The Impact Of Acute Hyperglycemia On Heart Rate Variability In Men And Women.
Bibliographic record
Abstract
BACKGROUND: Heart rate variability (HRV) is used to non-invasively assess autonomic nervous system (ANS) regulation of the heart. Chronic hyperglycemia has been known to reduce HRV; however, no research has examined the impact of acute hyperglycemia on HRV, considering the potential for sex- and menstrual cycle phase-based differences. PURPOSE: To examine the impact of acute hyperglycemia on HRV, in men and women during the early and late follicular phases of the menstrual cycle. METHODS: 41 healthy men and naturally menstruating women (17F, age: 21±1 years) were recruited. Women were assessed during the early and late follicular phases of the menstrual cycle. ‘Ultra short-term’ assessments of HRV (1-minute recordings) were completed using an electrocardiogram before, and 60- and 90-min after consuming a 75g oral glucose challenge. Analysis of HRV time-domain variables was performed. RESULTS: Acute hyperglycemia resulted in elevated HR (shorter R-R intervals) at 60- and 90-min post-glucose ingestion (Pre: 61±1, Post60: 65±1, Post90: 66±1 bpm; p=0.005, p<0.001 respectively), with no differences between men and women, or across phases of the menstrual cycle. The root mean square of successive differences between R-R intervals (RMSSD) and standard deviation in normal R-R intervals (SDNN) were significantly lower Post90 vs Pre (p=0.022, p=0.048 respectively), with no sex differences. Additionally, women, regardless of phase, had higher average HR compared to men (p<0.001). CONCLUSION: Acute hyperglycemia appears to decrease HRV, indicative of an acute change in ANS regulation. Furthermore, this study confirmed previously observed sex differences in HR, but not in HRV. Research supported by: NSERC Discovery Grant & Canadian Graduate Scholarship - Master’s
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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 | 0.000 | 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".