A novel framework for assessing causal effect of microbiome on health: long-term antibiotic usage as an instrument
Assessing causality is undoubtedly one of the key questions in microbiome studies for the upcoming years. Since randomized trials in human subjects are often unethical or difficult to pursue, analytical methods to derive causal effects from observational data deserve attention. As simple covariate a...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Gut Microbes |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19490976.2025.2453616 |
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| _version_ | 1832589937578344448 |
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| author | Nele Taba Krista Fischer Estonian Biobank Research Team Elin Org Oliver Aasmets |
| author_facet | Nele Taba Krista Fischer Estonian Biobank Research Team Elin Org Oliver Aasmets |
| author_sort | Nele Taba |
| collection | DOAJ |
| description | Assessing causality is undoubtedly one of the key questions in microbiome studies for the upcoming years. Since randomized trials in human subjects are often unethical or difficult to pursue, analytical methods to derive causal effects from observational data deserve attention. As simple covariate adjustment is not likely to account for all potential confounders, the idea of instrumental variable (IV) analysis is worth exploiting. Here we propose a novel framework of antibiotic instrumental variable regression (AB-IVR) for estimating the causal relationships between microbiome and various diseases. We rely on the recent studies showing that antibiotic treatment has a cumulative long-term effect on the microbiome, resulting in individuals with higher antibiotic usage to have a more perturbed microbiome. We apply the AB-IVR method on the Estonian Biobank data and show that the microbiome has a causal role in numerous diseases including migraine, depression and irritable bowel syndrome. We show with a plethora of sensitivity analyses that the identified causal effects are robust and propose ways for further methodological developments. |
| format | Article |
| id | doaj-art-c360a0ecb0a9481eb5159608188f0dae |
| institution | Kabale University |
| issn | 1949-0976 1949-0984 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Gut Microbes |
| spelling | doaj-art-c360a0ecb0a9481eb5159608188f0dae2025-01-24T05:10:33ZengTaylor & Francis GroupGut Microbes1949-09761949-09842025-12-0117110.1080/19490976.2025.2453616A novel framework for assessing causal effect of microbiome on health: long-term antibiotic usage as an instrumentNele Taba0Krista Fischer1Estonian Biobank Research Team2Elin Org3Oliver Aasmets4Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, EstoniaEstonian Genome Center, Institute of Genomics, University of Tartu, Tartu, EstoniaEstonian Genome Center, Institute of Genomics, University of Tartu, Tartu, EstoniaEstonian Genome Center, Institute of Genomics, University of Tartu, Tartu, EstoniaEstonian Genome Center, Institute of Genomics, University of Tartu, Tartu, EstoniaAssessing causality is undoubtedly one of the key questions in microbiome studies for the upcoming years. Since randomized trials in human subjects are often unethical or difficult to pursue, analytical methods to derive causal effects from observational data deserve attention. As simple covariate adjustment is not likely to account for all potential confounders, the idea of instrumental variable (IV) analysis is worth exploiting. Here we propose a novel framework of antibiotic instrumental variable regression (AB-IVR) for estimating the causal relationships between microbiome and various diseases. We rely on the recent studies showing that antibiotic treatment has a cumulative long-term effect on the microbiome, resulting in individuals with higher antibiotic usage to have a more perturbed microbiome. We apply the AB-IVR method on the Estonian Biobank data and show that the microbiome has a causal role in numerous diseases including migraine, depression and irritable bowel syndrome. We show with a plethora of sensitivity analyses that the identified causal effects are robust and propose ways for further methodological developments.https://www.tandfonline.com/doi/10.1080/19490976.2025.2453616gut microbiomeelectronic health registriesantibioticscausal inference |
| spellingShingle | Nele Taba Krista Fischer Estonian Biobank Research Team Elin Org Oliver Aasmets A novel framework for assessing causal effect of microbiome on health: long-term antibiotic usage as an instrument Gut Microbes gut microbiome electronic health registries antibiotics causal inference |
| title | A novel framework for assessing causal effect of microbiome on health: long-term antibiotic usage as an instrument |
| title_full | A novel framework for assessing causal effect of microbiome on health: long-term antibiotic usage as an instrument |
| title_fullStr | A novel framework for assessing causal effect of microbiome on health: long-term antibiotic usage as an instrument |
| title_full_unstemmed | A novel framework for assessing causal effect of microbiome on health: long-term antibiotic usage as an instrument |
| title_short | A novel framework for assessing causal effect of microbiome on health: long-term antibiotic usage as an instrument |
| title_sort | novel framework for assessing causal effect of microbiome on health long term antibiotic usage as an instrument |
| topic | gut microbiome electronic health registries antibiotics causal inference |
| url | https://www.tandfonline.com/doi/10.1080/19490976.2025.2453616 |
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