Physical activity and sedentary behavior show distinct associations with tissue-specific insulin sensitivity in adults with overweight

Aim: The aim of this study is to investigate associations between the physical activity (PA) spectrum (sedentary behavior to exercise) and tissue-specific insulin resistance (IR). Methods: We included 219 participants for analysis (median [IQR]: 61 [55; 67] years, BMI 29.6 [26.9; 32.0] kg/m2; 60% female) with predominant muscle or liver IR, as determined using a 7-point oral glucose tolerance test (OGTT). PA and sedentary behavior were measured objectively (ActivPAL) across 7 days. Context-specific PA was assessed with the Baecke questionnaire. Multiple linear regression models (adjustments include age, sex, BMI, site, season, retirement, and dietary intake) were used to determine associations between the PA spectrum and hepatic insulin resistance index (HIRI), muscle insulin sensitivity index (MISI) and whole-body IR (HOMA-IR, Matsuda index). Results: In fully adjusted models, objectively measured total PA (standardized regression coefficient β = 0.17, p = 0.020), light-intensity PA (β = 0.15, p = 0.045) and moderate-to-vigorous intensity PA (β = 0.13, p = 0.048) were independently associated with Matsuda index, but not HOMA-IR (p > 0.05). A higher questionnaire-derived sport index and leisure index were associated with significantly lower whole-body IR (Matsuda, HOMA-IR) in men but not in women. Results varied across tissues: more time spent sedentary (β = −0.24, p = 0.045) and a higher leisure index (β = 0.14, p = 0.034) were respectively negatively and positively associated with MISI, but not HIRI. A higher sport index was associated with lower HIRI (β = −0.30, p = 0.007, in men only). Conclusion: While we confirm a beneficial association between PA and whole-body IR, our findings indicate that associations between the PA spectrum and IR seem distinct depending on the primary site of insulin resistance (muscle or liver)

Cardiometabolic health improvements upon dietary intervention are driven by tissue-specific insulin resistance phenotype: A precision nutrition trial

Precision nutrition based on metabolic phenotype may increase the effectiveness of interventions. In this proof-of-concept study, we investigated the effect of modulating dietary macronutrient composition according to muscle insulin-resistant (MIR) or liver insulin-resistant (LIR) phenotypes on cardiometabolic health. Women and men with MIR or LIR (n = 242, body mass index [BMI] 25–40 kg/m2, 40–75 years) were randomized to phenotype diet (PhenoDiet) group A or B and followed a 12-week high-monounsaturated fatty acid (HMUFA) diet or low-fat, high-protein, and high-fiber diet (LFHP) (PhenoDiet group A, MIR/HMUFA and LIR/LFHP; PhenoDiet group B, MIR/LFHP and LIR/HMUFA). PhenoDiet group B showed no significant improvements in the primary outcome disposition index, but greater improvements in insulin sensitivity, glucose homeostasis, serum triacylglycerol, and C-reactive protein compared with PhenoDiet group A were observed. We demonstrate that modulating macronutrient composition within the dietary guidelines based on tissue-specific insulin resistance (IR) phenotype enhances cardiometabolic health improvements

Membrane protein structure, function, and dynamics: a perspective from experiments and theory

Membrane proteins mediate processes that are fundamental for the flourishing of biological cells. Membrane-embedded transporters move ions and larger solutes across membranes; receptors mediate communication between the cell and its environment and membrane-embedded enzymes catalyze chemical reactions. Understanding these mechanisms of action requires knowledge of how the proteins couple to their fluid, hydrated lipid membrane environment. We present here current studies in computational and experimental membrane protein biophysics, and show how they address outstanding challenges in understanding the complex environmental effects on the structure, function, and dynamics of membrane proteins.JTD, IA, and MR used the computational resources of the Modeling Facility of the Department of Chemistry, University of California Irvine funded by NSF Grant CHE-0840513 for this work. A-NB was supported in part by the Marie Curie International Reintegration Award IRG-26920.TWA was supported by ARC DP120103548, NSF MCB1052477, DE Shaw Anton (PSCA00061P; NRBSC, through NIH RC2GM093307), VLSCI (VR0200), and NCI (dd7). BA and SV acknowledge the support by ERC advanced Grant No. 268888. ZC and PG would like to acknowledge Reference Framework (NSRF) 2011–2013, National Action ‘‘Cooperation,’’ under grant entitled ‘‘Magnetic Nanoparticles for targeted MRI therapy (NANOTHER),’’ with code ‘‘11RYM-1-1799.’’ The program is cofunded by the European Regional Development Fund and national resources. Part of the calculations presented herein were performed using resources of the LinkSCEEM-2 project, funded by the EC under FP7 through Capacities Research Infrastructure, INFRA-2010-1.2.3 Virtual Research Communities, Combination of Collaborative Project and Coordination and Support Actions (CPCSA) under Grant agreement no. RI-261600. GB was supported in part by NSF grant MCB1330728 from the National Science Foundation and Grant PO1GM55876-14A1 from the National Institutes of Health. LD received funding from EU FP7 (PIOF-GA-2012-329534). LD, and MLK used the computational resources of Temple University, supported by the National Science Foundation through major research instrumentation grant number CNS-09-58854. JS acknowledges support from the Instituto de Salud Carlos III FEDER (CP12/03139