The indirect relationship between sleep and cognition in the PREVENT cohort: identifying targets for intervention

1. Introduction

As the global population grows older (United Nations, 2020) it has become increasingly important to understand the factors which contribute to healthy aging. The normal aging process impacts all physical and behavioral functions, including cognition (Harada et al., 2013). Broadly, cognition encompasses the crucial day-to-day abilities necessary to correctly respond to one's environment and includes components such as stimulus processing, memory (Harada et al., 2013), and higher-order executive functions (see Diamond, 2013). The trajectory of cognitive performance over time follows an inverted U. That is, cognition develops in childhood and adolescence, peaks during adulthood, and most domains begin to steadily decline after middle-age (Krivanek et al., 2021). Protecting cognition has garnered increased attention due to the massive economic and social burdens associated with its unhealthy decline and the onset of dementia (Alzheimer's Association, 2021). A myriad of factors may influence the rate at which cognition declines and the likelihood of developing dementia, including sleep patterns (Scullin and Bliwise, 2015).

Adequate sleep is vital for the maintenance of cognitive performance (Dzierzewski et al., 2018; Matricciani et al., 2019) and appears to be critical for the clearance of beta-amyloid (Aβ) protein (see Wang and Holtzman, 2020). Regarding the latter, self-reported poor sleep quality has been shown to be related to higher Aβ concentrations in middle-aged and older adults (Spira et al., 2013; Sprecher et al., 2015; but see Gabelle et al., 2019¹) and chronic sleep deprivation has resulted in greater accumulation of this neurotoxic protein (Tabuchi et al., 2015). More recently, Shokri-Kojori et al. (2018) demonstrated that acute sleep deprivation (i.e., one night) yielded increased Aβ concentrations in the hippocampus and thalamus of individuals aged between 22 and 72 years. These results may be related to decreased clearance of Aβ contingent upon adequate glymphatic function (e.g., Xie et al., 2013) and/or γ-oscillations during rapid eye-movement sleep (Aron and Yankner, 2016). Alternatively, results may reflect increased synthesis of Aβ in response to the lack of sleep (Castellano et al., 2011). These are important results as they demonstrate a potential link between acute and chronic sleep disruptions and the development of Alzheimer's disease pathologies (Ju et al., 2013, 2014; Wang and Holtzman, 2020). Moreover, studies of sleep deprivation demonstrate a general worsening of cognition (for review see Killgore, 2010). For example, Lo et al. (2016) found that acute partial (i.e., 5 h sleep for seven nights) and total (i.e., one night) sleep deprivation contributes to the formation of false memories, and Gevers et al. (2015) demonstrate a general slowing of Stroop task reaction times (RT). Sleep deprivation is thought to impair the ability of the brain to consolidate memories (Yoo et al., 2007) and reduce the availability of the resources necessary for adequate stimulus processing and top-down executive control (e.g., Botvinick et al., 2001). Similarly, too much sleep (e.g., 10 or more hours) has been found to be a risk factor for the development of global cognitive decline (Ma et al., 2020) and dementia onset (Cavaillès et al., 2022b). The mechanisms underlying the relationship between cognitive decline and sleep durations are not completely clear, but biological factors including elevated inflammation (Patel et al., 2009) and thinning in executive brain regions (Spira et al., 2016) have been proposed. Optimal health is likely supported by ~7 h of sleep per night (Watson et al., 2015); however, overall sleep quality (i.e., related to factors such as sleep disturbances, trouble falling asleep, long waketime after sleep onset) is also related to global cognition. In a recent systematic review, Casagrande et al. (2022) identified that a greater frequency of sleep disturbance is associated with impaired cognition. Moreover, individuals who regularly sleep poorly are at a higher risk for developing symptoms of depression (Riemann et al., 2020) and dementia (Sabia et al., 2021). The mechanism(s) by which sleep influences the onset of depression are unclear. However, evidence indicates that poorer sleep quality may disrupt neural plasticity and synaptic health within the brain's emotion processing regions (Disner et al., 2011; Riemann et al., 2020).

It is important to note that the relationship between sleep disruption and depressive symptoms is bi-directional. Symptoms of depression include poorer sleep quality, as well as increased fatigue, diminished concentration, decreased ability to make decisions, low mood, and apathy (Blazer, 2003). Importantly, and perhaps in conjunction with poorer sleep quality (Jaussent et al., 2011), depressive symptoms impair cognition (Varghese et al., 2022) and foster a greater likelihood for dementia development (Kessing, 2012; Cavaillès et al., 2022a). For example, Lindert et al. (2021) demonstrated that longitudinally, higher scores on the Center for Epidemiologic Studies Depression (CES-D) scale were positively related to worse scores on measures of episodic memory and executive function. This relationship has been demonstrated across the lifespan (see also Dotson et al., 2020) and has been attributed to elevated cortisol concentrations (Sapolsky, 2000) and increased neurotoxicity due to increased inflammation (Furtado and Katzman, 2015). Taken together, the current research landscape provides evidence for the links between sleep, depression and cognition. Importantly, however, the mechanistic nature of these associations remains poorly understood, especially in a mid-age population. Here, the PREVENT cohort—composed of individuals between the ages of 40 and 59—was used to examine the relationship between sleep, depression symptomology and cognition prior to any dementia diagnosis. We hypothesized that measures of sleep quality and quantity would be related to cognitive performance and it may be that this association is explained via an indirect association with depression or its symptoms.

2. Materials and methods

2.1. Participants

Seven hundred individuals (age range 40–59) from the PREVENT dementia programme (Ritchie et al., 2013) were included in this investigation. All participants self-reported being cognitively healthy at the time of collection. We note that a minority of participants reported a current diagnosis of depression (n = 23), sleep disorder (n = 93), anxiety disorder (n = 60), mood disorder (n = 24), psychotic disorder (n = 2), alcoholism (n = 3) and drug misuse (n = 2). We note that the data used in these analyses is secondary data where ethical approval has been obtained by the source cohort (i.e., PREVENT). All participants provided informed written consent.

2.2. Cognitive assessments

Participants' cognitive function was assessed via the COGNITO battery and tests included measures of executive function (i.e., Stroop color, word, and interference tasks), and memory (COGNITO Tasks 8—Articulation and Immediate Recall—and 17—Delayed Recall of Names). These tasks were selected due to their sensitivity to cognitive decline/disruption over the lifespan (Levy et al., 2002; Guarino et al., 2019; Taconnat et al., 2022).

2.3. Sleep assessments

Participants' sleep health was assessed via the Pittsburgh Sleep Quality Index (PSQI) (Buysse et al., 1989), the Epworth Sleepiness Scale (Johns, 1991) and the Lausanne NoSAS (Marti-Soler et al., 2016). The latter two scales were not included in baseline assessments and therefore constitute less of the dataset than the PSQI (see Table 1). Higher scores on the latter scales indicate increased daytime sleepiness and an increased risk of sleep-disordered breathing. Due to the low predictive validity of a total PSQI score (Landry et al., 2015; Parsey et al., 2015), we chose to use several of its components: hours of sleep per night, waking in the night or early morning, and self-reported overall sleep quality. Note that higher ratings of the latter three PSQI components (scored 0–3) correspond to worse sleep.

