Context: Common concerns when using low-calorie diets as a treatment for obesity are the reduction in fat-free mass, mostly muscular mass, that occurs together with the fat mass (FM) loss, and determining the best methodologies to evaluate body composition changes.Objective: This study aimed to evaluate the very-low-calorie ketogenic (VLCK) diet-induced changes in body composition of obese patients and to compare 3 different methodologies used to evaluate those changes.Design: Twenty obese patients followed a VLCK diet for 4 months. Body composition assessment was performed by dual-energy X-ray absorptiometry (DXA), multifrequency bioelectrical impedance (MF-BIA), and air displacement plethysmography (ADP) techniques. Muscular strength was also assessed. Measurements were performed at 4 points matched with the ketotic phases (basal, maximum ketosis, ketosis declining, and out of ketosis).Results: After 4 months the VLCK diet induced a −20.2 ± 4.5 kg weight loss, at expenses of reductions in fat mass (FM) of −16.5 ± 5.1 kg (DXA), −18.2 ± 5.8 kg (MF-BIA), and −17.7 ± 9.9 kg (ADP). A substantial decrease was also observed in the visceral FM. The mild but marked reduction in fat-free mass occurred at maximum ketosis, primarily as a result of changes in total body water, and was recovered thereafter. No changes in muscle strength were observed. A strong correlation was evidenced between the 3 methods of assessing body composition.Conclusion: The VLCK diet-induced weight loss was mainly at the expense of FM and visceral mass; muscle mass and strength were preserved. Of the 3 body composition techniques used, the MF-BIA method seems more convenient in the clinical setting.

The resting metabolic rate (RMR) decrease, observed after an obesity reduction therapy is a determinant of a short-time weight regain. Thus, the objective of this study was to evaluate changes in RMR, and the associated hormonal alterations in obese patients with a very low-calorie ketogenic (VLCK)-diet induced severe body weight (BW) loss. From 20 obese patients who lost 20.2 kg of BW after a 4-months VLCK-diet, blood samples and body composition analysis, determined by DXA and MF-Bioimpedance, and RMR by indirect calorimetry, were obtained on four subsequent visits: visit C-1, basal, initial fat mass (FM) and free fat mass (FFM); visit C-2, − 7.2 kg in FM, − 4.3 kg in FFM, maximal ketosis; visit C-3, − 14.4 kg FM, − 4.5 kg FFM, low ketosis; visit C-4, − 16.5 kg FM, − 3.8 kg FFM, no ketosis. Each subject acted as his own control. Despite the large BW reduction, measured RMR varied from basal visit C-1 to visit C-2, − 1.0%; visit C-3, − 2.4% and visit C-4, − 8.0%, without statistical significance. No metabolic adaptation was observed. The absent reduction in RMR was not due to increased sympathetic tone, as thyroid hormones, catecholamines, and leptin were reduced at any visit from baseline. Under regression analysis FFM, adjusted by levels of ketonic bodies, was the only predictor of the RMR changes (R2 = 0.36; p < 0.001). The rapid and sustained weight and FM loss induced by VLCK-diet in obese subjects did not induce the expected reduction in RMR, probably due to the preservation of lean mass. This is a follow up study on a published clinical trial.

The estimation of the time elapsed since death is of paramount importance in the field of forensic sciences and criminal investigation, owing, among other factors, to the possible legal repercussions. Over the past few years various formulae have been developed to calculate this interval using a combination of different statistical methods and the concentrations of substances found in the vitreous humor. Corrective factors, such as ambient temperature, cause of death or age, which can modify the concentration of these substances and therefore the estimation of the postmortem interval, have been incorporated into models. In this paper five simple and reliable models to estimate PMI based the on the analysis of potassium, hypoxanthine and urea in the vitreous humor are presented. Corrective factors, such as body weight, rectal temperature and ambient temperature, which can influence the estimation of this interval have been incorporated into the formulae. Finally, the R2 and the mean squared error have been calculated for each model in order to select the best of the five. A free software program which calculates the PMI from the model and parameters used is available from the authors. It provides quick and reliable results as well as the error committed and R2 for each case.