Bioelectrical Impedance Analysis Data Research Articles

Effect of peritoneal dialysate on bioelectrical impedance analysis variability in pediatric patients receiving peritoneal dialysis.

Previous adult studies have yielded conflicting results regarding whether the presence (D +) or absence (D-) of peritoneal dialysate affects the accuracy of bioelectrical impedance analysis (BIA) measurements. The aim of this study was to investigate whether the accuracy of BIA data varies between D + and D- measurements in children. This cross-sectional study recruited chronic kidney disease stage 5 patients aged 3 to 18years who received peritoneal dialysis. Body composition was assessed by multifrequency BIA, and values were compared between D + and D- measurements using the intraclass correlation coefficient (ICC). Fifty paired BIA measurements were collected from 18 patients with a mean age of 13.6 ± 4.1years and a mean dialysate fill volume of 1,006 ± 239.7ml/m2. Sixteen out of 17 BIA parameters (94.1%) exhibited excellent correlations between D + and D- measurements (ICC values = 0.954, 0.998). There was a trend of increased fluid status, including extracellular water, edema index, and overhydration, in D + measurements, with mean differences (95% CIs) of 0.5 (0.4, 0.6) L, 0.002 (0.001, 0.002), and 0.1 (0.1, 0.2) L, respectively. Soft lean mass and fat-free mass were higher in D + measurements, with mean differences (95% CIs) of 1.4 (1.2, 1.6), and 1.6 (1.4, 1.8) kg, respectively. In addition, patients older than 10years had a stronger correlation between D + and D- measurements than younger patients. A total of 94.1% of BIA parameters exhibited excellent correlations between D + and D- measurements, especially patients older than 10years. We recommend that BIA measurements be collected from children regardless of the presence of peritoneal dialysate.

PSXIV-29 Seminal Plasma Uterine Priming in Embryo-Recipient Beef Cows Alters Preweaning Offspring Growth But Not Indices of Body Composition

Abstract Seminal plasma primes the uterus for pregnancy through the recruitment of cells aiding in microbial clearance, tissue remodeling and angiogenesis, and embryo implantation and growth. Less is known about the effects of seminal plasma uterine priming in practices devoid of seminal plasma, specifically evaluating offspring growth in embryo-recipient beef cows. In the present study, we sought to determine the effects of seminal plasma uterine priming on postnatal body weights, carcass ultrasound measures, and indices of body composition. To realize these objectives, embryo-recipient cows randomly received 0.5 mL of pooled seminal plasma or no treatment (control) at estrus and underwent embryo transfer seven days later. Following parturition, offspring pre-weaning body weights were collected at 30, 90, and 180 days of age. Indices of body composition, carcass ultrasounds and bioelectrical impedance analysis (BIA), were collected pre-weaning at an average 209 days of age. Weaning weights and hip heights were collected at an average of 212 days of age. Ultrasound, BIA, and weaning weight data were analyzed using the MIXED procedure of SAS for fixed effects of treatment. Pre-weaning body weights were analyzed similarly with repeated measures for treatment, day, and their interaction. Covariates of sire, dam, sex, and age were included in all models if P ≤ 0.10. Correlations between carcass ultrasounds and BIA parameters of fat-free mass and fat-free soft tissue were analyzed using the CORR procedure of SAS. Significance is declared at P ≤ 0.05 and data were presented as means ± standard error. A treatment by day interaction was not observed for pre-weaning body weight. However, a main effect of treatment was observed for pre-weaning body weights, which was decreased (P = 0.0130) in seminal plasma calves compared with controls. Weaning weights and hip height were not different between treatments. There was not an effect of treatment on carcass ultrasound measures, including ribeye area, ribeye area adjusted by body weight, rib fat, intramuscular fat, or rump fat. Similarly, BIA measures of fat-free mass and fat-free soft tissue were not different between treatments. Ribeye area and rib fat ultrasound were moderately correlated (P < 0.05) with BIA measures of fat-free mass and fat-free soft tissue. Interestingly, previously reported data from this project show offspring from seminal plasma treated cows had decreased embryo size at days 35, 40, and 45 of gestation, and decreased birth weights. It appears seminal plasma uterine priming prior to embryo transfer negatively impacts offspring growth pre-weaning, however, by weaning this difference is diminished. It is less clear why pre-weaning weights differ due to similar body composition among treatment and control calves; however, these measures may have been collected after the period of compensatory gain occurred.

