Evidence map›Paper›PMID 38528230›Full record

ArticleNature aging2024

Integration of polygenic and gut metagenomic risk prediction for common diseases.

Yang Liu, Scott C Ritchie, Shu Mei Teo, Matti O Ruuskanen, Oleg Kambur, Qiyun Zhu, Jon Sanders, Yoshiki Vázquez-Baeza, Karin Verspoor, Pekka Jousilahti and 7 more

Open access · hybridAbstract read
In one paragraph

Article in Nature aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
5.1field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Article
  6. Review
  7. Prediction of Episodic Memory With Multiomics Scores.Biological psychiatry global open science · 2026
    Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Review
  13. Food substances alter gut resistome: Mechanisms, health impacts, and food components.Comprehensive reviews in food science and food safety · 2025
    Review
  14. Review
  15. Review
  16. Review
  17. Article
  18. Review
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors at 10 institutions in 5 countries.

Yang LiuCambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. yl985@medschl.cam.ac.uk.ORCID http://orcid.org/0000-0002-6023-3776
Scott C RitchieCambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0002-8454-9548
Shu Mei TeoCambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Matti O RuuskanenDepartment of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.
Oleg KamburDepartment of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.
Qiyun ZhuSchool of Life Sciences, Arizona State University, Tempe, AZ, USA.
Jon SandersDepartment of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA.
Yoshiki Vázquez-BaezaCenter for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
Karin VerspoorSchool of Computing Technologies, RMIT University, Melbourne, Victoria, Australia.ORCID http://orcid.org/0000-0002-8661-1544
Pekka JousilahtiDepartment of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.
Leo LahtiDepartment of Computing, University of Turku, Turku, Finland.
Teemu NiiranenDepartment of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.
Veikko SalomaaDepartment of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.ORCID http://orcid.org/0000-0001-7563-5324
Aki S HavulinnaDepartment of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.ORCID http://orcid.org/0000-0002-4787-8959
Rob KnightCenter for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
Guillaume MéricCambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.ORCID http://orcid.org/0000-0001-6288-9958
Michael InouyeCambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. mi336@cam.ac.uk.ORCID http://orcid.org/0000-0001-9413-6520
Baker Heart and Diabetes Institute · AUFinnish Institute for Health and Welfare · FIUniversity of Turku · FIUniversity of California San Diego · USArizona State University · USCornell University · USThe University of Melbourne · AUTuring Institute · GBUniversity of Helsinki · FIUppsala University · SE

Funding

British Heart Foundation RG/18/13/33946Wellcome Trust
6 · The paper itself

Abstract

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Indexed as

Coronary Artery DiseaseDiabetes Mellitus, Type 2Prostatic NeoplasmsGenetic Risk ScoreHumansMaleProspective StudiesRisk Factors

Identifiers

PMID38528230
PMCPMC11031402
OpenAlexW4393157157

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.