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Sleep and Related Disorders / Tracheal and Airway Disorders · Journal article
Communications Medicine · August 14, 2026
Well-designed and adequately powered for the question it asks.
Data-driven clustering of sleep-disordered breathing phenotypes identifies three distinct subgroups with divergent long-term cardiovascular risks. Clusters characterized by aging–hypertension and metabolic–hypoxia show 3.6–4 times higher composite cardiovascular event risk and 5.5 times higher heart failure risk compared to a healthy-profile cluster, and a phenotype-based risk score outperforms the apnea–hypopnea index for stratification both internally and in external validation.
Prospective cohort study with internal and external validation. Community-dwelling adults from the Sleep Heart Health Study baseline cohort, mean age 63.2 years, 52.7% female, with sleep testing and long-term adjudicated cardiovascular follow-up.. Intervention: Data-driven phenotypic clustering to define three SDB phenotypes: Cluster 1 (aging–hypertensive OSA), Cluster 2 (healthy sleep–CVD profile), and Cluster 3 (metabolic–hypoxic OSA). Compared with: Standard apnea–hypopnea index (AHI) risk stratification. n = 4,909. Sleep Heart Health Study (United States multicenter cohort, specific centres not detailed in abstract).
Cluster 1 (aging–hypertensive OSA) hazard ratio for composite cardiovascular endpoint: 4.06 (95% CI 3.50–4.71) vs Cluster 2 Cluster 3 (metabolic–hypoxic OSA) hazard ratio for composite endpoint: 3.62 (95% CI 2.94–4.44) vs Cluster 2 Heart failure risk for Cluster 1: HR 5.46 (95% CI 4.41–6.76) vs Cluster 2
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Phenotype-based risk stratification using routine clinical and sleep variables may improve identification of high-risk sleep apnea patients beyond AHI alone, potentially supporting more targeted prevention and treatment strategies. The approach shows promise for personalized risk assessment in clinical practice.
Rigorous prospective cohort study with adjudicated hard cardiovascular endpoints, clear phenotype-based risk stratification with internal and external validation, showing clinically meaningful hazard ratios that outperform standard AHI scoring.
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Phenotype-based risk stratification using routine clinical and sleep variables may improve identification of high-risk sleep apnea patients beyond AHI alone, potentially supporting more targeted prevention and treatment strategies. The approach shows promise for personalized risk assessment in clinical practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Sleep-disordered breathing (SDB) has heterogeneous cardiovascular risks that are not fully captured by the apnea–hypopnea index (AHI). We aimed to determine whether data-driven sleep–cardiometabolic phenotypes identify long-term cardiovascular risk and support phenotype-based risk stratification. We analyzed 5682 adults from the Sleep Heart Health Study baseline cohort enrolled in 1995-1998 (mean [SD] age, 63.2 [11.2] years; 52.7% female). Fifteen prespecified variables were used for k-means clustering to define SDB phenotypes. Participants with adjudicated follow-up data (n = 4909) underwent time-to-event analyses with a primary composite endpoint of various cardiovascular events. Three phenotypes were identified: Cluster 1 (aging–hypertensive OSA), Cluster 2 (healthy sleep-CVD profile), and Cluster 3 (metabolic–hypoxic OSA). Clusters 1 and 3 showed higher risks of the composite endpoint than Cluster 2 (hazard ratios [HRs], 4.06 [95% confidence interval (CI), 3.50–4.71] and 3.62 [95% CI, 2.94–4.44], respectively) and consistently elevated cardiovascular death, myocardial infarction, and stroke risks. Heart failure risk was pronounced for Clusters 1 and 3 (HRs, 5.46 [95% CI, 4.41–6.76] and 5.55 [95% CI, 4.23–7.29], respectively) compared with Cluster 2. The multinomial classifier demonstrated high accuracy and discrimination in the internal test set. The phenotype-based risk score exhibited a clear stepwise gradient in predicted and observed cardiovascular risk and retained prognostic value in the external cohort, outperforming the AHI with acceptable calibration. Multidimensional phenotyping identifies SDB subgroups with divergent cardiovascular risk. A phenotype-based score using routine variables may improve risk stratification beyond event frequency alone. Sleep-disordered breathing (sleep apnea) is common and can increase the risk of heart disease and stroke. It is usually graded by the apnea–hypopnea index (AHI), but AHI does not fully capture risk. We analyzed 5682 adults in a cohort with sleep testing and long follow-up. A computer method grouped people into three profiles: older with high blood pressure, low-risk healthy profile, and obesity with low oxygen at night. Compared with the group at lower risk, the older and obesity profiles had about four times more cardiovascular events, especially heart failure. A score using routine measures predicted risk better than AHI and also worked in an external cohort. This approach may help identify people at higher-risk earlier and support more personalized prevention and treatment. Huang, Xiang et al. identify sleep-disordered breathing phenotypes using clinical and sleep data from a community cohort. The phenotypes show divergent cardiovascular risks, and a phenotype-based score improves risk stratification beyond the apnea–hypopnea index.
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