Diet-gut bacteria-health interactions in older
consumers: new opportunities for food companies
Paul W. O’Toole
Dept. Microbiology, Univ. College Cork, Ireland
Alimentary Pharmabiotic Centre, Univ. College Cork, Ireland
http://apc.ucc.ie
http://eldermet.ucc.ie
May 22nd 2013
Bord Bia, Dublin
What are gut bacteria and why might they
be relevant?
• 10-100 trillion microbes in human gut
• ~10 times more bacterial cells in gut than human cells in
body!
• ~ 100 times more bacterial genes than human genes
• Human gut bacteria have a metabolic capacity similar to
that of the liver!
• Alterations in the gut microbiota are linked to
disease!
The plate-count anomaly
• Culturable fraction <30%
• We could not study the
intestinal microbiota
• Required
development
of culture-independent
techniques
Gut microbiota composition in healthy
adults
48%
51%
Eckburg et al, 2005.
Science 308: 1635-8
(85%)
http://apc.ucc.ie
Qin et al., 2010.
Nature 464: 59-65
http://apc.ucc.ie
Microbiota alterations are associated with
disease states
De Vos & De Vos, 2012. Nutrition Reviews 70 (S. ):S45–S56
Human diseases with microbiota
linkages
• Inflammatory Bowel Disease (IBD; eg
Crohn’s)
• Irritable Bowel Syndrome (IBS)
• Obesity
• Cardiovascular disease
Obese animals have gut bacteria
that extract calories and alter
metabolism
• Obesity is “transplantable” in mice
• Gut bacteria may be targeted to control human obesity
Cardiovascular disease risk is
heightened by gut bacteria
• Some gut bacteria convert dietary ingredients
into components of artery-blocking plaque
How nutrition works
Food
Cells & tissues
Metabolism
Heat
Growth
Energy
How nutrition really works
Food
Cells & tissues
Metabolism
Heat
Growth
Energy
The gut microbiota impacts on metabolism
The human-associated microbiota is
big news
June 14th 2012
June 8th 2012
Aug 18th 2012
ELDERMET
ELDERMET (Ireland)
€5 million, ~$6.6 Million
€5 million
€5 million
Amended from Nature,
May 2008
Ageing Population – a global
challenge....
 2000 -2030: worldwide >65
y.o. to double (420 million to
973 million)
Key changes in intestinal microbiota
in elderly
Reviewed in Woodmansey (2007)
J. App. Microbiol. 102: 1178-1186.
ELDERMET
Objectives
• Faecal microbiome 500 subjects >65 yrs,
T 0 , T3 , T6
• Clinical/health parameters, metabolome
• Microbial metagenome & metabolome test for correlations with health indices
• Stratification
STRATUM
Long stay
Rehab (<6 wks)
Day Hospital
Community
Community –antibiotic
Clostridium difficile positive
Colon cancer
TOTAL
SUBJECTS
100
50
50
50
100
100
50
500
Elderly subjects – sampling
•
•
•
•
•
•
•
•
Faeces
Blood
Urine
Saliva
Anthropometrics
Food Frequency Questionnaire (FFQ)
FIM, Barthel
MMSE, Geriatric Depression
Dietary Recommendations
Susan Power
Foods high in fat and/or sugar
(sparingly)
Meat, Fish, Poultry & Alternatives
(2 servings/day)
Milk, Cheese & Yoghurt
(3 servings/day)
Fruit & Vegetables
(5+ servings/day)
Breads, Cereals,
Potatoes, Rice &
Pasta
(6+ servings/day)
Reference: Irish Nutrition & Dietetic Institute http://www.indi.ie/index.php?page=32
Non-consumers (%) of particular food groups
Non-consumers (%) of particular food groups
Non-consumers (%) of particular food groups
Non-consumers (%) of particular food groups
Compliance with
dietary guidelines
Foods High in Fat and/or Sugar
Foods High in Fat and/or Sugar
(use sparingly) 19%
(use sparingly) 13%
Meat, Fish, Poultry & Alternatives
Meat, Fish, Poultry & Alternatives
(2 servings/day)
34%
(2 servings/day)
33%
Milk, Cheese, Yoghurt products
Milk, Cheese, Yoghurt products
(3 servings/day) 10%
(3 servings/day) 10%
Fruit & Vegetables
Fruit & Vegetables
(5+ servings/day) 64%
(5+ servings/day) 28%
Cereals, Breads, Potatoes, Rice &
Pasta
Cereals, Breads, Potatoes, Rice &
Pasta
(6+ servings/day) 18%
(6+ servings/day) 8%
Community
Long-stay care
The gut microbiota of elderly is different
to that of younger adults
Elderly adult intestinal microbiota
Young-adult intestinal microbiota
Claesson et al., 2011. PNAS USA.
