Autonomic Nervous System and Cognitive Impairment in Older Patients: Evidence From Long-Term Heart Rate Variability in Real-Life Setting
Bibliographic record
Abstract
BACKGROUND In geriatric age, cognitive impairment and cardiovascular disorders are frequent comorbidities. Age-related anatomical and functional cardiac changes, including the autonomic system, could interfere with the control of different cognitive domains. Therefore, we assess the relationship between long-term heart rate variability (HRV), as measure of autonomic nervous system (ANS) functioning, and cognitive performances, in elderly patients representative of the outpatients in a real-life setting. METHODS Of 155 elderly outpatients (aged >65) screened, 117 enrolled patients underwent anthropometric evaluation, cardiac assessment by 12-leads electrocardiogram, 24-hour ECG recording and blood pressure (BP) measurement as well as global cognitive evaluation by a standardized multidimensional assessment, including Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment test (MoCA). HRV analysis was performed on 24-hour ECG recordings focusing on Time-domain indices (SDNN, SDANN and RMSSD) and on Frequency-domain measurements (HR, LF, HF, LF/HF). Multivariate linear analysis was used to explore the influence of the HRV significant variables on MMSE and MoCA test values. RESULTS MMSE and MoCA scores were both significantly and positively correlated with sympathetic system parameters (SDNN, SDANN, LF and LF/HF ratio) but not with parasympathetic system (rMSSD and HF) parameters. Multivariate analysis confirms this relationship. CONCLUSIONS Our results show that, in a representative real-life community elderly population, increased sympathetic activity, but not decreased vagal activity is associated with better cognitive performances. These results support the sympathetic autonomic function, in that the relationship between better cognitive performances and moderate prevalence of autonomic function appears dependent on long-term changes in heart rate, mediated by sympathetic activation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".