Bone Marrow Adiposity, Bone Mineral Density and Wnt/β-catenin Pathway Inhibitors Levels in Hemodialysis Patients
Bibliographic record
Abstract
BACKGROUND: Marrow adipose tissue (MAT) is known to accumulate in patients with chronic kidney disease. This pilot study aimed to evaluate bone mineral density (BMD), MAT, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) using computed tomography (CT) scans and to explore correlations between bone parameters, circulating Wnt/β-catenin pathway inhibitor levels, and adipose tissue parameters. METHODS: Single-center cross-sectional pilot study conducted in hemodialysis patients at the Centre Universitaire de Québec, Hôtel-Dieu de Québec hospital, Canada. CT-scan slices were acquired at the levels of the hip, L3 vertebra, and tibia. Volumetric and areal BMD, tibia cortical thickness, VAT and SAT area, and fat marrow index (FMI) were analyzed using the Mindways QCT Pro software. Blood levels of sclerostin, dickkopf-related protein 1 (DKK1), fibroblast growth factor 23, and α-Klotho were assessed. Spearman's rho test was used to evaluate correlations. RESULTS: Fifteen hemodialysis patients (median age, 75 [66-82] years; 80% male; dialysis vintage, 39.3 [27.4-71.0] months) were included. While inverse correlations were obtained between L3 FMI and BMD, positive correlations were found between proximal tibial FMI and vertebral and tibial BMD, as well as with tibial (proximal and distal) cortical thickness. VAT had a positive correlation with α-Klotho levels, whereas L3 FMI had a negative correlation with DKK1 levels. CONCLUSIONS: CT-scan allows simultaneous evaluation of bone and marrow adiposity in dialysis patients. Correlations between MAT and BMD vary depending on the bone site evaluated. DKK1 and α-Klotho levels correlate with adipose tissue accumulation in dialysis 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".