FRI423 Obesity And Adiposity Are Associated With Decreased Anti-Mullerian Hormone (AMH) Levels In A Reproductive Aged Latina/LatinX Population

Abstract Disclosure: S. Schon: None. C. Jiang: None. E.L. Ferrell: None. F. Valbuena: None. L. Neff: Other; Self; Eli Lilly & Company, Dr. Neff is currently an employee and shareholder of Eli Lilly and Company, which has no affiliation with this work. At the onset of the study, she was employed by Northwestern University Feinberg Sch. M. Carnethon: None. E.E. Marsh: Consulting Fee; Self; Pfizer, Inc. Objective: Research suggests that obesity has an adverse effect on ovarian reserve, as assessed by AMH. Prior studies have explored this association predominantly in non-Hispanic Caucasian and African American cohorts. In addition, few studies have utilized metrics beyond BMI to characterize adiposity. The objective of this study was to examine the association of obesity/adiposity with AMH in a Latina/LatinX population using multiple measures of obesity/adiposity. Materials & Methods: This cross-sectional study utilized data from the Environment, Leiomyomas, Latinas, and Adiposity Study (ELLAS). ELLAS is a prospective longitudinal cohort study following Latina/LatinX females for 5 years. ELLAS used community based participatory research principles for engagement. Participants were between the ages of 21-50 at time of enrollment. Data from the first study visit were utilized for analysis. Assessment included anthropometrics, measurement of body composition using bioelectrical impedance analysis (BIA) (Tanita MC-280U) and serum AMH (pico-AMH assay, Ansh Labs). The association between BMI on AMH was assessed as a continuous and categorical outcome. Adiposity based on body fat %, and visceral adiposity index (VAI) was studied in relation to AMH using linear regression models. Statistical associations were determined using Chi-square, Wilcoxon rank-sum and linear or logistic regression as appropriate. Results: 621 women completed the first study visit and had BMI and AMH data available. BIA data was available on 591 participants. The mean age of participants was 37.5 ± 7.0 years. The mean BMI was 30.1 ± 6.8 kg/m2, with 261 (42%) of participants classified as having obesity by BMI (≥30 kg/m2). 214 women (34.5%) reported irregular menses and 89 reported currently taking hormonal contraception (HC) (14.3%). BMI was negatively associated with AMH (β=-0.055 p=0.014), however, this association was no longer significant after adjusting for age. Similarly, body fat % and VAI were also negatively associated with AMH (β=-0.058 p= 0.013, and β=-0.256 p<0.001), however this association was attenuated after adjusting for age. When the analysis was restricted to women not taking HC and with regular menses (n=290), higher BMI remained negatively associated with AMH after adjusting for age (β=-0.04, p=0.003). Similarly, higher body fat % and VAI were also negatively associated with AMH after adjusting for age (β=-0.04, p=0.005, and β=-0.090 p=0.009). Highest lifetime BMI was also associated with AMH after adjusting for age (β=-0.03, p=0.03). Conclusions: Among a cohort of Latina/LatinX females with normal menses, obesity and adiposity as assessed by BMI, body fat % and visceral adiposity index were negatively associated with AMH. This suggests that excess adiposity may compromise ovarian reserve and demonstrates the importance of assessing baseline gynecologic characteristics. Presentation: Friday, June 16, 2023

Using Body Composition Analysis for Improved Nutritional Intervention in Septic Patients: A Prospective Interventional Study.

The study aimed to determine whether using body composition data acquired through bio-electrical impedance analysis (BIA) to adjust diet formulas could improve outcomes in septic patients. There were 132 septic patients in medical intensive care units enrolled in the prospective, randomized, double-blind, interventional study. For the intervention group, dietitians had access to BIA data for adjusting diet formulas according to body composition variables on days 1, 3, and 8. The patients were also stratified based on nutritional risk using the modified Nutrition Risk in Critically ill (mNUTRIC) score. Patients with intervention were more likely to achieve caloric and protein intake goals compared to the control group, especially in the low-risk group. The intervention did not significantly affect mortality, but the survival curves suggested potential benefits. The high-risk group had longer ICU stays and mechanical ventilation duration, which were mitigated by the intervention. Certain body composition variables (e.g., extracellular water to total body water ratio and phase angle) showed differences between high-risk and low-risk groups and may be related to patient outcomes. Non-invasive body composition assessment using BIA can help dietitians adjust diet formulas for critically ill septic patients. Body composition variables may be associated with sepsis outcomes, but further research with larger patient numbers is needed to confirm these findings.

