A Body Shape Index (ABSI) serves as a potential indicator of fat distribution, offering a more reliable association with all-cause mortality compared to overall adiposity. The present cohort study aims to explore the relationship between ABSI and all-cause mortality in US adults with Type 2 Diabetes (T2D).

For this cohort study, we extracted information on 5,461 US adults with T2D from the National Health and Nutrition Examination Survey (NHANES) and the NHANES Linked Mortality File. Trends in ABSI from 1999 to 2018 were calculated and analyzed using partial Mann-Kendall tests. To assess the relationship between ABSI and all-cause mortality, as well as the robustness of the association results, we employed weighted restricted cubic splines (RCS), weighted Cox proportional hazards models, sensitivity analyses, and stratified analyses. Additionally, we conducted time-dependent receiver operating characteristic (ROC) curve analysis to evaluate ABSI's predictive capability for all-cause mortality over 3, 5, and 10 years.

Among US adults with Type 2 Diabetes (T2D), the mean ABSI gradually increased from 0.08333 to 0.08444 between 1999 and 2018. Following a median follow-up period of 90 months, 1,355 deaths (24.8% of the participants) occurred due to all causes. A left J-shaped association was observed between ABSI and all-cause mortality, with a 39% increased risk among US adults with T2D who had an ABSI below 0.08105 after full adjustment.

Our research has demonstrated a significant association between an elevated ABSI and the risk of all-cause mortality among US adults with T2D. These findings support the potential use of ABSI as a noninvasive tool to estimate mortality risk among US adults with T2D.

The online version contains supplementary material available at 10.1007/s40200-025-01570-3.

Severe respiratory infections pose a major challenge in clinical practice, especially in older adults. Body composition analysis could play a crucial role in risk assessment and therapeutic decision-making. This study investigates whether obesity or sarcopenia has a greater impact on mortality in patients with severe respiratory infections. The study focuses on the National Pandemic Cohort Network (NAPKON-SUEP) cohort, which includes patients over 60 years of age with confirmed severe COVID-19 pneumonia. An innovative approach was adopted, using pre-trained deep learning models for automated analysis of body composition based on routine thoracic CT scans.

The study included 157 hospitalized patients (mean age 70 ± 8 years, 41% women, mortality rate 39%) from the NAPKON-SUEP cohort at 57 study sites. A pre-trained deep learning model was used to analyze body composition (muscle, bone, fat, and intramuscular fat volumes) from thoracic CT images of the NAPKON-SUEP cohort. Binary logistic regression was performed to investigate the association between obesity, sarcopenia, and mortality.

Non-survivors exhibited lower muscle volume (p = 0.043), higher intramuscular fat volume (p = 0.041), and a higher BMI (p = 0.031) compared to survivors. Among all body composition parameters, muscle volume adjusted to weight was the strongest predictor of mortality in the logistic regression model, even after adjusting for factors such as sex, age, diabetes, chronic lung disease and chronic kidney disease, (odds ratio = 0.516). In contrast, BMI did not show significant differences after adjustment for comorbidities.

This study identifies muscle volume derived from routine CT scans as a major predictor of survival in patients with severe respiratory infections. The results underscore the potential of AI supported CT-based body composition analysis for risk stratification and clinical decision making, not only for COVID-19 patients but also for all patients over 60 years of age with severe acute respiratory infections. The innovative application of pre-trained deep learning models opens up new possibilities for automated and standardized assessment in clinical practice.

In clinical practice, there is an increasing demand for methods that provide accurate, precise, and real-time body composition measurements while reducing costs and data collection time.

This study aimed to evaluate the validity of an app-based bioelectric impedance analysis (BIA) system for determining body composition compared to a standard BIA method.

A total of 109 individuals with type 1 diabetes (56 men and 53 women), aged 18 to 70 years, with a BMI of 25.0-35.0 kg/m2 and glycated hemoglobin levels of 7.0-9.0%, were recruited. Participants were randomly assigned to start with either the app-based BIA system or the standard method (A-Wave BIA Quantum-V) and then switched to the other method. Anthropometric parameters, blood pressure, and metabolic profiles were also assessed.

The comparison between the two methods showed a negligible difference in resistance but a significant difference in reactance (-4.9%). The degree of consistency was excellent for both resistance (ICC=0.990, p<0.001) and reactance (ICC=0.903, p<0.001), with individual differences falling within acceptable ranges. Furthermore, body composition parameters, including fat-free mass, fat mass, skeletal muscle mass, total body water, and extracellular water, showed only minor and non-significant differences between the two methods.

These findings suggest that the app-based BIA system is a valid and reliable tool for assessing body composition, offering healthcare professionals, including dietitians and nutritionists, a cost-effective and time-efficient alternative to standard BIA methods.

Numerous clinical studies indicate that reproductive factors and physical performance levels are significant predictors of bone mineral density (BMD) in postmenopausal women. However, research on the relationship between these factors and body composition remains limited, and the mediating role of body composition in the impact of these factors on BMD remains unclear. This cross-sectional study aims to explore the correlation between reproductive factors, physical performance levels, BMD, and body composition in postmenopausal women, and to further investigate the mediating role of body composition in these relationships. This study enrolled 102 postmenopausal women meeting predetermined criteria from the osteoporosis outpatient clinic of Huzhou Central Hospital, China. Data were collected through surveys, assessments of physical performance function, and dual-energy X-ray absorptiometry (DXA) measurements. Correlation analysis and multiple linear regression analysis were used to determine the relationships among factors. Mediation analysis was conducted to explore the mediating role of body composition, followed by logistic regression analysis to investigate risk factors for osteoporosis. Gynoid Lean Muscle Mass (GLM) emerged as an independent predictive factor for BMD of spine, hip, and total body, and a protective factor against osteoporosis. Hand grip and 6-meter gait speed were positively correlated with BMD at these skeletal sites and GLM, also serving as protective factors against osteoporosis. Conversely, menarche age and Time taken for 5 sit-ups exhibited negative correlations with BMD and GLM, concurrently representing risk factors for osteoporosis. GLM mediated the relationship between menarche age, hand grip, and hip BMD. Prolonged reproductive periods and enhanced physical performance appear beneficial in mitigating Lean Mass loss, particularly GLM, in postmenopausal women, thereby protecting BMD and effectively preventing osteoporosis.

