The reversion of diabetes and the treatment of long-term obesity are difficult challenges. The failure mechanisms of rapid weight loss are mainly related to the wasting of lean mass. This single-arm study aims to evaluate the effects of a very low-calorie ketogenic diet (VLCKD) on body composition and resting energy expenditure in the short term reversal of diabetes mellitus Type 2. For eight weeks, subjects were administered a personalized VLCKD with protein intake based on lean mass and synthetic amino acidic protein supplementation. Each subject was assessed by anthropometry, Dual-energy X-ray Absorptiometry(DXA), bioimpedentiometric analysis (BIA), indirect calorimetry, and biochemical analysis. The main findings were the saving of lean mass, the reduction of abdominal fat mass, restored metabolic flexibility, the maintenance of resting energy expenditure, and the reversion of diabetes. These results highlight how the application of preventive, predictive, personalized, and participative medicine to nutrition may be promising for the prevention of diabetes and enhancement of obesity treatment.
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http://dx.doi.org/10.3390/nu11071526 | DOI Listing |
Nutrients
January 2025
Department of Sports Medicine and Sports Nutrition, Ruhr University Bochum, 44801 Bochum, Germany.
Background/objectives: Low energy availability (LEA) can cause impaired reproductive function, bone health issues, and suppressed immune function, and may result in decreased performance and overall health status. The purpose of this study was to investigate adaptions of body composition, blood status, resting metabolic rate, and endurance performance to gain more comprehensive insights into the symptoms of LEA and the adaptive effects in the athlete population (active women (n = 11) and men (n = 11)).
Methods: Three treatments were defined as 45 (EA45, control), 30 (EA30), and 10 (EA10) kcal/kg FFM/day and randomly assigned.
Nutrients
January 2025
Department of Endocrinology and Diabetes, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan.
Objective: In treating obesity, energy intake control is essential to avoid exceeding energy expenditure. However, excessive restriction of energy intake often leads to resting energy expenditure (REE) reduction, increasing hunger and making weight loss difficult. This study aimed to investigate whether providing nutritional guidance that considers energy expenditure based on the regular evaluation of REE and physical activity could effectively reduce body weight (BW) in patients with obesity.
View Article and Find Full Text PDFGenes (Basel)
January 2025
Fondazione IRCSS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milano, Italy.
Mendelian disorders of the epigenetic machinery (MDEMs) include a large number of conditions caused by defective activity of a member of the epigenetic machinery. MDEMs are characterized by multiple congenital abnormalities, intellectual disability and abnormal growth. that can be variably up- or down-regulated.
View Article and Find Full Text PDFGenes (Basel)
December 2024
Sport Sciences Research Centre, Rey Juan Carlos University, 28943 Fuenlabrada, Madrid, Spain.
: Previous studies suggest that there is a genetically determined component of fat oxidation at rest and during exercise. To date, the gene has been proposed as a candidate gene to affect fat oxidation during exercise because of the association of the "at-risk" A allele with different obesity-related factors such as increased body fat, higher appetite and elevated insulin and triglyceride levels. The A allele of the gene may also be linked to obesity through a reduced capacity for fat oxidation during exercise, a topic that remains largely underexplored in the current literature.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
Center for Wearable Intelligent Systems and Healthcare, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Recognizing human body motions opens possibilities for real-time observation of users' daily activities, revolutionizing continuous human healthcare and rehabilitation. While some wearable sensors show their capabilities in detecting movements, no prior work could detect full-body motions with wireless devices. Here, we introduce a soft electronic textile-integrated system, including nanomaterials and flexible sensors, which enables real-time detection of various full-body movements using the combination of a wireless sensor suit and deep-learning-based cloud computing.
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