Epidemiologic and mechanistic evidence is increasingly supporting the notion that obstructive sleep apnea is a risk factor for dementia. Hence, the identification of patients at risk of cognitive decline due to obstructive sleep apnea may significantly improve preventive strategies and treatment decision-making. Cerebrospinal fluid and blood biomarkers obtained through genomic, proteomic and metabolomic approaches are improving the ability to predict incident dementia. Therefore, fluid biomarkers have the potential to predict vulnerability to neurodegeneration in individuals with obstructive sleep apnea, as well as deepen our understanding of pathophysiological processes linking obstructive sleep apnea and dementia. Many fluid biomarkers linked to Alzheimer's disease and vascular dementia show abnormal levels in individuals with obstructive sleep apnea, suggesting that these conditions share common underlying mechanisms, including amyloid and tau protein neuropathology, inflammation, oxidative stress, and metabolic disturbances. Markers of these processes include amyloid-β, tau proteins, inflammatory cytokines, acute-phase proteins, antioxydants and oxidized products, homocysteine and clusterin (apolipoprotein J). Thus, these biomarkers may have the ability to identify adults with obstructive sleep apnea at high risk of dementia and provide an opportunity for therapeutic intervention. Large cohort studies are necessary to establish a specific fluid biomarker panel linking obstructive sleep apnea to dementia risk.

Aging is associated with reduced slow wave (SW) density (number SW/min in non-rapid-eye-movement sleep) and amplitude. It has been proposed that an age-related decrease in SW density may be due to a reduction in EEG amplitude instead of a decline in the capacity to generate SW. Here, we propose a data-driven approach to adapt SW amplitude criteria to age and sex. We predicted that the adapted criteria would reduce age and sex differences in SW density and SW characteristics but would not abolish them. A total of 284 healthy younger and older adults participated in one night of sleep EEG recording. We defined age- and sex-adapted SW criteria in a first cohort of younger (n=97) and older (n=110) individuals using a signal-to-noise ratio approach. We then used these age- and sex-specific criteria in an independent second cohort (n=77, 38 younger and 39 older adults) to evaluate age and sex differences on SW density and SW characteristics. After adapting SW amplitude criteria, we showed maintenance of an age-related difference for SW density whereas the sex-related difference vanished. Indeed, older adults produced less SW compared to younger adults. Specifically, the adapted SW amplitude criteria increased the probability of occurrence of low amplitude SW (<80µV) for older men especially. Our results thereby confirm an age-related decline in SW generation rather than an artefact in the detection amplitude criteria. As for the SW characteristics, the age- and sex-adapted criteria display reproducible effects across the two independent cohorts suggesting a more reliable inventory of the SW.

Sleep slow waves are studied for their role in brain plasticity, homeostatic regulation and their changes during aging. Here, we address the possibility that two types of slow waves co-exist in humans. Thirty young and 29 older adults underwent a night of polysomnographic recordings. Using the Transition frequency, slow waves with a slow transition (slow switchers) and with a fast transition (fast switchers) were discovered. Slow switchers had a high EEG connectivity along their depolarization transition while fast switchers had a lower connectivity dynamic and dissipated faster during the night. Aging was associated with lower temporal dissipation of sleep pressure in slow and fast switchers and lower EEG connectivity at the microscale of the oscillations, suggesting a decreased flexibility in the connectivity network of older individuals. Our findings show that two different types of slow waves with possible distinct underlying functions, coexist in the slow wave spectrum.