DECREASE HEART RATE VARIABILITY IN MORBID OBESITY
Bibliographic record
Abstract
Background: Obesity and type 2 diabetes are both associated with increased incidence of sudden death. However, cardiac autonomic neuropathy (CAN) is found in diabetes patients which are often obese. Thus, we have evaluated the presence of CAN in morbid obesity with type 2 diabetes compared to overweight diabetics and controls. Methods: We constituted three groups; one with morbid obesity and type 2 diabetes (OD; n = 9), one with diabetes only (D; n = 11) and one control (C; n = 9) matched for age. The diabetics (D) and controls (C) were matched for BMI (>30 kg/m2). No subject have evidence of coronary artery disease (CAD), congestive heart failure, thyroid or overt renal disease. CAN was evaluated using analysis of heart rate variability (HRV) from a 24-hr Holter recording. Results: SDNN (an estimate of overall HRV) was decreased in subjects with OD compared to D and C (78 ± 23 vs 136 ± 33 vs 144 ± 45 ms, mean ± SE, p < 0.001). SDANN (an estimate of long-term components of HRV) also showed a decrease in subjects with OD compared to D and C (60 ± 17 vs 127 ± 33 vs 126 ± 39 ms, p < 0.001). rMSSD (an estimate of short-term components of HRV) showed a decrease in subjects with OD compared to D (21 ± 9 vs 33 ± 12 ms, p = 0.02), but not different from C (21 ± 9 vs 31 ± 15 ms, p = 0.1). Conclusion: These results suggests that obesity per se is associated with important decrease in HRV. This alteration in the sympathovagal balance could be involved in the increase sudden death incidence found in this population.
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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) | 0.001 | 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".