Insights into diet, exercise and the gut microbiota in elite athletes

Transcription

1 Insights into diet, exercise and the gut microbiota in elite athletes Paul Cotter Head of Food Biosciences Teagasc Food Research Centre, Moorepark Principal Investigator, APC Microbiome Institute

2 Culture independent approaches Traditional, culture-based, approaches to microbiology only reveal the tip of the iceberg The success of culture based approaches is dependent on having types of agar/culture media/growth conditions that allow all microbes to grow Such an agar does not exist!! Indeed only a small % of microbes are easily cultured in the laboratory. DNA sequencing-based approaches can allow an analysis to the entire population regardless of whether it can be grown or not All of the DNA from the microbes present in a particular environment = Metagenomic DNA

3 Culture independent approaches Compositional sequencing Shotgun sequencing Community DNA Taxonomy Community Composition Genes or Pathways

4 Modulation Antimicrobials One can use Human studies Animal trials to assess the ability of Probiotics Prebiotics to positively influence the gut microbiota In vitro/ex vivo models Diet Exercise (?)

5 Aim To assess the relationship between diet, exercise, gut microbiota, metabolic & inflammatory markers in elite athletes relative to controls Subject Recruited Male Age: Elite Athletes n=4 Control BMI <25 n=23 Control BMI >28 n=23 Data collected Fasting blood sample (metabolic & inflammatory markers) Clinical bloods (CHOL, TG, fasting glucose etc.) Diet (FFQ) Body composition (DXA and W:H measurement) Faeces (microbial sequencing) Clarke et al Gut 214

6 Exercise Information (Controls) BMI <25 BMI >28 mean mean Walking winter (hours per week) summer (hours per week) Cycling winter (hours per week) summer (hours per week) Gardening winter (hours per week).9.17 summer (hours per week) Physical exercise e.g. aerobics, dancing, swimming etc. winter (hours per week) summer (hours per week) DIY (hours per week) Housework (hours per week) How many hours weekly of vigorous activity (sweating / heart rate) No. flights of stairs per day None have occupations that require a significant level of manual work 86% of the <25 BMI group have a sedentary occupation 91% of the >28 BMI group have sedentary occupation

7 Creatine Kinase levels Elevation of CK is an indication of damage to muscle The highest post-exercise serum enzyme activities are found after prolonged exercise such as ultradistance marathon running or weight-bearing exercises and downhill running, which include eccentric muscular contractions (Brancaccio et al., Br Med Bull : 29)

8 Macronutrient intake Macronutrients Recommende d intakes Athletes (n=4) * significant difference between athletes and >28 Controls ** significant difference between athletes and both control groups Controls BMI <25 (n=23) Controls BMI >28 (n=23) Median IQR Median IQR Median IQR Energy (Kcal) ** ** Energy (kj) Protein (g) 248** Fat (g) 131** Saturated Fat (g) 44** Monounsaturated Fat (g) 41* Polyunsaturated Fat (g) >1% TE 18* Carbohydrate (g) 572** Sugars (g) <1% TE 33** Starch (g) 25* Fibre (g) >25g 39* Cholesterol (mg) <3mg 74** Alcohol (g) <2g % Total Energy from Protein 1-15% % Total Energy from Total Fat 2-35% % Total Energy from Saturated Fat <1% % Total Energy from Carbohydrate >5%

9 Microbial diversity - Terminology Alpha diversity How many species are in an environment? Beta diversity How different species are distributed across environments?

