A Novel Role of Growth Differentiation Factor (GDF)-15 in Overlap with Sedentary Lifestyle and Cognitive Risk in COPD
Bibliographic record
Abstract
Sedentary behavior and cognitive impairment have a direct impact on patients’ outcomes. An energy metabolic disorder may be involved in the overlap of these comorbid conditions (motoric cognitive risk (MCR)) in patients with chronic obstructive pulmonary disease (COPD). We aimed to explore the linkage between a proapoptotic protein, growth differentiation factor (GDF)-15, and MCR. Physical activity (PA), cognitive function (Japanese version of the Montreal Cognitive Assessment: MOCA-J), and the serum GDF-15 levels were assessed in healthy subjects (n = 14), asthmatics (n = 22), and COPD patients (n = 28). In the entire cohort, serum GDF-15 had negative correlations with exercise (Ex) (ρ = −0.43, p < 0.001) and MoCA-J (ρ = −0.44, p < 0.001), and Ex and MOCA-J showed a positive correlation (ρ = 0.52, p < 0.0001). Compared to healthy subjects and asthmatics, COPD patients showed the highest serum GDF-15 levels and had a significantly higher proportion of subjects with MCR (both sedentary lifestyle (EX < 1.5) and cognitive risk (MoCA-J ≤ 25)). Also, we found that serum GDF-15 has a screening potential (100% sensitivity) greater than aging (67% sensitivity) for detecting MCR in COPD patients. In conclusion, higher serum GDF-15 had interrelationships with a sedentary lifestyle and cognitive risk. This protein was not disease-specific but could be a screening biomarker to detect MCR related to poor health outcomes of COPD patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".