Searching across hundreds of databases
Lipoprotein subclasses possess crucial cardiometabolic information. Due to strong multicollinearity among variables, little is known about the strength of influence of physical activity (PA) and adiposity upon this cardiometabolic pattern. Using a novel approach to adjust for covariates, we aimed at determining the "net" patterns and strength for PA and adiposity to the lipoprotein profile. Principal component and multivariate pattern analysis were used for the analysis of 841 prepubertal children characterized by 26 lipoprotein features determined by proton nuclear magnetic resonance spectroscopy, a high-resolution PA descriptor derived from accelerometry, and three adiposity measures: body mass index, waist circumference to height, and skinfold thickness. Our approach focuses on revealing and validating the underlying predictive association patterns in the metabolic, anthropologic, and PA data to acknowledge the inherent multicollinear nature of such data. PA associates to a favorable cardiometabolic pattern of increased high-density lipoproteins (HDL), very large and large HDL particles, and large size of HDL particles, and decreasedtriglyceride, chylomicrons, very low-density lipoproteins (VLDL), and their subclasses, and to low size of VLDL particles. Although weakened in strength, this pattern resists adjustment for adiposity. Adiposity is inversely associated to this pattern and exhibits unfavorable associations to low-density lipoprotein (LDL) features, including atherogenic small and very small LDL particles. The observed associations are still strong after adjustment for PA. Thus, lipoproteins explain 26.0% in adiposity after adjustment for PA compared to 2.3% in PA after adjustment for adiposity.
Pubmed ID: 34205279 RIS Download
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Commercial instrument supplier for human activity monitors. ActiGraph devices are used by researchers and clinicians in hundreds of universities and research organizations in more than 56 countries. ActiGraph activity monitors are the most validated and widely used devices of their kind. ActiGraph activity monitors use triaxial accelerometers and our validated proprietary filtering algorithms to accurately measure the amount and intensity of human activity. They are powered by rechargeable lithium ion batteries, and battery charging and communication are accomplished via a standard USB connection. All ActiGraph activity monitors can be worn at the waist or wrist and are suitable for subjects of all ages. ActiGraph devices have been used in hundreds of research studies in nearly 60 countries around the world since 1992. Leading research facilities including the U.S. National Institute of Health (NIH), the Institute of Child Health in the UK, and Karolinska Institutet in Sweden rely on our products to provide objective activity measurement in dozens of areas including obesity, diabetes, sleep, elderly behavior, and athletics. ActiGraph often works with scientific organizations to develop and implement software and hardware features that reflect the evolving needs of the research community. ActiGraph devices are well recognized as some of the most accurate activity measurement products on the market. Extensive research has confirmed our accuracy against the VO2 and doubly labeled water (DLW) methods of estimating energy expenditure. Keywords: Device, Researcher, Clinician, University, Research, Organization, Obesity, Diabetes, Sleep, Elderly, Behavior, Athletics, Scientific, Activity monitor, Physical, Measurement, Human, Activity,
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
scicrunch.org