Биоимпедансная оценка состава тела с использованием анализаторов АВС-01 «Медасс» и Диамант-АИСТ: результаты сравнения

Introduction. Bioelectrical impedance analysis (BIA) is an indirect method of body composition assessment. The equipment for BIA measurements is produced in many countries including Russia and may differ in electrical current frequencies, measurement schemes and built-in algorithms for the evaluation of body composition. In view of this, body composition estimates from various BIA instruments may be inconsistent which limits the ability to combine and generalize BIA data. Our purpose was to analyze the consistency and possibility of joint use of the BIA data obtained using the ABC-01 ‘Medas’ (SRC Medas, Moscow) and Diamant-AIST (LLC Diamant, St. Petersburg) instruments in adults. Materials and methods. 185 adult volunteers (107 women and 78 men) aged 18 to 61 years were examined according to the standard anthropometric measurement protocol and on the basis of paired consecutive measurements with the bioimpedance body composition instruments ABC-01 ‘Medas’ and Diamant-AIST. The estimates of fat-free mass (FFM), body fat (BF) and percentage body fat (%BF) were compared. Mutual calibration of the bioimpedance data was carried out based on transformation of the Diamant-AIST data using a conversion formula between the ABC-01 ‘Medas’ and Diamant-AIST resistances and subsequent application the ABC-01 ‘Medas’ body composition assessment algorithm. Results. There were significant differences in the median values of FFM, BF and %BF estimates provided by the ABC-01 ‘Medas’ and Diamant-AIST instruments. The FFM values in females using the ABC-01 ‘Medas’ instrument were significantly lower, and the BF and %BF were significantly higher as compared to the Diamant-AIST data. In males, the inverse relationship was observed while maintaining significant differences between the medians. The differences in paired estimates of body composition data provided by the ABC-01 ‘Medas’ instrument and the transformed Diamant-AIST data in males and females, respectively, were statistically insignificant. Also, the confidence intervals for the differences of paired body composition estimates reduced essentially, but still not ensured good consistency of individual body composition data. Conclusions. Significant differences in paired values of fat-free mass, fat mass and relative body fat using the ABC-01 ‘Medas’ and Diamant-AIST bioimpedance instruments are obtained. The possibility of mutual calibration and joint analysis of the related bioimpedance data at the group level is established. @ 2023. This work is licensed under a CC BY 4.0 license.

Machine learning-based obesity classification considering 3D body scanner measurements

Obesity can cause various diseases and is a serious health concern. BMI, which is currently the popular measure for judging obesity, does not accurately classify obesity; it reflects the height and weight but ignores the characteristics of an individual’s body type. In order to overcome the limitations of classifying obesity using BMI, we considered 3-dimensional (3D) measurements of the human body. The scope of our study was limited to Korean subjects. In order to expand 3D body scan data clinically, 3D body scans, Dual-energy X-ray absorptiometry, and Bioelectrical Impedance Analysis data was collected pairwise for 160 Korean subjects. A machine learning-based obesity classification framework using 3D body scan data was designed, validated through Accuracy, Recall, Precision, and F1 score, and compared with BMI and BIA. In a test dataset of 40 people, BMI had the following values: Accuracy: 0.529, Recall: 0.472, Precision: 0.458, and F1 score: 0.462, while BIA had the following values: Accuracy: 0.752, Recall: 0.742, Precision: 0.751, and F1 score: 0.739. Our proposed model had the following values: Accuracy: 0.800, Recall: 0.767, Precision: 0.842, and F1 score: 0.792. Thus, our accuracy was higher than BMI as well as BIA. Our model can be used for obesity management through 3D body scans.

Usefulness of skeletal muscle measurement by computed tomography in patients with esophageal cancer: changes in skeletal muscle mass due to neoadjuvant therapy and the effect on the prognosis.

