Medicine, Faculty of
Non UBC
Occupational Science and Occupational Therapy, Department of
Physical Therapy, Department of
DSpace
Brain Sciences 12 (10): 1360 (2022)
Djavad Mowafaghian Centre for Brain Health
Centre for Hip Health and Mobility
Alfini, Alfonso J.
Won, Junyeon
Weiss, Lauren R.
Nyhuis, Casandra C.
Zipunnikov, Vadim
Spira, Adam P.
Liu-Ambrose, Teresa
Shackman, Alexander J.
Smith, J. Carson
The objective of this study was to understand the associations of sleep and cardiorespiratory fitness with hippocampal volume and global cognition among older adults (n = 30, age = 65.8 years, female = 73.3%). Wrist actigraphy provided objective measures of nighttime sleep including sleep duration, average wake bout length (WBL; sleep disturbance), and wake-to-sleep transition probability (WTSP; sleep consolidation). Cardiorespiratory fitness was quantified via cycle exercise using a modified heart rate recovery approach. Magnetic resonance imaging was used to determine hippocampal volume and the Mini-Mental State Examination was used to assess global cognition. Fitness moderated associations of sleep with hippocampal volume and cognitive performance, whereby the association of WBL—an index of poor sleep—with hippocampal atrophy was stronger among less-fit individuals, and the association of sleep duration with cognitive performance was stronger among more-fit individuals. Across the fitness levels, a longer WBL was associated with lower cognitive performance, and a higher WTSP—an index of more consolidated sleep—was associated with greater hippocampal volume. Sleep and fitness were unrelated to the volume of an amygdala control region, suggesting a degree of neuroanatomical specificity. In conclusion, higher cardiorespiratory fitness may attenuate sleep disturbance-related hippocampal atrophy and magnify the cognitive benefits of good sleep. Prospective studies are needed to confirm these findings.
Citation: Alfini, A.J.; Won, J.; Weiss,L.R.; Nyhuis, C.C.; Zipunnikov, V.;Spira, A.P.; Liu-Ambrose, T.;Shackman, A.J.; Smith, J.C.Cardiorespiratory Fitness as aModerator of Sleep-RelatedAssociations with HippocampalVolume and Cognition. Brain Sci.2022, 12, 1360. https://doi.org/10.3390/brainsci12101360Academic Editor: Luigi De GennaroReceived: 12 August 2022Accepted: 3 October 2022Published: 7 October 2022Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2022 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).brainsciencesArticleCardiorespiratory Fitness as a Moderator of Sleep-RelatedAssociations with Hippocampal Volume and CognitionAlfonso J. Alfini 1, Junyeon Won 2, Lauren R. Weiss 2,3, Casandra C. Nyhuis 4 , Vadim Zipunnikov 5,Adam P. Spira 6,7,8, Teresa Liu-Ambrose 9,10,11, Alexander J. Shackman 3,12,13 and J. Carson Smith 2,3,13,*1 National Center on Sleep Disorders Research, Division of Lung Diseases, National Heart, Lung, and BloodInstitute, NIH, Bethesda, MD 20817, USA2 Department of Kinesiology, University of Maryland School of Public Health, College Park, MD 20742, USA3 Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742, USA4 Department of Public Health Sciences, Pennsylvania State University College of Medicine,Hershey, PA 17033, USA5 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA6 Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA7 Center on Aging and Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA8 Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine,Baltimore, MD 21205, USA9 Department of Physical Therapy, University of British Columbia, Vancouver, BC V6T 1Z3, Canada10 Djavad Mowafaghian Centre for Brain Health, University of British Columbia,Vancouver, BC V6T 1Z3, Canada11 Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute,Vancouver, BC V5Z 1M9, Canada12 Department of Psychology, University of Maryland, College Park, MD 20742, USA13 Maryland Neuroimaging Center, University of Maryland, College Park, MD 20742, USA* Correspondence: carson@umd.edu; Tel.: +1-301-405-0344Abstract: The objective of this study was to understand the associations of sleep and cardiorespiratoryfitness with hippocampal volume and global cognition among older adults (n = 30, age = 65.8 years,female = 73.3%). Wrist actigraphy provided objective measures of nighttime sleep including sleepduration, average wake bout length (WBL; sleep disturbance), and wake-to-sleep transition probabil-ity (WTSP; sleep consolidation). Cardiorespiratory fitness was quantified via cycle exercise usinga modified heart rate recovery approach. Magnetic resonance imaging was used to determine hip-pocampal volume and the Mini-Mental State Examination was used to assess global cognition. Fitnessmoderated associations of sleep with hippocampal volume and cognitive performance, whereby theassociation of