TABLE 1

Table 1. Participant characteristics, cognitive performance, sleep, and affect scores.

Finally, participants self-estimate of sleep duration (SleepGroup) was also assessed and reported in three groups: short- (i.e., <6 h), medium- (i.e., 6–7.99 h), and long-sleepers (i.e., >8 h).

2.4. Depression symptomology

The degree to which participants suffered from depressive symptoms was determined via the CES-D scale (Radloff, 1977). The CES-D is a 20-item depression symptom assessment with each item being scored on a scale 0–3 (i.e., total score from 0–60), and includes questions regarding participants' feelings of loneliness and the degree to which they enjoy life. Higher scores indicate more symptoms.

2.5. Apathy scores

Participants' apathy was assessed via a three-item apathy scale wherein participants reported whether they experienced “emotional blunting,” a “lack of initiative,” and/or a “lack of interest.” The frequency with which these symptoms occurred are summed to create a total score. Higher total scores are indicative of higher ratings of apathy. We chose to include a measure of apathy here because lack of interest/apathy has been defined as a core symptom of clinical depression (Blazer, 2003). Hence, apathy may indirectly mediate the association between depression symptoms and cognitive function (see Fishman et al., 2019).

2.6. Statistical analyses

2.6.1. Pre-processing

All data processing and subsequent analyses were performed in Stata SE 16.1. Prior to modeling, we assessed and processed responses to cued, free, immediate and list recall tasks; Stroop color, word and interference RTs; sleep, and depression and apathy scores. Where appropriate, skewed (i.e., g₁ > 1.0) data were log-transformed for normalization. We note that apathy scores remained skewed following log-transformation and were subsequently z-transformed to minimize their lack of normality. All cognitive variables of interest and sleep scores were z-transformed to normalize scaling (see Figure 1).

FIGURE 1

Figure 1. Structural equation model including predictor variables: Sleep, Depression (i.e., CES-D total score), and Apathy (i.e., apathy scale total score); mediator variables: Sex (i.e., male v. female), Age (in years), Education (i.e., total years of education), and SleepGroup (i.e., <6 h = Short; 6–7.99 h = Medium; >8 h = Long). Note that for data visualization purposes, sleep has been inserted into the model as a single measurement; however, it is composed of separate measures of hours of sleep per night, waking in the night or early morning, self-reported overall sleep quality, the Epworth Sleepiness Scale, and the Lausanne NoSAS. Paths extend from these variables to two latent constructs: Executive Function (EF; i.e., Stroop Color, Word and Interference task reaction times) and Memory (i.e., correct responses in Cued, Free, Immediate and List recall tasks). The latent constructs are joined via a covariance link.

2.6.2. Pairwise correlations

Pairwise correlations were employed to explore any associations between participant age, sex, years of education, sleep (i.e., hours of sleep per night, waking in the night or early morning, self-reported overall sleep quality, Epworth Sleepiness Scale, Lausanne NoSAS), depression and anxiety scores, Stroop color, word and interference task RTs, and correct responses to cued, free, immediate and list recall tasks. Correlations were Bonferroni corrected and associations were considered significant if p < 0.05.

2.6.3. Structural equation model

We employed a structural equation model (SEM) to assess direct and indirect effects between sleep, depression, apathy and cognitive function. That is, we aimed to create a single model to assess a mechanistic pathway by which our predictor variables may influence cognition. Prior to creating our model, simple regressions of the variables of interest were performed to better inform direct and indirect model paths. The presented model was estimated using a maximum likelihood with missing values (MLMV) test. We report standardized coefficients and beta values. The MLMV method assumes joint normality and, if present, randomly occurring missing values. The resulting model contains the following variables.

Cognition was assessed via two latent constructs. First, Executive Function (EF) was composed of Stroop color, word and interference task RTs (Periáñez et al., 2021). We then collated performance on the COGNITO tasks 8 and 17 into a Memory construct consisting of cued, free, immediate and list recall responses. A covariance link was applied between these constructs. Predictor variables included various indicators of sleep quality: TotalSleep (i.e., hours of sleep per night), WNEM (i.e., waking in the night or early morning), Quality (i.e., self-reported overall sleep quality), Epworth (i.e., the Epworth Sleepiness Scale), and NoSAS (i.e., Lausanne NoSAS). Depression and Apathy were included in our SEM as the total CES-D score, and total apathy score, respectively. Finally, Age and Education (i.e., years; continuous variables), SleepGroup (i.e., <6 h = 0, Short; 6–7.99 h = 1, Medium; >8 h = 2, Long), as well as Sex (i.e., females = 1, males = 2) were entered into our SEM as covariates to control for any confounds. Effects were deemed significant when p < 0.05.

3. Results

On average, the included sample was 51.17 years old (SD = 5.47), comprised of mostly females (i.e., 62%), had completed 16.69 (SD = 3.44) years of education, and most slept between 6 and 8 h a night (i.e., 52%; see Table 1).

3.1. Pairwise correlations

Initial pairwise correlations show no associations between predictor variables (i.e., indices of sleep, Apathy, Depression) and our chosen cognitive variables (rs <−0.002, ps > 0.99). We note, however, that Depression was related to TotalSleep, Quality, and WNEM (rs = −0.25, 0.36, 0.22, ps <0.001), whereas Apathy was only related to Quality (r = 0.14, p = 0.047); Depression and Apathy were also related to each other (r = 0.23, p < 0.001). In addition, the PSQI measures used here were correlated (rs > −0.27, ps <0.001) as well Stroop color, word and interference task RTs (rs > 0.51, ps <0.001), and the number of correct cued, free, immediate, and list recall responses (rs > 0.44, p < 0.001). Scores on the Epworth and NoSAS scales were not related to each other (r = 0.14, p > 0.99).

3.2. Structural equation model

3.2.1. Regression paths

As demonstrated in Figure 1, a direct path was extended from each predictor (i.e., Sleep including separate measures of TotalSleep, WNEM, Quality, Epworth, NoSAS, Depression, Apathy) and mediator (i.e., Sex, Age, Education, SleepGroup) variable to both cognitive latent constructs (i.e., EF and Memory). Links were also included between all sleep indices and Depression and Apathy, as well as between Depression and Apathy to assess the mediation of any relationship between our predictors and cognition.

3.2.2. Estimation and fit

Our model fit was deemed good according to accepted standards (e.g., Kline, 2016). Our model possesses a root mean squared error of approximation (RMSEA: differences between predicted and observed outcomes) = 0.025; the Tucker Lewis index (i.e., relative reduction in misfit per degree of freedom) = 0.982; the comparative fit index (CFI: metric of the model's improvement from baseline to proposed iterations) = 0.988.