Hormonal-induced water retention during the menstrual cycle (MC) may affect the estimates of body composition (BC) parameters depending on the MC phase if tissue hydration or volume is part of the BC analysis equations. Given this, MC phase-dependent changes of BC parameters might be expected within females for bioelectrical impedance analysis (BIA) or air-displacement plethysmography (ADP), whereas skin-fold calipometry (CAL) might not be affected. This study aimed to evaluate BC analyses during a regular MC by means of BIA and ADP with CAL serving as a control method in females with or without hormonal contraception with males serving as a control group. In a case-control design with repeated measurements, BC was determined using BIA, ADP, and CAL in 54 participants (age 18-33; BMI 17.0-27.8) divided into females using hormonal contraceptives (HC) (n = 19), females using no-hormonal contraceptives (no-HC) (n = 17), and males (n = 18). BC was assessed on four cycle-related days (menstruation, late follicular, ovulation, and late luteal). There were only small intraindividual BC variations during the MC (CV% 0.5-5.2) and neither significant time effects within any group (p = 0.065-0.939) nor significant time*group interactions (p = 0.151-0.956) for all devices (BIA, ADP, CAL) in any BC parameter. The results indicate that hormonal-induced water retention, if any, during MC had no effect on BC estimates of ADP, BIA, and CAL or were too small to be identified neither in females with HC nor in females with no-HC.

In December 2024, Nutrients launched a new section entitled "Nutrition and Neuro Sciences", with the scope of collecting review and research articles analyzing the impact of nutrition on cognitive function and brain physiology throughout life [...].

Methods used in daily clinical practice for the assessment of the nutritional status in patients with inflammatory bowel disease (IBD) are often based on simple indices and may not be sufficient in the case of minor or early changes. The purpose of this study was to analyze the nutritional status in patients with IBD.

The case-control study included 80 patients with IBD. The control group consisted of 80 healthy subjects matched based on age and gender. Body composition was measured using the electrical bioimpedance method.

Compared to the healthy women, the female patients with IBD had a significantly lower muscle mass (24.4 kg vs. 27.9 kg) and muscle strength (22.4 kg vs. 25.9 kg), as well as a lower MMI (7.8 kg/m2 vs. 9.9 kg/m2). Based on these findings, sarcopenia was diagnosed in 37.5% of the female patients, significantly more often than in the control group. In the group of men, there were no significant differences between the healthy controls and patients in terms of body composition and the prevalence of underweight and sarcopenia.

The patients with IBD were characterized by a poorer nutritional status than the healthy subjects, mainly in terms of fat-free body mass and muscle mass, and consequently a higher incidence of sarcopenia, especially in the female group.

This study aimed to determine the factors related to insufficient weight loss (IWL) following bariatric surgery.

The data for 3456 individuals who had bariatric surgery were obtained prospectively. A bioelectrical impedance analyzer was used to measure body composition changes and compare them between the sufficient (SWL) and IWL groups. The generalized estimated equation approach was used to assess changes in fat mass (FM), fat-free mass (FFM), FFM loss/weight loss percentage (FFML/WL%), and excess weight loss percentage (EWL%). Multivariate logistic regression models were used for IWL to establish independent baseline factors.

IWL was recorded in 8% of the cases. The data analysis revealed substantial differences in the changes in FM%, FFM%, FFML/WL%, and EWL% between the SWL and IWL groups after six months of follow-up. The IWL group demonstrated a greater FFML/WL% (Ptime before & after 6 months < 0.05). An older age, a higher baseline BMI, diabetes mellitus (DM), non-smoking, and sleeve gastrectomy (SG) were the predictors of IWL.

the significant predictors of IWL included older age, a higher baseline BMI, DM, SG, and non-smoking.

Metabolic syndrome (MS) is a risk factor for cardiovascular diseases, and its prevalence is increasing among children and adolescents. This study developed a machine learning model to predict MS using anthropometric and bioelectrical impedance analysis (BIA) parameters, highlighting its ability to handle complex, nonlinear variable relationships more effectively than traditional methods such as logistic regression.

The study included 359 youths from the Korea National Health and Nutrition Examination Survey (KNHANES; 16 MS, 343 normal) and 174 youths from real-world clinical data (66 MS, 108 normal). Model 1 used anthropometric data, Model 2 used BIA parameters, and Model 3 combined both. The eXtreme Gradient Boosting trained the models, and area under the receiver operating characteristic curve (AUC) evaluated performance. Shapley value analysis was applied to assess the contribution of each parameter to the model's prediction.

The AUCs for Models 1, 2, and 3 were 0.75, 0.66, and 0.90, respectively, in the KNHANES dataset, and 0.56, 0.61, and 0.74, respectively, in the real-world dataset. In pairwise comparison, Model 3 outperformed both Model 1 and Model 2 in both the KNHANES dataset (Model 1 vs. Model 3, p = 0.026; Model 2 vs. Model 3, p = 0.033) and the real-world dataset (Model 1 vs. Model 3, p = 0.035; Model 2 vs. Model 3, p = 0.008). Body fat mass was identified as the most significant contributor to Model 3.

The integrated model using both anthropometric and BIA parameters demonstrated strong predictability for pediatric MS, underlining its potential as an effective screening tool for MS in both clinical and general populations.

[Purpose] We aimed to assess the reliability of body composition measurements for individual limbs using a bioimpedance analyzer, with a focus on determining the minimal detectable change for each site. [Participants and Methods] This study included 55 healthy Japanese university students (28 men and 27 women). Each participant underwent two measurements using an InBody S10 body composition analyzer. Intraclass correlation coefficients and minimal detectable change values were calculated for muscle mass, water content, and phase angle at various limb sites. [Results] Muscle mass and water content measurements demonstrated high reliability across all limb sites, with intraclass correlation coefficients ranging from 0.996 to 0.998. Phase angle measurements also showed high reliability for the limbs, with values ranging from 0.936-0.975; however, reliability was lower for the trunk, with a value of 0.854. The minimal detectable change values indicated that detecting differences in trunk phase angle required a larger change than that required for limb sites. [Conclusion] Limb-specific measurements of body composition were highly reliable, showing stable and consistent muscle mass and water content. However, the low reliability of trunk phase angle measurements suggests that factors affecting trunk measurements warrant further exploration for accurate assessment.

This study investigated the association between fat-to-muscle ratio (FMR) and hypertension. A total of 1592 participants aged ≥ 40 years were included. Participants were divided into four groups by quartiles of FMR. Odds ratio (OR) and 95% confidence interval (CI) was calculated using logistic regression models. Restricted cubic spline was applied to examine the correlation of FMR and hypertension. Of 1592 participants, 943 (59.2%) participants had hypertension. Hypertension risk rose with FMR quartiles. Compared to FMR quartile 1, ORs were 1.496 (95% CI: 1.115-2.006), 2.445 (95% CI: 1.840-3.249), and 5.415 (95% CI: 3.993-7.344) for quartiles 2, 3, and 4, respectively (P for trend < 0.001). Adjusted OR in quartile 4 was 3.015 (95% CI: 2.083-4.365). Restricted cubic spline showed a linear relationship between FMR and hypertension. Adding FMR improved hypertension risk model performance (P = 0.006). Subgroup analysis revealed FMR interactions with sex (P = 0.010) and BMI (P < 0.016), with a higher hypertension risk in females and non-obese individuals. Additionally, versus FMR quartile 1, hypertensive individuals in quartiles 2 (OR: 1.370, 95% CI: 0.900-2.085), 3 (OR: 2.055, 95% CI: 1.374-3.073) and 4 (OR: 3.102, 95% CI: 2.055-4.682) exhibited a significantly elevated risk of atherosclerotic cardiovascular disease (ASCVD). In summary, Elevated FMR independently correlated with hypertension risk, especially in women, or even in non-obese individuals. FMR is a valuable tool for identifying populations with higher hypertension risk and assessing ASCVD risk in hypertensive individuals. Body composition warrants consideration in future hypertension risk studies.

