Endurance exercise does not protect the decrease in bone associated with Type 1 diabetes in adult rats
Bibliographic record
Abstract
Streptozotocin (STZ) induced type 1 diabetes mellitus (T1DM) decreases trabecular bone volume in mice. This study investigated the potential protective effects of endurance training (ET) on bone in STZ‐induced T1DM adult rats. Sixty four 8‐week old Sprague‐Dawley rats were randomly divided into 4 groups of 16: non‐T1DM sedentary (CS) and exercised (CX), T1DM sedentary (DS) and exercised (DX). Blood glucose was maintained at 9–15 mmol/L using subcutaneously implanted insulin pellets (Linplant, Linshin Canada, Inc.). ET was at ~75% VO 2max for 1 h/d, 5 d/wk for 10 wk. Areal and volumetric bone mineral density (aBMD and vBMD; excised femur) was measured using dual‐energy x‐ray absorptiometry (DXA; QDR 4500A) and micro computed tomography (μCT; Aloka LCT200); bone strength was estimated using μCT. Two‐way ANOVA was used to test for diabetes and exercise differences; significance was set at P<0.05. T1DM had lower body weight (20.0%), aBMD (8.6%), cortical vBMD (1.6%), and bending (12.4%) and torsional strength (13.4%) vs control (P<0.001) with no differences in trabecular vBMD. Exercise also decreased body weight (4.7%) vs sedentary (P=0.019). These results suggest that 10 wk of ET does not have bone protective effects and T1DM rats have lower aBMD and vBMD than control, which may be secondary to effects on body weight. Funded by CIHR .
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".