This analysis was performed to clarify the usefulness of skeletal muscle measurements using computed tomography (CT) in patients with esophageal cancer and the effect of treatment-induced changes in the skeletal muscle mass on the prognosis. Ninety-seven male patients who underwent thoracoscopic esophagectomy for esophageal squamous cell carcinoma were included in the study. The preoperative CT images were analyzed retrospectively. In a survival analysis performed according to the preoperative data of skeletal muscle, the low-skeletal muscle index (l-SMI) group had a poorer outcome than the normal skeletal muscle index (n-SMI) group in terms of both the overall survival (OS) and the relapse-free survival (RFS) (OS: P < 0.01, RFS: P = 0.01). In the multivariate analysis for the OS, preoperative l-SMI was an independent predictor (hazard ratio: 3.68, 95% confidence interval 1.32-10.2, P = 0.01). In patients who underwent neoadjuvant therapy (NAT), the SMI was significantly reduced after NAT (P < 0.01). The preoperative skeletal muscle area on CT was strongly correlated with the results of a bioelectrical impedance analysis (BIA) (ρ = 0.77, P < 0.01). A decreased preoperative skeletal muscle mass was associated with a poor outcome. In patients who underwent NAT, the SMI was significantly reduced after NAT. An analysis of the skeletal muscle mass using CT images was found to be useful for providing data that corresponded with BIA data.

Prediction of sarcopenia among peritoneal dialysis patients using a combination of irisin and phase angle

Sarcopenia is associated with significant morbidity and mortality in patients undergoing peritoneal dialysis (PD). Three different tools must be applied to measure the three indices for diagnosing sarcopenia. Considering the cumbersome diagnostic steps and multi-layered mechanisms underlying sarcopenia, we combined new biomarker with bioelectrical impedance analysis (BIA) data to predict PD sarcopenia. Patients underwent regular PD were asked to complete sarcopenia screening, including appendicular skeletal muscle mass, handgrip strength, and the 5-time chair stand time test according to the newly revised diagnosis consensus of Asian Working Group for Sarcopenia (AWGS2019). Serum was collected for centralized detection of the irisin levels. BIA data, especially the phase angle (PhA), as well as patient's general clinical information, dialysis related indices, laboratory data and body composition data were recorded. Among 105 enrolled PD patients (41.0% men, mean age 54.2 ± 8.89 years), the sarcopenia prevalence was 31.4% and the sarcopenic obesity was 8.6%. Binary regression analysis showed that serum irisin concentrations (OR = 0.98; 95% CI,0.97-0.99; p = 0.002), PhA (OR = 0.43; 95% CI, 0.21-0.90; p = 0.025) and body mass index (BMI) (OR = 0.64; 95% CI, 0.49-0.83; p = 0.001) were independently associated with PD sarcopenia. The AUC of the combination use of serum irisin concentrations and PhA for predicting PD sarcopenia was 0.925 with a sensitivity of 100% and specificity of 84.0% in male and was 0.880 with a sensitivity of 92.0% and specificity of 81.5% in female. PD sarcopenia score=1533.48+-0.75*Handgrip strength+4.63*BMI+-18.07*Total body water +-11.87*Extra-cellular water / total body water +9.26*Fat free mass index+-83.41*PhA+22.42*Albumin/Globulin+-26.38*blood phosphorus+-17.04*Total cholesterol+-29.02*Triglyceride+-0.29*Prealbumin+-0.17*Irisin. Sarcopenia is relatively common among PD patients. The combination of serum irisin concentrations and PhA facilitated the rapid prediction of PD sarcopenia and could serve as an optimal screening tool for PD sarcopenia in clinical settings.

The Russian Bioimpedance Database: An Update.

