Inter-individual responses to sprint interval training, a pilot study investigating interactions with the sirtuin system

Abstract

Sprint interval training (SIT) is reported to improve blood glucose control and may be a useful public health tool. The sirtuins and associated genes are emerging as key players in blood glucose control. This study investigated the interplay between the sirtuin/NAD system and individual variation in insulin sensitivity responses after SIT in young healthy individuals. Before and after 4 weeks of SIT, body mass and fat percentage were measured and oral glucose tolerance tests performed in 20 young healthy participants (7 females). Blood gene expression profiles (all 7 mammalian sirtuin genes and 15 enzymes involved in conversion of tryptophan, bioavailable vitamin B3, and metabolic precursors to NAD). NAD/NADP was measured in whole blood. Significant reductions in body weight and body fat post-SIT were associated with altered lipid profiles, NAD/NADP, and regulation of components of the sirtuin/NAD system (NAMPT, NMNAT1, CD38, and ABCA1). Variable improvements in measured metabolic health parameters were evident and attributed to different responses in males and females, together with marked inter-individual variation in responses of the sirtuin/NAD system to SIT.

Development of a multiplex assay to determine the expression of mitochondrial genes in human skeletal muscle

“…Previous studies have reported on the development of customdesigned multiplex gene expression assays to determine expression of targeted groups of genes regulating various metabolic processes, including inflammation, NAD + -dependent deacetylase activity, and oxidative stress, among others (Drew et al, 2011(Drew et al, , 2014a(Drew et al, , 2015(Drew et al, , 2016Gray et al, 2018). Additionally, studies have also validated GeXP assays for use in pathogen detection in clinical settings (Huang et al, 2020;Wang et al, 2016), and for characterisation of pathologies in various tissues (Drew et al, 2014b;Farquharson et al, 2012), highlighting their potential for use in multiple research settings.…”

Challenges of the heterogeneous nutrition response: interpreting the group mean

“…Analysis is inevitably directed to comparing group means to interpret nutrition responses to generate meaningful conclusions from nutrition research. However, this often contributes to recording non-significant differences from group comparisons, despite clear evidence of responders and non-responders within study groups (2,3) . Alternatively, depending on the recruitment and composition of the group, contradicting and contrasting results can be recorded for similar nutritional interventions (4,8) .…”

“…The application of omic technology platforms has the potential to provide detailed and robust data, which combined with bioinformatics, has the potential to characterise individual variation and identify associated biomarkers and nutrition intervention targets (65) . The incorporation of omic technologies, such as genomics, proteomics, metabolomics and epigenetics in nutrition research is elucidating genetic variants, gene, protein and metabolic biomarkers and signatures that may decipher interindividual variation in responses to nutrition and permit identification of determinants of nutritional responses (2,3,28–30) . This has created opportunities for the evolution of new research fields, such as personalised and precision nutrition (nutrition tailored to individual attributes), molecular epidemiology and nutritional bioinformatics (31,32) .…”

“…For example, the daily requirement for Se, an essential dietary nutrient, is significantly influenced by the genetic variants in the genes encoding selenoproteins (67) . Gene signatures have potential to stratify study populations and aid interpretation of inter-individual variation in study groups (2,3) . Ultimately, such developments will make it feasible to incorporate diverse individuals and populations that currently do not meet inclusion criteria for recruitment currently used to compile study cohorts for nutrition research.…”

Determinants of plasma adropin associated with metabolic control and links to lipid and glucose homeostasis

“…Plasma adropin data measured using an ELISA kit (Cusabio, USA) was combined from three study cohorts: SIT (Sprint Interval Training) (n = 20) (3) , OGHH (Oats, Gut and Heart Health, clinicaltrials.gov Identifier ISRCTN11665494) (n = 40) and VegGI (Impact of Vegetables on acute Glycaemia and glycaemia induced CVD risk in women: metabolic and Inter-individual variations, researchregistry3117) (n = 31). Combined analysis of data from the three cohorts was conducted to identify correlates (Pearson correlations, Microsoft Excel) of adropin with age, sex, BMI and markers of metabolic health (fasted plasma lipids, glucose and insulin).…”