This study reports several key findings related to cognitive impairment in patients with OSA. First, we observed a high prevalence of cognitive impairment (36%) that does not correlate with subjective symptoms such as daytime sleepiness or cognitive complaints. Second, we found that measures of sleep fragmentation and hypoxaemia were significantly associated with increased odds of cognitive impairment. Furthermore, a combination of these factors using a ‘rule-in’ approach did lead to a substantial improvement in impairment identification at the cost of specificity. The high sensitivity of these routinely collected PSG metrics in identifying cognitive impairment suggests that risk stratification may be possible for targeted cognitive screening in clinical settings.
Despite the well-established link between OSA and cognitive impairment, the prevalence of impairment in this population has not been thoroughly established. This is most likely due to the lack of standardisation in the tools and thresholds used to discern impairment, as well as the infrequent routine cognitive testing conducted during sleep studies.(14) For example, one study using the Montreal Cognitive Assessment screening instrument reported an increase in impairment prevalence of 42–55% in patients with moderate-to-severe OSA depending on the threshold that was applied.(15) However, comprehensive neuropsychological testing to clinically diagnose mild cognitive impairment (MCI) in patients with OSA shows a reported prevalence of 36–40%, which is consistent with our findings.(16, 17)
Use of the ACE-R has previously been documented in the population with OSA.(18–21) Previous findings include patients with OSA having worse cognitive recovery post-stroke, worse ACE-R performance compared to age-matched controls, and for increased transitional sleep and obstructive apnoeas to be negatively correlated with attention and verbal fluency deficits. Nonetheless, we noted considerable difference in the methods and population demographics compared to our investigation here. Additionally, our cut-off threshold for cognitive impairment (ACE-R ≤ 88) was set liberally to maximize sensitivity for detecting all-cause impairment, rather than exclusively identifying cases that are more likely to represent a population with dementia, for which a more conservative threshold (e.g., ≤ 82) would have been appropriate. However, the original paper by Mioshi et al., did publish a range of dementia likelihood ratios to overcome the ‘grey-zone’ between the proposed cut-off values of 88 and 82.(5) Whilst a score of 88 is 8.4 times more likely to come from someone with dementia than without, a score of 82 was 100 times more likely. Eleven individuals in our sample (12%) scored at or below an ACE-R score of 82.
The significant reduction across most cognitive domains in the impaired group relative to the unimpaired, coupled with the modest correlations between domains, indicates a complex pattern of underlying neurological impairments. This complexity is undoubtedly driven by the numerous ways OSA may induce cognitive dysfunction (e.g., sleep deficits vs neurological injury).(22) For this reason, repeat testing is recommended following OSA treatment to ascertain what neurocognitive impairments are due to reversible vs permanent damage.