Table 2 contains the SEM output for our model and demonstrates that worse sleep quality and fewer hours of sleep were associated with more depression symptoms (βs = 0.27, −0.10, ps <0.01). Moreover, higher scores on the Epworth and NoSAS scales were associated with more apathy symptoms (βs = 0.16, 0.13, ps <0.01) and depression and apathy symptoms were positively related (β = 0.20, p < 0.001). Neither Depression nor Apathy, nor any of the indices of sleep quality were related to memory performance (βs <−0.08, ps > 0.06); however, higher depression symptoms were found to be linked to worse executive function (i.e., longer RTs; β = 0.12, p = 0.005; see also Figure 2). Results demonstrated mediation of the effect of sleep on cognitive performance by depression symptoms. Indeed, β values regarding the relationship between sleep indices and executive function (β <−0.07) were attenuated by the effect of depression symptoms (β = 0.12). In terms of our covariates, we note that neither EF nor Memory were related to the sleep group to which participants belonged (i.e., Short: <6 h, Medium: 6–7.99 h, Long: >8 h; βs = −0.02, −0.11, ps > 0.21). Older age and less education were related to poorer executive function (βs = 0.27, −0.10, ps <0.02), whereas being more highly educated and female were related to better memory performance (βs = 0.11, −0.27, ps <0.01). The relationship between memory and participant age approached, but did not achieve conventional levels of statistical significance (β = −0.08, p = 0.06).

TABLE 2

Table 2. Structural equation model output.

FIGURE 2

Figure 2. Scatter plots depicting the relationship between the Center for Epidemiological Studies-Depression (CES-D) scale scores and Stroop color (top), word (center), and interference (bottom) task reaction times (ms). The panels include simple linear regressions accompanied by gray 95% confidence interval bands.

4. Discussion

Our investigation sought to explore the association between sleep, depressive symptoms and cognition in healthy middle-aged adults. Below, we discuss the links between four confounding variables and the assessed cognitive constructs prior to explaining a model which demonstrates a direct link between depression symptoms and executive function.

4.1. Age, sex and years of education: mediators of memory and executive function

Our model showed no association between the sleep group to which individuals belonged and cognition. This is contrary to recent results presented by Ma et al. (2020) demonstrating an inverted-U relationship between sleep duration and the likelihood of global cognitive decline. We note that the authors had a considerably larger sample size (N = 20,065) and this is a likely explanation as to why this result is absent from our model. Moreover, we demonstrated that sex and education were related to memory and executive function. That is, being male (e.g., Voyer et al., 2021) and less educated (e.g., Murayama et al., 2013) was associated with fewer correct responses to memory tasks, whereas more education was related to improved executive function. Our memory composite comprised verbal memory subtasks tapping episodic memory, a domain typically demonstrating a performance advantage for women relative to men (Asperholm et al., 2019), although inherent hormonal differences (i.e., estrogen concentrations) may modulate memory performance for females over time (Duarte-Guterman et al., 2015). Similarly, Staekenborg et al. (2020) and Han et al. (2023) indicate that increased cognitive reserve gained from more years of education may support memory and executive function in later life, while Joannette et al. (2020) show that educational attainment moderates the relationship between episodic memory and amyloid load. In contrast, being older was associated with worse performance on executive function. This is unsurprising as work has consistently demonstrated an association between increasing age and slowed RTs on executive functiontasks (Krivanek et al., 2021). What is more, detriments to global cognition related to age have been attributed to cortical thinning, demyelination, and brain volume loss (Blinkouskaya et al., 2021), as well as inefficient preparation of responses to stimuli (Williams et al., 2007; Hardwick et al., 2022). Accordingly, three of the confounding variables used here were related to cognition in a way that is aligned with the current corpus of literature.

4.2. Sleep may indirectly predict cognition via depression symptoms

Better sleep has been linked to improved mental health (Sadler et al., 2018), and has been shown to support cognition (Dzierzewski et al., 2018; Matricciani et al., 2019) and aide in the clearance of harmful Aβ protein (Xie et al., 2013; Tabuchi et al., 2015; Shokri-Kojori et al., 2018). In our model, however, none of the sleep measures investigated here were related to either cognitive construct. This may be an unexpected result given that cognitive dysfunction and insomnia share common neural mechanisms such as impaired functional connectivity and structural abnormalities within the amygdala, prefrontal cortex, anterior cingulate cortex and insula (e.g., Bagherzadeh-Azbari et al., 2019). Our null findings may be explained by the fact that the PREVENT cohort is composed of middle-, rather than old-aged, adults. This is notable because it is during this time in the lifespan where cognition is comparatively less vulnerable to insult (Diamond, 2013; Krivanek et al., 2021). However, even studies involving middle-aged individuals have demonstrated an association between sleep and impaired cognition (Ma et al., 2020), as well as Aβ concentrations (Sprecher et al., 2015). An alternative explanation may be that sleep is associated with cognition via some mediator(s) or that the relationship between sleep and poor cognitive outcome demonstrated in the literature may not always reflect cognitive deterioration toward organic dementia. Instead, this may be related to the compromised emotion-regulated processes underlying pseudo-dementia (e.g., Leonhardi et al., 2022). Although, depressive pseudo-dementia has shown to predict organic dementia longitudinally, at least in the elderly (Sáez-Fonseca et al., 2007; Diniz et al., 2013).

Our model found that fewer hours of sleep and lower self-reported sleep quality were related to more symptoms of depression, and that higher depression symptoms and more daytime sleepiness and sleep apnoea likelihood (i.e., Epworth Sleepiness and NoSAS Scales) were related to participants' total apathy score. We will address these results in turn. First, literature has previously demonstrated a link between sleep and depression (Scott et al., 2021; Joo et al., 2022). For example, a recent meta-analysis by Scott et al. (2021) provides evidence for a small-to-medium association between improved sleep quality and reduced symptoms of anxiety, stress, psychosis, and depression. Their analyses were conducted on studies from various countries and offers insight to the generalisability of this association across populations. In contrast, when Joo et al. (2022) assessed the relationship between each component of the PSQI and symptoms of depression, they found a dose-response relationship for each component of the index, except sleep duration. The link between sleep and depression has been explained biologically via increased activity in the amygdala (Yoo et al., 2007) and reduced functional connectivity between the amygdala and the prefrontal cortex (Motomura et al., 2013). What is more, one night of sleep deprivation has been linked with elevated sympathetic nervous system activity, increased heart rate variability and a subsequently diminished capacity to respond to emotional challenges (Zhong et al., 2005; Appelhans and Luecken, 2006; Sauvet et al., 2010; for review see Goldstein and Walker, 2014). Hence, that various measures of sleep were related to depression symptoms in our model was to be expected. Second, apathy affects various neurological outcomes and is common in individuals who present with symptoms of depression (Steffens et al., 2022); however, it is nosologically and neurobiologically distinct from depression (Tagariello et al., 2009). It is for this reason that we chose to include apathy in our model. We demonstrated that depression symptoms were unsurprisingly related to apathy scores and that apathy ratings were related to daytime sleepiness. Indeed, evidence has demonstrated that individuals with higher ratings of depression are less willing or likely to respond to rewards (Le Heron et al., 2018) and that resulting apathy symptoms are associated with alterations to frontoparietal executive networks (e.g., pre-frontal cortex and the anterior cingulate cortex; for review see Steffens et al., 2022). Previous work has shown that excessive sleepiness and sleep apnea disrupt normal activity within the pre-frontal cortex (e.g., Durning et al., 2014) and induce intermittent states of hypoxia (e.g., Bucks et al., 2013). These disruptions to neural activity and metabolism may be the mechanism(s) underlying how individuals develop/manifest feelings of apathy. When taken together, our results support literature demonstrating the links between less sleep, more depression symptoms and higher apathy present here.

