Association of plasma leptin, pro‐inflammatory adipokines and cancer‐related fatigue in early‐stage breast cancer patients: A prospective cohort study
Bibliographic record
Abstract
Cancer-related fatigue (CRF) is subjective and has wide inter-individual variability. Given that leptin is commonly associated with fatigue syndrome, its use as a potential biomarker for CRF is being investigated. The primary objective of this study was to evaluate the association between leptin and CRF in early-stage breast cancer patients receiving chemotherapy. In a prospective cohort study, patients completed assessments at baseline (T1), during chemotherapy (T2) and after chemotherapy (T3). Levels of plasma leptin and adipokines were measured using a Luminex bead-immunoassay and CRF was measured using the Multi-Dimensional Fatigue Symptom Inventory-Short Form (MFSI-SF). Data were analysed longitudinally using a generalised estimating equation incorporating clinically relevant parameters and pro-inflammatory adipokines. The analysis included 136 patients (mean age ± SD = 51.5 ± 8.8 years; 69.1% receiving anthracycline-based chemotherapy). More patients experienced CRF at T3 (23.8%) than at T2 (13.8%) compared to baseline. An increase was observed in the median plasma leptin level at T2, followed by a decrease at T3 (T1: 4.07 ng/mL, T2: 4.95 ng/mL and T3: 3.96 ng/mL). In the multivariate model, the change in leptin levels over time was significantly associated with the total MFSI-SF score (β = -0.15, P = 0.003) after adjusting for the tumour necrosis factor-α (TNF-α) level, anxiety, depression, insomnia, age, menopausal status and type of chemotherapy. This is the first study to report leptin as a biomarker that predicts the onset of CRF over time. Future studies are required to validate the findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".