Hypoxia and Cognitive Functions in Patients Suffering from Cardiac Diseases: A Narrative Review
Bibliographic record
Abstract
Background: Cardiovascular diseases (CVD) are major contributors to global morbidity and mortality, and their association with cognitive impairment has gained increasing attention. Recent studies indicate that the prevalence of post-myocardial infarction (MI) cognitive impairment ranges from 22% to 37%, with attention being one of the most frequently affected domains. Moreover, novel approaches, such as normobaric hypoxic training in cardiac rehabilitation, show potential in improving both cardiovascular and cognitive outcomes. Aim: This narrative review aims to synthesize current evidence on the role of hypoxia in the development of cognitive dysfunction among patients with cardiac diseases, emphasizing shared mechanisms along the heart–brain axis. Methods: We performed a narrative search of PubMed, Scopus, and Web of Science databases using the keywords “hypoxia”, “cognitive impairment”, “myocardial infarction”, “heart failure”, and “CABG surgery”. We included original studies, reviews, and meta-analyses published between 2000 and up to the present in English. Priority was given to peer-reviewed human studies; animal models were included when providing mechanistic insights. Exclusion criteria included case reports, conference abstracts, and non-peer-reviewed sources. Narrative reviews, while useful for providing a broad synthesis, carry an inherent risk of selective bias. To minimize this limitation, independent screening of sources and discussions among multiple authors were conducted to ensure balanced inclusion of the most relevant and high-quality evidence. Results: Hypoxia contributes to cognitive decline through multiple pathophysiological pathways, including blood–brain barrier disruption, white matter degeneration, oxidative stress, and chronic neuroinflammation. The concept of “cardiogenic dementia”, although not yet formally classified, highlights cardiac-related contributions to cognitive impairment beyond classical vascular dementia. Clinical assessment tools such as the Stroop test, Trail Making Test (TMT), and Montreal Cognitive Assessment (MoCA) are useful in detecting subtle executive dysfunctions. Both pharmacological treatments (ACE inhibitors, ARBs) and innovative rehabilitation methods (including normobaric hypoxic training) may improve outcomes. Conclusions: Cognitive impairment in cardiac patients is common, clinically relevant, and often underdiagnosed. Routine cognitive screening after cardiac events and integration of cognitive rehabilitation into standard cardiology care are recommended. Future studies should incorporate cognitive endpoints into cardiovascular trials.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".