Individuals with higher depressive and/or apathy symptoms often perform poorly on tests of executive function (e.g., McPherson et al., 2002; Funes et al., 2018). Here, our model demonstrated that depression, but not apathy, was associated with executive function. To understand these results, we considered the construction of our executive function latent construct. Rather than treating the Stroop color, word, and interference tasks strictly as measures of stimulus processing and inhibition, respectively, we grouped them into one latent construct. This is because recent work from Periáñez et al. (2021) has demonstrated that performance on the Stroop color, word, and interference tasks reflect visual search speed; Stroop color and interference performance are indicative of working memory; and execution of the Stroop interference task is related to conflict monitoring. Accordingly, the ability to complete the various iterations of the Stroop task is dependent on a combination of several higher-order executive functions. Despite some evidence affirming the link between apathy and executive function, the literature is mixed. Tests of executive function and global cognition have yielded no reliable association with symptoms of apathy (Marin et al., 2003; Brodaty et al., 2010) and this can be explained by the different pathways by which apathy and executive function are mediated in the brain (see Gonçalves et al., 2020). On the other hand, depression has been shown to negatively alter performance on tasks which require top-down control. A recent investigation identified that symptoms of depression are related to higher cortical noise which negatively impacts executive performance (Yao et al., 2022). Although not directly assessed here, it is therefore possible that higher and increasingly inefficient frontoparietal brain activity associated with less sleep and higher depression symptoms (e.g., Steffens et al., 2022) underlies the detrimental relationship between depression and cognition.

Last, our model demonstrated neither depression symptoms nor apathy scores were related to Memory. The literature regarding these associations is mixed. For example, Fishman et al. (2019) demonstrated that stroke patients with elevated apathy ratings performed worse on free recall tasks, whereas depression symptoms did not elicit a similar result. Conversely, Szymkowicz et al. (2018) found that Parkinson's disease patients with higher ratings of depression, but not apathy, performed worse on memory tasks. Our results do not seem to support these findings. However, it is worth noting that the relationships described above were found in individuals with psychiatric and/or physical co-morbidity, and in individuals who have been diagnosed with clinical depression and/or apathy disorders. Indeed, our results were obtained by modeling mostly cognitively and psychiatrically healthy individuals, and it may be the lack of relevant co-morbidity which spares any association between depression and/or apathy symptoms and memory performance.

4.3. Limitations, future directions, and conclusions

We are aware that our study presents several limitations. First, our sample is middle-aged, cognitively healthy, well-educated and ethnically homogenous. Therefore, it is unlikely that the present model can be generalized outside of this demographic. Future investigations should aim to explore a more diverse cohort in terms of age, socio-economic and cognitive status. Similarly, the presented model may serve as a launch point for investigations regarding the quantification of dementia likelihood via follow-up of this cohort, or further exploration of the biological mechanisms of the associations we present here; for example, via investigations of neurodegeneration and brain volume. It is possible our data show the effects of depression on both sleep and executive function which may be related to functional rather than organic mental disorder. Longitudinal data will be important to investigate this. Second, the CES-D and apathy scales used here are multi-component assessments of their respective constructs. That is, whether a specific sub-component of each scale drives the above interactions is not yet known. Moreover, as we incorporated self-report measures of sleep, future studies might benefit from incorporating objective measures to assess sleep duration/quality (e.g., actigraphy or polysomnography); this is especially relevant given data questioning the PSQI's predictive validity for objective sleep duration and quality (Landry et al., 2015; Parsey et al., 2015). As well, Evangelista et al. (2021) have demonstrated the importance for objective measures of sleep. The authors found discrepancies related to objective (i.e., polysomnography, multiple sleep latency test) and subjective (i.e., Epworth Sleepiness Scale) reports of sleep. Third, we note that the amount of data available for the Epworth and NoSAS scales was much less than other sleep measures due to their late introduction into this protocol. It may be that the related results observed here were due to this discrepancy in Ns. Finally, although the observed link between executive function and depressive symptoms intimates a possible deleterious neurological/physiological cascade across frontoparietal regions, the data necessary to confirm or quantify this relationship are unavailable. Regardless of the aforementioned limitations, our study provides evidence for an association between sleep and cognitive function mediated by depression symptoms in a middle-aged population. These results are pertinent in so much as they will encourage further investigation of the indirect relationship between sleep and cognition and the relevance of this relationship for the development of dementia. Furthermore, this work may serve as a basis to further explore the potential nature and timing of any treatments to prevent or ameliorate the development of dementia in later life.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found at: https://community.addi.ad-datainitiative.org/datasets/f/prevent-dementia-research-programme and/or https://portal.dementiasplatform.uk/.

Ethics statement

The studies involving humans were approved by the UK London-Camberwell St Giles National Health Service (NHS) Research Ethics Committee (REC reference: 12/LO/1023, IRAS project ID: 88938), which operates according to the Helsinki Declaration of 1975 (and as revised in 1983). Separate ethical applications for the Dublin site were submitted and given favorable opinions by the Trinity College School of Psychology Research Ethics Committee (SPREC022021-010) and the St James Hospital/Tallaght University Hospital Joint Research Ethics Committee. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

BT and IK wrote the first draft of the manuscript and analyzed the data. CR, KR, and IK are investigators for the PREVENT Dementia Program. CR, JO'B, BL, PM, and IK supervised the data collection. All authors edited and commented on subsequent versions of the manuscript and approved the final version of the manuscript.

Funding

The PREVENT study was funded by the Alzheimer's Society (grant numbers 178 and 264), the Alzheimer's Association (grant number TriBEKa-17-519007), and the Global Brain Health Institute and philanthropic donations. BT and VR would like to acknowledge funding for salary via the Medical Research Council (Dementias Platform UK). IK declares funding for this work through Medical Research Council (Dementias Platform UK) and NIHR Oxford Health Biomedical Research Centre and NIHR personal awards. AL acknowledges salary funding from NIHR. TW would like to acknowledge a NIHR and Alzheimer's Society Dementia Fellowship for salary support. BU's post is part-funded by a donation from Gnodde Goldman Sachs Giving. This funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. GMT acknowledges the support of the Osteopathic Heritage Foundation through funding for the Osteopathic Heritage Foundation Ralph S. Licklider, D.O., Research Endowment in the Heritage College of Osteopathic Medicine. JO'B was supported by the NIHR Cambridge Biomedical Research Centre.

Acknowledgments

The authors would like to thank and acknowledge the PREVENT dementia programme study participants. The data collection was also supported by research facilities at West London NHS Trust, NHS Lothian, Oxford Health NHS Foundation trust and Cambridgeshire and Peterborough NHS Foundation trust and St James's hospital, Dublin. This work has been previously shared as a pre-print on medRxiv.org (Tari et al., 2023).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

1. ^Results from this paper identified no relationship between amyloid burden and sleep quality in 143 elderly (i.e., 70–85 years of age) individuals.

