Changes in Human Muscle Transverse Relaxation Following Short‐Term Creatine Supplementation
Bibliographic record
Abstract
The rapid increase in body mass that often occurs following creatine (Cr) supplementation is believed to be due to intracellular water retention. The purpose of this study was to determine whether Cr consumption alters the magnetic resonance (MR) transverse relaxation (T(2)) distribution of skeletal muscle. Transverse relaxation can be used to model water compartments within a cell or tissue. In this double-blind study, subjects were asked to supplement their normal diet with creatine monohydrate (20 g day(-1) for 5 days) mixed with a grape drink (Creatine group, n = 7), or the grape drink alone (Placebo group, n = 8). Phosphorous MR spectroscopy was used to determine the effectiveness of the supplementation protocol. Subjects that responded to the Cr supplementation (i.e. showed a > 5 % increase in the ratio of the levels of phosphocreatine (PCr) and ATP) were placed in the Creatine group. Both proton MR imaging and spectroscopy were used to acquire T(2) data, at 1.89 T, from the flexor digitorum profundus muscle of each subject before and after supplementation. Following the supplementation period, the Creatine group showed a gain in body mass (1.2 +/- 0.8 kg, P < 0.05, mean +/- S.D.), and an increase in PCr/ATP ratio (23.8 +/- 16.4 %, P < 0.001). Neither group showed any changes in intracellular pH or T(2) calculated from MR images. However, the spectroscopy data revealed at least three components (> 5 ms) at approximately 20, 40 and 125 ms in both groups. Only in the Creatine group was there an increase in the apparent proton concentration of the two shorter components combined (+5.0 +/- 4.7 %, P < 0.05). According to the cellular water compartment model, the changes observed in the shorter T(2) components are consistent with an increase in intracellular water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".