Dietary soy and meat proteins induce distinct physiological and gene expression changes in rats

This study reports on a comprehensive comparison of the effects of soy and meat proteins given at the recommended level on physiological markers of metabolic syndrome and the hepatic transcriptome. Male rats were fed semi-synthetic diets for 1 wk that differed only regarding protein source, with casein serving as reference. Body weight gain and adipose tissue mass were significantly reduced by soy but not meat proteins. The insulin resistance index was improved by soy, and to a lesser extent by meat proteins. Liver triacylglycerol contents were reduced by both protein sources, which coincided with increased plasma triacylglycerol concentrations. Both soy and meat proteins changed plasma amino acid patterns. The expression of 1571 and 1369 genes were altered by soy and meat proteins respectively. Functional classification revealed that lipid, energy and amino acid metabolic pathways, as well as insulin signaling pathways were regulated differently by soy and meat proteins. Several transcriptional regulators, including NFE2L2, ATF4, Srebf1 and Rictor were identified as potential key upstream regulators. These results suggest that soy and meat proteins induce distinct physiological and gene expression responses in rats and provide novel evidence and suggestions for the health effects of different protein sources in human diets

Changes in weight loss, body composition and cardiovascular disease risk after altering macronutrient distributions during a regular exercise program in obese women

<p>Abstract</p> <p>Background</p> <p>This study's purpose investigated the impact of different macronutrient distributions and varying caloric intakes along with regular exercise for metabolic and physiological changes related to weight loss.</p> <p>Methods</p> <p>One hundred forty-one sedentary, obese women (38.7 ± 8.0 yrs, 163.3 ± 6.9 cm, 93.2 ± 16.5 kg, 35.0 ± 6.2 kg•m^-2, 44.8 ± 4.2% fat) were randomized to either no diet + no exercise control group (CON) a no diet + exercise control (ND), or one of four diet + exercise groups (high-energy diet [HED], very low carbohydrate, high protein diet [VLCHP], low carbohydrate, moderate protein diet [LCMP] and high carbohydrate, low protein [HCLP]) in addition to beginning a 3x•week^-1supervised resistance training program. After 0, 1, 10 and 14 weeks, all participants completed testing sessions which included anthropometric, body composition, energy expenditure, fasting blood samples, aerobic and muscular fitness assessments. Data were analyzed using repeated measures ANOVA with an alpha of 0.05 with LSD post-hoc analysis when appropriate.</p> <p>Results</p> <p>All dieting groups exhibited adequate compliance to their prescribed diet regimen as energy and macronutrient amounts and distributions were close to prescribed amounts. Those groups that followed a diet and exercise program reported significantly greater anthropometric (waist circumference and body mass) and body composition via DXA (fat mass and % fat) changes. Caloric restriction initially reduced energy expenditure, but successfully returned to baseline values after 10 weeks of dieting and exercising. Significant fitness improvements (aerobic capacity and maximal strength) occurred in all exercising groups. No significant changes occurred in lipid panel constituents, but serum insulin and HOMA-IR values decreased in the VLCHP group. Significant reductions in serum leptin occurred in all caloric restriction + exercise groups after 14 weeks, which were unchanged in other non-diet/non-exercise groups.</p> <p>Conclusions</p> <p>Overall and over the entire test period, all diet groups which restricted their caloric intake and exercised experienced similar responses to each other. Regular exercise and modest caloric restriction successfully promoted anthropometric and body composition improvements along with various markers of muscular fitness. Significant increases in relative energy expenditure and reductions in circulating leptin were found in response to all exercise and diet groups. Macronutrient distribution may impact circulating levels of insulin and overall ability to improve strength levels in obese women who follow regular exercise.</p

Adipokines: Linking metabolic syndrome, the immune system, and arthritic diseases