WBL—an index of poor sleep—with hippocampal atrophy was stronger among less-fitindividuals, and the association of sleep duration with cognitive performance was stronger amongmore-fit individuals. Across the fitness levels, a longer WBL was associated with lower cognitiveperformance, and a higher WTSP—an index of more consolidated sleep—was associated with greaterhippocampal volume. Sleep and fitness were unrelated to the volume of an amygdala control region,suggesting a degree of neuroanatomical specificity. In conclusion, higher cardiorespiratory fitnessmay attenuate sleep disturbance-related hippocampal atrophy and magnify the cognitive benefits ofgood sleep. Prospective studies are needed to confirm these findings.Keywords: sleep; actigraphy; brain volume; heart rate recovery; aging1. IntroductionA greater life expectancy and lower birth rate have accelerated the pace of populationaging. Recent estimates project that adults over age 65 will account for approximately 20%of the world’s population by 2050 [1]. Although increased longevity is a human successstory, older age is associated with increased morbidity and diminished capacity for workBrain Sci. 2022, 12, 1360. https://doi.org/10.3390/brainsci12101360 https://www.mdpi.com/journal/brainsciBrain Sci. 2022, 12, 1360 2 of 15and self-care [1]. Neurocognitive changes—including age-related declines in cognitiveperformance and brain health—reduce the opportunity for a long, productive, meaningfullife and increase the risk of dementia.Converging lines of evidence indicate that sleep plays a pivotal role in neurocognitiveaging [2,3]. Studies have shown that poor sleep quality is linked with cognitive deficits [4,5]and that both self-reported poor sleep quality and abnormal sleep duration, as well asobjective indices of sleep fragmentation, are associated with cortical and subcortical brainatrophy [6–8].Similar to poor sleep, lower cardiorespiratory fitness is implicated in poor neurocog-nitive outcomes in later life [9–11], and diminished aerobic capacity is a typical featureof the aging process, particularly among non-exercisers [12,13]. In general, older adultswith higher cardiorespiratory fitness perform better on tasks of cognitive, perceptual, andmotor ability than their less-fit counterparts [14–16]. Older master athletes—an elite groupof older, highly fit, physically active adults—have better cognitive performance, greatercerebral blood flow, and larger brain volume than age-matched controls [17–19].Although there is ample evidence that sleep and fitness enhance neurocognitive health,their potential interaction has rarely been examined. Nevertheless, an emerging body ofevidence hints at potential synergistic effects. Acute exercise has been shown to enhanceexecutive function to a greater degree among individuals with longer vs. shorter sleepduration [20], and higher levels of cardiorespiratory fitness weaken the association betweenself-reported sleep disturbance and cerebrospinal fluid (CSF) markers of neurodegenerationin older adults at risk of developing Alzheimer’s disease (AD) [21]. Despite this progress,the interactive consequences of sleep and fitness for brain structure and cognition in olderadults remain largely unknown. Gaining an understanding of these interactions may haveimportant implications regarding the need to make available low-risk, scalable, and cost-effective sleep and/or fitness interventions aimed at preserving neurocognitive function inolder adults.Converging lines of observational and mechanistic evidence suggest that the hip-pocampus plays a crucial role in mediating the impact of physical health on memory andcognitive function, and several reports have linked hippocampal structure to insomnia andcognitive decline [22,23]. Older individuals who engage in regular fitness training exhibitlarger hippocampal volume, better spatial memory, and higher levels of brain-derivedneurotrophic factor (BDNF) [24–26]. Randomized controlled trials (RCTs) demonstrate thatboth exercise training and higher fitness increase hippocampal volume and boost executivefunction and memory performance [24,27–30]. Likewise, short-term (10 days) exercisecessation has been shown to reduce hippocampal blood flow in master athletes [31]. Takentogether, these observations motivate the hypothesis that higher levels of cardiorespiratoryfitness help to preserve hippocampal volume and global cognition in the face of poor sleep.Here, we assessed the joint consequences of sleep and cardiorespiratory fitness onhippocampal volume and global cognitive function in older adults. We used wrist actigra-phy to obtain objective measures of nighttime sleep in the natural environment includingtotal sleep time (TST), mean wake bout length (WBL; an index of poor