routine clinical practice. We trained and externally validated a deep learning neural network (DLNN) to automatically segment L3-CT images. Expert-drawn segmentations of visceral and subcutaneous adipose tissue (VAT/SAT) and skeletal muscle (SM) of L3-CT-images of 3,187 patients undergoing abdominal surgery were used to train a DLNN. The external validation cohort was comprised of 2,535 patients with abdominal cancer. DLNN performance was evaluated with (geometric) Dice Similarity (DS) and Lin's Concordance Correlation Coefficient. There was a strong concordance between automatic and manual segmentations with median DS for SM, VAT, and SAT of 0.97 (interquartile range, IQR: 0.95-0.98), 0.98 (IQR: 0.95-0.98), and 0.95 (IQR: 0.92-0.97), respectively. Concordance correlations were excellent: SM 0.964 (0.959-0.968), VAT 0.998 (0.998-0.998), and SAT 0.992 (0.991-0.993). Bland-Altman metrics indicated only small and clinically insignificant systematic offsets; SM radiodensity: 0.23 hounsfield units (0.5%), SM: 1.26 cm2.m-2 (2.8%), VAT: -1.02 cm2.m-2 (1.7%), and SAT: 3.24 cm2.m-2 (4.6%). A robustly-performing and independently externally validated DLNN for automated body composition analysis was developed. CT-based body composition analysis is highly prognostic for long-term overall survival in oncology. This DLNN was succesfully trained and externally validated on several large patient cohorts and will therefore enable large scale population studies and implementation of body composition analysis into clinical practice.

Concurrent and predictive validity of the Global Leadership Initiative on Malnutrition criteria for adult patients in convalescent rehabilitation wards

Background & aimsThe Global Leadership Initiative on Malnutrition (GLIM) criteria has been recognised as major diagnostic criteria for malnutrition in adults worldwide; however, its validity in rehabilitation settings remains unclear. This study investigated the concurrent and predictive validity of the GLIM criteria for adult patients in convalescent rehabilitation wards. MethodsThis retrospective cohort study was conducted using pre-established datasets from convalescent rehabilitation wards in a hospital. The inclusion criteria were adults aged ≥18 years admitted to the wards between November 2018 and October 2020 who were available for body composition assessment. Malnutrition diagnoses were determined by registered dietitians (RDs) using the GLIM criteria. The Subjective Global Assessment (SGA) was performed by another RD and used for the malnutrition reference standard. The GLIM criteria sensitivity and specificity were examined for SGA. The odds ratios and hazard ratios of GLIM-defined malnutrition for the total score of the Functional Independence Measure (tFIM) effectiveness and non-home discharge were calculated using univariable and multivariable logistic regression analyses and Cox proportional hazard models. ResultsData from 723 patients were extracted from the dataset. GLIM-defined malnutrition was confirmed in 207 (28.6%) patients, 87 (12.0%) with moderate malnutrition and 120 (16.6%) with severe malnutrition. The SGA graded 146 (20.2%) patients with moderate malnutrition (grade B) and 86 (11.9%) with severe malnutrition (grade C). The GLIM criteria (malnutrition/ no malnutrition) had fair sensitivity (76.7%, 95% confidence interval [CI]: 70.7–82.0%) and good specificity (94.1%, 95% CI: 91.6–96.0%), indicating acceptable concurrent validity. GLIM-defined moderate malnutrition had poorer sensitivity than severe malnutrition (42.5% vs 81.4%). Logistic regression analyses revealed no evidence for the association between GLIM-defined malnutrition and poor tFIM effectiveness (adjusted odds ratio [AOR]: 1.09, 95% CI: 0.71–1.69) and non-home discharge (AOR: 1.19, 95% CI: 0.76–1.84). The Cox proportional hazard analyses also showed no effect of malnutrition on outcomes. ConclusionThe GLIM criteria had fair sensitivity and good specificity, indicating acceptable criteria for diagnosing malnutrition in rehabilitation settings. However, its predictive validity for functional recovery and discharge outcomes was insufficient.

The role of body composition in left ventricular remodeling, reverse remodeling, and clinical outcomes for heart failure with mildly reduced ejection fraction: more knowledge to the “obesity paradox”

BackgroundAlthough the “obesity paradox” is comprehensively elucidated in heart failure (HF) with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), the role of body composition in left ventricular (LV) remodeling, LV reverse remodeling (LVRR), and clinical outcomes is still unclear for HF with mildly reduced ejection fraction (HFmrEF).MethodsOur study is a single-centre, prospective, and echocardiography-based study. Consecutive HFmrEF patients, defined as HF patients with a left ventricular ejection fraction (LVEF) between 40 and 49%, between January 2016 to December 2021 were included. Echocardiography was re-examined at 3-, 6-, and 12-month follow-up to assess the LVRR dynamically. Body mass index (BMI), fat mass, fat-free mass, percent body fat (PBF), CUN-BAE index, and lean mass index (LMI) were adopted as anthropometric parameters in our study to assess body composition. The primary outcome was LVRR, defined as: (1) a reduction higher than 10% in LV end-diastolic diameter index (LVEDDI), or a LVEDDI < 33 mm/m2, (2) an absolute increase of LVEF higher than 10 points compared with baseline echocardiogram, or a follow-up LVEF ≥50%. The secondary outcome was a composite of re-hospitalization for HF or cardiovascular death.ResultsA total of 240 HFmrEF patients were enrolled in our formal analysis. After 1-year follow-up based on echocardiography, 113 (47.1%) patients developed LVRR. Patients with LVRR had higher fat mass (21.7 kg vs. 19.3 kg, P = 0.034) and PBF (28.7% vs. 26.6%, P = 0.047) compared with those without. The negative correlation between anthropometric parameters and baseline LVEDDI was significant (all P < 0.05). HFmrEF patients with higher BMI, fat mass, PBF, CUN-BAE index, and LMI had more pronounced and persistent increase of LVEF and decline in LV mass index (LVMI). Univariable Cox regression analysis revealed that higher BMI (HR 1.042, 95% CI 1.002–1.083, P = 0.037) and fat mass (HR 1.019, 95% CI 1.002–1.036, P = 0.026) were each significantly associated with higher cumulative incidence of LVRR for HFmrEF patients, while this relationship vanished in the adjusted model. Mediation analysis indicated that the association between BMI and fat mass with LVRR was fully mediated by baseline LV dilation. Furthermore, higher fat mass (aHR 0.957, 95% CI 0.917–0.999, P = 0.049) and PBF (aHR 0.963, 95% CI 0.924–0.976, P = 0.043) was independently associated with lower risk of adverse clinical events.ConclusionsBody composition played an important role in the LVRR and clinical outcomes for HFmrEF. For HFmrEF patients, BMI and fat mass was positively associated with the cumulative incidence of LVRR, while higher fat mass and PBF predicted lower risk of adverse clinical events but not LMI.Graphical abstract