Insomnia and Cardiometabolic Health: Bridging the Gap Between Sleep Deficit and Disease Prevention
Bibliographic record
Abstract
Insomnia is a condition characterized by difficulty initiating or maintaining sleep, or experiencing early morning awakenings despite having a sufficient opportunity for rest. It affects up to one-third of adults, with around 10% meeting the criteria for insomnia disorder. Emerging research increasingly points to insomnia as a significant, modifiable risk factor for cardiometabolic diseases, including type 2 diabetes, cardiovascular disease, chronic kidney disease, and metabolic dysfunction-associated steatotic liver disease. This narrative review synthesizes the latest evidence linking insomnia to heightened cardiometabolic risk, especially type 2 diabetes. Additionally, we discuss how sleep deprivation affects metabolic processes and cardiovascular health, highlighting the connection between insomnia and cardiometabolic disease. Despite its prevalence and clear impact on health, insomnia remains trivialized, underdiagnosed, and inadequately managed. Only a minority of individuals seek medical advice for sleep disturbances, highlighting an urgent need for improved screening and management, particularly for those with cardiometabolic conditions. In this review, we aim to provide health-care professionals with practical recommendations for identifying and managing insomnia, a condition that is often underrecognized, with the ultimate goal of reducing the burden of cardiometabolic diseases. Integrating sleep health into cardiometabolic care will represent a significant step forward in reducing the global burden of chronic cardiometabolic diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".