ArticleAlzheimer's research & therapy2022
Blood-based lipidomic signature of severe obstructive sleep apnoea in Alzheimer's disease.
Article in Alzheimer's research & therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 14 citations in OpenAlex.
- Lipidomic signatures reveal biomarkers of mild cognitive impairment.Translational psychiatry · 2026Article
- Chronic Intermittent Hypoxia Exposure Induces a Unique Microglial Transcriptome in 5XFAD Mice.Molecular neurobiology · 2025Article
- Sleep Disordered Breathing and Neurocognitive Disorders.Journal of clinical medicine · 2024Review
- Cerebrospinal fluid lipidomic fingerprint of obstructive sleep apnoea in Alzheimer's disease.Alzheimer's research & therapy · 2023Article
- Chewing the fat: How lipidomics is changing our understanding of human health and disease in 2022.Analytical science advances · 2023Review
- Proteostasis failure exacerbates neuronal circuit dysfunction and sleep impairments in Alzheimer's disease.Molecular neurodegeneration · 2023Review
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundObstructive 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 different 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.
methodsThe 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 profiling was performed using liquid chromatography coupled with mass spectrometry.
resultsWe identified a subset of 44 lipids (mainly phospholipids and glycerolipids) that were expressed differently between patients with AD and severe and nonsevere OSA. Among the lipids in this profile, 30 were significantly correlated with specific 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).
conclusionOur results show a plasma lipidomic fingerprint that allows the identification of patients with AD and severe OSA, allowing the personalized management of these individuals. The findings suggest that oxidative stress and inflammation are potential prominent mechanisms underlying the association between OSA and AD.
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