Macro and micro sleep architecture and cognitive performance in older adults

Djonlagic, Ina; Mariani, Sara; Fitzpatrick, Annette L.; Klei, Veerle M G T H Van Der; Johnson, Dayna A.; Wood, Alexis C.; Seeman, Teresa E.; Nguyen, Ha; Prerau, Michael J.; Luchsinger, José A.; Dzierzewski, Joseph M.; Rapp, Stéphen R.; Tranah, Gregory J.; Yaffe, Kristine; Burdick, Katherine E.; Stone, Katie L.; Redline, Susan; Purcell, Shaun

doi:10.1038/s41562-020-00964-y

Cited by 90 publications

(137 citation statements)

References 117 publications

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“…Model 5, was further adjusted for NT-proBNP (Zonneveld et al, 2017;Ferguson et al, 2018;Nagata et al, 2019;Ostovaneh et al, 2020). Finally, we considered models that included the variables in Model 4 and one of the following variables previously reported to be associated with overall or specific domains of cognitive function: CAC scores (Bos et al, 2015;Deckers et al, 2017), % SWS (Wunderlin et al, 2020) and % REM (Djonlagic et al, 2021), % time SpO 2 < 90% (Yaffe et al, 2011;Johnson et al, 2017) and ODI (Johnson et al, 2017).…”

Section: Statistical Analysesmentioning

confidence: 99%

Prediction of Cognitive Decline Using Heart Rate Fragmentation Analysis: The Multi-Ethnic Study of Atherosclerosis

Costa

Redline

Hughes

et al. 2021

Front. Aging Neurosci.

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Background: Heart rate fragmentation (HRF), a new non-invasive metric quantifying cardiac neuroautonomic function, is associated with increasing age and cardiovascular disease. Since these are risk factors for cognitive decline and dementia, in the Multi-Ethnic Study of Atherosclerosis (MESA), we investigated whether disrupted cardiac neuroautonomic function, evidenced by increased HRF, would be associated with worse cognitive function assessed concurrently and at a later examination, and with greater cognitive decline.Methods: HRF was derived from the ECG channel of the polysomnographic recordings obtained in an ancillary study (n = 1,897) conducted in conjunction with MESA exam 5 (2010–2012). Cognitive function was assessed at exam 5 and 6.4 ± 0.5 years later at exam 6 (2016–2018) with tests of global cognitive performance (the Cognitive Abilities Screening Instrument, CASI), processing speed (Digit Symbol Coding, DSC) and working memory (Digit Span). Multivariable regression models were used to quantify the associations between HRF indices and cognitive scores.Results: The participants’ mean age was 68 ± 9 years (54% female). Higher HRF at baseline was independently associated with lower cognitive scores at both exams 5 and 6. Specifically, in cross-sectional analyses, a one-standard deviation (SD) (13.7%) increase in HRF was associated with a 0.51 (95% CI: 0.17–0.86) points reduction in CASI and a 1.12 (0.34–1.90) points reduction in DSC. Quantitatively similar effects were obtained in longitudinal analyses. A one-SD increase in HRF was associated with a 0.44 (0.03–0.86) and a 1.04 (0.28–1.81) points reduction in CASI and DSC from exams 5 to 6, respectively. HRF added predictive value to the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE-APOE-ε4) risk score and to models adjusted for serum concentration of NT-proBNP, an analyte associated with cognitive impairment and dementia.Conclusion: Increased HRF assessed during sleep was independently associated with diminished cognitive performance (concurrent and future) and with greater cognitive decline. These findings lend support to the links between cardiac neuroautonomic regulation and cognitive function. As a non-invasive, repeatable and inexpensive probe, HRF technology may be useful in monitoring cognitive status, predicting risk of dementia and assessing therapeutic interventions.

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Section: Statistical Analysesmentioning

confidence: 99%

Prediction of Cognitive Decline Using Heart Rate Fragmentation Analysis: The Multi-Ethnic Study of Atherosclerosis

Costa

Redline

Hughes

et al. 2021

Front. Aging Neurosci.

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“…Therefore, in middle-age or older cohorts where sleep disorders and circadian disruption are prevalent, there is value in extending global sleep health assessments with data from PSG and actigraphy. Moreover, metrics of OSA, altered sleep architecture, and sleep duration variability predict adverse health outcomes, cognitive decline and mortality, underscoring the potential utility of these data for informing sleep health assessments (26)(27)(28).…”

Section: Discussionmentioning

confidence: 98%

“…A canon of sleep health parameters for evolving measures of sleep micro-architecture (e.g. spindles, k-complexes) has yet to be established despite emerging data on the unique information contained in these measures (27,46). These limitations highlight that sleep health is undergoing conceptual advances and scale development (1, 6), scale validation (5,6), implementations of sleep health in cohort studies and community samples (21,47), innovations in methodological approaches (4,7,41,48), optimal range and threshold determination (18,22), and many practical choices on how best to analyze multidimensional sleep data.…”

