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Microbiome-based risk prediction in incident heart failure: a community challenge

Heart failure (HF) is a major public health problem. Early identification of at-risk individuals could allow for interventions that reduce morbidity or mortality. The community-based FINRISK Microbiome DREAM challenge (synapse.org/finrisk) evaluated the use of machine learning approaches on shotgun...

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Autores principales: Erawijantari, Pande Putu, Kartal, Ece, Liñares-Blanco, José, Laajala, Teemu D., Feldman, Lily Elizabeth, Carmona-Saez, Pedro, Shigdel, Rajesh, Claesson, Marcus Joakim, Bertelsen, Randi Jacobsen, Gomez-Cabrero, David, Minot, Samuel, Albrecht, Jacob, Chung, Verena, Inouye, Michael, Jousilahti, Pekka, Schultz, Jobst-Hendrik, Friederich, Hans-Christoph, Knight, Rob, Salomaa, Veikko, Niiranen, Teemu, Havulinna, Aki S., Saez-Rodriguez, Julio, Levinson, Rebecca T., Lahti, Leo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10593042/
https://www.ncbi.nlm.nih.gov/pubmed/37873403
http://dx.doi.org/10.1101/2023.10.12.23296829
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author Erawijantari, Pande Putu
Kartal, Ece
Liñares-Blanco, José
Laajala, Teemu D.
Feldman, Lily Elizabeth
Carmona-Saez, Pedro
Shigdel, Rajesh
Claesson, Marcus Joakim
Bertelsen, Randi Jacobsen
Gomez-Cabrero, David
Minot, Samuel
Albrecht, Jacob
Chung, Verena
Inouye, Michael
Jousilahti, Pekka
Schultz, Jobst-Hendrik
Friederich, Hans-Christoph
Knight, Rob
Salomaa, Veikko
Niiranen, Teemu
Havulinna, Aki S.
Saez-Rodriguez, Julio
Levinson, Rebecca T.
Lahti, Leo
author_facet Erawijantari, Pande Putu
Kartal, Ece
Liñares-Blanco, José
Laajala, Teemu D.
Feldman, Lily Elizabeth
Carmona-Saez, Pedro
Shigdel, Rajesh
Claesson, Marcus Joakim
Bertelsen, Randi Jacobsen
Gomez-Cabrero, David
Minot, Samuel
Albrecht, Jacob
Chung, Verena
Inouye, Michael
Jousilahti, Pekka
Schultz, Jobst-Hendrik
Friederich, Hans-Christoph
Knight, Rob
Salomaa, Veikko
Niiranen, Teemu
Havulinna, Aki S.
Saez-Rodriguez, Julio
Levinson, Rebecca T.
Lahti, Leo
author_sort Erawijantari, Pande Putu
collection PubMed
description Heart failure (HF) is a major public health problem. Early identification of at-risk individuals could allow for interventions that reduce morbidity or mortality. The community-based FINRISK Microbiome DREAM challenge (synapse.org/finrisk) evaluated the use of machine learning approaches on shotgun metagenomics data obtained from fecal samples to predict incident HF risk over 15 years in a population cohort of 7231 Finnish adults (FINRISK 2002, n=559 incident HF cases). Challenge participants used synthetic data for model training and testing. Final models submitted by seven teams were evaluated in the real data. The two highest-scoring models were both based on Cox regression but used different feature selection approaches. We aggregated their predictions to create an ensemble model. Additionally, we refined the models after the DREAM challenge by eliminating phylum information. Models were also evaluated at intermediate timepoints and they predicted 10-year incident HF more accurately than models for 5- or 15-year incidence. We found that bacterial species, especially those linked to inflammation, are predictive of incident HF. This highlights the role of the gut microbiome as a potential driver of inflammation in HF pathophysiology. Our results provide insights into potential modeling strategies of microbiome data in prospective cohort studies. Overall, this study provides evidence that incorporating microbiome information into incident risk models can provide important biological insights into the pathogenesis of HF.
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spelling pubmed-105930422023-10-24 Microbiome-based risk prediction in incident heart failure: a community challenge Erawijantari, Pande Putu Kartal, Ece Liñares-Blanco, José Laajala, Teemu D. Feldman, Lily Elizabeth Carmona-Saez, Pedro Shigdel, Rajesh Claesson, Marcus Joakim Bertelsen, Randi Jacobsen Gomez-Cabrero, David Minot, Samuel Albrecht, Jacob Chung, Verena Inouye, Michael Jousilahti, Pekka Schultz, Jobst-Hendrik Friederich, Hans-Christoph Knight, Rob Salomaa, Veikko Niiranen, Teemu Havulinna, Aki S. Saez-Rodriguez, Julio Levinson, Rebecca T. Lahti, Leo medRxiv Article Heart failure (HF) is a major public health problem. Early identification of at-risk individuals could allow for interventions that reduce morbidity or mortality. The community-based FINRISK Microbiome DREAM challenge (synapse.org/finrisk) evaluated the use of machine learning approaches on shotgun metagenomics data obtained from fecal samples to predict incident HF risk over 15 years in a population cohort of 7231 Finnish adults (FINRISK 2002, n=559 incident HF cases). Challenge participants used synthetic data for model training and testing. Final models submitted by seven teams were evaluated in the real data. The two highest-scoring models were both based on Cox regression but used different feature selection approaches. We aggregated their predictions to create an ensemble model. Additionally, we refined the models after the DREAM challenge by eliminating phylum information. Models were also evaluated at intermediate timepoints and they predicted 10-year incident HF more accurately than models for 5- or 15-year incidence. We found that bacterial species, especially those linked to inflammation, are predictive of incident HF. This highlights the role of the gut microbiome as a potential driver of inflammation in HF pathophysiology. Our results provide insights into potential modeling strategies of microbiome data in prospective cohort studies. Overall, this study provides evidence that incorporating microbiome information into incident risk models can provide important biological insights into the pathogenesis of HF. Cold Spring Harbor Laboratory 2023-10-12 /pmc/articles/PMC10593042/ /pubmed/37873403 http://dx.doi.org/10.1101/2023.10.12.23296829 Text en https://creativecommons.org/licenses/by-nc/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Erawijantari, Pande Putu
Kartal, Ece
Liñares-Blanco, José
Laajala, Teemu D.
Feldman, Lily Elizabeth
Carmona-Saez, Pedro
Shigdel, Rajesh
Claesson, Marcus Joakim
Bertelsen, Randi Jacobsen
Gomez-Cabrero, David
Minot, Samuel
Albrecht, Jacob
Chung, Verena
Inouye, Michael
Jousilahti, Pekka
Schultz, Jobst-Hendrik
Friederich, Hans-Christoph
Knight, Rob
Salomaa, Veikko
Niiranen, Teemu
Havulinna, Aki S.
Saez-Rodriguez, Julio
Levinson, Rebecca T.
Lahti, Leo
Microbiome-based risk prediction in incident heart failure: a community challenge
title Microbiome-based risk prediction in incident heart failure: a community challenge
title_full Microbiome-based risk prediction in incident heart failure: a community challenge
title_fullStr Microbiome-based risk prediction in incident heart failure: a community challenge
title_full_unstemmed Microbiome-based risk prediction in incident heart failure: a community challenge
title_short Microbiome-based risk prediction in incident heart failure: a community challenge
title_sort microbiome-based risk prediction in incident heart failure: a community challenge
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10593042/
https://www.ncbi.nlm.nih.gov/pubmed/37873403
http://dx.doi.org/10.1101/2023.10.12.23296829
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