Metabolic syndrome (MetS) represents a cluster of metabolic and cardiovascular complications, including obesity and visceral adiposity, insulin resistance, dyslipidemia, hyperglycemia and hypertension, which directly increase the risk of cardiovascular diseases (CVD) and diabetes mellitus type 2 (DM2). Patients with arthritic diseases, such as rheumatoid arthritis and osteoarthritis, have a higher incidence of CVD. Although recent advances in the treatment of arthritic diseases, the incidence of CVD remains elevated, and MetS has been identified as a possible link between CVD and arthritic diseases. Chronic low-grade inflammation associated with obesity has been established as a significant contributing factor to the increased prevalence of MetS. Adipokines, which play important physiological roles in metabolic activities contributing to the pathogenesis of MetS, are also involved in the regulation of autoimmune and/or inflammatory processes associated with arthritic diseases. Therefore, MetS and dysregulated secretion of pro-inflammatory adipokines have been recognized as a molecular link between CVD and arthritis diseases. In the present paper, we review recent evidence supporting the role played by adipokines, in particular leptin, adiponectin, and lipocalin-2, in the modulation of the immune system, MetS and arthritic diseases. The underlying cellular and molecular mechanisms are discussed, as well as potential new therapeutic strategies.Acknowledgments: OG and FL are Staff Personnel of Xunta de Galicia (Servizo Galego de Saude, SERGAS) through a research-staff stabilization contract (ISCIII/SERGAS). VF is a “Sara Borrell” Researcher funded by ISCIII and FEDER (CD16/00111). RG is a “Miguel Servet” Researcher funded by Instituto de Salud Carlos III (ISCIII) and FEDER. CRF is a pre-doctoral research scholar funded by ISCIII and FEDER (Exp.18/00188). OG, MAGG, and RG are members of RETICS Programme, RD16/0012/0014 (RIER: Red de Investigación en Inflamación y Enfermedades Reumáticas) via Instituto de Salud Carlos III (ISCIII) and FEDER. FL is a member of CIBERCV (Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares). The work of OG and JP (PI17/00409), RG (PI16/01870 and CP15/00007) and FL (PI15/00681 PI18/00821 and CB16/11/00226) were funded by Instituto de Salud Carlos III and FEDER. OG is a beneficiary of a project funded by Research Executive Agency of the European Union in the framework of MSCA-RISE Action of the H2020 Programme (Project number 734899). RG is beneficiary of a project funded by Mutua Madrileña 2018. AM wishes to acknowledge financial support from the European Structural and Social Funds through the Research Council of Lithuania (Lietuvos Mokslo Taryba) according to the activity ‘Improvement of researchers’ qualification by implementing world-class R&D projects’ of Measure No. 09.3.3-LMT-K-712 (grant application code: 09.3.3-LMT-K-712-01-0157, agreement No. DOTSUT-215) and the new funding programme: Attracting Foreign Researchers for Research Implementation (2018–2022). The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript

Energy expenditure of rugby players during a 14-day in-season period, measured using doubly labelled water.

Criterion data for total energy expenditure (TEE) in elite rugby are lacking, which prediction equations may not reflect accurately. This study quantified TEE of 27 elite male rugby league (RL) and rugby union (RU) players (U16, U20, U24 age groups) during a 14-day in-season period using doubly labelled water (DLW). Measured TEE was also compared to estimated, using prediction equations. Resting metabolic rate (RMR) was measured using indirect calorimetry, and physical activity level (PAL) estimated (TEE:RMR). Differences in measured TEE were unclear by code and age (RL, 4369 ± 979; RU, 4365 ± 1122; U16, 4010 ± 744; U20, 4414 ± 688; U24, 4761 ± 1523 Kcal.day-1). Differences in PAL (overall mean 2.0 ± 0.4) were unclear. Very likely differences were observed in RMR by code (RL, 2366 ± 296; RU, 2123 ± 269 Kcal.day-1). Differences in relative RMR between U20 and U24 were very likely (U16, 27 ± 4; U20, 23 ± 3; U24, 26 ± 5 Kcal.kg-1.day-1). Differences were observed between measured and estimated TEE, using Schofield, Cunningham and Harris-Benedict equations for U16 (187 ± 614, unclear; -489 ± 564, likely and -90 ± 579, unclear Kcal.day-1), U20 (-449 ± 698, likely; -785 ± 650, very likely and -452 ± 684, likely Kcal.day-1) and U24 players (-428 ± 1292; -605 ± 1493 and -461 ± 1314 Kcal.day-1, all unclear). Rugby players have high TEE, which should be acknowledged. Large inter-player variability in TEE was observed demonstrating heterogeneity within groups, thus published equations may not appropriately estimate TEE

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field