Sample size estimations based on human microbiome temporal stability over six months: a shallow shotgun metagenome sequencing analysis.

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Tác giả: Christian C Abnet, Carolina Porras, Jianxin Shi, Rashmi Sinha, Emily Vogtmann, Yunhu Wan, Semi Zouiouich

Ngôn ngữ: eng

Ký hiệu phân loại: 002.074 Museums, collections, exhibits

Thông tin xuất bản: United States : Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 12055

 BACKGROUND: Biological factors impact the human microbiome, highlighting the need for reasonably estimating sample sizes in future population studies. METHODS: We assessed the temporal stability of fecal microbiome diversity, species composition, and genes and functional pathways through shallow shotgun metagenome sequencing. Using intraclass correlation coefficients (ICC), we measured biological variability over six months. We estimated case numbers for 1:1 or 1:3 matched case-control studies, considering significance levels of 0.05 and 0.001 with 80% power, based on the collected fecal specimens per participant. RESULTS: The fecal microbiome's temporal stability over six months varied (ICC <
 0.6) for most alpha and beta diversity metrics. Heterogeneity was seen in species, genes, and pathways stability (ICC 0.0-0.9). Detecting an odds ratio of 1.5 per standard deviation required 1,000-5,000 cases (0.05 significance for alpha and beta
  0.001 for species, genes, pathways) with equal cases and controls. Low-prevalent species needed 15,102 cases
  high-prevalent species required 3,527. Similar needs applied to genes and pathways. In a 1:3 matched case-control study with one fecal specimen, 10,068 cases were needed for low-prevalent species
  2,351 for high-prevalent species. For odds ratios of 1.5 with multiple specimens, cases needed for low-prevalent species were 15,102 (one specimen), 8,267 (two specimens), and 5,989 (three specimens). CONCLUSIONS: Detecting disease associations requires a large number of cases. Repeating prediagnostic samples and matching cases to more controls could decrease the needed number of cases for such detections. IMPACT: Our results will help future epidemiologic studies design and implement well-powered microbiome studies.
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