IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-44800-7.html
   My bibliography  Save this article

Investigating the etiologies of non-malarial febrile illness in Senegal using metagenomic sequencing

Author

Listed:
  • Zoë C. Levine

    (Broad Institute of Harvard and MIT
    Harvard Graduate Program in Biological and Biomedical Science
    Harvard/MIT MD-PhD Program)

  • Aita Sene

    (Cheikh Anta Diop University Dakar
    Centre International de Recherche et de Formation en Génomique Appliquée et de la Surveillance Sanitaire)

  • Winnie Mkandawire

    (Broad Institute of Harvard and MIT
    University of Massachusetts Medical School)

  • Awa B. Deme

    (Centre International de Recherche et de Formation en Génomique Appliquée et de la Surveillance Sanitaire)

  • Tolla Ndiaye

    (Cheikh Anta Diop University Dakar
    Centre International de Recherche et de Formation en Génomique Appliquée et de la Surveillance Sanitaire)

  • Mouhamad Sy

    (Cheikh Anta Diop University Dakar
    Centre International de Recherche et de Formation en Génomique Appliquée et de la Surveillance Sanitaire)

  • Amy Gaye

    (Cheikh Anta Diop University Dakar
    Centre International de Recherche et de Formation en Génomique Appliquée et de la Surveillance Sanitaire)

  • Younouss Diedhiou

    (Cheikh Anta Diop University Dakar
    Centre International de Recherche et de Formation en Génomique Appliquée et de la Surveillance Sanitaire)

  • Amadou M. Mbaye

    (Cheikh Anta Diop University Dakar
    Centre International de Recherche et de Formation en Génomique Appliquée et de la Surveillance Sanitaire)

  • Ibrahima M. Ndiaye

    (Cheikh Anta Diop University Dakar
    Centre International de Recherche et de Formation en Génomique Appliquée et de la Surveillance Sanitaire)

  • Jules Gomis

    (Cheikh Anta Diop University Dakar
    Centre International de Recherche et de Formation en Génomique Appliquée et de la Surveillance Sanitaire)

  • Médoune Ndiop

    (Programme National de lutte contre le Paludisme, Ministère de la Santé)

  • Doudou Sene

    (Programme National de lutte contre le Paludisme, Ministère de la Santé)

  • Marietou Faye Paye

    (Broad Institute of Harvard and MIT)

  • Bronwyn L. MacInnis

    (Broad Institute of Harvard and MIT
    Harvard T.H. Chan School of Public Health, Harvard University)

  • Stephen F. Schaffner

    (Broad Institute of Harvard and MIT
    Harvard T.H. Chan School of Public Health, Harvard University
    Harvard University)

  • Daniel J. Park

    (Broad Institute of Harvard and MIT)

  • Aida S. Badiane

    (Cheikh Anta Diop University Dakar
    Centre International de Recherche et de Formation en Génomique Appliquée et de la Surveillance Sanitaire)

  • Andres Colubri

    (Broad Institute of Harvard and MIT
    University of Massachusetts Medical School)

  • Mouhamadou Ndiaye

    (Cheikh Anta Diop University Dakar
    Centre International de Recherche et de Formation en Génomique Appliquée et de la Surveillance Sanitaire)

  • Ngayo Sy

    (Service de Lutte Anti Parasitaire)

  • Pardis C. Sabeti

    (Broad Institute of Harvard and MIT
    Harvard T.H. Chan School of Public Health, Harvard University
    Harvard University
    Howard Hughes Medical Institute)

  • Daouda Ndiaye

    (Cheikh Anta Diop University Dakar
    Centre International de Recherche et de Formation en Génomique Appliquée et de la Surveillance Sanitaire)

  • Katherine J. Siddle

    (Broad Institute of Harvard and MIT
    Brown University)

Abstract

The worldwide decline in malaria incidence is revealing the extensive burden of non-malarial febrile illness (NMFI), which remains poorly understood and difficult to diagnose. To characterize NMFI in Senegal, we collected venous blood and clinical metadata in a cross-sectional study of febrile patients and healthy controls in a low malaria burden area. Using 16S and untargeted sequencing, we detected viral, bacterial, or eukaryotic pathogens in 23% (38/163) of NMFI cases. Bacteria were the most common, with relapsing fever Borrelia and spotted fever Rickettsia found in 15.5% and 3.8% of cases, respectively. Four viral pathogens were found in a total of 7 febrile cases (3.5%). Sequencing also detected undiagnosed Plasmodium, including one putative P. ovale infection. We developed a logistic regression model that can distinguish Borrelia from NMFIs with similar presentation based on symptoms and vital signs (F1 score: 0.823). These results highlight the challenge and importance of improved diagnostics, especially for Borrelia, to support diagnosis and surveillance.

