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Record W4417362837 · doi:10.1038/s41514-025-00293-2

Sympathetic-parasympathetic system deregulation theory of aging

2025· review· en· W4417362837 on OpenAlexafffund
J.P. Errico, Benneth Ben‐Azu, Makenna Gargus, M. Karen Newell Rogers, Marie‐Ève Tremblay

Bibliographic record

Venuenpj Aging · 2025
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
FundersNational Institute of Neurological Disorders and StrokeCanadian Institutes of Health ResearchNational Institutes of HealthUniversity of Victoria
KeywordsAutonomic nervous systemNervous systemSympathetic nervous systemCentral nervous systemEpigeneticsParasympathetic nervous system

Abstract

fetched live from OpenAlex

The central nervous system, comprised of the brain, spinal cord, and nerves, includes the autonomic nervous system (ANS) that regulates involuntary functions. Within the ANS, the sympathetic and the parasympathetic nervous systems (SNS and PNS, respectively) control the same bodily functions, but in opposing directions. For example, the sympathetic nervous system elicits our "fight or flight" response, while the parasympathetic system supports "rest and repair" mechanisms in the broadest possible sense. With age, changes occur in how information is transmitted, in energetic requirements and expenditures, and in the ability to respond to change. These alterations with age result in the "hallmarks of aging", specifically including genomic instability, telomere attrition, epigenetic changes, loss of proteostasis, deregulated nutrient-sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and chronic inflammation. Understanding these age-dependent changes is essential for promoting healthy aging and longevity. We propose that, at the core of aging, there is an imbalance between the SNS and PNS, which provides opportunities for therapeutic intervention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.936
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.331
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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