When investigating the individual items comprising the ACE-R (see supplementary Table S1), we found that most items presented with a possible ceiling effect, notably in the attention & orientation domain, and some items of the memory, language, and visuospatial domains. Without further testing, it is unclear whether these items were not sufficiently sensitive to detect mild cognitive deficits, or if participants were indeed unimpaired and performing at maximal test levels. If the former were true, this limitation may mask subtle relationships between OSA and cognitive function. This is especially pertinent with attentional function, as this domain is known to be significantly affected by sleep disruption/fragmentation.(23) Conversely, we observed that three specific tasks—the delayed verbal recall test, and both phonemic and semantic verbal fluency tasks— accounted for a substantial portion of variation in total ACE-R score. These cognitive tasks have previously been found to be impaired in the population with OSA and are sensitive measures of both MCI and Alzheimer’s Disease.(24–27)
Regarding our regression analyses, it was not unexpected that daytime sleepiness or presence of cognitive complaints were not significantly associated with impairment status, as these self-reported measures have been shown to poorly reflect their objective counterparts in this population.(4) Similarly, the AHI is known to be an unreliable marker of comorbid prediction.(8) Patients with identical AHI values can experience varying levels of hypoxaemia and sleep fragmentation, leading to inconsistent results when correlating AHI severity with multiple cognitive endpoints.(28–31)
We did, however, find significant correlations between traditional indices of OSA pathophysiology and cognitive performance, and although methodological limitations prevent causal inference, there is strong biological plausibility for these mechanisms contributing to cognitive harm. Intermittent hypoxia is considered one of the primary mechanisms of neurocognitive injury in OSA, causing short-term, localized neural tissue damage during sleep (e.g., ischemia-reperfusion injury) and systemic metabolic maladaptation’s in response to chronic exposure.(32–34) The hippocampus, an important neural correlate of memory recall and verbal fluency, and one observed to be compromised in patients with OSA, has been shown to be particularly sensitive to oxygen fluctuations and hypoxic degeneration in rodent models.(32) Similarly, sleep fragmentation can induce cognitive harm through chronic sympathetic overactivity and disruption of homeostatic mechanisms essential for healthy cognitive function, such as memory consolidation.(35, 36) Studies of rodents have found sleep fragmentation to impair neurogenesis, neuroplasticity, and cell excitability of the hippocampus.(37–40) As such, Mean SpO2, T90, ODI, and arousal index have all been previously shown to be correlated with multiple cognitive endpoints, however this is the first study showing their relationship with cognitive performance using the ACE-R instrument.(15, 41–43)
Limitations and Future Research
Methodological limitations within this cross-sectional investigation include the small sample size and potential bias introduced by the modest difference in inclusion criteria between the two datasets (see Table S2 and S3 for statistical comparison). Additionally, given the exploratory nature of our second aim, we chose not to apply a correction for multiple comparisons. This decision was made to minimize the risk of Type II errors (i.e., false negatives) which could potentially mask important correlates of cognitive performance. However, we acknowledge both the increase in risk of Type 1 errors, and the overfitting of our variables to this cohort, therefore further larger studies are needed to confirm the associations here and validate their clinical use as predictors of impairment. Additionally, novel methods that provide greater precision in measuring hypoxaemia and sleep disruption, such as the hypoxic burden or arousal intensity may provide more accurate methods of detection, however these may not be available in routine clinical settings.(44, 45)
It is also important to note that the ACE-R has been superseded by the ACE-III. However, given the almost perfect degree of correlation between the two (99.3% R2) with most items remaining unchanged (no changes to memory/fluency items), the results of this study would remain generalisable for both instruments.(46) Although this study did not assess cognitive performance post-treatment with CPAP, the long-term utility of cognitive screening tests for monitoring treatment effects requires further exploration.
Clinical Impact
OSA has been associated with cognitive impairment for over five decades, yet there remains a lack of routine objective cognitive monitoring in a cohort with frequent cognitive complaints. This slow transition of research into clinical practice might be explained by the common barrier of insufficient resources to perform routine testing in sleep disorder services. Recognizing the constraints of time and labour, especially in busy public hospital systems globally, our study advocates for a targeted approach to optimize resource allocation. Our findings suggest that implementing specific combinations of routinely collected PSG metrics can significantly enhance the identification of cognitive impairment in patients with OSA. However, while this approach may allow over a third of patients to avoid being screened, it is important to note that more than half of the patients identified by the rule-in criteria will have an ACE-R score in the normal range. Despite this limitation to our targeted approach, we believe this trade-off is necessary to ensure that individuals with true impairment are identified and can benefit from early detection. Even a one-third reduction in those needing to be screened will result in substantially reduced labour costs, while those identified as cognitively impaired can benefit from post-OSA treatment testing to monitor treatment response. Moreover, our research, alongside existing literature, underscores the inadequacy of current patient-reported measures of sleepiness and cognitive complaints in capturing the full spectrum of neurocognitive health in patients with OSA. Although our results are preliminary, they provide foundational insights that could guide further research in this field.