Suggested Citation

  • Zoë C. Levine & Aita Sene & Winnie Mkandawire & Awa B. Deme & Tolla Ndiaye & Mouhamad Sy & Amy Gaye & Younouss Diedhiou & Amadou M. Mbaye & Ibrahima M. Ndiaye & Jules Gomis & Médoune Ndiop & Doudou Se, 2024. "Investigating the etiologies of non-malarial febrile illness in Senegal using metagenomic sequencing," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-44800-7
    DOI: 10.1038/s41467-024-44800-7
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-44800-7
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-44800-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Judith U. Oguzie & Brittany A. Petros & Paul E. Oluniyi & Samar B. Mehta & Philomena E. Eromon & Parvathy Nair & Opeoluwa Adewale-Fasoro & Peace Damilola Ifoga & Ikponmwosa Odia & Andrzej Pastusiak & , 2023. "Metagenomic surveillance uncovers diverse and novel viral taxa in febrile patients from Nigeria," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    2. Rui Wang & Naihua Xiu & Kim-Chuan Toh, 2021. "Subspace quadratic regularization method for group sparse multinomial logistic regression," Computational Optimization and Applications, Springer, vol. 79(3), pages 531-559, July.
    3. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    4. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.
    5. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
    6. Xiangwei Li & Thomas Delerue & Ben Schöttker & Bernd Holleczek & Eva Grill & Annette Peters & Melanie Waldenberger & Barbara Thorand & Hermann Brenner, 2022. "Derivation and validation of an epigenetic frailty risk score in population-based cohorts of older adults," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    7. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    8. Heng Chen & Daniel F. Heitjan, 2022. "Analysis of local sensitivity to nonignorability with missing outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(4), pages 1342-1352, December.
    9. S Ariane Christie & Amanda S Conroy & Rachael A Callcut & Alan E Hubbard & Mitchell J Cohen, 2019. "Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-13, April.
    10. Zhu Wang, 2022. "MM for penalized estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 54-75, March.
    11. Ida Kubiszewski & Kenneth Mulder & Diane Jarvis & Robert Costanza, 2022. "Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 139-148, February.
    12. Gustavo A. Alonso-Silverio & Víctor Francisco-García & Iris P. Guzmán-Guzmán & Elías Ventura-Molina & Antonio Alarcón-Paredes, 2021. "Toward Non-Invasive Estimation of Blood Glucose Concentration: A Comparative Performance," Mathematics, MDPI, vol. 9(20), pages 1-13, October.
    13. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    14. Naimoli, Antonio, 2022. "Modelling the persistence of Covid-19 positivity rate in Italy," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    15. Gurgul Henryk & Machno Artur, 2017. "Trade Pattern on Warsaw Stock Exchange and Prediction of Number of Trades," Statistics in Transition New Series, Polish Statistical Association, vol. 18(1), pages 91-114, March.
    16. Ahmed Ismaïl & Hartikainen Anna-Liisa & Järvelin Marjo-Riitta & Richardson Sylvia, 2011. "False Discovery Rate Estimation for Stability Selection: Application to Genome-Wide Association Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-20, November.
    17. Vitaly Meursault & Daniel Moulton & Larry Santucci & Nathan Schor, 2022. "One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas," Working Papers 22-39, Federal Reserve Bank of Philadelphia.
    18. Michael Funke & Kadri Männasoo & Helery Tasane, 2023. "Regional Economic Impacts of the Øresund Cross-Border Fixed Link: Cui Bono?," CESifo Working Paper Series 10557, CESifo.
    19. Wang, Wenjia & Zhou, Yi-Hui, 2021. "Eigenvector-based sparse canonical correlation analysis: Fast computation for estimation of multiple canonical vectors," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    20. Lu Tang & Ling Zhou & Peter X. K. Song, 2019. "Fusion learning algorithm to combine partially heterogeneous Cox models," Computational Statistics, Springer, vol. 34(1), pages 395-414, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-44800-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.