Abstract 11335: Does Reduced Parasympathetic Heart Rate Variability Promote Obesity in Children?
Bibliographic record
Abstract
Introduction: Considering the contribution of the autonomic nervous system (ANS) in regulating both energy expenditure and energy intake, assessment of heart rate variability (HRV) may elucidate the etiology and adaptive processes in the development of obesity. Variations in the cardiac ANS are associated with changes in energy balance and eating behaviours, highlighting the potential key function of the ANS in the etiology of obesity. While it is broadly accepted that obesity impacts HRV, potential impacts of HRV patterns on obesity remain unexplored. We hypothesize that certain HRV patterns increase risk of excess weight in children. Methods: Participants included 406 White youth from the QUALITY cohort (56% boys). Children were considered at risk of CVD due to confirmed parental obesity status. Time- and frequency-domain HRV indices derived from short-term ECG recordings (SDNN, RMSSD, pNN50, LF, HF, and LF/HF ratio) were analyzed at baseline (mean = 9.6 years, SD = 0.9). Adiposity at 2-year follow-up was evaluated through dual-energy X-ray absorptiometry. Total body fat (BF) and BF distribution were captured; central adiposity was estimated using the android-to-gynoid ratio. Prospective associations between HRV at baseline and adiposity at 2-year follow-up were estimated using regression models including weights for well-established confounders (difference in adiposity measures over time, sex, age, sexual maturity, and physical activity). Results: 8-10 year olds with a sympathovagal imbalance described by a sympathetic predominance (LF/HF) presented with higher BF (β = 3.32, p < 0.001) and higher android-to-gynoid ratio (β =0.03, p<0.001) at age 10-12 years. Furthermore, lower levels of parasympathetic indices at age 8-10 years predicted higher BF (RMSSD: β = -0.09, p < 0.01; pNN50: β = -0.13, p < 0.01) and higher android-to-gynoid ratio (RMSSD: β -0.001, p < 0.05; pNN50: β = -0.001, p < 0.01) two years later. Conclusions: Impaired cardiac ANS modulation appears associated with the occurrence of childhood obesity two years later. A sympathetic predominance paired with reduced parasympathetic indices in otherwise healthy children may foreshadow pathophysiological processes that lead to obesity and may help to identify intervention targets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".