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Record W4384299365 · doi:10.3390/nu15143132

The Role of the Kynurenine Pathway in the Pathophysiology of Frailty, Sarcopenia, and Osteoporosis

2023· review· en· W4384299365 on OpenAlexaff
Juan R Ballesteros, Daniel Rivas, Gustavo Duque

Bibliographic record

VenueNutrients · 2023
Typereview
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsSarcopeniaKynurenineMedicineOsteoporosisAnabolismKynurenine pathwayInternal medicineEndocrinologyCatabolismNiacinInflammationTryptophanBiologyMetabolismBiochemistryAmino acid

Abstract

fetched live from OpenAlex

Tryptophan is an essential nutrient required to generate vitamin B3 (niacin), which is mainly involved in energy metabolism and DNA production. Alterations in tryptophan metabolism could have significant effects on aging and musculoskeletal health. The kynurenine pathway, essential in tryptophan catabolism, is modulated by inflammatory factors that are increased in older persons, a process known as inflammaging. Osteoporosis, sarcopenia, osteosarcopenia, and frailty have also been linked with chronically increased levels of inflammatory factors. Due to the disruption of the kynurenine pathway by chronic inflammation and/or changes in the gut microbiota, serum levels of toxic metabolites are increased and are associated with the pathophysiology of those conditions. In contrast, anabolic products of this pathway, such as picolinic acid, have demonstrated a positive effect on skeletal muscle and bone. In addition, physical activity can modulate this pathway by promoting the secretion of anabolic kynurenines. According to the evidence collected, kynurenines could have a promising role as biomarkers for osteoporosis sarcopenia, osteosarcopenia, and frailty in older persons. In addition, some of these metabolites could become important targets for developing new pharmacological treatments for these conditions.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.047
GPT teacher head0.304
Teacher spread0.257 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations47
Published2023
Admission routes1
Has abstractyes

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