Background. The association between malnutrition and poor outcomes in stroke patients has, to date, been evaluated using composite scores derived from laboratory measurements. However, Bioelectrical Impedance Analysis (BIA) and its advanced application, Bioelectrical Impedance Vector Analysis (BIVA), offer a non-invasive, cost-efficient, and rapid alternative. These methods enable precise assessment of body composition, nutritional status, and hydration levels, making them valuable tools in the clinical evaluation of stroke patients. Objective. This study aimed to compare the ordinal distribution of modified Rankin Scale (mRS) scores at 90 days following an acute ischemic stroke, stratifying patients based on their nutritional status at the time of Stroke Unit admission, as determined by the Bioelectrical Impedance Vector Analysis (BIVA) malnutrition parameter. Methods. We conducted a single-centre prospective observational study on all consecutive patients admitted for acute ischemic stroke to our Stroke Unit between 1 April 2024, and 30 September 2024. We applied the IPW (Inverse Probability Weighting) statistical technique and ordinal logistic regression to compare mRS scores in malnourished and non-malnourished patients. Results. Overall, our study included 195 patients with ischemic stroke assessed using BIVA. Of these, 37 patients (19%) were malnourished. After IPW, we found that malnourished patients had significantly lower rates of favorable 90-day functional outcomes (cOR 3.34, 95% CI 1.74-6.41; p = 0.001). Even after accounting for relevant covariates, malnutrition remained an independent predictor of unfavorable outcomes (acOR 2.79, 95% CI 1.37-5.70; p = 0.005), along with NIHSS score at admission (acOR 1.19, 95% CI 1.11-1.28; p < 0.001), intravenous thrombolysis (acOR 0.28, 95% CI 0.15-0.52; p < 0.001), absolute lymphocyte count (cOR 1.01, 95% CI 1.00-1.02; p = 0.027), and albumin concentration (cOR 0.82, 95% CI 0.75-0.89; p < 0.001). Conclusions. Malnutrition, assessed through Bioelectrical Impedance Vector Analysis (BIVA) at the time of admission to the Stroke Unit, is associated with worse clinical outcomes at 90 days following the ischemic cerebrovascular event.

Obesity, measured by body mass index, is a risk factor for cardiovascular disease. However, the role of body composition, including body fat percentage and lean body mass (LBM), in cardiovascular outcomes has not been well studied in patients with coronary artery disease (CAD). This study aims to evaluate the association of body composition with cardiovascular outcomes and all-cause mortality in patients with CAD.

Body composition was obtained via bioelectrical impedance analysis from 1291 patients with CAD before starting cardiac rehabilitation. Patients were divided into quintiles by body composition and analyzed in total and after sex stratification. All-cause mortality and a composite of major adverse cardiovascular events, including acute coronary syndrome, coronary revascularization, heart failure hospitalization, and stroke, were primary study outcomes. In the total cohort adjusted analyses, body mass index, body fat percentage, and LBM were not predictors of all-cause mortality or major adverse cardiovascular events. In sex-stratified analyses, among women, the third LBM quintile was associated with decreased risk of all-cause mortality compared with the lowest LBM quintile (adjusted hazard ratio, 0.07 [95% CI, 0.01-0.57]; P=0.01). No other body composition variables were associated with all-cause mortality or major adverse cardiovascular events in either sex.

In women with CAD, moderate LBM was associated with lower mortality when compared with low LBM, whereas body fat percentage and body mass index were not associated with mortality or major adverse cardiovascular events in either sex. Future research studying the implications of changes in body composition on outcomes in men and women with CAD is warranted.

Sarcopenic obesity (SO), characterized by the coexistence of obesity and sarcopenia, is an increasingly prevalent condition in aging populations, associated with numerous adverse health outcomes. We aimed to identify and validate an explainable prediction model of SO using easily available clinical characteristics.

A preliminary cohort of 1,431 participants from three community regions in Ziyang city, China, was used for model development and internal validation. For external validation, we utilized data from 832 residents of multi-center nursing homes.

The diagnosis of SO was based on the European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) criteria. Five machine learning models (support vector machine, logistic regression, random forest, light gradient boosting machine, and extreme gradient boosting) were used to predict SO. The performance of these models was assessed by the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) approach was used for model interpretation.

After feature reduction, an 8-feature model demonstrated good predictive ability. Among the five models tested, the support vector machine (SVM) model performed best in SO prediction in both internal (AUC = 0.862) and external (AUC = 0.785) validation sets. The eight key predictors identified were BMI, gender, neck circumference, waist circumference, thigh circumference, time to full tandem standing, time to five-times sit-to-stand, and age. SHAP analysis revealed BMI and gender as the most influential predictors. To facilitate the utilization of the SVM model in clinical setting, we developed a web application ( https://svcpredictapp.streamlit.app/ ).

We developed an explainable machine learning model to predict SO in aging community and nursing populations. This model offers a novel, accessible, and interpretable approach to SO prediction with potential to enhance early detection and intervention strategies. Further studies are warranted to validate our model in diverse populations and evaluate its impact on patient outcomes when integrated into comprehensive geriatric assessments.

This study aims to explore the combined risk of metabolic syndrome (MetS) and low fat-free mass (FFM) on an individual's disability-free survival (DFS). Disability is defined as a composite of dementia, physical disability, and mortality.

Using data from the Korean Genome and Epidemiology Study, we divided 3721 participants aged 40-69 years based on their MetS status and FFM index (FFMI) score. Kaplan-Meier survival analysis and Cox regression were used to analyze differences in DFS between the four groups.

From 108 events, MetS group had significantly shorter DFS than the non-MetS group regardless of FFMI (p < 0.0001). After adjusting other potential confounding variables, the MetS group had a higher risk of shortened DFS regardless of FFMI, and the MetS group with low FFMI had a 2.06-fold increased risk compared to the non-MetS group with high FFMI (p < 0.001). Older age and lower income were also associated with higher risk of shorter DFS (p < 0.001).

The combination of MetS and low FFMI contribute to a cumulative risk of shortened DFS. Community nurses can perform MetS screening and body composition assessment to predict and control the risk of developing disability over time.

We aimed to develop and validate a novel prediction model for osteoporosis based on serotonin, fat-soluble vitamins, and bone turnover markers to improve prediction accuracy of osteoporosis.