References

Alzheimer's Association (2021). 2021 Alzheimer's disease facts and figures. Alzheimers Dement. 17, 327–406. doi: 10.1002/alz.12328

CrossRef Full Text | Google Scholar

Appelhans, B. M., and Luecken, L. J. (2006). Heart rate variability as an index of regulated emotional responding. Rev. Gen. Psychol. 10, 229–240. doi: 10.1037/1089-2680.10.3.229

CrossRef Full Text | Google Scholar

Aron, L., and Yankner, B. A. (2016). Neurodegenerative disorders: neural synchronization in Alzheimer's disease. Nature 540, 207–208. doi: 10.1038/540207a

PubMed Abstract | CrossRef Full Text | Google Scholar

Asperholm, M., Högman, N., Rafi, J., and Herlitz, A. (2019). What did you do yesterday? A meta-analysis of sex differences in episodic memory. Psychol. Bull. 145, 785–821. doi: 10.1037/bul0000197

PubMed Abstract | CrossRef Full Text | Google Scholar

Bagherzadeh-Azbari, S., Khazaie, H., Zarei, M., Spiegelhalder, K., Walter, M., Leerssen, J., et al. (2019). Neuroimaging insights into the link between depression and insomnia: a systematic review. J. Affect. Disord. 258, 133–143. doi: 10.1016/j.jad.2019.07.089

CrossRef Full Text | Google Scholar

Blazer, D. G. (2003). Depression in late life: review and commentary. J. Gerontol. A Biol. Sci. Med. Sci. 58, 249–265. doi: 10.1093/gerona/58.3.M249

PubMed Abstract | CrossRef Full Text | Google Scholar

Blinkouskaya, Y., Caçoilo, A., Gollamudi, T., Jalalian, S., and Weickenmeier, J. (2021). Brain aging mechanisms with mechanical manifestations. Mech. Ageing Dev. 200, 111575. doi: 10.1016/j.mad.2021.111575

PubMed Abstract | CrossRef Full Text | Google Scholar

Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., and Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychol. Rev. 108, 624–652. doi: 10.1037/0033-295X.108.3.624

CrossRef Full Text | Google Scholar

Brodaty, H., Altendorf, A., Withall, A., and Sachdev, P. (2010). Do people become more apathetic as they grow older? A longitudinal study in healthy individuals. Int. Psychogeriatr. 22, 426–436. doi: 10.1017/S1041610209991335

PubMed Abstract | CrossRef Full Text | Google Scholar

Bucks, R. S., Olaithe, M., and Eastwood, P. (2013). Neurocognitive function in obstructive sleep apnoea: a meta-review. Respirology 18, 61–70. doi: 10.1111/j.1440-1843.2012.02255.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Buysse, D. J., Reynolds, C. F. III, Monk, T. H., Berman, S. R., and Kupfer, D. J. (1989). The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 28, 193–213. doi: 10.1016/0165-1781(89)90047-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Casagrande, M., Forte, G., Favieri, F., and Corbo, I. (2022). Sleep quality and aging: a systematic review on healthy older people, mild cognitive impairment and Alzheimer's disease. Int. J. Environ. Res. Public Health 19, 8457. doi: 10.3390/ijerph19148457

PubMed Abstract | CrossRef Full Text | Google Scholar

Castellano, J. M., Kim, J., Stewart, F. R., Jiang, H., DeMattos, R. B., Patterson, B. W., et al. (2011). Human apoE isoforms differentially regulate brain amyloid-β peptide clearance. Sci. Transl. Med. 3, 89ra57. doi: 10.1126/scitranslmed.3002156

PubMed Abstract | CrossRef Full Text | Google Scholar

Cavaillès, C., Berr, C., Helmer, C., Gabelle, A., Jaussent, I., and Dauvilliers, Y. (2022a). Complaints of daytime sleepiness, insomnia, hypnotic use, and risk of dementia: a prospective cohort study in the elderly. Alzheimers. Res. Ther. 14, 12. doi: 10.1186/s13195-021-00952-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Cavaillès, C., Carrière, I., Wagner, M., Dartigues, J. F., Berr, C., Dauvilliers, Y., et al. (2022b). Trajectories of sleep duration and timing before dementia: a 14-year follow-up study. Age Ageing 51, afac186. doi: 10.1016/j.sleep.2022.05.279

PubMed Abstract | CrossRef Full Text | Google Scholar

Diamond, A. (2013). Executive functions. Annu. Rev. Psychol. 64, 135–168. doi: 10.1146/annurev-psych-113011-143750

CrossRef Full Text | Google Scholar

Diniz, B. S., Butters, M. A., Albert, S. M., Dew, M. A., and Reynolds, C. F. III (2013). Late-life depression and risk of vascular dementia and Alzheimer's disease: systematic review and meta-analysis of community-based cohort studies. Br. J. Psychiatry 202, 329–335. doi: 10.1192/bjp.bp.112.118307

PubMed Abstract | CrossRef Full Text | Google Scholar

Disner, S. G., Beevers, C. G., Haigh, E. A., and Beck, A. T. (2011). Neural mechanisms of the cognitive model of depression. Nat. Rev. Neurosci. 12, 467–477. doi: 10.1038/nrn3027

CrossRef Full Text | Google Scholar

Dotson, V. M., McClintock, S. M., Verhaeghen, P., Kim, J. U., Draheim, A. A., Syzmkowicz, S. M., et al. (2020). Depression and cognitive control across the lifespan: a systematic review and meta-analysis. Neuropsychol. Rev. 30, 461–476. doi: 10.1007/s11065-020-09436-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Duarte-Guterman, P., Yagi, S., Chow, C., and Galea, L. A. (2015). Hippocampal learning, memory, and neurogenesis: effects of sex and estrogens across the lifespan in adults. Horm. Behav. 74, 37–52. doi: 10.1016/j.yhbeh.2015.05.024

PubMed Abstract | CrossRef Full Text | Google Scholar

Durning, S. J., Capaldi, V. F. 2nd, Artino, A. R. Jr, Graner, J., van der Vleuten, C., Beckman, T. J., et al. (2014). A pilot study exploring the relationship between internists' self-reported sleepiness, performance on multiple-choice exam items and prefrontal cortex activity. Med. Teach. 36, 434–440. doi: 10.3109/0142159X.2014.888408

PubMed Abstract | CrossRef Full Text | Google Scholar

Dzierzewski, J. M., Dautovich, N., and Ravyts, S. (2018). Sleep and cognition in older adults. Sleep Med. Clin. 13, 93–106. doi: 10.1016/j.jsmc.2017.09.009

CrossRef Full Text | Google Scholar

Evangelista, E., Rassu, A. L., Barateau, L., Lopez, R., Chenini, S., Jaussent, I., et al. (2021). Characteristics associated with hypersomnia and excessive daytime sleepiness identified by extended polysomnography recording. Sleep 44, zsaa264. doi: 10.1093/sleep/zsaa264

PubMed Abstract | CrossRef Full Text | Google Scholar

Fishman, K. N., Ashbaugh, A. R., Lanctôt, K. L., Cayley, M. L., Herrmann, N., Murray, B., et al. (2019). The role of apathy and depression on verbal learning and memory performance after stroke. Arch. Clin. Neuropsychol. 34, 327–336. doi: 10.1093/arclin/acy044

