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Record W4392814213 · doi:10.1002/jcsm.13430

Cancer anorexia‐cachexia syndrome is characterized by more than one inflammatory pathway

2024· article· en· W4392814213 on OpenAlexafffund
Bruno Gagnon, Jessica Murphy, David Simonyan, Claudia A. Penafuerte, Jacinthe Sirois, Martin Chasen, Michel L. Tremblay

Bibliographic record

VenueJournal of Cachexia Sarcopenia and Muscle · 2024
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of TorontoUniversité LavalMcMaster UniversityNEOMED InstituteMcGill UniversityHôtel-Dieu de QuébecConcordia UniversityMcGill University Health Centre
FundersCancer Research Society
KeywordsAnorexiaCachexiaCancerCancer cachexiaMedicineInflammationInternal medicineOncologyEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: The interdependence of cytokines and appetite-modifying hormones implicated in cancer anorexia-cachexia syndrome (CACS) remains unclear. This study aimed to regroup these cytokines and hormones into distinct inflammatory (or non-inflammatory) pathways and determine whether these pathways can classify patients with CACS phenotypes. METHODS: Clinical characteristics of 133 patients [61.7% male; mean age = 63.4 (SD: 13.1) years] with advanced cancer prior to oncology treatments were documented, including weight loss history. Patients completed the Functional Assessment of Anorexia-Cachexia Therapy (FAACT) questionnaire and Timed Up and Go test and had their sex-standardized skeletal muscle index (z-SMI) and fat mass index (z-FMI) derived using computed tomography scans. Their plasma levels of cytokines and appetite-modifying hormones were also determined. Date of death was recorded. Exploratory factor analysis (EFA) was used to regroup 15 cytokines and hormone into distinct inflammatory pathways (factors). For each patient, regression factor scores (RFS), which tell how strongly the patient associates with each factor, were derived. Two-step cluster analysis on the RFS was used to classify patients into groups. CACS phenotypes were correlated with RFS and compared between groups. Groups' survival was estimated using Kaplan-Meier analysis. RESULTS: ; SD: 56.25), and 62 (46.6%) had cachexia. EFA identified three factors: (F-1) IFN-γ, IL-1β, Il-4, IL-6, IL-10, IL-12, TGFβ1 (positive contribution), and IL-18 (negative); (F-2) IL-8, IL-18, MCP-1, TGFβ1, TNF-α (positive), and ghrelin (negative); and (F-3) TRAIL and leptin (positive), and TGFβ1 and adiponectin (negative). RFS-1 was associated with cachexia (P = 0.002); RFS-2, with higher CRP (P < 0.0001) and decreased physical function (P = 0.01); and RFS-3 with better appetite (P = 0.04), lower CRP (P = 0.002), higher z-SMI (P = 0.04) and z-FMI (P < 0.0001), and less cachexia characteristics (all P < 0.001). Four patient groups were identified with specific RFS clusters aligning with the CACS continuum from no cachexia to pre-cachexia, cachexia, and terminal cachexia. Compared to the other two groups, groups 1 and 2 had higher plasma levels of IL-18 and TRAIL. Group 1 also had lower inflammatory cytokines, adiponectin, and CRP compared to the other three groups. Group 3 had inflammatory cytokine levels similar to group 2, except for TNF-α and leptin which were lower. Group 4 had very high inflammatory cytokines, adiponectin, and CRP compared to the other 3 groups (all P < 0.0001). Groups 3 and 4 had worse cachexia characteristics (P < 0.05) and shorter survival (log rank: P = 0.0009) than the other two groups. CONCLUSIONS: This exploratory study identified three distinct pathways of inflammation, or lack thereof, characterizing different CACS phenotypes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.312
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations14
Published2024
Admission routes2
Has abstractyes

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