Locomotor Training and Factors Associated with Blood Glucose Regulation After Spinal Cord Injury
Bibliographic record
Abstract
BACKGROUND: Individuals with spinal cord injury (SCI) have increased rates of glucose intolerance, insulin insensitivity, and type II diabetes caused mainly by the deconditioning of paralyzed muscle. The purpose of this systematic review was to determine the effectiveness of locomotor training in individuals with SCI on blood glucose control. METHODS: We searched studies on locomotor training for individuals with SCI with outcomes of glucose, insulin, or outcomes that could change glucose handling (i.e. increases in muscle mass, shifts in muscle fiber type composition, changes in transport proteins, or enzymes involved in glucose metabolism) in PubMed and EMBASE. RESULTS: Eleven studies (10 with incomplete SCI; 1 with complete SCI) were included in our review. Locomotor training included body weight supported treadmill training (BWSTT) with manual or robotic assistance, with and without functional electrical stimulation (FES), or involved FES-assisted over ground training. Six months of locomotor training in individuals with SCI resulted in significant decreases in glucose (15%) and insulin (33%) areas under the curve during oral glucose tolerance tests. Two to twelve months of locomotor training reversed some of the muscle atrophy - with muscle being the site of most glucose consumption, this is important for glucose control. Training also increased capacity for glucose storage, enzymes involved in glucose phosphorylation (hexokinase) and oxidation (citrate synthase), and glucose transport proteins (GLUT-4). Fiber type composition shifted to a slower fiber type, which favors glucose handling. There were no effects on fat mass. CONCLUSION: Locomotor training in individuals with SCI (generally an incomplete injury) increases capacity to handle glucose and results in muscular changes that should reduce the risk of type II diabetes.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".