Effects of weight loss and weight loss maintenance on cardiac autonomic function in obesity: a randomized controlled trial
Bibliographic record
Abstract
To investigate relationships between weight loss and weight loss maintenance with cardiac autonomic function and exercise in obesity, 39 adults (45.7 ± 10.7 years; BMI: 34.2 ± 3.4 kg·m−2) participated in a 10-week, medical weight loss program combined with aerobic exercise. A subset ( n = 18) participated in an aerobic exercise weight loss maintenance program (550 or 970 MET min·week−1) for 18 additional weeks. Primary outcomes included markers of cardiac autonomic function assessed by heart rate variability (HRV) (i.e., SDNN, RMSSD, HFln). Following weight loss, we observed significant improvements for SDNN (48.2 [41.4–55.1] vs. 55.1 [45.7–64.4] ms, p = 0.03), RMSSD (37.7 [29.1–46.4] vs. 47.9 [37.4–58.4] ms, p = 0.002), and HFln (5.88 [5.39–6.36] vs. 6.32 [5.86–6.78] ms, p = 0.001). Regression analyses showed fasting insulin concentration predicted 24% and 27% of the variance in RMSSD ( r2 = 0.236, p = 0.007) and HFln ( r2 = 0.274, p = 0.004), respectively. Following weight loss maintenance, no significant changes in HRV were observed. Changes in LDL ( r=–0.54, p = 0.04) and non-HDL ( r=–0.77, p = 0.001) were inversely associated with RMSSD changes. Clinically significant weight loss via caloric restriction and aerobic exercise improved HRV markers of cardiac vagal modulation. Following weight loss maintenance, we did not observe any further changes in HRV. Thus, our data suggest that commonly prescribed exercise volumes contribute to maintenance of parasympathetic modulation following medical weight loss programming and exercise. Novelty Caloric restriction and exercise exert significant improvements in cardiac autonomic function as measured by HRV in overweight and obesity. Aerobic exercise training, within recommended guidelines coupled with weight loss maintenance, retains cardiac autonomic function benefits from weight loss in previously obese individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".