The Bacteroidetes : Firmicutes ratio varies
considerably in elderly subjects
n = 160
(14-91)% Bacteroidetes : (81-10%) Firmicutes
Claesson et al., 2011. PNAS USA.
Is variation in microbiota
composition related to community
location, diet or metadata?
Marcus Claesson
Ian Jeffery
•
•
•
•
•
83 Community-dwelling
20 Day hospital (out-patient)
15 Rehabilitation (≤6 weeks)
60 Long-stay (>6 weeks)
(13 Young healthy controls)
191
Mean age 78+/- 8 yrs; 65-102 yrs.
Gut bacteria depend on where you live
Community
Long-stay
Young control
Claesson et al., 2012. Nature.
Diet co-segregates with microbiota and
residence location
Complete-linkage clustering based on
Euclidean distances to PC1
Driving food types
FFQ PCA
Correspondence analysis
DG1: “low fat / high fibre”
DG2: “moderate fat / high fibre”
DG3: “moderate fat / low fibre”
DG4: “high fat / moderate fibre”
Microbiota diversity correlates
with diet diversity
What are the consequences for the
host of microbiota differences?
The measurable metabolies are
different depending on location
Long-stay
Community
Rehab
Community
Dr. Martina Wallace and Dr. Lorraine Brennan, Univ. College Dublin
Integrating metabolome, metabolites
& genus-level microbiota
Co-inertia of
microbiota &
metabolome 
by location
NMR spectrum
metabolite PCA
Normalized gene counts
Shotgun metagenome:
differentially abundant SCFA genes
Butyrate
•
•
•
Acetate
Propionate
BCoAt: Butyryl-CoA transferase/Acetyl-CoA hydrolase
ACS: Acetate-formyltetrahydrofolate synthetase/Formate-tetrahydrofolate ligase
PCoAt: Propionyl-CoA:succinate-CoA transferase/Propionate CoA-transferase
Claesson et al., 2012. Nature.
Who cares?
Inflammatory markers vary by
community location
Microbiota-health correlations
Health/clinical markers
•
•
•
•
•
•
•
•
•
•
BMI: Body Mass Index
CC: Calf Circumference
MAC: Mid-Arm Circumference
SBP: Systolic Blood Pressure
DBP: Diastolic Blood Pressure
CCI: Charlson Comorbidity Index
Barthel Index of Activities of Daily Living
FIM: Functional Independence Measure
MMSE: Mini-Mental State Exam
MNA: Mini-Nutritional Assessment
Possible confounders
– Antibiotics:
•
•
exclude <1mo
>1mo had no sign. effect on
µ-biota
– Adjust quantile regression
model for:
•Age and gender
•Location
•Medication
Microbiota separation correlates
with health measures
Claesson et al., 2012. Nature.
LONG-STAY, UNHEALTHY
COMMUNITY, HEALTHY
Microbiota changes across location are
mirrored by changes in health
Claesson et al., 2012. Nature.
The microbiota of elderly - summary
• Microbiota type correlates with habitual diet
• Movement from community dwelling to residential care
associated with altered diet
• Diet changes; microbiota follows
• Microbiota alterations correlate with health changes
especially in long-stay subjects
• Metagenomics and metabolomics support a dietmicrobiota-health axis
n = 1,250
UK, NL, FR,
IT, PL
T0
12 mo.’s
5 x 25 subjects
Opportunities for food companies in the
elderly nutrition space
• Cleaner products – defined targets
• Elimination of undesirable microbial blooms
• Consumer friendly
- Pack size
- Cost
- Palatability
- Convenience
- Multiplex ingredients
• Enhancement of existing ingredients
Acknowledgements
Prof. R. Paul Ross
Prof. Colin Hill
Dr. Catherine Stanton
Prof. Gerald Fitzgerald
Prof. Fergus Shanahan
Prof. Ted Dinan
Dr. Julian Marchesi
Dr. Douwe van Sinderen
Dr. Anthony Fitzgerald
Dr. Denis O’Mahony
Prof. Cillian Twomey
Dr. Suzanne Timmons
Prof. Willy Molloy
Dr. Marcus Claesson
Dr. Ian Jeffery
Dr. Siobhán Cusack
Dr. Eibhlis O’Connor
Dr. Eileen O’Herlihy
Ms. Karen O’Donovan RN
Ms. Patricia Egan RN
Dr. Orla O’Sullivan
Ms. Jennifer Deane B. Sc.
Ms. Mairead Coakley M. Sc.
Ms. Bhuna Laks M. Sc.
Dr. Susana Conde
Mr. Hugh Harris M.Sc.
Dr. Mary Rea
Ms. Susan Power B.Sc.
Plus
Dr. Martina Wallace
Dr. Lorraine Brennan
The Cork City
Geriatricians Group