Effect of Creatine Supplementation on Body Composition and Malnutrition-Inflammation Score in Hemodialysis Patients: An Exploratory 1-Year, Balanced, Double-Blind Design
Bibliographic record
Abstract
Hemodialysis has a detrimental effect on fat-free mass (FFM) and muscle strength over time. Thus, we aimed to evaluate the effect of creatine supplementation on the body composition and Malnutrition-Inflammation Score (MIS) in patients with chronic kidney disease (CKD) undergoing hemodialysis. An exploratory 1-year balanced, placebo-controlled, and double-blind design was conducted with hemodialysis patients (≥18 years). The creatine group (CG) received 5 g of creatine monohydrate and 5 g of maltodextrin per day and the placebo group (PG) received 10 g of maltodextrin per day. MIS and body composition were analyzed at three time points: pre, intermediate (after 6 months), and post (after 12 months). After 6 months, 60% of patients on creatine experienced an increase in FFM compared to a 36.8% increase for those on placebo. Moreover, 65% of patients on creatine increased their skeletal muscle mass index (SMMI) compared to only 15.8% for those on placebo. Creatine increased intracellular water (ICW) in 60% of patients. MIS did not change after the intervention. In the CG, there was an increase in body weight (p = 0.018), FFM (p = 0.010), SMMI (p = 0.022). CG also increased total body water (pre 35.4 L, post 36.1 L; p = 0.008), mainly due to ICW (pre 20.2 L, intermediate 20.7 L, post 21.0 L; p = 0.016). Long-term creatine supplementation in hemodialysis patients did not attenuate the MIS, but enhanced FFM and SMMI, which was likely triggered by an increase in ICW.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".