Significant differences between two commonly used bioimpedance methods in hemodialysis patients.

INTRODUCTION: Bioimpedance methods are currently used abundantly in patients on chronic hemodialysis. In this population, their most important role is to determine the level of fluid volume, respectively its intra- and extracellular components. There are several bioimpedance devices on the market. In this project, we compared two frequently used devices: Body Composition Monitor and InBody S10. MATERIALS AND METHODS: We invited patients on chronic hemodialysis who are being treated in our institution. Inclusion criteria were: clinically stable condition, lack of artificial joints, pacemakers, or other implanted metal objects. The examinations were performed just prior to hemodialysis by both methods 5 minutes apart. Patients were examined in the supine position after 15 minutes at rest to stabilize body fluids. Studied parameters were those that are obtainable by both methods: total body water (TBW) (L), extracellular water (ECW) (L) and intracellular water (ICW) (kg), lean tissue mass (LTM) (L), and fat tissue mass (kg). RESULTS: We included 14 participants (aged 64.4 ± 18.0 years). Statistically and clinically significant differences between data from compared devices were observed for all variables. Inbody S10 overestimated TBW by 2.58 ± 2.73 L and ICW by 4.56 ± 2.27 L in comparison to BCM. The highest difference (27%) was measured for LTM and ICW 22%. LTM, fat, and ECW were higher when measured by BCM (LTM by 8.54 ± 6.43 kg, p < 0.001; fat by 3.41 ± 4.22, p = 0.01; ECW by 2.01 ± 0.89 L, p < 0.001). CONCLUSION: The differences between tested devices were significant not only statistically, but also clinically. These two devices cannot be used interchangeably for dry weight setting of hemodialysis patients.

Physical activity in the treatment of obesity in practice.

Physical activity is an integral part of the obesity treatment. Curiously, even among health professionals, the evaluation of obesity treatment persists only based on weight loss. However, the treatment of obesity is not only a simple weight loss, but above all the treatment of associated diseases such as hypertension, diabetes, dyslipidemia, etc. Physical fitness is very rarely assessed, although it best predicts the future and reduces mortality from all causes. It is mainly a cardiovascular disease, where it is shown that the parameter VO2Max is the strongest predictor of not only cardiovascular mortality. The specifics of the prescription of physical activity bring with it the presence not only of diabetes, hypertension and cardiovascular diseases, but also osteoarthritis, possibly undergoing bariatric surgery. To objectify physical activity, including subjective symptoms, it is appropriate to use modern methods of telemedicine, which is the future in the treatment of not only obesity.

Resting Energy Expenditure in Patients with Extreme Obesity: Comparison of the Harris-Benedict Equation with Indirect Calorimetry.

Background: The main objective of the work was the analysis and description of data on body composition and resting energy expenditure (REE) values of selected groups of patients with obesity whose REE measurement results using indirect calorimetry reached a level below 95% of the predicted REE calculated using the Harris-Benedict (H-B) equation. The sub-goals were to describe the dependence of body composition on the size of the REE and to find out if the deviations between the number of the total measured REE and the REE calculated using H-B in the adapted group (patients with altered REE values, lower than expected caused by long caloric restriction) are significant. Methods: For the research, 71 (39 women and 32 men) patients treated in obesitology were selected. Patients underwent the measurement of resting metabolism using indirect calorimetry (IC) and body composition measurement on the bioimpedance device and, at the same time, the value of resting metabolism was calculated for everyone using the H-B equation. The whole group was divided into five groups according to the deviation of the measurement using IC and the calculation of the H-B equation. Results: In the total set of examined individuals, there were 32.4% with a reduced REE value compared to the REE calculation according to the H-B equation, which corresponds to 23 individuals. In the adapted group, the average measured REE was 2242 ± 616 kcal compared to the H-B calculation of 2638 ± 713 kcal. Statistically, these results were not significant, but a high case-to-case variation was found. The highest deviation from the H-B predictive calculation was -42% and +43% in the whole research group. The amount of muscle tissue in the adapted group averaged 44.3 ± 11.9 kg and the amount of fat-free mass (FFM) 77.9 ± 20.1 kg. When statistically testing the dependence of REE on FFM and muscle tissue in the adapted group, a strong correlation was found. Conclusions: The H-B equation alone is not suitable for setting a suitable diet therapy for an individual with obesity. In order to select and characterize a group of adapted individuals, it will be necessary to use other methods or a larger research sample, and preferably examine and divide patients with specific comorbidities or include their health status.

Real-time CGM Is Superior to Flash Glucose Monitoring for Glucose Control in Type 1 Diabetes: The CORRIDA Randomized Controlled Trial.

OBJECTIVE: The aim of this trial was to compare the efficacy of real-time and intermittently scanned continuous glucose monitoring (rtCGM and isCGM, respectively) in maintaining optimal glycemic control. RESEARCH DESIGN AND METHODS: In this randomized study, adults with type 1 diabetes (T1D) and normal hypoglycemia awareness (Gold score <4) used rtCGM (Guardian Connect Mobile) or isCGM (FreeStyle Libre) during 4 days of physical activity (exercise phase) and in the subsequent 4 weeks at home (home phase). Primary end points were time in hypoglycemia (<3.9 mmol/L [<70 mg/dL]) and time in range (3.9-10.0 mmol/L [70-180 mg/dL]). The isCGM group wore an additional masked Enlite sensor (iPro2) for 6 days to check for bias between the different sensors used by the rtCGM and isCGM systems. RESULTS: Sixty adults with T1D (mean age 38 ± 13 years; A1C 62 ± 12 mmol/mol [7.8 ± 1.1%]) were randomized to rtCGM (n = 30) or isCGM (n = 30). All participants completed the study. Percentage of time in hypoglycemia (<3.9 mmol/L [<70 mg/dL]) was lower among rtCGM versus isCGM participants in the exercise phase (6.8 ± 5.5% vs. 11.4 ± 8.6%, respectively; P = 0.018) and during the home phase (5.3 ± 2.5% vs. 7.3 ± 4.4%, respectively; P = 0.035). Hypoglycemia differences were significant and most notable during the night. rtCGM participants spent more time in range (3.9-10 mmol/L [70-180 mg/dL]) than isCGM participants throughout both the exercise (78.5 ± 10.2% vs. 69.7 ± 16%, respectively; P = 0.0149) and home (75.6 ± 9.7% vs. 67.4 ± 17.8%, respectively; P = 0.0339) phases. The results were robust to the insignificant bias between rtCGM and isCGM sensors that masked CGM found in the isCGM arm. CONCLUSIONS: rtCGM was superior to isCGM in reducing hypoglycemia and improving time in range in adults with T1D with normal hypoglycemia awareness, demonstrating the value of rtCGM alarms during exercise and in daily diabetes self-management.