Postmenopausal women aged 55 to 65 years were recruited and divided into three groups based on DXA (normal, osteopenia, and osteoporosis). A total of 109 participants were included in this study and split into healthy (39/109, 35.8%), osteopenia (35/109, 32.1%), and osteoporosis groups (35/109, 32.1%). Serum concentrations of serotonin, fat-soluble vitamins, and bone turnover markers of participants were measured. Stepwise discriminant analysis was performed to identify efficient predictors for osteoporosis. The prediction model was developed based on Bayes and Fisher's discriminant functions, and validated via leave-one-out cross-validation. Normal and empirical volume under the receiver operating characteristic (ROC) surface (VUS) tests were used to evaluate predictive effects of variables in the prediction model.

Significant variables including oestrogen (E2), total procollagen type 1 amino-terminal propeptide (TP1NP), parathyroid hormone (PTH), BMI, vitamin K, serotonin, osteocalcin (OSTEOC), vitamin A, and vitamin D3 were used for the development of the prediction model. The training accuracy for normal, osteopenia, and osteoporosis is 74.4% (29/39), 80.0% (28/35), and 85.7% (30/35), respectively, while the total training accuracy is 79.8% (87/109). The internal validation showed excellent performance with 72.5% testing accuracy (72/109). Among these variables, serotonin and vitamin K exert important roles in the prediction of osteoporosis.

We successfully developed and validated a novel prediction model for osteoporosis based on serum concentrations of serotonin, fat-soluble vitamins, and bone turnover markers. In addition, interactive communication between serotonin and fat-soluble vitamins was observed to be critical for bone health in this study.

This study aimed to identify factors associated with the 10-year risk of fatal and non-fatal cardiovascular disease (CVD) events in apparently healthy individuals aged 40-69 years.

148 patients without established CVD were divided into low-risk (70 patients) and high-risk (78 patients) groups based on their CVD risk in SCORE2.

High-risk patients presented with higher left atrial volume index (LAVI) (p = 0.003), left ventricular mass index (LVMI) (p < 0.001), and ratio of peak velocity of early diastolic transmitral flow to peak velocity of early diastolic mitral annular motion (E/E') (p < 0.001) but lower oxygen uptake at anaerobic threshold (VO₂AT) (p = 0.02) and maximal oxygen uptake (VO2max) (p = 0.008), compared to their counterparts. High-risk patients also had higher values of high-sensitivity cardiac troponin T (hs-cTnT) (p < 0.001) and N-terminal prohormone of brain natriuretic peptide (NT-proBNP) (p<0.001), and lower level of glomerular filtration rate (GFR) (p < 0.001). In a multiple logistic regression model, E/E' > 6.75 cm/s (OR 3.9, 95% CI: 1.5-10.3; p = 0.004) andhs-cTnT > 4.8 pg/ml (OR 6.02, 95% CI: 2.3-15.8; p < 0.001) were independently associated with high and very high CVD risk. SCORE2 (%) correlated positively with metabolic age (R Spearman = 0.79; p < 0.001), hs-cTnT (R = 0.6; p < 0.001), and NT-proBNP (R = 0.5; p < 0.001) and negatively with GFR (R = -0.5; p < 0.001) and VO2max (ml/min/kg) (R = -0.3; p < 0.001).

Elevated E/E' and higher hs-cTnT level independently predict high and very high risk in SCORE2. The increasing 10-year cardiovascular disease risk correlates with higher metabolic age, higher levels of NT-proBNP and hs-cTnT, and lower level of GFR.

Bioelectrical impedance analysis (BIA) has a wide range of applications. For over 25 years, it has primarily been utilized for assessing body composition. This method is non-invasive, portable, widely available, cost-effective, and user-friendly, offering the advantage of repeatability and minimal dependence on patient cooperation. BIA measures the impedance of the whole body, specifically the body's resistance to alternating current. In postpartum women, who undergo significant physiological changes following childbirth, BIA can serve as a valuable diagnostic and monitoring tool. It is commonly employed to track body weight and fat reduction, and it facilitates the differentiation of fat mass, muscle mass, and body water content. This enables the customization of nutritional plans and the development of individualized training regimens tailored to the patient's health status. Additionally, BIA aids in the assessment of hydration status, which is particularly critical during the postpartum period when women often experience fluid retention. Furthermore, optimal hydration is essential for lactation and maintaining favorable conditions for breastfeeding. BIA is also invaluable for evaluating nutritional status, micronutrient balance, and preventing both overweight and malnutrition. Moreover, BIA supports physical recovery by monitoring muscle mass, thereby assisting in the assessment of pelvic floor muscle regeneration following childbirth.

Lateralized overgrowth (LO) is characterized by excessive growth of one side of the body compared to the other. LO can present as isolated (ILO) or within syndromes, like Beckwith-Wiedemann Spectrum (BWSp) and PIK3CA-related overgrowth spectrum (PROS). Currently, the diagnosis of LO relies on clinical evaluation and lacks a standardized method. In this study, we evaluated total body dual-energy X-ray absorptiometry (TB-DXA) as a potential tool for standardizing LO assessment. Patients with LO underwent both clinical evaluation and TB-DXA. TB-DXA data, including total mass, mass of the three main tissue components (adipose, muscle, and bone), total mass discrepancy ratio, relative tissue composition, and discrepancy of relative tissue composition were calculated and compared with clinical findings. Differences between affected regions and the contralateral side were assessed. A total of 46 patients (61% PROS, 24% BWSp, 15% ILO) were included in this study. TB-DXA detected overgrowth regions aligned with clinical evaluation in 91% of cases and was able to identify localized overgrowth even when clinically overlooked. Additionally, TB-DXA revealed differences in tissue composition between affected and unaffected regions for symmetrical body areas, with these differences varying by diagnostic subgroup. Different patterns of tissue composition overgrowth were observed among different conditions, with PROS predominantly showing adipose tissue overgrowth, while BWSp/ILO mainly osteo-muscular overgrowth. TB-DXA is an accurate, safe, and reproducible tool in the clinical setting providing an objective method for identifying and quantifying LO. It offers valuable guidance for clinicians in the diagnosis and management of LO.

3D body scan has been adopted for body composition assessment due to its ability to accurately capture body shape measurements. However, the complexity of mesh representation and the lack of fine-shape descriptors limit its applications in body fat percentage analysis. Most studies rely on algorithms applied to anthropometric values derived from 3D scans, such as multiple girth measurements, which fail to account for the body's detailed shape. To address these issues, we explore the feasibility of using point cloud representation. However, few existing point-based methods are aimed at the human body or regression tasks. In this study, we introduce a new model, D3BT, which utilizes a transformer-based network on the body point cloud to efficiently learn shape information for regional and global fat percentage regression tasks. The model dynamically divides the points into voxels for enhanced transformer training, providing higher density and better alignment across different subjects, which is more suitable for body shape learning. We evaluate various models for predicting body fat percentage from 3D body scans, using ground truth data from dual-energy X-ray absorptiometry (DXA) reports. Compared to traditional methods that depend on anthropometric measurements and other point-based approaches, the proposed model shows superior results. In extensive experiments, the model reduces the Root Mean Square Error (RMSE) by an average of 10.30% and achieves an average R-squared score of 0.86.