10 Microbiota - diversity *** *** ** ** ** * ** **

11 Correlation - microbial diversity and protein Protein Protein Protein Protein Protein Correlation coefficient:.427 p value:. Correlation coefficient:.241 p value:.7 Low BMI Controls High BMI Controls Athletes Phylogenetic diversity Correlation coefficient:.289 p value:.7 Shannon index Correlation coefficient:.258 p value:.17 Correlation coefficient:.236 P value:.3 Observed species Chao1 Simpson

12 Creatine kinase Creatine kinase Creatine kinase Correlation - microbial diversity and CK Low BMI Controls High BMI Controls Athletes Phylogenetic diversity Shannon Simpson

13 Microbiota - diversity Unweighted Unifrac Green = High BMI Controls Blue = Low BMI Controls Black = Athletes

14 Culture independent approaches Compositional sequencing Shotgun sequencing Community DNA Taxonomy Community Composition Genes or Pathways Barton et al. Gut 218

15 Rugby team Shotgun vs. 16S

16 Microbiota Diversity - Pathways URINE NMR Faecal NMR

17 Functional Metagenomic Variation Thousands of pathways to analyse!!! High BMI control group had the lowest average abundance scores across 31 metabolic pathway categories. Athlete group had the highest mean abundance across 29 of the 34 metabolic categories

18 # of Correlations Functional Metagenomic Variation Carbohydrate(g./day) CK(IU/L) Energy(KJ/day) Fat(g./day) Fibre(g./day) Protein(g./day) 8 15 Dietary factors, sugars and other carbohydrates, as well as energy intake, provide the majority of the correlation for the control groups whereas the athlete group was predominantly correlated with CK, total bilirubin, and total energy intake Athlete Low.BMI High.BMI Starch(g./day) Sugars(g./day) Total.Bilirubin(IU/L)

19 # of Correlations AthMET- Pathway-metabolite associations SCFAs (produced by specific microbes) have been shown to alter chemotaxis and phagocytosis; induce reactive oxygen species (ROS); change cell proliferation and function; have antiinflammatory, anti-tumorigenic, and antimicrobial effects; and alter gut integrity Urine Acetoacetate Acetone Allantoin Creatine Dimethylglycine Glycerate L.Carnitine N.methylnicotinamide O.Acetyl.Carnitine PAG Proline.Betaine Succinate TMAO Trigonelline Unkown.A X3.OH.Isovaleric.acid Athlete High.BMI Low.BMI

20 SCFAs correlate with diet or microbial diversity Simpson diversity Shannon diversity PD whole tree diversity Lean mass Protein Fibre Spearman's Correlation Acetic acid Coefficient.54 Sig. (2-tailed). Spearman's Correlation Propioninc Coefficient acid Sig. (2-tailed) Spearman's Correlation Butyric acid Coefficient Sig. (2-tailed) Spearman's Correlation Isobutyric Coefficient acid Sig. (2-tailed) Isovaleric acid Valeric acid Spearman's Correlation Coefficient Sig. (2-tailed) Spearman's Correlation Coefficient Sig. (2-tailed) Acetic,propionic, butyric correlate with diet Isobutyric, valeric and valeric correlate with bacterial diversity

21 Applications to interventions 8 weeks Exercise (n=25) Exercise + whey (n=27) Fitter Whey protein (n=22) Casey et al MSys

22 Diet and exercise as modulators Cronin et al MSys 218 ExMet Submitted In prep Mycobiome ExMet IBD Rugby study Submitted In prep N=1 CricketMet In prep OlympicMet *REDSMet *Relative Energy Deficiency in Sport

23 Conclusion Gut microbiota contributes significantly to health and disease Diet and, now, exercise appear to be modulators of the gut microbiota Potential to use diet and exercise to improve the microbiota in non-athletes (and athletes) Potential to develop new probiotics/biotherapeutics for athletes

24 Acknowledgements Teagasc Orla O Sullivan Wiley Barton Ciara O Donovan Siobhan Clarke UCC Owen Cronin Fergus Shanahan Mick Molloy Eileen Murphy Eanna Falvey Imperial College London Elaine Holmes Nicholas Penney Isabel Garcia-Perez All athletes both elite and non Irish Sports Institute Sharon Madigan

25 Acknowledgements All athletes both elite and non Vision 1 group Prof. Michael Molloy Mardyke arena staff Metabolomics ( Imperial College London) Prof. Elaine Holmes Dr. Nicholas Penney Isabel Garcia Perez

26 Next Gen Sequencing Platform

27 NO OF 1 STUDY DESIGN

28 (+) INFLUENCE OF TRAINING Improved body composition Improved cardiorespiratory fitness

