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652
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ShareScore release 0.9.0
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652 results for “Body Composition”
A Double-Blind, Randomized, Placebo-Controlled Study Evaluating the Effects of Bardoxolone Methyl on Body Composition in Patients With Stage 4 Chronic Kidney Disease and Type 2 Diabetes Mellitus
ClinicalTrials.gov study NCT01655186. IPD Sharing: YES. Countries: 0. Publications: 0.
Mi Band Step Challenge to Improve Body Composition in Overweight University Students
ClinicalTrials.gov study NCT02893566. IPD Sharing: Not stated. Countries: 0. Publications: 0.
The Influence of Probiotics on Body Weight and Composition
ClinicalTrials.gov study NCT02465294. IPD Sharing: NO. Countries: 0. Publications: 0.
Impact of Protein Supplementation and Resistance Training on Body Composition, Functional Parameters, Metabolic Adaptation, and Nitrogen Balance in Adults Aged 45 Years and Older With Overweight or Ob
ClinicalTrials.gov study NCT06950684. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Effects of Pistachio Consumption on Body Composition and Blood and Bone Indicators
ClinicalTrials.gov study NCT02849392. IPD Sharing: YES. Countries: 0. Publications: 0.
Effects of Whey Protein Intervention on Body Composition and Muscle Function in the Elderly With Sacropenia
ClinicalTrials.gov study NCT02490410. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Prognostic Effect of Body Composition in Cancer Survivors
ClinicalTrials.gov study NCT06398782. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Study of Body Composition Methodology in Young Children (InfantPod)
ClinicalTrials.gov study NCT00730587. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Combining Prebiotic and Diet/Physical Activity Educational Program Effects on Body Composition and Intestinal Microbiota in Children
ClinicalTrials.gov study NCT05671731. IPD Sharing: NO. Countries: 0. Publications: 0.
Study of the Nutritional, Metabolic, and Body Composition Profile in Children and Adolescents With Epidermolysis Bullosa
ClinicalTrials.gov study NCT00004791. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Evaluation of body composition as a potential biomarker in spinal muscular atrophy
<p><strong>Introduction: </strong>We aimed to investigate the correlation between body composition (BC) and spinal muscular atrophy (SMA)-specific motor function assessments.</p> <p><strong>Methods: </strong>Patients with SMA types I or II, aged 1 to 10 years, were recruited in this cross-sectional study. The protocol included anthropometric measurements, and dual-energy X-ray absoprtiometry to assess fat mass (FM), lean mass (LM), fat-free mass (FFM), FM and FFM indexes (FMI, FFMI), and motor function assessments (Children's Hospital of Philadelphia Infant Test of Neuromuscular Disorders scale for SMAI, and Hammersmith Functional Motor Scale-Expanded for SMAII).</p> <p><strong>Results: </strong>Eighty-eight children were included. All had a higher FM percentage than reference values. Motor function was moderately correlated with body mass index (BMI), FFMI, and LMI in SMAI, and weakly correlated with FFMI, LMI, and LM:FM ratio in SMAII.</p> <p><strong>Discussion: </strong>BC shows promise as a potential biomarker for SMA, but further studies are needed.</p> <p> </p>
Indexes of adiposity and body composition in the prediction of metabolic syndrome in obese children and adolescents: Which is the best?
<p>Background and aim</p> <p>There is no agreement about which index of adiposity and/or body composition is the most accurate in identifying the metabolic syndrome (METS). The aim of our study was to compare the accuracy of the different indexes in order to recognize the most reliable.</p> <p>Methods and results</p> <p>We evaluated 1332 obese children and adolescents (778 females and 554 males), aged 14.4 ± 1.8 yrs, Body Mass Index (BMI) standard deviation scores (SDS) 2.99 ± 0.55, followed at the Istituto Auxologico Italiano, a tertiary center for childhood obesity. For each subject the following indexes were assessed: BMI, BMI SDS, Fat-Free Mass Index (FFMI), Fat Mass Index (FMI), Tri-Ponderal Mass Index (TMI), Waist-to-Height ratio (WtHR) and a new one, the Body Mass Fat Index (BMFI), which normalizes the BMI for percentage of body fat and the waist circumference. Thereafter we calculated for each index a threshold value for age and sex, in order to compare their accuracy, sensitivity and specificity in identifying the METS. There was a good correlation among indexes (p < 0.0001 for all). However, when the area under the curve (AUC) was compared, some of them, in particular the BMFI and the BMI, performed better than the other ones, although the differences were small.</p> <p>Conclusions</p> <p>BMI, which neither considers body composition nor fat distribution, performs as good as other indexes, and should therefore be the preferred one, also because of the easiness of its calculation.</p>
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