Postoperative pancreatic fistula (POPF) remains one of the most severe complications after pancreatic surgery. The methods for predicting pancreatic fistula are limited. We aimed to investigate the predictive value of body composition parameters measured by preoperative bioelectrical impedance analysis (BIA) on the development of POPF.

A total of 168 consecutive patients undergoing pancreatic surgery from March 2022 to December 2022 at our institution were included in the study and randomly assigned at a 3:2 ratio to the training group and the validation group. All data, including previously reported risk factors for POPF and parameters measured by BIA, were collected. Risk factors were analyzed by univariable and multivariable logistic regression analysis. A prediction model was established to predict the development of POPF based on these parameters.

POPF occurred in 41 of 168 (24.4%) patients. In the training group of 101 enrolled patients, visceral fat area (VFA) (odds ratio [OR] = 1.077, P = 0.001) and fat mass index (FMI) (OR = 0.628, P = 0.027) were found to be independently associated with POPF according to multivariable analysis. A prediction model including VFA and FMI was established to predict the development of POPF with an area under the receiver operating characteristic curve (AUC) of 0.753. The efficacy of the prediction model was also confirmed in the internal validation group (AUC 0.785, 95% CI 0.659-0.911).

Preoperative assessment of body fat distribution by BIA can predict the risk of POPF after pancreatic surgery.

Cachexia affects up to 60% of patients with lung cancer, with its prevalence rising up to 80% in advanced stages of disease. In approximately 20% of cases, it is the primary cause of mortality. Five studies, including a total of 4467 patients, across range of cancer types reported data on weight gain in cancer patients undergoing chemotherapy. Across all five studies, an average of 18.3% of patients experienced weight gain > 5% (816 out of 4467 patients). The frequency of weight gain > 5% was highest among breast cancer patients, 18.9% in Pedersini et al (n = 169) and 33.0% in Sella et al (n = 687). In NSCLC patients, weight gain was reported in 18.3% in patients in Patel et al (n = 2301) and 11.7% in Roeland et al (n = 1030). In contrast, colorectal cancer patients showed only 5.7% of weight gain > 5% (Zutphen et al, n = 280). Additionally, weight loss > 5% was reported in 15.1% of breast cancer patients and 28.3% of colorectal cancer patients. Despite weight loss being quantified as a common endpoint in clinical trials focused on cancer cachexia, there is limited data on the impact of weight gain as a marker of a positive outcome among cancer patients. Studies have shown that weight gain of more than 5% within 3 months in NSCLC patients can be associated with improvement in overall survival (OS) and progression-free survival (PFS) scores. In this post hoc analysis by Roeland et al., the authors defined different percentage cut-off values for maximum weight gain among patients with non-small cell lung cancer within 3 months of starting platinum-based chemotherapy. Among all categories, namely, weight gain > 0%, > 2.5% and > 5%, a significant benefit in overall and progression-free survival was seen and was comparable among all groups. These findings highlight the clinical significance of incorporating strategies that encourage weight gain and to prevent weight loss at the least among cancer patients. Along with further delving into the prognostic value of weight gain and developing methods to encourage this response among cancer patients, future studies should use standardized assessment tools to identify weight gain that could be attributed to underlying pathologic processes such as oedema and congestion. We also suggest that monitoring and reporting of weight changes should be done in all cancer trials.

Nutritional support in critically ill patients has been acknowledged as a pillar of ICU care, playing a pivotal role in preserving muscle mass, supporting immune function, and promoting recovery during and after critical illness. Providing effective nutritional support requires adapting it to the patient's diagnosis, unique characteristics, and metabolic state to minimize the risks of overfeeding or underfeeding while mitigating muscle loss. This level of care requires a comprehensive nutritional assessment and the establishment of a nutrition-focused protocol. Regular, consistent and detailed nutritional evaluation can influence both therapeutic decisions and clinical interventions, thus ensuring that the specific needs of critically ill patients are met from the acute phase through their entire recovery process. Bioelectrical impedance analysis (BIA) is increasingly recognized as a valuable tool for enhancing nutritional care in critically ill patients. By delivering precise, real-time insights into key aspects of body composition, BIA is thought to provide clinicians with a more comprehensive understanding of the complex physiological changes that occur during critical illness. This narrative review highlights the potential of BIA in offering these precise assessments, facilitating the development of more accurate and personalized nutritional strategies for critically ill patients. If BIA can reliably assess dynamic shifts in hydration and tissue integrity, it holds the promise of further advancing individualized care and optimizing clinical outcomes in this vulnerable population.

Obesity is a public health problem worldwide, and body composition assessment is a very important diagnostic tool. Bioelectrical Impedance Analysis (BIA) is a fast, non-invasive, relatively low-cost, and user-friendly technique; however, to obtain greater validity of the estimates, the predictive equations used must be population specific. Thus, the objectives of this study were: (1) to test the validity of four BIA equations used for fat-free mass (FFM) estimation and one model for fat mass (FM) estimation in adults with overweight or obesity; (2) develop and cross-validate new equations to estimate FFM to adults with overweight or obesity, and specific for those with obesity.

The non-probabilistic sample included 269 individuals, 53.2% with overweight and 46.8% with obesity, aged 18-79 years, randomly divided into two groups: development (n = 178) and cross-validation (n = 91), stratified by sex and classification as overweight or obese. The criterion technique was dual-energy-x-ray absorptiometry (DXA), whereas a tetrapolar single-frequency BIA equipment was used as the alternative method. Paired t-test, multiple regression, concordance correlation coefficient, and Bland-Altman analysis were used.

Most existing equations were not valid and new equations were derived: (1) for individuals with overweight or obesity: CCC = 0.982; r2 = 0.95; standard error of estimate (SEE) = 2.50 kg; limits of agreement (LOA) = -5.0 to 4.8; and (2) specific for individuals with obesity: CCC = 0.968; r2 = 0.94; SEE = 2.53 kg; LOA = -5.3 to 5.2. No FFM differences were observed between the new models and the reference method (p > 0.05).

The new proposed models provide valid options to estimate FFM in an adult population with overweight/obesity.