Section: Limitationsmentioning

confidence: 99%

Multidimensional Sleep Health: Concepts, Advances, and Implications for Research and Intervention

Chung

Goodman

Huang

et al. 2021

Preprint

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The new paradigm of multidimensional sleep health (‘sleep health’) offers both challenges and opportunities for sleep science. Buysse (2014) has described sleep health to be multidimensional, framed as positive attributes, operationalizable into composite measures of global sleep health, sensitive to upstream exposures, and consequential for downstream health. We highlight two paradigm-shifting effects of a multidimensional sleep health perspective. The first is the use of composite sleep metrics which i) enable quantification of population shifts in sleep health, ii) with possibly reduced measurement error, iii) greater statistical stability, and iv) reduced multiple-testing burdens. The second is that sleep dimensions do not occur in isolation, that is, they are commonly biologically or statistically dependent. These dependencies complicate hypothesis tests yet can be leveraged to inform scale construction, model interpretation, and inform targeted interventions. To illustrate these points, we i) extended Buysse’s Ru SATED model; ii) constructed a conceptual model of sleep health; and iii) showed exemplar analyses from the Multi-Ethnic Study of Atherosclerosis (n=735). Our findings support that sleep health is a distinctively useful paradigm to facilitate interpretation of a multitude of sleep dimensions. Nonetheless, the field of sleep health is still undergoing rapid development and is currently limited by: i) a lack of evidence-based cut-offs for defining optimal sleep health; ii) longitudinal data to define utility for predicting health outcomes; and iii) methodological research to inform how to best combine multiple dimensions for robust and reproducible composites.

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“…Increased sleep fragmentation could accelerate neurodegeneration via the accumulation of abnormal proteins such as tau and amyloid beta in the brain, leading to dementia [22]. In older people, better cognitive performance was related to having pro les of sleep metrics observed in younger people [23]. Insomnia is associated with impairments in cognitive performance, and thus, relieving insomnia could potentially improve cognitive outcome [24].…”

Section: Discussionmentioning

confidence: 99%

The Effect of Rotigotine on Cognitive Function and Sleep Problems in Parkinson's Disease: an Open-Label Pilot Study

Suzuki

Funakoshi

Fujita

et al. 2021

Preprint

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Background: We hypothesized that rotigotine may have a positive effect on cognitive function in patients with Parkinson’s disease (PD) by improving daytime motor function and nighttime sleep status due to its 24-hour sustained properties.Methods: We evaluated the effect of rotigotine on motor symptoms, cognitive function, daytime sleepiness, sleep disturbances, and motor symptoms in 10 PD patients with sleep disturbances, defined as a PD Sleep Scale (PDSS)-2 score of ≥ 15, in a single-center, 3-month open-label study. Participants received 24 mg/24 h (patch content: 4.5-9 mg) rotigotine for a 3-month period. At baseline and 3 months, patients were evaluated on the Movement Disorder Society Revision of the Unified PD Rating Scale (MDS-UPDRS) parts III and IV and cognitive assessments, such as the Mini-Mental State Examination (MMSE), frontal assessment battery (FAB) and Montreal Cognitive Assessment (MoCA). The Epworth Sleepiness Scale (ESS) and PDSS-2 were administered at baseline and at 1 month, 2 months and 3 months.Results: At 3 months, MDS-UPDRS part III (-10.7, p<0.001) and MDS-UPDRS part IV (-1.0, p=0.023) scores significantly decreased, MoCA scores (1.7, p=0.0095) significantly increased, and off time significantly decreased (-43.0 min; p=0.029) from baseline. PDSS-2 scores significantly decreased from baseline at 2 months (-14.5, p<0.05) and 3 months (-20.0, p<0.001). ESS, MMSE or FAB scores did not significantly change after rotigotine treatment.Conclusion: Our preliminary findings suggest that low-dose rotigotine could improve motor symptoms, sleep disturbance, and cognitive function without worsening daytime sleepiness in patients with PD.

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Macro and micro sleep architecture and cognitive performance in older adults

Cited by 90 publications

References 117 publications

Prediction of Cognitive Decline Using Heart Rate Fragmentation Analysis: The Multi-Ethnic Study of Atherosclerosis

Prediction of Cognitive Decline Using Heart Rate Fragmentation Analysis: The Multi-Ethnic Study of Atherosclerosis

Multidimensional Sleep Health: Concepts, Advances, and Implications for Research and Intervention

The Effect of Rotigotine on Cognitive Function and Sleep Problems in Parkinson's Disease: an Open-Label Pilot Study

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