Independent effect of type 2 diabetes beyond characteristic comorbidities and medications on immediate but not continued knee extensor exercise hyperemia
Bibliographic record
Abstract
We tested the hypothesis that type 2 diabetes (T2D), when present in the characteristic constellation of comorbidities (obesity, hypertension, dyslipidemia) and medications, slows the dynamic adjustment of exercising muscle perfusion and blunts the steady state relative to that of controls matched for age, body mass index, fitness, comorbidities, and non-T2D medications. Thirteen persons with T2D and 11 who served as controls performed rhythmic single-leg isometric quadriceps exercise (rest-to-6 kg and 6-to-12 kg transitions, 5 min at each intensity). Measurements included leg blood flow (LBF, femoral artery ultrasound), mean arterial pressure (MAP, finger photoplethysmography), and leg vascular conductance (LVK, calculated). Dynamics were quantified using mean response time (MRT). Measures of amplitude were also used to compare response adjustment: the change from baseline to 1) the peak initial response (greatest 1-s average in the first 10 s; ΔLBFPIR, ΔLVKPIR) and 2) the on-transient (average from curve fit at 15, 45, and 75 s; ΔLBFON, ΔLVKON). ΔLBFPIR was significantly blunted in T2D vs. control individuals (P = 0.037); this was due to a tendency for reduced ΔLVKPIR (P = 0.063). In contrast, the overall response speed was not different between groups (MRT P = 0.856, ΔLBFON P = 0.150) nor was the change from baseline to steady state (P = 0.204). ΔLBFPIR, ΔLBFON, and LBF MRT did not differ between rest-to-6 kg and 6-to-12 kg workload transitions (all P > 0.05). Despite a transient amplitude impairment at the onset of exercise, there is no robust or consistent effect of T2D on top of the comorbidities and medications typical of this population on the overall dynamic adjustment of LBF, or the steady-state levels achieved during low- or moderate-intensity exercise.
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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 | 0.001 |
| 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) | 0.002 | 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".