Extensive bioelectrical impedance analysis (BIA) data have the potential of health monitoring and the assessment of health risks at the population level. The importance of BIA data lies in their availability and abundance for many countries. In Russia, mass BIA data are generated by the national network of health centers (HCs). Our aim was to describe the structure and capabilities of the updated HCs' BIA database. Upon several requests between 2012 and 2020, 369 HCs representing all Federal districts of Russia and 60 out of 85 Federal subjects in them, submitted raw bioimpedance data which were obtained using the same type of BIA instrument, namely ABC-01 'Medas' (SRC Medas, Russia). After application of strict selection criteria, 2,429,977 BIA measurement records were selected that formed the updated 2010-2019 HCs' database. Various slices of the BIA data are described according to spatiotemporal, demographic and other characteristics. Reference curves of the bioimpedance phase angle according to age and sex are presented. Limitations and prospects for further work are outlined. We believe that, after appropriate sampling, the database can be utilized to study biological, geographical, social and other associations of the bioimpedance and body composition parameters, for generating updated national references, international comparisons and data standardization.

Application of Benford's Law for Quality Assessment of Preventive Screening Data

An empirical Benford's law which describes the probability of the appearance of certain first significant digits in many distributions taken from real life, is used to identify anomalies in various kinds of data. Our aim was to test Benford's law to assess the quality of mass preventive screening data on the example of bioelectrical impedance analysis (BIA) data from Moscow health centers. As was shown earlier, such a data is characterized by a high level of contamination by artificially generated and falsified data. A generated 2010–2019 database of BIA measurements contained 1361019 measurement records in the age range of the examined persons from 5 to 96 years. Application of the expert quality assessment algorithm, which was used as a reference for evaluation of the effectiveness of Benford analysis, revealed a high percentage of incorrect data (66.5 %) which was dominated by falsified data. To characterize the degree of the data compliance with Benford's law, the mean absolute deviations of the frequency distributions of the first and first two significant digits deviations from the proper values and chi-squared statistics for the tenth powers of the standardized resistance, reactance, and resistance index values were assessed for each health center. A significant correlation was observed between the data deviation from Benford's law and the percentage of incorrect data as provided by the expert quality assessment algorithm (ρmax = 0.66 and 0.62 for the mean absolute deviations and χ2 statistics, respectively, based on the resistance value and the first significant digit). It is suggested that deviation of the BIA data from Benford's law serves as a sufficient, but not a necessary, condition for their contamination. For those health centers, in which most of the incorrect data were represented by multiple measurements of the same person under the guise of different ones, the data were in good agreement with Benford's law. If the structure of incorrect data was dominated by measurements of the calibration block, software emulations of BIA measurements and outliers, then the use of Benford's law made it possible to effectively rank health centers by the level of data authenticity.

Hydration status during commercial saturation diving measured by bioimpedance and urine specific gravity.

Excessive fluid loss triggered by hyperbaric pressure, water immersion and hot water suits causes saturation divers to be at risk of dehydration. Dehydration is associated with reductions in mental and physical performance, resulting in less effective work and an increased risk of work-related accidents. In this study we examined the hydration status of 11 male divers over 19 days of a commercial saturation diving campaign to a working depth of 74 m, using two non-invasive methods: Bioelectrical impedance analysis (BIA) and urine specific gravity (USG). Measurements were made daily before and after bell runs, and the BIA data was used to calculated total body water (TBW). We found that BIA and USG were weakly negatively correlated, probably reflecting differences in what they measure. TBW was significantly increased after bell runs for all divers, but more so for bellmen than for in-water divers. There were no progressing changes in TBW over the 19-day study period, indicating that the divers’ routines were sufficient for maintaining their hydration levels on short and long term.

Patient and Staff Perceptions on Using Bioelectrical Impedance Analysis in an Outpatient Haemodialysis Setting: A Qualitative Descriptive Study.

Bioelectrical impedance analysis (BIA) is an objective hydration and body composition assessment method recommended for use in haemodialysis patients. Limited research exists on the acceptability and utility of BIA in clinical practice. This qualitative study explored patient and staff acceptability and perceived value of BIA in an outpatient haemodialysis setting at a tertiary public hospital in Queensland, Australia. Participants included five patients receiving outpatient haemodialysis and 12 multidisciplinary clinical staff providing care to these patients. Semi-structured interviews were employed and data were analysed thematically. Patients were satisfied with the BIA measurement process and most thought the BIA data would be useful for monitoring changes in their nutrition status. Clinical staff valued BIA data for improving fluid management, assessing nutrition status and supporting patient care. Staff recommended targeting BIA use to patient groups who would benefit the most to improve its uptake in the haemodialysis setting. Conclusions: BIA use in the outpatient haemodialysis setting is acceptable and provides valuable objective data to support health-related behaviour changes in patients and enhance clinical practice. Implementation of BIA should be tailored to the local context and staff should be supported in its use.

