National Cancer Institute Microbiome measurements in epidemiological studies

National Cancer Institute Chronic 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

National Cancer Institute Four major concerns for microbiome 1. Study design - prospective 2. Efficient biosample collections 3. Stability of exposure metrics 4. Replication of findings

National Cancer Institute 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

National Cancer Institute 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?

National Cancer Institute 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

National Cancer Institute 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 1000 cases controls ICC = cases controls ICC = 0.4 = power

relative abundanceα-diversityβ-diversity ICC Temporal Variability Based on HMP Data

relative abundanceα-diversityβ-diversity ICC Temporal Variability Based on HMP Data

relative abundanceα-diversityβ-diversity ICC Temporal Variability Based on HMP Data average ICC for top five PCoA scores

Temporal Variability Based on HMP Data

National Cancer Institute MBQC ICCs

National Cancer Institute 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

National Cancer Institute Meta-analysis Regular aspirin use and risk of esophageal/gastric cancer

National Cancer Institute Publication bias

National Cancer Institute Four major concerns for microbiome 1. Study design - prospective 2. Efficient biosample collections 3. Stability of exposure metrics 4. Replication of findings