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Is obstructive sleep apnoea more than an epiphenomenon in patients with obesity? A cardiovascular perspective from a real-world propensity score-matched study

2025· article· en· W7130427994 on OpenAlexaff
B S Prado, A Franci, Luciano Baracioli, K Razimavicius, Roberta Saretta, R Kalil-Filho, L F Drager

Bibliographic record

VenueEuropean Heart Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsObesityStroke (engine)Myocardial infarctionPropensity score matchingIncidence (geometry)Retrospective cohort studyMaceCohort studyRisk factorObesity paradox

Abstract

fetched live from OpenAlex

Abstract Background Obesity is the main risk factor for Obstructive Sleep Apnoea (OSA). Previous studies have shown that OSA affects ~50-70% of patients with obesity and exacerbates sympathetic activation, inflammation and endothelial dysfunction in these patients. However, it is still unclear whether OSA exacerbates cardiovascular (CV) events in patients with obesity. Purpose To test the hypothesis that OSA increases CV events in patients with obesity regardless of its grade. Methods We conducted a retrospective cohort analysis using the TriNetX Global Health Research Network through anonymised electronic medical records. We identified adult patients >40 years with obesity without previous anti-obesity therapy (GLP-1 agonists and tirzepatide) or bariatric surgery. We excluded patients with previous episodes of myocardial infarction and/or stroke. We used multiple code recommendations for tracking the presence of OSA, according to the international Classification of Diseases 10th edition: G47.33, G47.3, G47.39 or G47.30. We performed a propensity score matching in a 1:1 ratio for age, sex, ethnicity, hypertension, diabetes, dyslipidemia, smoking and chronic kidney disease. The combined endpoint included the incidence of 3-point MACE (non-fatal myocardial infarction, non-fatal stroke and all-cause mortality). Results In a crude analysis, patients with obesity+OSA represented approximately 15% of the sample. After the propensity score matching, 1,534,850 patients with obesity were included for the analysis (50% in each group: with and without OSA). After a median of 730 days of follow-up, OSA increased the incidence of combined events by 11% (OR:1.11; 95% CI: 1.09 – 1.13). A stratified analysis by different grades of obesity revealed that this result was driven by more severe classes of obesity: grade 1 (OR: 0.92; 95% CI: 0.85 – 1.00), grade 2 (OR: 1.06; 95% CI: 0.98 –1.17) and grade 3 (OR:1.08; 95% CI: 1.04 – 1.13). Conclusion OSA is associated with a modest increase in the incidence of MACE in patients with obesity. These results are probably attenuated by not capturing potential OSA underdiagnosis and OSA treatment in patients with obesity. Increasing OSA awareness in patients with obesity may be an interesting strategy for decreasing the cardiovascular burden associated with obesity in parallel to the improvement of sleep-related symptoms in these patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.041
GPT teacher head0.309
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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