Paul Cotter Teagasc Food Research Centre & APC

Transcription

1 Research into the relationship between digestive bacteria, nutrition and health Paul Cotter Teagasc Food Research Centre & APC

2 Our microbes - Some impressive facts Microbes are a part of human life, living on all the surfaces and cavities of the human body. - More microbes than human cells (10:1) - Microbe genes:human genes = 100:1 (regarded as the other genome) -Majority of these microbes are found in the gut -Many of these can t be grown in the lab but thanks to DNA sequencing technologies we have a much better understanding of the composition and role of our microbes Unlike our human genome, our microbial genome can be altered to improve health Some things that alter our gut microbes (gut microbiota) include diet (+ or -), probiotics (+), prebiotics (+), antibiotics (usually -)

3 Microbiota - terminology Alpha diversity How many types of sequences in a sample? Beta diversity How different types are distributed among samples? Low total diversity within the gut microbiota is generally regarded as less desirable and has been observed in children that are more susceptible to allergies as well as sufferers of IBD, IBS and C. difficile infection (among others)

4 Eldermet Subjects separated by community location Differences driven by diet Beta diversity Community Long-stay Young control laesson, O Toole et al Nature 2012

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=40 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 2014

6 Lean Body Mass (Kg) Body Fat % BMI Waist Hip Ratio Physical Characteristics 50 P< P< p= P< P< Athlete Control BMI <25 Control BMI > Athlete Control BMI <25 Control BMI > P< P< p= P< p= P< Athlete Control BMI <25 Control BMI >28 0 Athlete Control BMI <25 Control BMI >28

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 : 209)

8 CK IU/L Creatine Kinase levels 4000 P< P< P= Athlete Control BMI <25 Control BMI >28

9 IL-6 (pg/ml) IL-8 (pg/ml) Cytokines IL-1 b (pg/ml) p< Athlete Control BMI <25 Control BMI > p= p< Athlete Control BMI <25 Control BMI > p=0.02 TNF- a (pg/ml) p= Athlete Control BMI <25 Control BMI >28 0 Athlete Control BMI <25 Control BMI >28

10 Leptin (pg/ml) Insulin (pg/ml) Adiponectin ( g/ml) Insulin, adiponectin and leptin 3000 p= p= p= Athlete Control BMI <25 Control BMI >28 0 p< Athlete Control BMI <25 Control BMI >28 No significant difference was seen in IFN, IL-10, Il-12p70, IL-6, CRP and glucose between the cohorts p< Athlete Control BMI <25 Control BMI >28

11 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

12 Macronutrients Macronutrient intake Recommended intakes Athletes (n=40) * 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) 18182** Protein (g) 248** Fat (g) 131** Saturated Fat (g) 44** Monounsaturated Fat (g) 41* Polyunsaturated Fat (g) >10% TE 18* Carbohydrate (g) 572** Sugars (g) <10% TE 330** Starch (g) 205* Fibre (g) >25g 39* Cholesterol (mg) <300mg 704** Alcohol (g) <20g % Total Energy from Protein 10-15% % Total Energy from Total Fat 20-35% % Total Energy from Saturated Fat <10% % Total Energy from Carbohydrate >50%

13 Top 5 food groups contributing to protein

14 Microbiota - a diversity *** *** ** ** ** * ** **

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

16 Correlation - microbial diversity and protein Protein Protein Protein Protein Protein Correlation coefficient: p value: Correlation coefficient: p value: Low BMI Controls High BMI Controls Athletes Phylogenetic diversity Correlation coefficient: p value: Shannon index Correlation coefficient: p value: Correlation coefficient: P value: Observed species Chao1 Simpson

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

18 Summary/Conclusion Elevated Creatine Kinase in athletes indicator of extreme exercise Athletes significantly less total body fat than controls Low BMI cohort physically more active than high BMI cohort Athletes are extremely fit and exercise a lot! Athletes diet is quite different from that of controls Athletes consume more macronutrients (except alcohol) A greater % of the energy intake of athletes is provided through protein Lower IL-Iβ, IL-6 and TNF-α in athletes Lower Athletes Adiponectin have in athletes lower than low inflammatory BMI cohort markers Lower Leptin in athletes and low BMI cohort compared to high BMI cohort than controls Higher diversity of gut microbiota in athletes Clear separation of microbial communities between athletes and controls using unweighted UniFrac analysis Increasing Athletes protein have intake a correlates more with diverse increased gut diversity microbiota Higher Firmicutes and lower Bacteroidetes found in athletes compared to high BMI control cohort

19 ExerciseMet Sedentary/inactive individuals i.e. low physical activity category according to International physical activity questionnaire (IPAQ). Male and Female BMI 22-35, age 18-40, minor non-confounding medical conditions or medications only. Group one - Exercise only for 8 weeks 30 recruited, 25 finished program Group two - Exercise plus 30g Whey protein once daily supplement for 8 weeks 30 recruited, 22 finished program Group three - 16 week study period; phase 1 - once daily protein supplementation alone for 8 weeks, 30 recruited, 27 finished program phase 2 - protein supplement plus exercise program 22 finished program

20 Exercise program 3 times a week in the Mardyke Sports Arena (mixture of aerobic and resistance) Aerobic based around couch to 5km models and include treadmill, bike, cross-trainer and stepper. Resistance training progressive based on 70% of one repetition max and includes upper body, lower body and core work outs. Calories expended and weight lifted measured using the online FitLinxx recording system attached to each machine in the gym.

21 Exercise program

22 Outputs Intestinal microbiota Urinary microbial metabolites Cytokines Routine haematology and Body composition Resting heart rate, Blood pressure Weight Waist Hip ratio Predicted VO2max (Rockport One mile walk test) Change in everyday physical activity using the International Physical Activity Questionnaire Dietary habits using a Food Frequency Questionnaire Cross sectional analysis by Cantab test/bdnf

23 Helping companies globally to prove food, beverage and supplement health claims

24 Health Area Expertise Gastrointestinal health Mental & cognitive health Cardiovascular health Healthy aging Sports performance Nutritional health Immune and inflammatory health Contact: Barry Skillington Business Development Manager Tel:

25 Thanks to ExMet AthleteMet & many others

26 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) 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

27 Microbiota taxonomic changes (16S rrna) Genus Family Phylum Bacteroidetes Firmicutes Candidate division TM6 Planctomycetes Chlamydiae Chloroflexi Spirochaetes Cynobacteria Pasteurellaceae Erysipelotrichaceae Lactobacillaceae Peptostreptococcaceae Veillonellaceae Ruminococcaceae Clostridiaceae Family XIII Incertae Sedis Peptococcaceae Coriobacteriaceae Akkermansiaceae Family XI Incertae Sedis Enterobacteriaceae S24-7 Succinivibrionaceae Comamonadaceae Spirochaetaceae Chitinophagaceae Saprospiraceae Bdellovibrionaceae Prevotellaceae Streptococcaceae 4Cod2 Haemophilus Bacteroides Lactobacillus Prevotella Peptostreptococcaceae Incertae Sedis Ruminococcaceae Incertae Sedis Clostridium Erysipelotrichaceae Incertae Sedis Roseburia Phascolarctobacterium Akkermansia Howardella Collinsella Holdemania Escherichia-Shigella Veillonella Coriobacteriaceae bacterium WAL RC9 gut group Succinivibrio Acinetobacter Treponema Bdellovibrio Ferruginibacter Filimonas Sedimentibacter Dorea Pseudobutyrivibrio A : >28 A : <25 <25 : >28 Athlete Athlete Low v High v Low v High Significant increase No change Significant decrease