Proteomic profiling for prediction of recurrent cardiovascular event in patients with acute coronary syndrome and obstructive sleep apnea: A post-hoc analysis from the ISAACC study

Zapater, Andrea; Gràcia-Lavedán, Esther; Torres, Gerard; Mínguez, Olga; Pascual, Lydia; Cortijo, Anunciación; Martínez, Dolores; Batlle, Jordi de; Henríquez-Beltrán, Mario; Abad, Jorge; Durán-Cantolla, Joaquín; Urrutia, Amaia; Mediano, Olga; Masdeu, María José; Ordax-Carbajo, Estrella; Masa, Juan F.; Peña, Mónica de la; Mayos, Merçé; Coloma, Ramón; Montserrat, Josep M.; Chiner, Eusebi; Barbé, Ferrán; Sánchez‐de‐la‐Torre, Manuel

doi:10.1016/j.biopha.2022.114125

Cited by 3 publications

(1 citation statement)

References 53 publications

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“…This panel has also been used in OSA patients to identify subgroups based on differential inflammatory protein expression. For example, in a recent post hoc analysis of the ISAACC study [ 8 ], Zapater et al analyzed the proteomic profiles in 86 OSA patients admitted for acute coronary syndrome, divided into those with and without recurrent CVD events [ 64 ]. Using a supervised random forest algorithm to select relevant proteins and generate a predictive model of recurrent CVD, the authors identified 38 (of 276) cardiovascular and inflammatory proteins that were differentially expressed between the two groups.…”

Section: Applying Machine Learning and Artificial Intelligence To Obs...mentioning

confidence: 99%

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review

Cohen,

Kundel,

Robson

et al. 2024

JCM

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Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea–hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.

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Section: Applying Machine Learning and Artificial Intelligence To Obs...mentioning

confidence: 99%

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review

Cohen,

Kundel,

Robson

et al. 2024

JCM

View full text Add to dashboard Cite

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Proteomics in Cardiovascular disease

Gong,

Zhang,

Chen

et al. 2024

Clinica Chimica Acta

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Predictors and markers of the cardiovascular impact of obstructive sleep apnoea

Dodani,

Pinilla,

Sánchez-de-la-Torre

2024

Current Opinion in Pulmonary Medicine

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Purpose of review Obstructive sleep apnoea (OSA) is the most common form of sleep-disordered breathing and has been linked to cardiovascular health. However, some of the findings supporting this are controversial. These discrepancies might be a result of heterogeneity among OSA patients, and thus, additional information would be required to better stratify OSA patients according to cardiovascular risk. In this review, we aim to discuss the potential of biomarkers to fulfil this role. Recent findings Randomized controlled trials have been unable to confirm whether OSA treatment with continuous positive airway pressure (CPAP) has a positive effect on cardiovascular outcomes. Emerging physiology-based metrics of OSA seem to be more suitable for identifying patients at higher risk of cardiovascular disease and predicting the effects of CPAP outcomes on cardiovascular health. Similarly, blood-based molecular markers have gained attention in this context over the last few years. Summary Accurate cardiovascular risk stratification and appropriate treatment allocation for OSA patients remain challenging. However, significant efforts are being made to develop novel tools to address these important issues.

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Proteomic profiling for prediction of recurrent cardiovascular event in patients with acute coronary syndrome and obstructive sleep apnea: A post-hoc analysis from the ISAACC study

Cited by 3 publications

References 53 publications

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review

Proteomics in Cardiovascular disease

Predictors and markers of the cardiovascular impact of obstructive sleep apnoea

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