PubMed Abstract | CrossRef Full Text | Google Scholar

Funes, C. M., Lavretsky, H., Ercoli, L., St Cyr, N., and Siddarth, P. (2018). Apathy mediates cognitive difficulties in geriatric depression. Am. J. Geriatr. Psychiatry 26, 100–106. doi: 10.1016/j.jagp.2017.06.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Furtado, M., and Katzman, M. A. (2015). Neuroinflammatory pathways in anxiety, posttraumatic stress, and obsessive compulsive disorders. Psychiatry Res. 229, 37–48. doi: 10.1016/j.psychres.2015.05.036

PubMed Abstract | CrossRef Full Text | Google Scholar

Gabelle, A., Gutierrez, L. A., Jaussent, I., Ben Bouallegue, F., De Verbizier, D., Navucet, S., et al. (2019). Absence of relationship between self-reported sleep measures and amyloid load in elderly subjects. Front. Neurol. 10, 989. doi: 10.3389/fneur.2019.00989

PubMed Abstract | CrossRef Full Text | Google Scholar

Gevers, W., Deliens, G., Hoffmann, S., Notebaert, W., and Peigneux, P. (2015). Sleep deprivation selectively disrupts top-down adaptation to cognitive conflict in the Stroop test. J. Sleep Res. 24, 666–672. doi: 10.1111/jsr.12320

PubMed Abstract | CrossRef Full Text | Google Scholar

Goldstein, A. N., and Walker, M. P. (2014). The role of sleep in emotional brain function. Annu. Rev. Clin. Psychol. 10, 679–708. doi: 10.1146/annurev-clinpsy-032813-153716

PubMed Abstract | CrossRef Full Text | Google Scholar

Gonçalves, S. A. B., Caramelli, P., Mariano, L. I., Guimarães, H. C., Gambogi, L. B., Resende, E. P. F., et al. (2020). Apathy in frontotemporal dementia is related to medial prefrontal atrophy and is independent of executive dysfunction. Brain Res. 1737, 146799. doi: 10.1016/j.brainres.2020.146799

PubMed Abstract | CrossRef Full Text | Google Scholar

Guarino, A., Favieri, F., Boncompagni, I., Agostini, F., Cantone, M., and Casagrande, M. (2019). Executive functions in Alzheimer disease: a systematic review. Front. Aging Neurosci. 10, 437. doi: 10.3389/fnagi.2018.00437

CrossRef Full Text | Google Scholar

Han, S., Lee, J. Y., Cho, S. I., Oh, D. J., and Yoon, D. H. (2023). Risk factors for various cognitive function decline trajectories in adults over 40 years of age: a retrospective cohort study. Psychiatry Investig. 20, 293–300. doi: 10.30773/pi.2022.0188

PubMed Abstract | CrossRef Full Text | Google Scholar

Harada, C. N., Natelson Love, M. C., and Triebel, K. L. (2013). Normal cognitive aging. Clin. Geriatr. Med. 29, 737–752. doi: 10.1016/j.cger.2013.07.002

CrossRef Full Text | Google Scholar

Hardwick, R. M., Forrence, A. D., Costello, M. G., Zackowski, K., and Haith, A. M. (2022). Age-related increases in reaction time result from slower preparation, not delayed initiation. J. Neurophysiol. 128, 582–592. doi: 10.1152/jn.00072.2022

PubMed Abstract | CrossRef Full Text | Google Scholar

Jaussent, I., Bouyer, J., Ancelin, M. L., Akbaraly, T., Pérès, K., Ritchie, K., et al. (2011). Insomnia and daytime sleepiness are risk factors for depressive symptoms in the elderly. Sleep 34, 1103–1110. doi: 10.5665/SLEEP.1170

PubMed Abstract | CrossRef Full Text | Google Scholar

Joannette, M., Bocti, C., Dupont, P. S., Lavallée, M. M., Nikelski, J., Vallet, G. T., et al. (2020). Education as a moderator of the relationship between episodic memory and amyloid load in normal aging. J. Gerontol. A Biol. Sci. Med. Sci. 75, 1820–1826. doi: 10.1093/gerona/glz235

PubMed Abstract | CrossRef Full Text | Google Scholar

Johns, M. W. (1991). A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep 14, 540–545. doi: 10.1093/sleep/14.6.540

PubMed Abstract | CrossRef Full Text | Google Scholar

Joo, H. J., Kwon, K. A., Shin, J., Park, S., and Jang, S. I. (2022). Association between sleep quality and depressive symptoms. J. Affect. Disord. 310, 258–265. doi: 10.1016/j.jad.2022.05.004

CrossRef Full Text | Google Scholar

Ju, Y. E., Lucey, B. P., and Holtzman, D. M. (2014). Sleep and Alzheimer disease pathology–a bidirectional relationship. Nat. Rev. Neurol. 10, 115–119. doi: 10.1038/nrneurol.2013.269

CrossRef Full Text | Google Scholar

Ju, Y. E., McLeland, J. S., Toedebusch, C. D., Xiong, C., Fagan, A. M., Duntley, S. P., et al. (2013). Sleep quality and preclinical Alzheimer disease. JAMA Neurol. 70, 587–593. doi: 10.1001/jamaneurol.2013.2334

PubMed Abstract | CrossRef Full Text | Google Scholar

Kessing, L. V. (2012). Depression and the risk for dementia. Curr. Opin. Psychiatry 25, 457–461. doi: 10.1097/YCO.0b013e328356c368

CrossRef Full Text | Google Scholar

Killgore, W. D. (2010). Effects of sleep deprivation on cognition. Prog. Brain Res. 185, 105–129. doi: 10.1016/B978-0-444-53702-7.00007-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Kline, R. B. (2016). Principles and practice of structural equation modeling, 4th ed. New York, NY: Guilford Press.

Google Scholar

Krivanek, T. J., Gale, S. A., McFeeley, B. M., Nicastri, C. M., and Daffner, K. R. (2021). Promoting successful cognitive aging: a ten-year update. J. Alzheimers. Dis. 81, 871–920. doi: 10.3233/JAD-201462

PubMed Abstract | CrossRef Full Text | Google Scholar

Landry, G. J., Best, J. R., and Liu-Ambrose, T. (2015). Measuring sleep quality in older adults: a comparison using subjective and objective methods. Front. Aging Neurosci. 7, 166. doi: 10.3389/fnagi.2015.00166

PubMed Abstract | CrossRef Full Text | Google Scholar

Le Heron, C., Plant, O., Manohar, S., Ang, Y. S., Jackson, M., Lennox, G., et al. (2018). Distinct effects of apathy and dopamine on effort-based decision-making in Parkinson's disease. Brain 141, 1455–1469. doi: 10.1093/brain/awy110

PubMed Abstract | CrossRef Full Text | Google Scholar

Leonhardi, J., Barthel, H., Speerforck, S., Dietzel, J., Schroeter, M. L., Saur, D., et al. (2022). Differential diagnosis between Alzheimer's disease-related depression and pseudo-dementia in depression: a new indication for amyloid-β imaging? J. Alzheimers. Dis. 88, 1029–1035. doi: 10.3233/JAD-215619

