National Cancer Institute Microbiome measurements in epidemiological studies.

transcript

Microbiome measurements in epidemiological studies

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

Four major concerns for microbiome

1. Study design - prospective2. Efficient biosample collections3. Stability of exposure metrics4. Replication of findings

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

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?

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

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

)/( 222wbb

1000 cases + 1000 controlsICC = 0.8

2000 cases + 2000 controlsICC = 0.4=

Actinobact

Bactero

idetes

Firmicu

bacteri

Proteo

bacteri

PD_tree

Observe

d_speci

non_index

Unweighted

0.10.20.30.40.50.60.70.8

0.51 0.48

0.610000000000001

0.650000000000001

0.570.47 0.440.48 0.5 0.48

0.690000000000001

0.520.45

0.640000000000001

0.47 0.5

0.610000000000001

NCI_Stool HMP_Stool HMP_Oral HMP_Anteriornares HMP_Skin HMP_Vagina

relative abundance α-diversity β-diversity

Temporal Variability Based on HMP Data

Actinobact

Bactero

idetes

Firmicu

bacteri

Proteo

bacteri

PD_tree

Observe

d_speci

non_index

Unweighted

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

0.640000000000001

0.47 0.5

0.610000000000001

Actinobact

Bactero

idetes

Firmicu

bacteri

Proteo

bacteri

PD_tree

Observe

d_speci

non_index

Unweighted

0.10.20.30.40.50.60.70.8

0.51 0.48

0.610000000000001

0.650000000000001

0.570.47 0.440.48 0.5 0.48

0.690000000000001

0.520.45

0.640000000000001

0.47 0.5

0.610000000000001

average ICC for top five PCoA scores

MBQC ICCs

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

Meta-analysis

Regular aspirin use and risk of esophageal/gastric cancer

Publication bias

1. Study design - prospective2. Efficient biosample collections3. Stability of exposure metrics4. Replication of findings

National Cancer Institute Microbiome measurements in epidemiological studies.

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