Josephine Reinert Quist

Background : Body composition measurement is an integral part of estimating nutritional status according to the Global Leadership Initiative on Malnutrition (GLIM). Bioelectrical impedance analysis (BIA) is suggested for measuring body composition. This study mimicked the BIA potential use in the everyday clinic. In the practical clinical setting, there is not always the possibility to adopt a strict protocol such as drinking a glass of water or voiding immediately before measurement for all patients. Aim : To investigate the effect of oral fluid intake and voiding on body composition measurements using a multi-frequency BIA device. Methods : This was a single-centre intervention crossover study. Body composition was measured four times in healthy adults: one prior to oral fluid intake and urinary voiding, twice immediately after voiding, and once 15 minutes after drinking 500 ml tap water. The main parameters were body weight, whole-body resistance (R, ohm), capacitive reactance (X _C ), and phase angle (PhA, ^o ). Results : We examined 31 healthy adults with a mean age of 34 (SD = 12) years; height, 173 (SD = 9) cm; body weight, 76.8 (SD = 18) kg; the baseline mean BIA outcomes were R 597 (SD = 88) ohm, X _C 59 (SD = 8) ohm and PhA 5.7 (SD = 0.7) . Oral fluid intake increased all BIA outcomes compared to baseline, i.e., with R 4.71 (SD = 7.17) ohm, p = 0.001; X _C with 0.73 (SD = 1.07) ohm, p = 0.001; PhA with 0.12 (SD = 0,52), p == 0.199, fat mass (FM) with 0.26 (SD = 0.57) kg, p = 0.008 and fat-free-mass (FFM) 0.08 (SD = 0.42) kg, p = 0.423. After urinary voiding the BIA outcomes decreased with R 3.52 (SD= 3.94) ohm, p = 0.000; X _C 0.38 (SD = 0.91) ohm, p = 0.029, and PhA 0.03 (SD = 0.17) . Conclusions : Oral fluid intake and urinary voiding have detectable effects on BIA outcomes but that the magnitude of these effects is small, and hence, in most cases, may be ignored in routine clinical practice.

Background Nephrotic syndrome (NS) in children, entailing kidney-related protein leakage and peripheral oedema, remains difficult to assess. Bioelectrical impedance analysis (BIA) provides several body composition measures, and integration of machine learning (ML) may improve clinical care. We tested an ML model to identify NS in children, compared with healthy children. Methods This was a cross-sectional study, conducted on children with active NS in the acute phase (aNS group) included from the Department of Paediatrics and Adolescent Medicine, Aarhus University Hospital, Denmark. Anonymized MF-BIA data from frequences between 5-1000 kHz were added to the JustAddDataBio (JADBio)®, a web-based ML platform for analysing potential biomarkers to diagnose. Results Eight children with aNS and 38 age-matched healthy children were included. The ML software employed a ridge logistic regression with the penalty hyperparameter lambda = 0.001, with a selected threshold of 0.81 by JADBio, and the area under the curve (AUC) was 0.84 [95% confidence interval (CI): 0.72;0.94] as the best model. The software selected the following features: height, age, resistance at 50 kHz, impedance at 50 kHz, the characteristic frequency, phase angle at 50 kHz and sex. The model had a statistically significant true positive classification of a healthy child of 0.92 (92%) [CI: 0.88;0.96], and a specificity of 0.22 (22%) [CI: 0.08;0.36]. Conclusion Applying an ML-supported evaluation of BIA improved diagnostics. A low specificity limits the clinical application. To obtain a more acceptable model, a larger population of patients and the inclusion of more biomarkers may be needed.

Bioelectrical impedance analysis (BIA) is a non-invasive and inexpensive technique for assessing body composition. Predictive models can estimate fluid volumes and fat-free mass but they depend on assumptions. Although less easy to interpret, raw data like phase angle are assumption-free. Phase angle relates to cellular health, with high values in healthy populations. BIA finds multiple clinical applications in nutrition, chronic diseases, and rehabilitation. Its implementation in clinical practice and research requires standardized protocols, and better reference material should be established, as argued in this review.

Patients with an ileostomy may experience postoperative electrolyte derangement and dehydration but are presumed to stabilise thereafter. We aimed to investigate the prevalence of sodium depletion in stable outpatients with an ileostomy and applied established methods to estimate their fluid status. We invited 178 patients with an ileostomy through a region-wide Quality-of-Life-survey to undergo outpatient evaluation of their sodium and fluid status. The patients delivered urine and blood samples, had bioelectrical impedance analysis performed and answered a questionnaire regarding dietary habits. Out of 178 invitees, 49 patients with an ileostomy were included; 22 patients (45%, 95% CI, 31–59%) had unmeasurably low urinary sodium excretion (2) and low venous blood plasma CO₂ (median 24, IQR 21–26, mmol/L), indicative of chronic renal impairment and metabolic acidosis. Bioelectrical impedance analysis, plasma osmolality, creatinine and sodium values were not informative in determining sodium status in this population. A high proportion of patients with an ileostomy may be chronically sodium depleted, indicated by absent urinary sodium excretion, secondary hyperaldosteronism and chronic renal impairment, despite normal standard biochemical tests. Sodium depletion may adversely affect longstanding renal function. Future studies should investigate methods to estimate and monitor fluid status and aim to develop treatments to improve sodium depletion and dehydration in patients with an ileostomy.