PubMed Abstract | CrossRef Full Text | Google Scholar

Levy, G., Jacobs, D. M., Tang, M. X., Côté, L. J., Louis, E. D., Alfaro, B., et al. (2002). Memory and executive function impairment predict dementia in Parkinson's disease. Mov. Disord. 17, 1221–1226. doi: 10.1002/mds.10280

PubMed Abstract | CrossRef Full Text | Google Scholar

Lindert, J., Paul, K. C., Lachman, M. E., Ritz, B., and Seeman, T. E. (2021). Depression-, anxiety-, and anger and cognitive functions: findings from a longitudinal prospective study. Front. Psychiatry 12, 665742. doi: 10.3389/fpsyt.2021.665742

PubMed Abstract | CrossRef Full Text | Google Scholar

Lo, J. C., Chong, P. L., Ganesan, S., Leong, R. L., and Chee, M. W. (2016). Sleep deprivation increases formation of false memory. J. Sleep Res. 25, 673–682. doi: 10.1111/jsr.12436

PubMed Abstract | CrossRef Full Text | Google Scholar

Ma, Y., Liang, L., Zheng, F., Shi, L., Zhong, B., and Xie, W. (2020). Association between sleep duration and cognitive decline. JAMA Netw. Open 3, e2013573. doi: 10.1001/jamanetworkopen.2020.13573

PubMed Abstract | CrossRef Full Text | Google Scholar

Marin, R. S., Butters, M. A., Mulsant, B. H., Pollock, B. G., and Reynolds III, C. F. (2003). Apathy and executive function in depressed elderly. J. Geriatr. Psychiatry Neurol. 16, 112–116. doi: 10.1177/0891988703016002009

PubMed Abstract | CrossRef Full Text | Google Scholar

Marti-Soler, H., Hirotsu, C., Marques-Vidal, P., Vollenweider, P., Waeber, G., Preisig, M., et al. (2016). The NoSAS score for screening of sleep-disordered breathing: a derivation and validation study. Lancet Respir. Med. 4, 742–748. doi: 10.1016/S2213-2600(16)30075-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Matricciani, L., Paquet, C., Galland, B., Short, M., and Olds, T. (2019). Children's sleep and health: a meta-review. Sleep Med. Rev. 46, 136–150. doi: 10.1016/j.smrv.2019.04.011

PubMed Abstract | CrossRef Full Text | Google Scholar

McPherson, S., Fairbanks, L., Tiken, S., Cummings, J. L., and Back-Madruga, C. (2002). Apathy and executive function in Alzheimer's disease. J. Int. Neuropsychol. Soc. 8, 373–381. doi: 10.1017/S1355617702813182

PubMed Abstract | CrossRef Full Text | Google Scholar

Motomura, Y., Kitamura, S., Oba, K., Terasawa, Y., Enomoto, M., Katayose, Y., et al. (2013). Sleep debt elicits negative emotional reaction through diminished amygdala-anterior cingulate functional connectivity. PLoS ONE 8, e56578. doi: 10.1371/journal.pone.0056578

PubMed Abstract | CrossRef Full Text | Google Scholar

Murayama, N., Iseki, E., Tagaya, H., Ota, K., Kasanuki, K., Fujishiro, H., et al. (2013). Intelligence or years of education: which is better correlated with memory function in normal elderly Japanese subjects?. Psychogeriatrics 13, 9–16. doi: 10.1111/j.1479-8301.2012.00408.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Parsey, C. M., Schmitter-Edgecombe, M., and Belenky, G. (2015). Sleep and everyday functioning in older adulthood. J. Appl. Gerontol. 34, 48–72. doi: 10.1177/0733464812458364

PubMed Abstract | CrossRef Full Text | Google Scholar

Patel, S. R., Zhu, X., Storfer-Isser, A., Mehra, R., Jenny, N. S., Tracy, R., et al. (2009). Sleep duration and biomarkers of inflammation. Sleep 32, 200–204. doi: 10.1093/sleep/32.2.200

PubMed Abstract | CrossRef Full Text | Google Scholar

Periáñez, J. A., Lubrini, G., García-Gutiérrez, A., and Ríos-Lago, M. (2021). Construct validity of the Stroop color-word test: influence of speed of visual search, verbal fluency, working memory, cognitive flexibility, and conflict monitoring. Arch. Clin. Neuropsychol. 36, 99–111. doi: 10.1093/arclin/acaa034

PubMed Abstract | CrossRef Full Text | Google Scholar

Radloff, L. S. (1977). The CES-D scale: a self-report depression scale for research in the general population. Appl. Psychol. Meas. 1, 385–401. doi: 10.1177/014662167700100306

CrossRef Full Text | Google Scholar

Riemann, D., Krone, L. B., Wulff, K., and Nissen, C. (2020). Sleep, insomnia, and depression. Neuropsychopharmacology 45, 74–89. doi: 10.1038/s41386-019-0411-y

CrossRef Full Text | Google Scholar

Ritchie, C. W., Wells, K., and Ritchie, K. (2013). The PREVENT research programme–a novel research programme to identify and manage midlife risk for dementia: the conceptual framework. Int. Rev. Psych. 25, 748–754. doi: 10.3109/09540261.2013.869195

PubMed Abstract | CrossRef Full Text | Google Scholar

Sabia, S., Fayosse, A., Dumurgier, J., van Hees, V. T., Paquet, C., Sommerlad, A., et al. (2021). Association of sleep duration in middle and old age with incidence of dementia. Nat. Commun. 12, 2289. doi: 10.1038/s41467-021-22354-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Sadler, P., McLaren, S., Klein, B., Harvey, J., and Jenkins, M. (2018). Cognitive behavior therapy for older adults with insomnia and depression: a randomized controlled trial in community mental health services. Sleep 41, zsy104. doi: 10.1093/sleep/zsy104

PubMed Abstract | CrossRef Full Text | Google Scholar

Sáez-Fonseca, J. A., Lee, L., and Walker, Z. (2007). Long-term outcome of depressive pseudodementia in the elderly. J. Affect. Disord. 101, 123–129. doi: 10.1016/j.jad.2006.11.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Sapolsky, R. M. (2000). Glucocorticoids and hippocampal atrophy in neuropsychiatric disorders. Arch. Gen. Psychiatry 57, 925–935. doi: 10.1001/archpsyc.57.10.925

PubMed Abstract | CrossRef Full Text | Google Scholar

Sauvet, F., Leftheriotis, G., Gomez-Merino, D., Langrume, C., Drogou, C., Van Beers, P., et al. (2010). Effect of acute sleep deprivation on vascular function in healthy subjects. J. Appl. Physiol. 108, 68–75. doi: 10.1152/japplphysiol.00851.2009

PubMed Abstract | CrossRef Full Text | Google Scholar

Scott, A. J., Webb, T. L., Martyn-St James, M., Rowse, G., and Weich, S. (2021). Improving sleep quality leads to better mental health: a meta-analysis of randomised controlled trials. Sleep Med. Rev. 60, 101556. doi: 10.1016/j.smrv.2021.101556

