Heart rate variability and the metabolic syndrome: a systematic review of the literature
Bibliographic record
Abstract
BACKGROUND: A number of cross-sectional studies have examined associations between heart rate variability and metabolic syndrome, but differences in study populations, data collection and analysis methodologies make synthesis difficult. The purpose of this study was to systematically review published primary research examining associations between heart rate variability and metabolic syndrome or its individual risk factors. METHODS: A systematic literature search of PubMed and EMBASE was conducted to identify relevant articles published from January 1999 to December 2012. Studies were included if they examined associations between heart rate variability analysed by standard protocols and metabolic syndrome risk factors according to published definitions. All papers were scored with a modified Downs and Black instrument, and data were extracted. RESULTS: Fourteen studies were included. Heart rate variability generally was reduced in women with metabolic syndrome compared to those without, while results in men were inconsistent. Time and frequency domain heart rate variability parameters were associated with individual metabolic syndrome risk factors, though sex differences exist. Only two studies considered nonlinear and Poincaré plot heart rate variability parameters, which were reduced in metabolic syndrome. CONCLUSIONS: Heart rate variability is altered differently in men and women with metabolic syndrome. Future studies should follow consistent heart rate variability analysis protocols and metabolic syndrome definitions and include more comprehensive analyses to investigate potential mechanisms.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".