The role of muscle ultrasound to predict sarcopenia

ObjectivesThis study aimed to provide data about the role of muscle ultrasound (US) to predict sarcopenia. MethodsA total of 313 geriatric outpatients (age ≥65 y) were enrolled in the study. After a comprehensive geriatric assessment, anthropometric measurement and handgrip strength (HGS) data were obtained and a bioelectrical impedance analysis was performed. Sarcopenia was diagnosed using HGS and bioelectrical impedance analysis data. Gastrocnemius medialis (GC), rectus femoris (RF), and rectus abdominis (RA) muscle thickness as well as the RF cross-sectional area (CSA) were measured with US. The role of muscle US to predict sarcopenia was defined with a receiver operating characteristics analysis. ResultsThe prevalence of probable and confirmed sarcopenia were 43.8% (n = 137) and 13.4% (n = 42), respectively. All muscle US parameters had positive correlations with both HGS and the fat-free mass index. There were inverse correlations between all muscle US parameters and the five-item sarcopenia questionnaire. The RF CSA had stronger correlations with the five-item sarcopenia questionnaire, HGS, and the fat-free mass index than others. The values of GC, RF, and RA muscle thickness and the RF CSA to predict sarcopenia for women/men were 13.9/13.8 mm (area under the curve [AUC]: 0.817/0.707 mm), 13/15.5 mm (AUC: 0.760/0.736 mm), 4.3/5.2 cm2 (AUC: 0.766/0.773 cm2), and 6.6/7.0 mm (AUC: 0.740/0.688 mm), respectively. ConclusionsGC, RF, and RA muscle thickness and the RF CSA all may predict sarcopenia accurately in geriatric outpatients.

Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR).

The performances of three non-destructive sensors, based on different principles, bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR) and time domain reflectometry (TDR), were studied to discriminate between unfrozen and frozen-thawed fish. Bigeye tuna (Thunnus obesus) was selected as a model to evaluate these technologies. The addition of water and additives is usual in the fish industry, thus, in order to have a wide range of possible commercial conditions, some samples were injected with different water solutions (based on different concentrations of salt, polyphosphates and a protein hydrolysate solution). Three different models, based on partial least squares discriminant analysis (PLS-DA), were developed for each technology. This is a linear classification method that combines the properties of partial least squares (PLS) regression with the classification power of a discriminant technique. The results obtained in the evaluation of the test set were satisfactory for all the sensors, giving NIR the best performance (accuracy = 0.91, error rate = 0.10). Nevertheless, the classification accomplished with BIA and TDR data resulted also satisfactory and almost equally as good, with accuracies of 0.88 and 0.86 and error rates of 0.14 and 0.15, respectively. This work opens new possibilities to discriminate between unfrozen and frozen-thawed fish samples with different non-destructive alternatives, regardless of whether or not they have added water.

Comparison of lateralized bioelectrical impedance analysis data and lateralized manual muscle testing score in amyotrophic lateral sclerosis (als) during the initial and final nutritional assessment

Rationale: Amyotrophic lateral sclerosis (ALS) is a rare disease with a poor prognosis. During follow-up, manual muscle testing (MMT) makes it possible to determine the laterality of the functional impairment. Bioelectrical impedance analysis (BIA) determines the body composition and therefore the fat-free mass (FFM), reflecting muscle mass. The BIA can be lateralized by right or left hemi-body. The objectives of this work were to determine if there was a link between the lateralized changes in body composition in BIA and the lateralized motor deficit objectified by MMT, and to determine if this link was stable during the course of the disease.

Serial measurements of body composition using bioelectrical impedance and clinical usefulness of phase angle in colorectal cancer.

