Background & Aims: With the rise of global cardiometabolic diseases, it is important to investigate risk factors such as obesity. Metabolic flexibility, the ability to maintain metabolic homeostasis following an acute challenge, can reflect cardiometabolic health. We investigated the association between body composition and the metabolic flexibility following meal consumption in an adult population.
Methods: In this study of 1027 participants (mean age 44.0 y ± SD 4.2 y), we administered a mixed-macronutrient meal challenge. Fasting and 2-h postprandial plasma were assayed for lipids, glycemic, and inflammation biomarkers. We characterized metabolic flexibility through meal-induced biomarker responses (%Δ, the difference between postprandial and fasting concentrations, divided by fasting concentration). We then compared the responses by sex-specific tertiles of body mass index (BMI) and percent body fat.
Results: With every unit (kg/m) increase in BMI, %Δ (95% confidence interval) increased by 0.17% (0.09, 0.26%) for total cholesterol, 0.31% (0.07, 0.54%) for triglycerides, and 0.11% (0.01, 0.20%) for apoA-I, whereas insulin elevation was reduced (-6.30%; -8.41, -4.20%), and the reduction in leptin was attenuated (0.64%; 0.25, 1.05%). With each unit (percent) increase in body fat, we observed similar changes in the %Δ of total cholesterol and leptin but not in triglycerides, apoA-I, or insulin. Glucose response increased by 0.29% (0.06, 0.51%) as body fat increases by one unit.
Conclusion: Metabolic flexibility, as assessed by biomarker responses to an acute physiological meal challenge, differed by body composition. These findings may help elucidate the pathways through which obesity contributes to cardiometabolic diseases.
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http://dx.doi.org/10.1016/j.clnesp.2021.09.730 | DOI Listing |
Methods Mol Biol
December 2024
Department of Environmental Toxicology, The University of California, Davis, Davis, CA, USA.
Biological fluids are proteinaceous liquids or suspensions released through different body orifices or through penetration of the skin. These fluids are the result of multiple tissues and cell types and contain extensive, highly complex, and dynamic protein populations that reflect both the transcriptional program of the originating cells and a record of the individual's health status. Body fluids are readily accessible to clinicians and researchers, and as such proteomic analyses are an important component of clinical studies, fertility studies, oral health studies, and forensic investigations.
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December 2024
Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, 030032, China.
A pasting-3D microfluidic paper-based analytical device (P-3D μPAD) was developed. It enabled an efficient cascade reaction between urate oxidase (UOX) and Fe/Pt-doped carbon nanoparticles (Fe/Pt-CNPs) for visual colorimetric detection of uric acid (UA). The novel Fe/Pt-CNP nanozyme performed high peroxidase-like activity toward 3,3',5,5'-tetramethylbenzidine (TMB) and HO with Michaelis - Menten constants (K) of 0.
View Article and Find Full Text PDFBMC Med Res Methodol
December 2024
Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, 180 Madison Avenue, New York, NY, USA.
Background: In cohort studies with time-to-event outcomes, covariates of interest often have values that change over time. The classical Cox regression model can handle time-dependent covariates but assumes linear effects on the log hazard function, which can be limiting in practice. Furthermore, when multiple correlated covariates are studied, it is of great interest to model their joint effects by allowing a flexible functional form and to delineate their relative contributions to survival risk.
View Article and Find Full Text PDFJ Pharm Sci
December 2024
Research and Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, Illinois, 60064, United States.
Biopharmaceutical companies generate a wealth of data, ranging from in silico physicochemical properties and machine learning models to both low and high-throughput in vitro assays and in vivo studies. To effectively harnesses this extensive data, we introduce a statistical methodology facilitated by Accuracy, Utility, and Rank Order Assessment (AURA), which combines basic statistical analyses with dynamic data visualizations to evaluate endpoint effectiveness in predicting intestinal absorption. We demonstrated that various physicochemical properties uniquely influence intestinal absorption on a project-specific basis, considering factors like intestinal efflux, passive permeability, and clearance.
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