Intermuscular Differences in Leg Muscle Protein Quality in Human Diabetic Neuropathy
Bibliographic record
Abstract
The magnetization transfer (MT) ratio, derived from magnetic resonance (MR) imaging, has been used to non-invasively evaluate pathological changes in the central nervous system. Use of MT imaging has been recently extended to evaluate muscle protein quality in natural adult aging, and in some clinical populations with neuromuscular disease. Peripheral neuropathy due to diabetes is related to loss of strength, motor axon loss, muscle denervation and subsequent atrophy of skeletal muscle. PURPOSE: To assess whether individuals with a diabetic neuropathy (DN) have a reduced muscle protein quality in the flexor and extensor leg muscles compared to age-matched control subjects. METHODS: Using a 3T MR scanner, MT images of the tibialis anterior (TA), soleus (SOL) and gastrocnemius (GA) muscles were recorded from 8 individuals (5 men) with DN (∼60y) and 7 (4 men) age and sex-matched controls. Using image analysis software, a magnetization transfer ratio (MTR) was calculated offline from pairs of images with (MT) and without (M0) an off-resonance prepulse as: [(M0-MT)/M0]. Outside the MR scanner, maximal dorsiflexion strength and voluntary activation were assessed using an ankle joint dynamometer. Absolute maximal dorsiflexion strength was normalized to TA muscle volume in a subset (n=9; 4 DN vs. 5 control) of each group. RESULTS: From MTR calculations, the muscle protein quality of the TA, SOL and GA were significantly reduced (∼13%, ∼5%, ∼5%, respectively) in DN subjects compared to controls. MTR values from an average of all 3 leg muscles were significantly lower in DN subjects (0.28 ± 0.02 vs. 0.30 ± 0.02 p.u.) compared to control. Despite equal voluntary activation abilities, absolute maximal dorsiflexion strength was significantly lower in the DN (∼29%) group. When normalized to muscle tissue volume, dorsiflexion strength was significantly lower in DN (∼34%) compared to control. CONCLUSIONS: In addition to reductions in overall leg muscle protein quality, these findings indicate preferential decline in the structural integrity and contractile quality of the extensor (TA) leg muscles when compared with the flexors (SOL, GA). Thus, for those with DN, muscle weakness may primarily be due to a loss of muscle mass, and reduction of contractile quality per volume of viable muscle tissue. Supported by NSERC
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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 | 0.000 | 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".