Objectives. The study aimed to assess the relationship between body fat and each of body mass index (BMI) and waist circumference (WC), and to test the effectiveness of fat mass (FM), percent of body fat (PBF), BMI, and WC in predicting high levels of fasting blood glucose (FBG). Methods. A total of 167 adolescents aged 11-17 years were recruited from Rabat region. BMI and WC were determined using standard equipments. FM and PBF were derived from isotope dilution technique. FBG was determined by the hexokinase method. Results. Regardless of the weight status, BMI showed a strong positive correlation with FM and PBF in both genders. WC was significantly correlated with FM in boys and girls, and with PBF in different groups of girls and boys of the study sample. However, there was no significant relationship between WC and PBF in normal weight and overweight-obese groups of boys. FBG was highly correlated with FM and PBF in girls of the study sample and in overweight-obese girls. Similar significant relationship between FBG and both BMI and WC was observed in overweight-obese girls, while there was no significant association between FBG and other variables in boys and normal-weight girls. Conclusion. BMI and WC were closely associated with FM and PBF, respectively. However, the degree of these associations depends on gender and weight status. BMI may provide a better proxy estimate of overall adiposity than WC; nevertheless, both of them would appear to be a reasonable surrogate for FM and PBF as screening tools to identify adolescents at risk of developing excess body fat and high level of FBG.
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http://dx.doi.org/10.1155/2012/510458 | DOI Listing |
Cancer Med
January 2025
The Huntsman Cancer Institute at the University of Utah, Salt Lake City, Utah, USA.
Introduction: The purpose of this study was to evaluate the association between body composition, overall survival, odds of receiving treatment, and patient-reported outcomes (PROs) in individuals living with metastatic non-small-cell lung cancer (mNSCLC).
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Eur Heart J Cardiovasc Imaging
January 2025
Sorbonne Université, unité d'imagerie cardiovasculaire et thoracique, Hôpital La Pitié Salpêtrière (AP-HP), Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Institute of Cardiometabolism and Nutrition, ACTION Group, Paris, France.
Purpose: Epicardial adipose tissue (EAT) could contribute to the specific atherosclerosis profile observed in premature coronary artery disease (pCAD) characterized by accelerated plaque burden (calcified and non-calcified), high risk plaque features (HRP) and ischemic recurrence. Our aims were to describe EAT volume and density in pCAD compared to asymptomatic individuals matched on CV risk factors and to study their relationship with coronary plaque severity extension and vulnerability.
Materials And Methods: 208 patients who underwent coronary computed tomography angiography (CCTA) were analyzed.
EClinicalMedicine
October 2024
Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Center, St. Michael's Hospital, Unity Health Toronto, Toronto, ON M5B 1W8, Canada.
Background: Use of health applications (apps) to support healthy lifestyles has intensified. Different app features may support effectiveness, including gamification defined as the use of game elements in a non-game situation. Whether health apps with gamification can impact behaviour change and cardiometabolic risk factors remains unknown.
View Article and Find Full Text PDFSarcopenia is defined as a muscle-wasting syndrome that occurs with accelerated aging, while cachexia is a severe wasting syndrome associated with conditions such as cancer and immunodeficiency disorders, which cannot be fully addressed through conventional nutritional supplementation. Sarcopenia can be considered a component of cachexia, with the bidirectional interplay between adipose tissue and skeletal muscle potentially serving as a molecular mechanism for both conditions. However, the underlying mechanisms differ.
View Article and Find Full Text PDFWorld J Clin Cases
January 2025
Department of Gastroenterology, Laiko General Hospital, National and Kapodistrian University of Athens, Athens 11527, Greece.
Machine learning (ML) is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis, thus creating machines that can complete tasks otherwise requiring human intelligence. Among its various applications, it has proven groundbreaking in healthcare as well, both in clinical practice and research. In this editorial, we succinctly introduce ML applications and present a study, featured in the latest issue of the .
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