There is a growing need to evaluate the agreement between the field methods and integrate artificial intelligence (AI) using two-dimensional (2D) photos for enhanced real-world analysis. This study evaluated the agreement between AI-2D photos and the clinical reference method, dual-energy X-ray absorptiometry (DXA) to estimate the body fat percentage (BFP). Other methods were also investigated, including skinfolds, A-mode ultrasound, and bioelectrical impedance analysis (BIA). This cross-sectional study was conducted on 1273 adults of both sexes. The Bland-Altman plots, Lin's Correlation Coefficient of Agreement (CCC), and error analyses were calculated. AI-2D photos demonstrated substantial agreement with DXA presenting the highest agreement (CCC ≥ 0.96) among all the investigated methods. InBody-270 and Omron HBF-514 BIA devices showed moderate agreement (CCC = 0.90 to 0.95) for all participants, age groups >30 years, and body mass index >25 kg/m2. AI-2D photos can be interchangeable with DXA, providing a practical, accessible alternative and an easy-to-use system for BFP estimation.

This study aimed to evaluate the association between substituting 10, 30, and 60 min/day of physical activity and sitting time with obesity indicators among workers. It is a cross-sectional study involving 394 adults (76.6% women) from São Paulo, Brazil. Physical activity and sitting time (min/day) were measured using the short version of the International Physical Activity Questionnaire (IPAQ) to determine physical activity levels. Obesity indicators included body mass index (BMI; kg/m²), waist circumference (cm), and body fat (kg). Isotemporal substitution models were analyzed using multivariate linear regression, adjusted for sociodemographic variables. Participants reported median values of 150.0 min/day (IQR: 60.0-360.0) for walking and 445.7 min/day (IQR: 317.1-565.7) for sitting time. The median values for BMI, waist circumference, and body fat were 27.1 kg/m², 91.0 cm, and 23.5 kg, respectively. Substituting 10 min/day of sitting time for 10 min/day of walking was associated with waist circumference (β: -0.216: 95% CI: -0.041; -0.007). In addition, substituting 60 min/day of sitting time for moderate physical activity (β -0.206; 95% CI: -0.123; -0.008) was negatively associated with body fat. The equivalent substitution of different periods of sitting time for more active behaviors was favorably associated with obesity indicators. These findings suggest that lifestyle interventions based on replacing sitting time with physical activity can reduce the obesity epidemic in adults.

Accurate and efficient body composition assessment is essential for diagnosing and monitoring childhood obesity in clinical and research settings. This study evaluated the agreement between bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) in assessing body composition and its changes during a long-term lifestyle intervention in children and adolescents with obesity. We hypothesized that BIA is comparable to DXA in tracking longitudinal changes in body composition in this population. The study included 92 children and adolescents 5 to 17 years of age (mean 10.5 ± 2.9 years) classified as obese at the time of measurement (body mass index-standard deviation score ≥ 2 SD). From January 1, 2014, to December 31, 2017, they participated in a lifestyle intervention at Randers Regional Hospital. Body composition was assessed using BIA and DXA at baseline and follow-up (mean 20.5 ± 9.3 months). Bland-Altman plots, correlation- and concordance analyses were used to evaluate the agreement between methods. Bland-Altman plots demonstrated wide limits of agreement without significant bias for longitudinal changes in fat mass (FM), FM%, fat-free mass (FFM), and FFM% (0.39 kg (CI -0.14, 0.93), -0.39% (CI -1.06, 0.26), -0.21 kg (CI -0.73, 0.31), and 0.39% (CI -0.27, 1.05), respectively). Correlations between methods for changes in FM, FM%, FFM, and FFM% were 0.93, 0.83, 0.88, and 0.83, respectively. In cross-sectional assessments, BIA underestimated FM and FM% and overestimated FFM and FFM% compared to DXA. Conclusively, BIA effectively monitors longitudinal changes in body composition at a group level in a pediatric population with obesity. However, caution is needed for individual assessments.

The positive impact of prunes on body measurements and blood pressure has been suggested in previous research.

The purpose of this extensive meta-analysis and systematic review was to examine the effects of adult consumption of prunes on anthropometric parameters and blood pressure.

To identify relevant randomized controlled trials (RCTs) (till August 9th, 2024), databases from ISI Web of Science, Scopus, and PubMed/Medline were searched. The current systematic review and meta-analysis covered 14 studies that met the eligibility requirements. The weighted mean difference was used to compute the effect size. The Cochrane Q test and I2 index were used to measure study heterogeneity. The random effects model was utilized to get the overall effect magnitude.

The meta-findings analysis's showed that eating prunes had no appreciable effects on, body mass index (BMI) (p = 0.267), waist circumference (WC) (p = 0.562), fat mass (FM) (p = 0.840), systolic blood pressure (SBP) (p = 0.338), or diastolic blood pressure (DBP) (p = 0.233). However, we observed significantly reduction in body weight (p < 0.001) after prunes consumption.

Our findings showed that the consumption of prune leads to a significant reduction in body weight, but the results indicated non-significant effects on blood pressure, BMI, WC and FM.

Background: This study sought to assess how body mass (BM) and body composition in post-COVID-19 elderly adults were affected by 8 weeks of resistance training. An additional goal was to determine the agreement between Bioelectrical Impedance Analysis (BIA) and Dual Energy X-Ray Absorptiometry (DXA) in elderly people. Methods: Participants were randomly assigned to an intervention Group, which engaged in 8 weeks of resistance training, and a Control Group, which was advised to maintain their usual activity levels. Before and after the intervention, the body composition was analyzed via the BIA and DXA methods. Results: We found no statistically significant changes in BM or body composition following resistance training. BIA was found to overestimate the participants' baseline BM and fat-free mass (FFM) and to underestimate the fat mass (FM), compared to the DXA method. There were no significant differences in intervention-induced changes in FM and FFM measured by BIA and DXA. Conclusions: Moderate intensity resistance training lasting 8 weeks was not found to be a sufficient stimulus to improve BM and body composition in post-COVID-19 elderly adults. We also conclude that BIA may serve as a viable alternative to DXA for measuring longitudinal changes in body composition in elderly people.

Aims/Background This study expanded the existing literature on obesity and distortion of body image by examining subjective and objective body type among young medical workers, specifically investigating whether fat percentage independently influences body type cognitive bias. Methods We recruited 264 participants (41.29% male, mean age 26.2 ± 3.1 years, mean body mass index (BMI) 21.7 ± 3.1 kg/m2) at a comprehensive hospital in central China. The questionnaire responses of the enrolled participants concerning basic information and body type self-assessment were extracted. Their weights and body compositions were evaluated by the bioelectrical impedance analysis technique (BIA). Objective indicators included measured weight, BMI, fat percentage, waist circumference (WC) and waist-to-hip ratio (WHR). Results Based on this sample, the prevalence rate of body type cognitive bias was 28.79%. For women, the univariate regression analysis showed that fat percentage was significantly correlated with body type cognitive bias (odds ratio [OR] 1.107, 95% confidence interval [CI] 1.033-1.185, p = 0.004). BMI (OR 1.303, 95% CI 1.098-1.546, p = 0.002) and WC (OR 1.109, 95% CI 1.043-1.180, p = 0.001) might also be positively associated with body type cognitive bias. After adjusting for age, BMI, WHR, and job position, the risk of body type cognitive bias decreased with fat percentage up to the first inflection point (26.8%) (OR 0.78, 95% CI 0.62-0.98, p = 0.036), and then it increased up to the second inflection point (33.0%). When fat percentage exceeded 33.0%, the relationship lost statistical significance. No significant relationships were found for men. Conclusion Fat percentage is an independent, nonlinear factor influencing women's body type cognitive bias. Gender and fat percentage should be considered when establishing weight management intervention strategies to prevent obesity from becoming a public health problem.