29 distance distance

30 METABOLIC CHANGES OVER TIME Participant 1 Participant 2 Urine Faecal

31 SIGNIFICANT URINARY METABOLITES

32 Summay n of 1 Improvements in fitness parameters Changes in metabolic and microbial dynamics over time 32

33 Culture independent approaches Extremes of life Health vs Disease Dietary & other interventions Food products Food chain Animal Health Environment Athletes vs Non-athletes Obese vs Lean Impact of antibiotics and other drugs Cystic fibrosis Bladder disease Impact of specific diets Microbiota of fermented foods Dairy powders Identification of new spoilage agents Microbiology of brewing Processing facilities Feed conversion efficiency pigs Milking equipment Cow rumen Mystery diseases in horses Viral diarrhoea in infants Plant pathogens Soil microbiology

34 What do we mean by Next Gen? 1 M reads Roche - GSFLX Whole-genome Composition 4 M Whole-genome Composition Shotgun 15 M ION - PGM Shotgun 3 M Shotgun 13 4 M Oxford -MinION Illumina - MiSeq ION - Proton Illumina - NextSeq

35 Gut microbiota and health Function Vitamin synthesis Digestion and absorption Immune stimulation Control pathogens Influencing the efficacy of energy harvest from ingested food Intestinal epithelial cell proliferation & differentiation, ph Signaling from the periphery to the brain

36 CK IU/L Creatine Kinase levels 4 P<.1 P<.1 3 P= Athlete Control BMI <25 Control BMI >28

37 IL-6 (pg/ml) TNF- (pg/ml) IL-1 (pg/ml) IL-8 (pg/ml) Cytokines p< p=.3 p<.1 2 Athlete Control BMI <25 Control BMI > Athlete Control BMI <25 Control BMI >28 3 p=.2 15 p= Athlete Control BMI <25Control BMI >28 Athlete Control BMI <25 Control BMI >28

38 Leptin (pg/ml) Insulin (pg/ml) Insulin, adiponectin and leptin p=.78 p=.2 Athlete Control BMI <25 Control BMI >28 Adiponectin ( g/ml) p<.1 p=.9 Athlete Control BMI <25 Control BMI >28 No significant difference was seen in IFN, IL-1, Il-12p7, IL-6, CRP and glucose between the cohorts 6 4 p<.1 2 Athlete Control BMI <25 Control BMI >28

39 Food Frequency Questionnaire An FFQ is a limited checklist of foods and beverages, with a frequency response section for subjects to report how often each items was consumed over a specified periods of time. Advantages: Representative of usual intake Preferable method for nutrients with very high day-to-day variability Less expensive than food records or diet recalls Suitable for very large studies Disadvantages: Relies upon the respondent s memory Less sensitive to measures of absolute intake Exclusion of foods popular to ethnic minority groups Possible response distortion of healthy foods (e.g. fruits, vegetables) Underreporting of energy intake was highest in men and individuals with a higher body mass index

40 Lean Body Mass (Kg) Body Fat % BMI Waist Hip Ratio Physical Characteristics 5 P<.1 P<.1 p= P<.1 P< Athlete Control BMI <25 Control BMI >28.7 Athlete Control BMI <25 Control BMI > P<.1 P<.1 p= P<.1 p=.52 P< Athlete Control BMI <25 Control BMI >28 Athlete Control BMI <25 Control BMI >28

41 Top 5 food groups contributing to protein

42 Diet and exercise as modulators ExMet Mycobiome ExMet IBD AthleteMet N=2 CricketMet OlympicMet *REDSMet *Relative Energy Deficiency in Sport

43 OlympicMet Baseline study of the gut microbiota of athletes participating in international competition including Olympians Recruited 35 individuals participating in international competition Males (N=2) Age Females (N=1) Age Range of sports including; Marathon running Swimming Rowing Paralympic cycling Paralympic swimming Race walking Boxing Cycling Triathlete Hockey Steeplechase