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Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia

Author

Listed:
  • Marie-Laure Charpignon

    (Institute for Data, Systems, and Society, Massachusetts Institute of Technology)

  • Bella Vakulenko-Lagun

    (University of Haifa)

  • Bang Zheng

    (Ageing Epidemiology Research Unit, School of Public Health, Imperial College London)

  • Colin Magdamo

    (Massachusetts General Hospital/Harvard Medical School)

  • Bowen Su

    (Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London)

  • Kyle Evans

    (Massachusetts General Hospital/Harvard Medical School
    Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School)

  • Steve Rodriguez

    (Massachusetts General Hospital/Harvard Medical School
    Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School)

  • Artem Sokolov

    (Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School)

  • Sarah Boswell

    (Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School)

  • Yi-Han Sheu

    (Massachusetts General Hospital/Harvard Medical School)

  • Melek Somai

    (Inception Labs, Collaborative for Health Delivery Sciences, Medical College of Wisconsin)

  • Lefkos Middleton

    (Ageing Epidemiology Research Unit, School of Public Health, Imperial College London
    Public Health Directorate, Imperial College London NHS Healthcare Trust)

  • Bradley T. Hyman

    (Massachusetts General Hospital/Harvard Medical School)

  • Rebecca A. Betensky

    (New York University)

  • Stan N. Finkelstein

    (Institute for Data, Systems, and Society, Massachusetts Institute of Technology
    Division of Clinical Informatics, Beth Israel Deaconess Medical Center)

  • Roy E. Welsch

    (Institute for Data, Systems, and Society, Massachusetts Institute of Technology
    Sloan School of Management, Massachusetts Institute of Technology)

  • Ioanna Tzoulaki

    (Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London
    Dementia Research Institute, Imperial College London
    University of Ioannina)

  • Deborah Blacker

    (Massachusetts General Hospital/Harvard Medical School
    Harvard T.H. Chan School of Public Health)

  • Sudeshna Das

    (Massachusetts General Hospital/Harvard Medical School)

  • Mark W. Albers

    (Massachusetts General Hospital/Harvard Medical School
    Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School)

Abstract

Metformin, a diabetes drug with anti-aging cellular responses, has complex actions that may alter dementia onset. Mixed results are emerging from prior observational studies. To address this complexity, we deploy a causal inference approach accounting for the competing risk of death in emulated clinical trials using two distinct electronic health record systems. In intention-to-treat analyses, metformin use associates with lower hazard of all-cause mortality and lower cause-specific hazard of dementia onset, after accounting for prolonged survival, relative to sulfonylureas. In parallel systems pharmacology studies, the expression of two AD-related proteins, APOE and SPP1, was suppressed by pharmacologic concentrations of metformin in differentiated human neural cells, relative to a sulfonylurea. Together, our findings suggest that metformin might reduce the risk of dementia in diabetes patients through mechanisms beyond glycemic control, and that SPP1 is a candidate biomarker for metformin’s action in the brain.

Suggested Citation

  • Marie-Laure Charpignon & Bella Vakulenko-Lagun & Bang Zheng & Colin Magdamo & Bowen Su & Kyle Evans & Steve Rodriguez & Artem Sokolov & Sarah Boswell & Yi-Han Sheu & Melek Somai & Lefkos Middleton & B, 2022. "Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35157-w
    DOI: 10.1038/s41467-022-35157-w
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    References listed on IDEAS

    as
    1. Steve Rodriguez & Clemens Hug & Petar Todorov & Nienke Moret & Sarah A. Boswell & Kyle Evans & George Zhou & Nathan T. Johnson & Bradley T. Hyman & Peter K. Sorger & Mark W. Albers & Artem Sokolov, 2021. "Machine learning identifies candidates for drug repurposing in Alzheimer’s disease," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. P. Ding & T.J. Vanderweele & J. M. Robins, 2017. "Instrumental variables as bias amplifiers with general outcome and confounding," Biometrika, Biometrika Trust, vol. 104(2), pages 291-302.
    Full references (including those not matched with items on IDEAS)

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