Metabolic syndrome (MetS) and sarcopenia together pose significant health risks, increasing frailty, falls, and fractures in older adults. This study compared muscle mass measurements obtained using two different dual-energy X-ray absorptiometry (DXA) machines and bioelectrical impedance analysis (BIA), and evaluated the accuracy of these measurements in these older adults.

In this prospective multicenter cohort study, patients aged ≥ 65 years with MetS had their muscle mass assessed using both BIA and DXA. Two DXA devices, Hologic Horizon and GE Lunar Prodigy, were used as clinical standards for sarcopenia diagnosis. Statistical analyses generated equations for transforming BIA results to match those from DXA, enhancing comparability.

Participants had a mean age of 73.2 ± 5.3 years. The mean appendicular skeletal muscle mass (ASM) measured by BIA and DXA was 19.7 ± 3.1 kg (BIA) and 18.1 ± 2.9 kg (DXA) for males, and 13.7 ± 2.2 kg (BIA) and 12.6 ± 1.8 kg (DXA) for females. Device-specific equations were developed to estimate DXA-measured ASM based on BIA results. These equations are presented for all participants and for each DXA device, highlighting significant differences in prediction models between the two DXA machines.

The study developed device-specific equations for sarcopenia diagnosis in older adults with MetS, highlighting substantial differences between Hologic and GE Lunar devices. While BIA may offer a more accessible alternative to DXA, the variation in prediction formulas underscores the need for standardized equipment to ensure consistency in sarcopenia diagnosis.

Osteosarcopenia is a combination of sarcopenia and osteoporosis that increases mortality rates among older people compared with either alone. This study aimed to identify the contribution of osteosarcopenia to the development and severity of dysphagia.

We retrospectively reviewed the medical charts of 211 patients aged ≥ 65 years who were referred to the dysphagia rehabilitation team. Based on Functional Oral Intake Scale (FOIS) scores, we classified the patients with (FOIS scores 1-5) and without (FOIS scores 6, 7) dysphagia as Type A and those with (FOIS scores 1, 2) and without (FOIS score 3-7) enteral feeding as Type B. Based on chest computed tomography (CT) findings we then defined patients with T4 (MI) and pectoralis (PMI) muscle indexes, L1 attenuation, and T4MI, PMI, and L1 attenuation below the cutoff values as having sarcopenia, osteoporosis, and osteosarcopenia, respectively.

The FOIS scores were significantly lower among patients with osteosarcopenia than among those without sarcopenia or osteoporosis. Moreover, PMI and FOIS scores significantly and positively correlated, and PMI was significantly lower in the group with, than without, enteral feeding. Osteoporosis and osteosarcopenia were significant in the patients who were fed enterally (p = 0.032 and 0.047, respectively).

Patients with sarcopenia and osteoporosis undergoing swallowing rehabilitation tended to have severe dysphagia that required much medical attention.

A commercial three-dimensional optical (3DO) scanning system was reported to be used in body composition assessment. However, the applicability in Chinese adults has yet to be well-studied.

This secondary analysis was based on a 16-week weight-loss clinical trial with an optional extension to 24 weeks. Waist and hip circumference and body composition were measured by 3DO scanning at each follow-up visit during the study. Bioelectrical impedance analysis (BIA) was also performed to confirm the reliability of 3DO scanning at each visit. We used Lin's concordance correlation coefficients (CCC) to evaluate the correlation between the two methods above-mentioned. Bland-Altman analysis was also performed to evaluate the agreement and potential bias between different methods.

A total number of 70 Chinese adults overweight and obese (23 men and 47 women, aged 31.8 ± 5.8 years) were included in the analysis, which resulted in 350 3DO scans and corresponding 350 BIA measurements. The percent body fat, fat mass, and fat-free mass were 33.9 ± 5.4%, 26.7 ± 4.6 kg, and 50.3 ± 8.7 kg before the trial by 3DO scanning. And they were 30.5 ± 5.8%, 22.5 ± 4.7 kg, and 49.4 ± 8.3 kg after 16 weeks of the trial. Compared with BIA, 3DO scanning performed best in the assessment of fat-free mass (CCC = 0.89, 95%CI: 0.86, 0.90), then followed by fat mass (CCC = 0.76, 95%CI: 0.71, 0.80) and percent body fat (CCC = 0.70, 95%CI: 0.64, 0.75). Subgroup analysis showed that 3DO scanning and BIA correlated better in women than that in men, and correlated better in measuring fat-free mass in participants with larger body weight (BMI ≥28.0 kg/m2) than those with smaller body weight (<28.0 kg/m2).

3DO scanning is an effective technology to monitor changes in body composition in Chinese adults overweight and obese. However its accuracy and reliability in different ethnicities needs further exploration.

Chronic kidney disease (CKD) is a serious health concern with an estimated prevalence of about 13.4% worldwide. It is cause and consequence of various comorbidities, including cardiovascular diseases. In parallel, common pathological conditions closely related to ageing and unhealthy dietary habits increase the risk of CKD development and progression, including type 2 diabetes and obesity. Among these, obesity is either independent risk factor for new onset kidney disease or accelerates the rate of decline of kidney function by multiple mechanisms. Therefore, the role of diets aimed at attaining weight loss in patients with obesity is clearly essential to prevent CKD as to slow disease progression. Various dietary approaches have been licensed for the medical dietary therapy in CKD, including low-protein diet and Mediterranean diet. Interestingly, emerging evidence also support the use of low-carbohydrate/ketogenic diet (LCD/KD) in these patients. More specifically, LCD/KDs may efficiently promote weight loss, improve metabolic parameters, and reduce inflammation and oxidative stress, resulting in a dietary strategy that act globally in managing collateral conditions that are directly and indirectly related to the kidney function.

This consensus statement from the Italian Society of Endocrinology (SIE), working group of the Club Nutrition - Hormones and Metabolism; the Italian Society of Nutraceuticals (SINut), Club Ketodiets and Nutraceuticals "KetoNut-SINut"; and the Italian Society of Nephrology (SIN) is intended to be a guide for Endocrinologist, Nutritionists and Nephrologist who deal with the management of patients with obesity with non-dialysis CKD providing a practical guidance on assessing nutritional status and prescribing the optimal diet in order to best manage obesity to prevent CKD and its progression to dialysis.