Although body composition (BC) can be measured easily using bioelectrical impedance analysis (BIA), there are few studies of serial BC measurements in colorectal cancer (CRC). The purpose of the present study was to observe the serial change of BC in patients with CRC surgery from the initiation to the end of chemotherapy and to evaluate its clinical usefulness. From July 2018 to November 2019, patients undergoing elective CRC surgery were enrolled. All clinical data were reviewed retrospectively. BIA data were collected prospectively at four time points (initial, discharge day, first chemotherapy, and 6 months later). BC was measured using a commercial BIA device. A total of 160 patients were enrolled, and 110 (68.8%) patients were followed. Most BC measurements, such as weight, body mass index, skeletal muscle mass, skeletal muscle index, and fat mass index, were lowest at the first chemotherapy and rebounded after 6 months. Phase angle (PhA) and the ratio of extracellular water to total body water (ECW/TBW) were "V" shaped and inverted "V" shaped, respectively, and the peaks were on discharge days. This pattern of BC showed significant difference according to sarcopenia, old age (>70 years), and advanced stage (III or IV). The change of PhA and ECW/TBW sensitively pattern differences according to clinical aspect. Using BIA, serial BC measurements were taken to establish a pattern based on clinical characteristics. PhA showed the most sensitive change according to the patient's clinical aspect.

Acceptability of Artificial Intelligence in Poultry Processing and Classification Efficiencies of Different Classification Models in the Categorisation of Breast Fillet Myopathies.

Breast meat from modern fast-growing big birds is affected with myopathies such as woody breast (WB), white striping, and spaghetti meat (SM). The detection and separation of the myopathy-affected meat can be carried out at processing plants using technologies such as bioelectrical impedance analysis (BIA). However, BIA raw data from myopathy-affected breast meat are extremely complicated, especially because of the overlap of these myopathies in individual breast fillets and the human error associated with the assignment of fillet categories. Previous research has shown that traditional statistical techniques such as ANOVA and regression, among others, are insufficient in categorising fillets affected with myopathies by BIA. Therefore, more complex data analysis tools can be used, such as support vector machines (SVMs) and backpropagation neural networks (BPNNs), to classify raw poultry breast myopathies using their BIA patterns, such that the technology can be beneficial for the poultry industry in detecting myopathies. Freshly deboned (3–3.5 h post slaughter) breast fillets (n = 100 × 3 flocks) were analysed by hand palpation for WB (0-normal; 1-mild; 2-moderate; 3-Severe) and SM (presence and absence) categorisation. BIA data (resistance and reactance) were collected on each breast fillet; the algorithm of the equipment calculated protein and fat index. The data were analysed by linear discriminant analysis (LDA), and with SVM and BPNN with 70::30: training::test data set. Compared with the LDA analysis, SVM separated WB with a higher accuracy of 71.04% for normal (data for normal and mild merged), 59.99% for moderate, and 81.48% for severe WB. Compared with SVM, the BPNN training model accurately (100%) separated normal WB fillets with and without SM, demonstrating the ability of BIA to detect SM. Supervised learning algorithms, such as SVM and BPNN, can be combined with BIA and successfully implemented in poultry processing to detect breast fillet myopathies.

Impedance-based measures of muscle mass can be used to predict severity of hepatic steatosis in pediatric nonalcoholic fatty liver disease

ObjectivesThe objective of this study was to investigate the association between measures of body composition based on bioelectrical impedance analysis (BIA) and histologic severity of liver disease in a pediatric cohort with nonalcoholic fatty liver disease (NAFLD). MethodsThis was a cross-sectional study of patients < 20 y old with histologically confirmed NAFLD followed in our Steatohepatitis Center from 2017 to 2019. Contemporaneous body-composition data were obtained using a multifrequency octopolar BIA device (InBody 370, InBody, Seoul, South Korea). BIA data collected were skeletal muscle mass, appendicular muscle mass, and percentage body fat. Skeletal and appendicular muscle mass were corrected for height (dividing by the square of height), generating their respective indices. Univariate linear and logistic regression, followed by multivariable logistic regression analyses, were used. ResultsOf the 79 children included (27% female, 73% male; 38% Hispanic; median age, 13 y; median body mass index Z-score, 2.43), the median NAFLD Activity Score was 4 (interquartile range, 3–5). In multivariable regression analyses, the skeletal muscle mass index was negatively associated with hepatic steatosis after controlling for confounders (odds ratio, 0.76; 95% confidence interval, 0.62–0.93). Similarly, the appendicular muscle mass index was negatively associated with severity of hepatic steatosis severity (odds ratio, 0.69; 95% confidence interval, 0.53–0.90). In contrast, percentage body fat was not associated with hepatic steatosis. NAFLD Activity Score, lobular inflammation, ballooning scores, and fibrosis stage were not associated with measures of body composition. ConclusionsThere is an inverse association between BIA-based measures of muscle mass and severity of hepatic steatosis in children with NAFLD. BIA data could further inform clinical decision making in this context.