PubMed Abstract | CrossRef Full Text | Google Scholar

Scullin, M. K., and Bliwise, D. L. (2015). Sleep, cognition, and normal aging: integrating a half century of multidisciplinary research. Perspect. Psychol. Sci. 10, 97–137. doi: 10.1177/1745691614556680

PubMed Abstract | CrossRef Full Text | Google Scholar

Shokri-Kojori, E., Wang, G. J., Wiers, C. E., Demiral, S. B., Guo, M., Kim, S. W., et al. (2018). β-Amyloid accumulation in the human brain after one night of sleep deprivation. Proc. Natl. Acad. Sci. USA. 115, 4483–4488. doi: 10.1073/pnas.1721694115

PubMed Abstract | CrossRef Full Text | Google Scholar

Spira, A. P., Gamaldo, A. A., An, Y., Wu, M. N., Simonsick, E. M., Bilgel, M., et al. (2013). Self-reported sleep and β-amyloid deposition in community-dwelling older adults. JAMA Neurol. 70, 1537–1543. doi: 10.1001/jamaneurol.2013.4258

PubMed Abstract | CrossRef Full Text | Google Scholar

Spira, A. P., Gonzalez, C. E., Venkatraman, V. K., Wu, M. N., Pacheco, J., Simonsick, E. M., et al. (2016). Sleep duration and subsequent cortical thinning in cognitively normal older adults. Sleep 39, 1121–1128. doi: 10.5665/sleep.5768

PubMed Abstract | CrossRef Full Text | Google Scholar

Sprecher, K. E., Bendlin, B. B., Racine, A. M., Okonkwo, O. C., Christian, B. T., Koscik, R., et al. (2015). Amyloid burden is associated with self-reported sleep in nondemented late middle-aged adults. Neurobiol. Aging 36, 2568–2576. doi: 10.1016/j.neurobiolaging.2015.05.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Staekenborg, S. S., Kelly, N., Schuur, J., Koster, P., Scherder, E., Tielkes, C. E. M., et al. (2020). Education as proxy for cognitive reserve in a large elderly memory clinic: ‘Window of Benefit'. J. Alzheimers. Dis. 76, 671–679. doi: 10.3233/JAD-191332

PubMed Abstract | CrossRef Full Text | Google Scholar

Steffens, D. C., Fahed, M., Manning, K. J., and Wang, L. (2022). The neurobiology of apathy in depression and neurocognitive impairment in older adults: a review of epidemiological, clinical, neuropsychological and biological research. Transl. Psychiatry 12, 525. doi: 10.1038/s41398-022-02292-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Szymkowicz, S. M., Dotson, V. M., Jones, J. D., Okun, M. S., and Bowers, D. (2018). Symptom dimensions of depression and apathy and their relationship with cognition in Parkinson's disease. J. Int. Neuropsychol. Soc. 24, 269–282. doi: 10.1017/S1355617717001011

PubMed Abstract | CrossRef Full Text | Google Scholar

Tabuchi, M., Lone, S. R., Liu, S., Liu, Q., Zhang, J., Spira, A. P., et al. (2015). Sleep interacts with aβ to modulate intrinsic neuronal excitability. Curr. Biol. 25, 702–712. doi: 10.1016/j.cub.2015.01.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Taconnat, L., Bouazzaoui, B., Bouquet, C., Larigauderie, P., Witt, A., and Blaye, A. (2022). Cognitive mechanisms underlying free recall in episodic memory performance across the lifespan: testing the control/representation model. Psychol. Res. 87, 1370–1388. doi: 10.1007/s00426-022-01736-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Tagariello, P., Girardi, P., and Amore, M. (2009). Depression and apathy in dementia: same syndrome or different constructs? A critical review. Arch. Gerontol. Geriatr. 49, 246–249. doi: 10.1016/j.archger.2008.09.002

CrossRef Full Text | Google Scholar

Tari, B., Ben Yehuda, M., Laurell, A. A. S., Ritchie, K., Dauvilliers, Y., Ritchie, C., et al. (2023). The indirect relationship between sleep and cognition in the PREVENT cohort: identifying targets for intervention. medRxiv. [preprint]. doi: 10.1101/2023.05.18.23290160

CrossRef Full Text | Google Scholar

United Nations (2020). Department of Economic and Social Affairs. Population Division. World Population Prospects: The 2015 Revision: Key Findings and Advance Tables. United Nations Working Paper ESA/P/WP.241. Washington, DC: United Nations.

Google Scholar

Varghese, S., Frey, B. N., Schneider, M. A., Kapczinski, F., and de Azevedo Cardoso, T. (2022). Functional and cognitive impairment in the first episode of depression: a systematic review. Acta Psychiatr. Scand. 145, 156–185. doi: 10.1111/acps.13385

CrossRef Full Text | Google Scholar

Voyer, D., Saint Aubin, J., Altman, K., and Gallant, G. (2021). Sex differences in verbal working memory: a systematic review and meta-analysis. Psychol. Bull. 147, 352–398. doi: 10.1037/bul0000320

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, C., and Holtzman, D. M. (2020). Bidirectional relationship between sleep and Alzheimer's disease: role of amyloid, tau, and other factors. Neuropsychopharmacology 45, 104–120. doi: 10.1038/s41386-019-0478-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Watson, N. F., Badr, M. S., Belenky, G., Bliwise, D. L., Buxton, O. M., Buysse, D., et al. (2015). Recommended amount of sleep for a healthy adult: a joint consensus statement of the American academy of sleep medicine and sleep research society. Sleep 38, 843–844. doi: 10.5665/sleep.4716

PubMed Abstract | CrossRef Full Text | Google Scholar

Williams, B. R., Strauss, E. H., Hultsch, D. F., and Hunter, M. A. (2007). Reaction time inconsistency in a spatial Stroop task: age-related differences through childhood and adulthood. Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn. 14, 417–439. doi: 10.1080/13825580600584590

PubMed Abstract | CrossRef Full Text | Google Scholar

Xie, L., Kang, H., Xu, Q., Chen, M. J., Liao, Y., Thiyagarajan, M., et al. (2013). Sleep drives metabolite clearance from the adult brain. Science 342, 373–377. doi: 10.1126/science.1241224

PubMed Abstract | CrossRef Full Text | Google Scholar

Yao, H. K., Guet-McCreight, A., Mazza, F., Moradi Chameh, H., Prevot, T. D., Griffiths, J. D., et al. (2022). Reduced inhibition in depression impairs stimulus processing in human cortical microcircuits. Cell Rep. 38, 110232. doi: 10.1016/j.celrep.2021.110232

PubMed Abstract | CrossRef Full Text | Google Scholar

Yoo, S. S., Hu, P. T., Gujar, N., Jolesz, F. A., and Walker, M. P. (2007). A deficit in the ability to form new human memories without sleep. Nat. Neurosci. 10, 385–392. doi: 10.1038/nn1851

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhong, X., Hilton, H. J., Gates, G. J., Jelic, S., Stern, Y., Bartels, M. N., et al. (2005). Increased sympathetic and decreased parasympathetic cardiovascular modulation in normal humans with acute sleep deprivation. J. Appl. Physiol. 98, 2024–2032. doi: 10.1152/japplphysiol.00620.2004

PubMed Abstract | CrossRef Full Text | Google Scholar