Near-infrared spectroscopy can detect differences in vascular responsiveness to a hyperglycaemic challenge in individuals with obesity compared to normal-weight individuals
Bibliographic record
Abstract
Objective: To examine whether the near-infrared spectroscopy combined with vascular occlusion test technique could detect differences in vascular responsiveness during hyperglycaemia between normal-weight individuals and individuals with obesity. Methods: A total of 16 normal-weight individuals (body mass index, 21.3 ± 1.7 kg/m 2 ) and 13 individuals with obesity (body mass index, 34.4 ± 2.0 kg/m 2 ) were submitted to five vascular occlusion tests (Pre, 30, 60, 90 and 120 min after glucose challenge). Vascular responsiveness was determined by the Slope 2 (Slope 2 StO 2 ) and the area under the curve (StO 2AUC ) of oxygen saturation derived from near-infrared spectroscopy–vascular occlusion test. Results: The Slope 2 StO 2 increased from 1.07 ± 0.16%/s (Pre) to 1.53 ± 0.21%/s at 90 min ( p < 0.05) in the control group, while in obese it increased from 0.71 ± 0.09%/s (Pre) to 0.92 ± 0.14%/s at 60 min ( p < 0.05), and to 0.97 ± 0.10%/s ( p < 0.01) at 120 min after glucose ingestion. The StO 2AUC decreased from 1729 ± 214% . sec (Pre) to 1259 ± 232% . sec at 60 min ( p < 0.05) and to 1034 ± 172% . sec at 90 min ( p < 0.05) in the normal-weight group, whereas it decreased at 90 min (637 ± 98% . sec; p < 0.05) and at 120 min (590 ± 93% . sec; p < 0.01) compared to 30 min (1232 ± 197% . sec) after glucose ingestion in individuals with obesity. Conclusion: Near-infrared spectroscopy–vascular occlusion test technique was capable of detecting differences in vascular responsiveness during hyperglycaemia between normal-weight individuals and individuals with obesity.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".