NutriCatt Protocol Improves Body Composition and Clinical Outcomes in Elderly Patients Undergoing Colorectal Surgery in ERAS Program: A Retrospective Cohort Study.

Background: A poor body composition, often found in elderly patients, negatively impacts perioperative outcomes. We evaluated the effect of a perioperative nutritional protocol (NutriCatt) on body composition and clinical outcomes in a cohort of elderly patients undergoing colorectal surgery in a high-volume center adopting the ERAS program. Methods: 302 out of 332 elderly (>75 years) patients from 2015 to 2020 were identified. Patients were divided according to their adherence, into “NutriCatt + ERAS” (n = 166) or “standard ERAS” patients (n = 136). Anthropometric and bioelectrical impedance analysis data were evaluated for NutriCatt + ERAS patients. Complications, length of hospital stay (LOS), and other postoperative outcomes were compared between both groups. Results: In NutriCatt + ERAS patients, significant improvements of phase angle (pre-admission vs. admission 4.61 ± 0.79 vs. 4.84 ± 0.85; p = 0.001; pre-admission vs. discharge 4.61 ± 0.79 vs. 5.85 ± 0.73; p = 0.0002) and body cell mass (pre-admission vs. admission 22.4 ± 5.6 vs. 23.2 ± 5.7; p = 0.03; pre-admission vs. discharge 22.4 ± 5.6 vs. 23.1 ± 5.8; p = 0.02) were shown. NutriCatt + ERAS patients reported reduced LOS (p = 0.03) and severe complications (p = 0.03) compared to standard ERAS patients. A regression analysis confirmed the protective effect of the NutriCatt protocol on severe complications (OR 0.10, 95% CI 0.01–0.56; p = 0.009). Conclusions: The NutriCatt protocol improves clinical outcomes in elderly patients and should be recommended in ERAS colorectal surgery.

Application of Artificial Neural Network to Somatotype Determination

Somatotype characteristics are important for the selection of sporting activities, as well as and the prevalence of several chronic diseases. Nowadays the most common method of somatotyping is the Heath–Carter method, which calculates the somatotype base on 10 anthropometric parameters. Another possibility for evaluation of somatotype gives commonly used bioelectrical impedance analysis), but the accuracy of the proposed formulas is questioned. Therefore, we aimed to investigate the possibility of applying an artificial neural network to achieve the formulas, which allow us to determine the endomorphy and mesomorphy using data on body height and weight and raw bioelectrical impedance analysis data in young women. The endomorphy (Endo), ectomorphy (Ecto), and mesomorphy (Meso) ratings were determined using artificial neural networks and the Heath–Carter method. To identify critical parameters and their degree of impact on the artificial neural network outputs, a sensitivity analysis was performed. The multi-layer perceptron MLP 4-4-1 (input: body mass index (BMI), reactance, resistance, and resting metabolic rate) for the Endo somatotype was proposed (root mean squared error (RMSE) = 0.66, χ2 = 0.66). The MLP 4-4-1 (input: BMI, fat-free mass, resistance, and total body water) for the Meso somatotype was proposed (RMSE = 0.76, χ2 = 0.87). All somatotypes (Endo, Meso and Ecto) can be calculated using MLP 2-4-3 (input: BMI and resistance) with accuracy RMSE = 0.67 and χ2 = 0.51. The bioelectrical impedance analysis and Heath–Carter method compliance was evaluated with the statistical algorithm proposed by Bland and Altman. The artificial neural network-based formulas allow us to determine the endomorphy and mesomorphy in young women’s ratings with high accuracy and agreement with the Heath–Carter method. The results of our study indicate the successful application of artificial neural network-based model in predicting the somatotype of young women. The artificial neural network model can be practically used in bioelectrical impedance analysis devices in the future.