Response of Circulating Inflammatory Markers to Intermittent Hypoxia-Hyperoxia Training in Healthy Elderly People and Patients with Mild Cognitive Impairment
Bibliographic record
Abstract
Intermittent hypoxia-hyperoxia training (IHHT) is a non-pharmacological therapeutic modality for management of some chronic- and age-related pathologies, such as Alzheimer’s disease (AD). Our previous studies demonstrated significant improvement of cognitive function after IHHT in the patients with mild cognitive impairment (MCI). The present study further investigated the effects of IHHT on pro-inflammatory factors in healthy elderly individuals and patients with early signs of AD. Twenty-nine subjects (13 healthy subjects without signs of cognitive impairment syndrome and 16 patients diagnosed with MCI; age 52 to 76 years) were divided into four groups: Healthy+Sham (n = 7), Healthy+IHHT (n = 6), MCI+Sham (n = 6), and MCI+IHHT (n = 10). IHHT was carried out 5 days per week for 3 weeks (total 15 sessions), and each daily session included 4 cycles of 5-min hypoxia (12% FIO2) and 3-min hyperoxia (33% FIO2). Decline in cognitive function indices was observed initially in both MCI+Sham and MCI+IHHT groups. The sham training did not alter any of the parameters, whereas IHHT resulted in improvement in latency of cognitive evoked potentials, along with elevation in APP110, GDF15 expression, and MMP9 activity in both healthy subjects and those with MCI. Increased MMP2 activity, HMGB1, and P-selectin expression and decreased NETs formation and Aβ expression were also observed in the MCI+IHHT group. There was a negative correlation between MoCA score and the plasma GDF15 expression (R = −0.5799, p < 0.05) before the initiation of IHHT. The enhanced expression of GDF15 was also associated with longer latency of the event-related potentials P330 and N200 (R = 0.6263, p < 0.05 and R = 0.5715, p < 0.05, respectively). In conclusion, IHHT upregulated circulating levels of some inflammatory markers, which may represent potential triggers for cellular adaptive reprogramming, leading to therapeutic effects against cognitive dysfunction and neuropathological changes during progression of AD. Further investigation is needed to clarify if there is a causative relationship between the improved cognitive function and the elevated inflammatory markers following IHHT.
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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) | 0.001 | 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".