Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury

  1. Abel Torres-Espín
  2. Jenny Haefeli
  3. Reza Ehsanian
  4. Dolores Torres
  5. Carlos A Almeida
  6. J Russell Huie
  7. Austin Chou
  8. Dmitriy Morozov
  9. Nicole Sanderson
  10. Benjamin Dirlikov
  11. Catherine G Suen
  12. Jessica L Nielson
  13. Nikos Kyritsis
  14. Debra D Hemmerle
  15. Jason F Talbott
  16. Geoff T Manley
  17. Sanjay S Dhall
  18. William D Whetstone
  19. Jacqueline C Bresnahan
  20. Michael S Beattie
  21. Stephen L McKenna
  22. Jonathan Z Pan  Is a corresponding author
  23. Adam R Ferguson  Is a corresponding author
  24. The TRACK-SCI Investigators
  1. University of California, San Francisco, United States
  2. University of New Mexico School of Medicine, United States
  3. Lawrence Berkeley National Laboratory, United States
  4. Santa Clara Valley Medical Center, United States
  5. University of Minnesota, United States

Abstract

Background: Predicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients.

Methods: Intra-operative monitoring records and neurological outcome data were extracted (n=118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods.

Results: Network analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO regression confirmed these findings, revealing an optimal MAP range of 76-[104-117] mmHg associated with neurological recovery.

Conclusion: We show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention.

Funding: NIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ARF)(ATE); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB).

Data availability

Source data has been deposited to the Open Data Commons for Spinal Cord Injury (odc-sci.org; RRID:SCR_016673) under the accession number ODC-SCI:245 (doi: 10.34945/F5R59) and ODC-SCI:246 (doi: 10.34945/F5MG68)

The following data sets were generated

Article and author information

Author details

  1. Abel Torres-Espín

    Weill Institute for Neurosciences; Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Jenny Haefeli

    Weill Institute for Neurosciences; Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Reza Ehsanian

    Division of Physical Medicine and Rehabilitation, Department of Orthopaedics and Rehabilitation, University of New Mexico School of Medicine, Albuquerque,, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Dolores Torres

    Weill Institute for Neurosciences; Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Carlos A Almeida

    Weill Institute for Neurosciences; Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. J Russell Huie

    Weill Institute for Neurosciences; Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Austin Chou

    Weill Institute for Neurosciences; Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Dmitriy Morozov

    Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Nicole Sanderson

    Lawrence Berkeley National Laboratory, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Benjamin Dirlikov

    Rehabilitation Research Center, Santa Clara Valley Medical Center, San Jose, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Catherine G Suen

    Weill Institute for Neurosciences; Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Jessica L Nielson

    Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, United States
    Competing interests
    The authors declare that no competing interests exist.
  13. Nikos Kyritsis

    Weill Institute for Neurosciences; Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7801-5796
  14. Debra D Hemmerle

    Weill Institute for Neurosciences; Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2796-6107
  15. Jason F Talbott

    Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francsico, United States
    Competing interests
    The authors declare that no competing interests exist.
  16. Geoff T Manley

    Weill Institute for Neurosciences; Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  17. Sanjay S Dhall

    Weill Institute for Neurosciences; Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  18. William D Whetstone

    Department of Emergency Medicine, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  19. Jacqueline C Bresnahan

    Weill Institute for Neurosciences; Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  20. Michael S Beattie

    Weill Institute for Neurosciences; Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  21. Stephen L McKenna

    Department of Physical Medicine and Rehabilitation, Santa Clara Valley Medical Center, San Jose, United States
    Competing interests
    The authors declare that no competing interests exist.
  22. Jonathan Z Pan

    Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, United States
    For correspondence
    jonathan.pan@ucsf.edu
    Competing interests
    The authors declare that no competing interests exist.
  23. Adam R Ferguson

    Weill Institute for Neurosciences; Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    For correspondence
    adam.ferguson@ucsf.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7102-1608
  24. The TRACK-SCI Investigators

Funding

National Institute of Neurological Disorders and Stroke (R01NS088475)

  • Adam R Ferguson

National Institute of Neurological Disorders and Stroke (UG3NS106899)

  • Adam R Ferguson

U.S. Department of Veterans Affairs (1I01RX002245)

  • Adam R Ferguson

U.S. Department of Veterans Affairs (I01RX002787)

  • Adam R Ferguson

Wings for Life Foundation

  • Abel Torres Espín

Wings for Life Foundation

  • Adam R Ferguson

Craig H. Neilsen Foundation

  • Adam R Ferguson

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: This study constitutes a retrospective data analysis. All data was de-identified before pre-processing and analysis. Protocols for retrospective data extraction were approved by Institutional Research Board (IRB) under protocol numbers 11-07639 and 11-06997.

Copyright

© 2021, Torres Espín et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,960
    views
  • 282
    downloads
  • 27
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Abel Torres-Espín
  2. Jenny Haefeli
  3. Reza Ehsanian
  4. Dolores Torres
  5. Carlos A Almeida
  6. J Russell Huie
  7. Austin Chou
  8. Dmitriy Morozov
  9. Nicole Sanderson
  10. Benjamin Dirlikov
  11. Catherine G Suen
  12. Jessica L Nielson
  13. Nikos Kyritsis
  14. Debra D Hemmerle
  15. Jason F Talbott
  16. Geoff T Manley
  17. Sanjay S Dhall
  18. William D Whetstone
  19. Jacqueline C Bresnahan
  20. Michael S Beattie
  21. Stephen L McKenna
  22. Jonathan Z Pan
  23. Adam R Ferguson
  24. The TRACK-SCI Investigators
(2021)
Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury
eLife 10:e68015.
https://doi.org/10.7554/eLife.68015

Share this article

https://doi.org/10.7554/eLife.68015

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Fangluo Chen, Dylan C Sarver ... G William Wong
    Research Article

    Obesity is a major risk factor for type 2 diabetes, dyslipidemia, cardiovascular disease, and hypertension. Intriguingly, there is a subset of metabolically healthy obese (MHO) individuals who are seemingly able to maintain a healthy metabolic profile free of metabolic syndrome. The molecular underpinnings of MHO, however, are not well understood. Here, we report that CTRP10/C1QL2-deficient mice represent a unique female model of MHO. CTRP10 modulates weight gain in a striking and sexually dimorphic manner. Female, but not male, mice lacking CTRP10 develop obesity with age on a low-fat diet while maintaining an otherwise healthy metabolic profile. When fed an obesogenic diet, female Ctrp10 knockout (KO) mice show rapid weight gain. Despite pronounced obesity, Ctrp10 KO female mice do not develop steatosis, dyslipidemia, glucose intolerance, insulin resistance, oxidative stress, or low-grade inflammation. Obesity is largely uncoupled from metabolic dysregulation in female KO mice. Multi-tissue transcriptomic analyses highlighted gene expression changes and pathways associated with insulin-sensitive obesity. Transcriptional correlation of the differentially expressed gene (DEG) orthologs in humans also shows sex differences in gene connectivity within and across metabolic tissues, underscoring the conserved sex-dependent function of CTRP10. Collectively, our findings suggest that CTRP10 negatively regulates body weight in females, and that loss of CTRP10 results in benign obesity with largely preserved insulin sensitivity and metabolic health. This female MHO mouse model is valuable for understanding sex-biased mechanisms that uncouple obesity from metabolic dysfunction.

    1. Computational and Systems Biology
    Huiyong Cheng, Dawson Miller ... Qiuying Chen
    Research Article

    Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.