Type 1 Diabetes Mellitus (DM1) affects a small percentage of the population. Nevertheless, its prevalence is currently growing with alarming data on uncontrolled cases. The importance of body composition and Phase Angle (PA), assessed by Bioelectrical Impedance (BIA), in long- term DM1 patients lies in the fact that alterations in cellular integrity and body compartments may affect risk profiles and metabolic control. The objective of this study was to compare PA and body composition parameters between adults with DM1 and healthy controls.

A comparative study was carried out in a public university outpatient clinic including a cohort of adult patients of both sexes diagnosed with DM1 and healthy controls matched by age and sex in a 2:1 ratio. Anthropometric measurements included weight, height and BMI. Using the raw BIA data of Resistance and Reactance, fat-free mass (FFM), fat mass (FM), fat-free mass index (FFMI), fat mass index (FMI), PA and standardized PA (SPA) were calculated. Means or medians were compared between the groups. Regression models were used to identify distinguishing characteristics of the groups and associations within the DM1 group (i.e. glycated hemoglobin (HbA1c), disease duration, presence of microvascular complications, capillary blood glucose, BMI and FMI).

88 patients with DM1and 46 healthy controls were evaluated. PA (6.05 vs. 6.85, p = 0.000) and SPA (-1.47 vs. -0,37, p = 0.000) were lower in patients with DM1 compared to healthy controls. People with DM1 displayed higher adiposity (%FM = 29.6 vs. 27.6, p = 0.016; FMI = 7.00 vs. 6.33, p = 0.016) and lower %FFM compared to healthy controls. Most of the differences were maintained after sex stratification; however, men with DM1 showed a lower FFMI than male controls (18.2 vs. 20.16, p = 0.029).

Patients with DM1 present lower PA than healthy controls, which may be related to worse cell membrane integrity. Significant body composition differences between the groups and between sexes were identified, with data showing greater adiposity in women with DM1 and men displaying lower muscle mass. These findings suggest the importance of including PA and body composition evaluations in the follow-up of patients with DM1. The ultimate goal is to obtain a better metabolic control and, consequently, a better prognosis.

In this study, we evaluated the contribution of the putative genetic factors into the established associations between selected circulating adipokine levels, body composition measurements, and low-back-pain-related disability scores (LBP_DS). A total of 1078 individuals from 98 nuclear families (with 1 to 11 siblings per family) were examined. A detailed self-report questionnaire was used to collect LBP disability data; body composition (fat, skeletal muscle mass, and extracellular water (ECW)) was assessed using the bioimpedance method; plasma levels of adipokines were measured by ELISA. Pedigree-based statistical analysis methods were used, including family-based variance component analysis (VCA) and principal phenotype analysis (PPA), to estimate the contribution of potential genetic and environmental factors. The VCA revealed a significant additive genetic component in LBP_DS and for the selected body composition phenotypes and adipokines. The study also revealed that both adipokines (GDF-15, chemerin, and follistatin) and body composition variables (BMI, fat mass/weight, waist circumference, and ECW) were genetically correlated with LBP_DS. Next, PPA generated two synthetic phenotypes: PPCT (combining cytokines) and PPBC (combining body composition variables). There was no significant correlation between the putative genetic factors underlying the created PPs. However, each of them displayed a significant genetic correlation with LBP_DS. These findings indicate that genetic factors that are assumingly common for several adipokine variations and several body composition measurements, respectively, presumably have a pleotropic genetic influence on the LBP_DS variation, independently from one another. This, in turn, suggests that the alleged genetic factors employing pleiotropic effects on LBP_DS have a complex and probably non-overlapping composition.

Although body composition (BC) has been associated with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), there is little evidence of differences in BC in patients with MASLD regarding body mass index (BMI). The aim of this study was to determine differences in BC in terms of BMI and metabolic comorbidities in patients with MASLD.

It is a cross-sectional study with patients who attended the check-up unit. Liver steatosis was evaluated by controlled attenuation parameter, and patients were classified into five groups according to BMI, presence of MASLD, and metabolic characteristics: <25 kg/m2 non-MASLD; <25 kg/m2-MASLD; Overweight-MASLD; Metabolically Healthy Obese (MHO)-MASLD; and Metabolically Unhealthy Obese (MUO)-MASLD. BC was assessed by bioelectrical impedance and a Bioimpedance Vectorial Analysis (BIVA) was carried out. Differences in BC were analyzed by a One-Way ANOVA test. Univariate and multivariate analyses were performed for factors associated with abnormal BC.

A total of 316 patients were included. 59% (n = 189) were male, with a mean age of 49 ± 10 years. Fat% significantly higher according to BMI was not different between BMI <25 kg/m2-MASLD and Overweight-MASLD groups. Skeletal muscle mass (SMM) was significantly lower in obesity groups with respect to overweight and normal weight groups (p < 0.05); however, no differences were observed in the post-hoc analysis. Extracellular Water/Intracellular Water ratio was significantly higher in the MHO-MASLD group and MUO-MASLD group compared with the BMI <25 kg/m2 non-MASLD group and with the BMI <25 kg/m2-MASLD group. Abnormal Waist Circumference (WC) and liver steatosis were independent factors associated with abnormal BC.

BC in MASLD patients varies according to BMI increase; changes could be explained by loss of SMM and not necessarily by the presence of metabolic abnormalities. High WC and the presence of steatosis are independent factors associated with altered BC.

Micronutrient deficiencies are common in inflammatory bowel disease (IBD). The aim of this study was to evaluate micronutrient deficiencies and identify muscular status of patients with IBD. From June 2019 to October 2021, a total of 105 patients with IBD were enrolled prospectively. To obtain objective data, micronutrients were measured in the patients' serum, and body composition analysis was performed using bioelectrical impedance analysis. There were 51 patients with ulcerative colitis (UC) and 54 with Crohn's disease (CD), while the gender ratio (M: F) was 54:51. The average age was 37 ± 18 years, which was significantly lower in patients with CD than UC (29 ± 16 vs. 45 ± 16, p < 0.001). Iron and magnesium were lower in patients with CD compared to UC, respectively (63.3 ± 42.5 vs. 82.8 ± 44.0 µg/dL, p = 0.024, 2.08 ± 0.15 vs. 2.15 ± 0.19 mg/dL, p = 0.036). Vitamin D levels showed insufficiency in patients with UC and deficiency (below 20 ng/mL) in patients with CD (20.1 ± 10.6 vs. 19.0 ± 9.9 ng/mL, p = 0.567). In the UC and CD patient groups, skeletal muscle index (SMI) and adjusted skeletal muscle mass were lower in patients with CD compared to UC (SMI: 32.8 ± 4.7 vs. 35.8 ± 5.5%, p < 0.004, adjusted skeletal muscle: 7.0 ± 1.5 vs. 8.2 ± 1.9 kg/m2, p < 0.001). In conclusion, decreased trace elements, specifically iron, magnesium, and vitamin D, as well as skeletal muscle mass were observed to be prominent in patients with CD as compared to UC.