Blood-based lipidomic signature of severe obstructive sleep apnoea in Alzheimer's disease

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Dakterzada, Farida
Benítez, IvánBenítez, Iván - ORCID ID
Targa, Adriano
Carnes, Anna
Pujol, Montserrat
Minguez Roure, Olga
Vaca, Rafaela
Sánchez de la Torre, ManuelSánchez de la Torre, Manuel - ORCID ID
Barbé Illa, FerranBarbé Illa, Ferran - ORCID ID
Pamplona Gras, ReinaldPamplona Gras, Reinald - ORCID ID
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cc-by (c) authors, 2022
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Background: Obstructive sleep apnoea (OSA) is the most frequent form of sleep-disordered breathing in patients with Alzheimer’s disease (AD). Available evidence demonstrates that both conditions are independently associated with alterations in lipid metabolism. However, it is unknown whether the expression of lipids is diferent between AD patients with and without severe OSA. In this context, we examined the plasma lipidome of patients with suspected OSA, aiming to identify potential diagnostic biomarkers and to provide insights into the pathophysiological mechanisms underlying the disease. Methods: The study included 103 consecutive patients from the memory unit of our institution with a diagnosis of AD. The individuals were subjected to overnight polysomnography (PSG) to diagnose severe OSA (apnoea-hypopnea index ≥30/h), and blood was collected the following morning. Untargeted plasma lipidomic profling was performed using liquid chromatography coupled with mass spectrometry. Results: We identifed a subset of 44 lipids (mainly phospholipids and glycerolipids) that were expressed diferently between patients with AD and severe and nonsevere OSA. Among the lipids in this profle, 30 were signifcantly correlated with specifc PSG measures of OSA severity related to sleep fragmentation and hypoxemia. Machine learning analyses revealed a 4-lipid signature (phosphatidylcholine PC(35:4), cis-8,11,14,17-eicosatetraenoic acid and two oxidized triglycerides (OxTG(58:5) and OxTG(62:12)) that provided an accuracy (95% CI) of 0.78 (0.69–0.86) in the detection of OSA. These same lipids improved the predictive power of the STOP-Bang questionnaire in terms of the area under the curve (AUC) from 0.61 (0.50–0.74) to 0.80 (0.70–0.90). Conclusion: Our results show a plasma lipidomic fngerprint that allows the identifcation of patients with AD and severe OSA, allowing the personalized management of these individuals. The fndings suggest that oxidative stress and infammation are potential prominent mechanisms underlying the association between OSA and AD.
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Alzheimer's Research & Therapy, 2022, vol. 14, núm. 1