National Cancer Institute Microbiome measurements in epidemiological studies.
17
National Cancer Institute Microbiome measurements in epidemiological studies
-
Upload
laurence-thornton -
Category
Documents
-
view
214 -
download
1
Transcript of National Cancer Institute Microbiome measurements in epidemiological studies.
Nati
onal
Can
cer I
nstit
ute
Microbiome measurements in epidemiological studies
Nati
onal
Can
cer I
nstit
ute
Chronic Disease EpidemiologyChronic Disease Epidemiology
• Etiologic studies focused on understanding the association between diet, lifestyle, environmental exposures, genetics, etc. and the risk of cancer, heart disease, …• e.g. Helicobacter pylori and gastric cancer• Chronic Hp infection has multiple outcomes• Multiple cancer associations• Burn out
Nati
onal
Can
cer I
nstit
ute
Four major concerns for microbiome
1. Study design - prospective2. Efficient biosample collections3. Stability of exposure metrics4. Replication of findings
Nati
onal
Can
cer I
nstit
ute
Four major concerns for microbiome
1. Study design – prospective• For many exposures, optimal studies are prospective
• exposure information collected when subjects are ‘healthy’
• For cancer, it requires very large sample sizes• cancer is a rare disease• 50,000 – 500,000 subjects
• Most biosamples are never measured
Nati
onal
Can
cer I
nstit
ute
Four major concerns for microbiome
2. Efficient collection of large number of subjects• often completed at home• most previous studies had one contact
• or contact every 4-5 years• or more frequent contact with a subset of subjects
• oral microbiome collections?• fecal microbiome collections?• Absolute abundance
• How to normalize?
Nati
onal
Can
cer I
nstit
ute
Four major concerns for microbiome
3. Stability of exposure metrics• we need exposure metrics that are ‘the same’ when
measured (6, 12, 24, …) months apart
• The intraclass correlation coefficient (ICC) is very useful• systolic and diastolic blood pressure
• 0.87 and 0.77 between operators • 0.91 and 0.77 between devices• >0.60 in an individual when measured 12 months apart
Nati
onal
Can
cer I
nstit
ute
Intraclass correlation coefficient (ICC)
Temporal Variability and Implications to Epidemiological Studies
• Intraclass correlation coefficient (ICC)– A set of subjects with two time-points measurement. – For a given feature (relative abundance, α/β-diversity), let
be between-subject variance and be within-subject variance. – ICC =
• ICC ~ effective sample size for epidemiological Studies
• Human microbiome project (HMP)– Subjects with two visits, 18 body sites– linear mixed model, adjusting for age/sex/sequencing center
2b
2w
)/( 222wbb
1000 cases + 1000 controlsICC = 0.8
2000 cases + 2000 controlsICC = 0.4=
power
Actinobact
eria
Bactero
idetes
Firmicu
tes
Fuso
bacteri
a
Proteo
bacteri
a
PD_tree
Chao1
Observe
d_speci
es
Shan
non_index
Unweighted
Weig
hted0
0.10.20.30.40.50.60.70.8
0.51 0.48
0.610000000000001
0.52
0.650000000000001
0.570.47 0.440.48 0.5 0.48
0.690000000000001
0.520.45
0.4
0.640000000000001
0.47 0.5
0.610000000000001
0.4
NCI_Stool HMP_Stool HMP_Oral HMP_Anteriornares HMP_Skin HMP_Vagina
relative abundance α-diversity β-diversity
ICC
Temporal Variability Based on HMP Data
Actinobact
eria
Bactero
idetes
Firmicu
tes
Fuso
bacteri
a
Proteo
bacteri
a
PD_tree
Chao1
Observe
d_speci
es
Shan
non_index
Unweighted
Weig
hted0
0.10.20.30.40.50.60.70.8
0.51 0.48 0.52
0.650000000000001
0.570.47 0.440.48 0.5 0.48
0.690000000000001
0.520.45
0.4
0.640000000000001
0.47 0.5
0.610000000000001
0.4
NCI_Stool HMP_Stool HMP_Oral HMP_Anteriornares HMP_Skin HMP_Vagina
relative abundance α-diversity β-diversity
ICC
Temporal Variability Based on HMP Data
Actinobact
eria
Bactero
idetes
Firmicu
tes
Fuso
bacteri
a
Proteo
bacteri
a
PD_tree
Chao1
Observe
d_speci
es
Shan
non_index
Unweighted
Weig
hted0
0.10.20.30.40.50.60.70.8
0.51 0.48
0.610000000000001
0.52
0.650000000000001
0.570.47 0.440.48 0.5 0.48
0.690000000000001
0.520.45
0.4
0.640000000000001
0.47 0.5
0.610000000000001
0.4
NCI_Stool HMP_Stool HMP_Oral HMP_Anteriornares HMP_Skin HMP_Vagina
relative abundance α-diversity β-diversity
ICC
Temporal Variability Based on HMP Data
average ICC for top five PCoA scores
Temporal Variability Based on HMP Data
Nati
onal
Can
cer I
nstit
ute
MBQC ICCs
Nati
onal
Can
cer I
nstit
ute
Four major concerns for microbiome
4. Replication of findings• Observational studies require many independent similar
findings before we draw conclusions or consider intervention
• requires harmonizing the exposure metric• α-diversity seems easy enough
• interpolation of study means could be employed• β-diversity metrics may be hard
• allows quantification of the consistency
Nati
onal
Can
cer I
nstit
ute
Meta-analysis
Regular aspirin use and risk of esophageal/gastric cancer
Nati
onal
Can
cer I
nstit
ute
Publication bias
Nati
onal
Can
cer I
nstit
ute
Four major concerns for microbiome
1. Study design - prospective2. Efficient biosample collections3. Stability of exposure metrics4. Replication of findings