sleep), and wake-to-sleep transition probability (WSTP; an index of sleep consolidation). In the laboratory,we quantified individual differences in cardiorespiratory fitness (i.e., Modified Heart RateRecovery, HRRM) and hippocampal volume. Global cognitive function was assessed usingthe Mini-Mental State Examination (MMSE) [32]. This novel combination of approachesallowed us to rigorously test whether higher levels of cardiorespiratory fitness attenuatesleep-related (a) hippocampal atrophy and (b) cognitive decline. To assess specificity, aparallel series of control analyses were performed for the amygdala, a neighboring regionof the medial temporal lobe. We hypothesized that higher levels of cardiorespiratory fitnesswould mitigate the adverse associations of poor sleep (i.e., shorter sleep duration, lessconsolidated sleep, and longer wake bout length) with hippocampal volume and globalcognition and that these effects would not generalize to the amygdala.Brain Sci. 2022, 12, 1360 3 of 152. Materials and Methods2.1. Experimental Design and SettingThe current cross-sectional analyses were conducted in the broader context of a ran-domized cross-over design study, in which participants engaged in a 30 min aerobic exercisesession or a sedentary control condition followed immediately by a structural MRI assess-ment. Participants performed each arm of the trial (exercise/control) on separate days(mean inter-session interval = 12.3 ± 17.6 days) and the order of both was randomized andcounterbalanced across participants. Actigraphic sleep assessments occurred prior to thefirst imaging visit. To maximize reliability, analyses of the hippocampal structure employedMRI data from both visits (see below). Although the cross-over design was integral tothe broader study, it is not central to the goals, implementation, or interpretation of thecross-sectional analyses that are the focus of the present report.2.2. ParticipantsDetails of the study procedures have been reported elsewhere [20,33]. Briefly, westudied 30 physically active, cognitively normal older adults (MMSE score ≥ 26, 73.3%women, age 55 to 81 years) (Table 1). Participant recruitment included in-person solicitationat local senior recreation centers, newspaper advertisements, and study fliers posted onuniversity listservs. Individuals were screened using a structured telephone interview toascertain health history and identify potential contraindications. Eligible individuals ob-tained written physician approval to engage in moderate-intensity exercise and participatein an in-person laboratory screening session, during which they were familiarized withthe study procedures and provided written informed consent (Figure 1). This study wasapproved by the Institutional Review Board of the University of Maryland in accordancewith the Helsinki Declaration.Table 1. Participant characteristics.Characteristic Mean ± SDAge, years 65.8 ± 7.3Female 22 (73.3)Education > 12 years 28 (93.3)White 23 (76.7)Body Mass Index, kg/m2 25.2 ± 4.07-Day PA, kcal/kg/day 223.3 ± 28.1Actigraphy, days 7.9 ± 3.4Total Sleep Time, min 403.6 ± 71.9Wake Bout Length, min 5.7 ± 2.8Wake-to-Sleep Transition Probability 0.2 ± 0.1Borg’s Rating of Perceived Exertion 14.6 ± 1.2Baseline Heart Rate, bpm 76.9 ± 10.1Post-Exercise Heart Rate, bpm 92.8 ± 13.5Exercise Intensity as a Percentage of HRReserve, % 73.7 ± 20.7Hippocampal Volume, mm3/ICV 0.0052 ± 0.0006Amygdala Volume, mm3/ICV 0.0020 ± 0.0002Mini-Mental State Exam 29.1 ± 1.2Note. Female, education, and white are expressed as n (%). The Borg’s Rating of Perceived Exertion and all heartrate metrics reflect measurements during the exercise condition. Abbreviations. PA—physical activity, HRReserve—heart rate reserve (calculated as max HR—resting HR), SD—standard deviation, kg—kilograms, m—meters,min—minutes, kcal—kilocalories, bpm—beats per minute, ICV—intracranial volume.Brain Sci. 2022, 12, 1360 4 of 15Brain Sci. 2022, 12, x FOR PEER REVIEW 4 of 15 Note. Female, education, and white are expressed as n (%). The Borg’s Rating of Perceived Exertion and all heart rate metrics reflect measurements during the exercise condition. Abbreviations. PA—physical activity, HRReserve—heart rate reserve (calculated as max HR—resting HR), SD—standard deviation, kg—kilograms, m—meters, min—minutes, kcal—kilocalories, bpm—beats per minute, ICV—intracranial volume. Figure 1. Participant recruitment, screening, exclusion, and enrollment between 2016 and 2018. 