Determination of antioxidant activity of polyphenol extract from grape seeds

The significance of carbohydrates for endurance training has been well established, whereas the role of protein and the adaptive response with endurance training is unclear. Therefore, the aim of this perspective is to discuss the current evidence on the role of dietary protein and the adaptive response with endurance training. On a metabolic level, a single bout of endurance training stimulates the oxidation of several amino acids. Although the amount of amino acids as part of total energy expenditure during exercise is relatively low compared to other substrates (e.g., carbohydrates and fat), it may depress the rates of skeletal muscle protein synthesis, and thereby have a negative effect on training adaptation. A low supply of amino acids relative to that of carbohydrates may also have negative effects on the synthesis of capillaries, synthesis and turn-over of mitochondrial proteins and proteins involved in oxygen transport including hamoglobin and myoglobin. Thus far, the scientific evidence demonstrating the significance of dietary protein is mainly derived from research with resistance exercise training regimes. This is not surprising since the general paradigm states that endurance training has insignificant effects on skeletal muscle growth. This could have resulted in an underappreciation of the role of dietary protein for the endurance athlete. To conclude, evidence of the role of protein on endurance training adaptations and performance remains scarce and is mainly derived from acute exercise studies. Therefore, future human intervention studies must unravel whether dietary protein is truly capable of augmenting endurance training adaptations and ultimately performance

Association of Cardiometabolic Multimorbidity With Mortality.

IMPORTANCE: The prevalence of cardiometabolic multimorbidity is increasing. OBJECTIVE: To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. DESIGN, SETTING, AND PARTICIPANTS: Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689,300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128,843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499,808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates. EXPOSURES: A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). MAIN OUTCOMES AND MEASURES: All-cause mortality and estimated reductions in life expectancy. RESULTS: In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy. CONCLUSIONS AND RELEVANCE: Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity

Association of Cardiometabolic Multimorbidity With Mortality.

IMPORTANCE: The prevalence of cardiometabolic multimorbidity is increasing. OBJECTIVE: To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. DESIGN, SETTING, AND PARTICIPANTS: Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689,300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128,843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499,808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates. EXPOSURES: A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). MAIN OUTCOMES AND MEASURES: All-cause mortality and estimated reductions in life expectancy. RESULTS: In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy. CONCLUSIONS AND RELEVANCE: Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity

World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions

BACKGROUND: To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. METHODS: In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40-80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. FINDINGS: Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0·685 (95% CI 0·629-0·741) to 0·833 (0·783-0·882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40-64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. INTERPRETATION: We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. FUNDING: World Health Organization, British Heart Foundation (BHF), BHF Cambridge Centre for Research Excellence, UK Medical Research Council, and National Institute for Health Research

Nutritional impact on molecular and physiological adaptations to exercise : nutrition matters

Skeletal muscle responds to exercise by a diversity of processes that collectively contribute to short-term and structural adaptations to the demanded performance capacities. There is common consensus that, in general, adequate nutrient availability during and after exercise is important to maximise skeletal muscle adaptation and ultimately performance. At the same time, several knowledge gaps remain regarding the precise mechanisms underlying these effects on adaptation, the most optimal nutrient composition in relation to type of exercise, optimal timing etc. This dissertation addresses some of these unsolved issues by studying the role of carbohydrates and proteins during adaptation following different forms of exercise. The first part (chapters 2 – 4) focusses on carbohydrate availability with resistance exercise, whereas the second part (chapters 5 - 7) specifically addresses the effects and potential of protein supplementation with endurance training. In chapter 2 we reviewed the existing literature regarding the role of skeletal muscle glycogen with endurance and resistance exercise. Based on this review we concluded that the role of muscle glycogen levels and/or carbohydrate availability on the skeletal muscle adaptive response to resistance exercise requires further scientific attention. To experimentally explore this, we assessed the impact of a pre-exercise meal that differed in macronutrient content on skeletal muscle glycogen levels and acute transcriptional level analysing specific mRNAs in the post-resistance exercise period in chapter 3. Specifically, after a glycogen depleting endurance exercise session in the morning, subjects received an isocaloric mixed meal containing different amounts of carbohydrates and fat 2 hours before a resistance exercise session in the afternoon, while ample protein was provided throughout the day. We hypothesized that some of the selected mRNAs associated with substrate metabolism and mitochondrial biogenesis would differ between the nutritional conditions, without any changes in proteolytic genes. The findings described in chapter 3 demonstrated that muscle mRNA responses related to exercise adaptation were minimally affected by the pre-exercise meals that differed in macronutrient composition. In chapter 4, derived from the same study, we describe the analysis of a number of plasma cytokine patterns during the day to investigate whether these mediators were affected by carbohydrate availability. We hypothesized that some selected cytokines would differ between nutritional conditions, whereas other circulating cytokines suggested to be involved in skeletal muscle adaptation would not respond differently. Our main finding was that a pre-exercise meal in general did not influence plasma cytokine responses in the post-resistance exercise period. Findings of chapter 3 and 4 contribute to the view that carbohydrate availability during resistance exercise is of minor importance when aiming for an acute positive skeletal muscle adaptive response. In addition, our data question the importance of carbohydrates as both substrate for resistance exercise and as modulator of the skeletal muscle response that underlies adaptation. Yet, at present it might be premature to change carbohydrate recommendations for individuals performing resistance exercise. Shifting our focus to proteins, we first reviewed the effects and possible underlying physiological mechanisms of protein supplementation on the adaptive response to endurance training in Chapter 5. To further explore these insights, we performed a double-blind randomised controlled trial with repeated measures to determine whether protein supplementation impacts the adaptive response to endurance training. In chapter 6 we provide proof-of-concept that protein supplementation elicited greater increases in VO2max and stimulated lean mass gain in response to prolonged endurance training. To our knowledge, this was the first double-blind randomised controlled trial with repeated measures showing that protein supplementation enhances the adaptive response to endurance training. These remarkable effects of protein on VO2max that were observed give rise to questions regarding their underlying mechanisms. To this end, we analysed the muscle transcriptome to gain insight into changes in the steady-state gene expression. In chapter 7, we demonstrated that prolonged endurance training changed expression of genes involved in extracellular matrix organisation, energy metabolism and oxidative phosphorylation. Changes in extracellular matrix organisation tended to be greater in the protein group than in the control group and these greater transcriptional changes may reflect the enhanced physiological adaptation as a result of protein supplementation.</p

Protein supplementation elicits greater gains in maximal oxygen uptake capacity and stimulates lean mass accretion during prolonged endurance training: a double-blind randomized controlled trial

Contains fulltext : 206248.pdf (publisher's version ) (Open Access

Plasma cytokine responses to resistance exercise with different nutrient availability on a concurrent exercise day in trained healthy males

Contains fulltext : 192632.pdf (publisher's version ) (Open Access

Koolhydraten en trainingsadaptatie: een beetje minder voor meer resultaat?

Koolhydraten spelen een belangrijke rol tijdens en na inspanning. Ze leveren energie en zorgen voor herstel. Recent onderzoek toont echter aan dat je door minder koolhydraten in te nemen de prestaties juist kunt verbeteren. Pim Knuiman vertelt hier meer over. Hij doet onderzoek naar de adaptieve respons van de skeletspier bij training en voeding, en bekijkt dit van molecuul tot prestatie