Evidence map›Paper›PMID 37557961›Full record

ReviewBrain, behavior, and immunity2023

Machine learning models of plasma proteomic data predict mood in chronic stroke and tie it to aberrant peripheral immune responses.

Neda H Bidoki, Kristy A Zera, Huda Nassar, Lauren L Drag, Michael Mlynash, Elizabeth Osborn, Muhith Musabbir, Da Eun Kim, Maria Paula Mendez, Maarten G Lansberg and 2 more

Open access · greenAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Brain, behavior, and immunity, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
1.2field-weighted citation impact, top 24% 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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Review
  6. The Immunologic Underpinnings of Post-Stroke Depression.Biological psychiatry global open science · 2025
    Review
  7. Article
  8. Article
  9. 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

12 authors at 1 institution in 1 country.

Neda H BidokiDepartment of Anesthesiology, Perioperative, & Pain Medicine, Stanford University Medical Center, Stanford, CA 94305, USA.
Kristy A ZeraDepartment of Neurology and Neurological Sciences, Stanford University Medical Center, Palo Alto, CA 94305, USA.
Huda NassarDepartment of Anesthesiology, Perioperative, & Pain Medicine, Stanford University Medical Center, Stanford, CA 94305, USA.
Lauren L DragDepartment of Neurology and Neurological Sciences, Stanford University Medical Center, Palo Alto, CA 94305, USA; Stanford Stroke Recovery Program, Stanford University Medical Center, Stanford, CA 94305, USA.
Michael MlynashDepartment of Neurology and Neurological Sciences, Stanford University Medical Center, Palo Alto, CA 94305, USA; Stanford Stroke Recovery Program, Stanford University Medical Center, Stanford, CA 94305, USA.
Elizabeth OsbornDepartment of Neurology and Neurological Sciences, Stanford University Medical Center, Palo Alto, CA 94305, USA; Stanford Stroke Recovery Program, Stanford University Medical Center, Stanford, CA 94305, USA.
Muhith MusabbirDepartment of Neurology and Neurological Sciences, Stanford University Medical Center, Palo Alto, CA 94305, USA; Stanford Stroke Recovery Program, Stanford University Medical Center, Stanford, CA 94305, USA.
Da Eun KimDepartment of Neurology and Neurological Sciences, Stanford University Medical Center, Palo Alto, CA 94305, USA; Stanford Stroke Recovery Program, Stanford University Medical Center, Stanford, CA 94305, USA.
Maria Paula MendezDepartment of Neurology and Neurological Sciences, Stanford University Medical Center, Palo Alto, CA 94305, USA; Stanford Stroke Recovery Program, Stanford University Medical Center, Stanford, CA 94305, USA.
Maarten G LansbergDepartment of Neurology and Neurological Sciences, Stanford University Medical Center, Palo Alto, CA 94305, USA; Stanford Stroke Recovery Program, Stanford University Medical Center, Stanford, CA 94305, USA.
Nima AghaeepourDepartment of Anesthesiology, Perioperative, & Pain Medicine, Stanford University Medical Center, Stanford, CA 94305, USA; Department of Biomedical Data Sciences, Stanford University Medical Center, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University Medical Center, Stanford, CA 94305, USA.
Marion S BuckwalterDepartment of Neurology and Neurological Sciences, Stanford University Medical Center, Palo Alto, CA 94305, USA; Stanford Stroke Recovery Program, Stanford University Medical Center, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University Medical Center, Stanford, CA 94305, USA. Electronic address: marion.buckwalter@stanford.edu.
Stanford Medicine · US

Funding

BBB Dysfunction in Post-Stroke DementiaR01NS124927 · NINDS · STANFORD UNIVERSITY · PI MARION S BUCKWALTER · 2022 to 2026
$3.2M
Machine Learning for Integrative Modeling of the Immune System in Clinical SettingsR35GM138353 · NIGMS · STANFORD UNIVERSITY · PI AGHAEEPOUR, NIMA · 2020 to 2024
$2.2M
Neuropathology of synapses in AD and ADRDRF1AG077443 · NIA · STANFORD UNIVERSITY · PI AGHAEEPOUR, NIMA, MONTINE, THOMAS J · 2023 to 2023
$2.2M
Neuropathology of synapses in AD and ADRDR01AG077443 · NIA · STANFORD UNIVERSITY · PI Nima Aghaeepour, Thomas J Montine · 2026 to 2026
$707k
NIA NIH HHS R01 AG077443NIA NIH HHS RF1 AG077443NIGMS NIH HHS R35 GM138353NINDS NIH HHS R01 NS124927
6 · The paper itself

Abstract

Post-stroke depression is common, long-lasting and associated with severe morbidity and death, but mechanisms are not well-understood. We used a broad proteomics panel and developed a machine learning algorithm to determine whether plasma protein data can predict mood in people with chronic stroke, and to identify proteins and pathways associated with mood. We used Olink to measure 1,196 plasma proteins in 85 participants aged 25 and older who were between 5 months and 9 years after ischemic stroke. Mood was assessed with the Stroke Impact Scale mood questionnaire (SIS3). Machine learning multivariable regression models were constructed to estimate SIS3 using proteomics data, age, and time since stroke. We also dichotomized participants into better mood (SIS3 > 63) or worse mood (SIS3 ≤ 63) and analyzed candidate proteins. Machine learning models verified that there is indeed a relationship between plasma proteomic data and mood in chronic stroke, with the most accurate prediction of mood occurring when we add age and time since stroke. At the individual protein level, no single protein or set of proteins predicts mood. But by using univariate analyses of the proteins most highly associated with mood we produced a model of chronic post-stroke depression. We utilized the fact that this list contained many proteins that are also implicated in major depression. Also, over 80% of immune proteins that correlate with mood were higher with worse mood, implicating a broadly overactive immune system in chronic post-stroke depression. Finally, we used a comprehensive literature review of major depression and acute post-stroke depression. We propose that in chronic post-stroke depression there is over-activation of the immune response that then triggers changes in serotonin activity and neuronal plasticity leading to depressed mood.

Indexed as

ProteomicsStrokeAffectDepressionHumansMachine LearningBiomarkerDepressionMachine learningProteomicsProximity extension assayStroke

Identifiers

PMID37557961
PMCPMC10792657
OpenAlexW4385623702

What OpenQuestion holds

Textmetadata
LicenceTDM
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.