2.3. Eligibility Criteria Individuals were excluded if they reported a history of heart attack, stroke, transient ischemic attack, seizures, epilepsy, brain tumor, closed head injury, alcohol/substance abuse, psychosis; current visual/auditory limitations; or a current diagnosis of AD, atrial fibrillation, cardiovascular disease, pulmonary disease, diabetes, hypertension, Parkin-son’s disease, sleep apnea, depression, anxiety, cognitive impairment, left-handedness, severe obesity (body mass index ≥40 kg/m2), self-reported low physical activity (defined here as not achieving at least 30 min of physical activity at least 3 times/week during the previous 6 months), limited English language proficiency, and MRI contraindications. 2.4. Sleep Assessment A wrist-worn actigraph (Motionlogger Watch; Ambulatory Monitoring, Inc., Ards-ley, NY, USA) was used to measure sleep behavior for approximately one week. Wrist actigraphy is a valid and reliable tool for the objective measurement of sleep [34]. During the in-person screening visit, participants were trained to properly use the actigraph. On average, the participants wore the actigraph on their non-dominant wrist for 7.9 ± 3.4 days, throughout which the actigraphic data were continuously collected in one-minute Figure 1. Participant recruitment, screening, exclusion, and enrollment between 2016 and 2018.2.3. Eligibility CriteriaIndividuals were excluded if they reported a history of heart attack, stroke, transientischemic attack, seizures, epilepsy, brain tumor, closed head injury, alcohol/substanceabuse, psychosis; current visual/auditory limitations; or a current diagnosis of AD, atrialfibrillation, cardiovascular disease, pulmonary disease, diabetes, hypertension, Parkinson’sdisease, sleep apnea, depression, anxiety, cognitive impairment, left-handedness, severeobesity (body mass index ≥ 40 kg/m2), self-reported low physical activity (defined here asnot achieving at least 30 min of physical activity at least 3 times/week during the previous6 months), limited English language proficiency, nd MRI contraindications.2.4. Sleep AssessmentA wrist-wor actigraph (Motionlogger Watch; Ambulatory Monitoring, Inc., Ards-ley, NY, USA) was used to measure sleep beh vior for appr ximately one week. Wristactigraphy is a valid an reliable tool for the objective measurement of sleep [34]. Duringthe in- erson screening visit, participants were trained to properly use the actigraph. Onaverage, the participants wore the actigraph on their non-dominant wrist for 7.9 ± 3.4 days,throughout which the actigraphic data were continuously collected in one-minute epochs.Participants were trained to indicate bedtime (i.e., intention to sleep) and awakening (i.e.,intention to start the day) by pressing an event-marker button on the actigraph at thosetimes. Button-presses were then used to establish the in-bed interval. A daily sleep diaryprovided an ancillary source of information about the in-bed interval and actigraph removal(e.g., due to bathing) [33]. Actigraphic data and diary information were visually inspectedby two trained raters to set the in-bed interval and identify invalid data (i.e., non-wearperiods and periods of device malfunction). The raters were naïve to the participants’Brain Sci. 2022, 12, 1360 5 of 15level of cardiorespiratory fitness, and inter-rater discrepancies were resolved via consensus.Action-W software (Ambulatory Monitoring, Inc.) was used to derive the following sleepparameters: total sleep time (TST; minutes) and average wake bout length (WBL; minutes).We then calculated the wake-to-sleep transition probability (WSTP) as a measure of sleepconsolidation. WSTP was defined as the probability of transitioning from an awake toa sleep state, with higher values indicating a greater sleep probability [35]. WSTP wascalculated as the reciprocal of the average WBL. Each of these parameters was averagedacross nights.2.5. Cognitive AssessmentThe Mini-Mental State Exam (MMSE), which is a valid and reliable assessment tool,was used to measure global cognitive performance [32]. The MMSE is a well-established11-item (yes/no) measure (score range 0–30) of global cognitive function including thedomains of executive function, memory, language, and visuospatial skills [32].2.6. Acute ExerciseA single, 30 min session of moderate-intensity aerobic exercise was performed on amechanically braked cycle ergometer (Monark 828 E, Monark Exercise AB, Vansbro Swe-den). The exercise session followed a well-established cycling protocol [20] and consistedof 20 min of steady-state exercise bound between a 5 min self-directed warm-up periodand cool-down period. To establish the standardized exercise intensity during the exercisesession, participants maintained an RPE of 15 (associated with the verbal anchor ‘Hard’)and a pedal cadence of 60–80 revolutions per minute. Participants manually adjustedthe ergometer’s resistance level to maintain the subjective effort of ‘Hard’ throughout thesession while maintaining pedal cadence. The RPE was measured every five minutes andwas indexed using the