Relation between myostatin levels and malnutrition and muscle wasting in hemodialysis patients
Bibliographic record
Abstract
BACKGROUND AND AIM: Malnutrition is one of the most troublesome comorbidities among hemodialysis patients (HD). Myostatin (MSTN) belongs to the transforming growth factor-β superfamily. In HD patients, MSTN effects are not limited to skeletal muscle growth. The present study aimed to assess MSTN levels in HD patients and its relation to various clinical and biochemical parameters. PATIENTS AND METHODS: The present case control study included 60 patients on HD for at least three years. In addition, there were age and sex-matched healthy subjects who constitutes the control group. Nutritional status was evaluated using the malnutrition inflammation score (MIS). Muscle wasting in the present study was evaluated using the lean tissue index (LTI) as assessed by the body composition monitor (BCM). Rectus Femoris Muscle (RFM) thickness was also measured as indicator for nutritional status of patient. RESULTS: The present study included 60 HD patients, and ageand sex-matched healthy controls. Patients expressed significantly higher myostatin levels when compared to controls [median (IQR): 221.3 (153.5-688.2) versus 144.8 (97.0-281.7), p < 0.001]. According to MIS, patients were classified into those with no/mild malnutrition (n = 22) and others with moderate/severe malnutrition (n = 38). Comparison between the two subgroups revealed that the former group had significantly lower myostatin levels [167.7 (150.3-236.3) versus 341.7 (160.9-955.9), p = 0.004]. According to LTI, patients were classified into those with muscle wasting (n = 23) and others without muscle wasting (n = 37). Comparative analysis showed that patients in the former group had significantly higher myostatin levels [775.1 (325.1-2133.7) versus 161.8 (142.6-302.3), p < 0.001]. CONCLUSIONS: Myostatin seems to be a promising marker for identification of malnutrition and muscle wasting in HD 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 | 0.001 |
| 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".