Borg’s 6–20 RPE scale [36]. Importantly, the RPE during the exercisesession was used as the primary determinant of exercise intensity because of the evidencefor its safety and validity among older adults [37]. This approach has been shown to controlthe relative exercise intensity among individuals who may vary in their absolute maximalcapacity to perform cycle ergometer work [38]. Participants also wore a heart rate (HR)monitor (Polar RS800CX, Polar Electro, Oy, Kempele, Finland). The age-predicted max HRwas calculated using the Tanaka method (208–0.7 × age) for older adults [39], which wasthen used to compute exercise intensity as a percentage of HR reserve (used for comparisonpurposes only, see Table 1). Water was provided ad libitum during exercise.2.7. Modified Heart Rate Recovery (HRRM)Individual differences in cardiorespiratory fitness were quantified using a modifiedheart rate recovery (HRRM) approach, defined as the difference between pre- and post-exercise HR. Although this approach is not equivalent to other HRR methodologies [40,41],it is consistent with established literature, which demonstrates that (a) fitness-relatedimprovements in vagal tone facilitate more rapid decreases in post-exercise HR towardsbaseline levels [42–46]; (b) HR remains higher (5 min) post-exercise vs. pre-exercise [47];and (c) more-fit individuals demonstrate more rapid decreases in post-exercise HR (vs.HR at the onset of submaximal work) [43]. The HRRM score was computed using theequation below, where HRpre as measured while participants were seated immediatelybefore initiating exercise, and HRpost as measured in the same position 5 min after thecompletion of exercise.HRRM = HRpost − HRpreSimilar to previous studies of this nature [8,48,49], our index of cardiorespiratoryfitness (HRRM) was dichotomized using a median split (≥14 beats per minute; bpm) andanalyzed as a binary variable (high fitness = 0, low fitness = 1).Brain Sci. 2022, 12, 1360 6 of 152.8. MRI AssessmentMRI data were acquired using a Siemens Trio Tim 3.0 Tesla MRI scanner (Erlangen,Germany) and a 32-channel head coil. Sagittal T1-weighted anatomical images wereacquired using a magnetization prepared rapid gradient echo (MPRAGE) imaging sequence(TR = 1900 ms, TE = 2.3 ms, TI = 900 ms, flip angle = 9◦, slice thickness = 0.9 mm, in-planeresolution = 0.9 × 0.9 mm, matrix = 256 × 256, field of view = 230 × 230).2.9. MRI Data ProcessingMRI data were manually inspected before and after processing for quality assur-ance. T1-weighted images were processed using the FreeSurfer (version 5.3.0) automatedvolumetric reconstruction pipeline (recon-all), which includes tissue segmentation andintensity-based surface deformation [50]. For each participant, MRI data from the two imag-ing assessments were integrated using the FreeSurfer longitudinal function and normalizedto stereotaxic standard space (MNI305) via nonlinear transformation. Analyses focused onbilateral hippocampal volume (Figure 2). To assess specificity, a parallel series of controlanalyses were performed for the amygdala (Figure 2). Regional volumetric estimates weresummed across hemispheres and normalized by intracranial volume (ICV).Brain Sci. 2022, 12, x FOR PEER REVIEW 6 of 15 before initiating exercise, and 𝐻𝑅௣௢௦௧ as measured in the same position 5 min after the completion of exercise. 𝐻𝑅𝑅ெ ൌ 𝐻𝑅௣௢௦௧ െ 𝐻𝑅௣௥௘ Similar to previous studies of this nature [8,48,49], our index of cardiorespiratory fit-ness (𝐻𝑅𝑅ெ) was dichotomized using a median split (≥14 beats per minute; bpm) and an-alyzed as a binary variable (high fitness = 0, low fitness = 1). 2.8. MRI Assessment MRI data were acquired using a Siemens Trio Tim 3.0 Tesla MRI scanner (Erlangen, Germany) and a 32-channel head coil. Sagittal T1-weighted anatomical images were ac-quired using a magnetization prepared rapid gradient echo (MPRAGE) imaging sequence (TR = 1900 ms, TE = 2.3 ms, TI = 900 ms, flip angle = 9°, slice thickness = 0.9 mm, in-plane resolution = 0.9 × 0.9 mm, matrix = 256 × 256, field of view = 230 × 230). 2.9. MRI Data Processing MRI data were manually inspected before and after processing for quality assurance. T1-weighted images were processed using the FreeSurfer (version 5.3.0) automated volu-metric reconstruction pipeline (recon-all), which includes tissue segmentation and inten-sity-based surface deformation [50]. For each participant, MRI data from the two imaging assessments were integrated using the FreeSurfer longitudinal function and normalized to stereotaxic standard space (MNI305) via nonlinear transformation. Analyses focused on bilateral hippocampal volume (Figure 2). To assess specificity, a parallel series of con-trol analyses were performed for the amygdala (Figure 2). Regional volumetric estimates were summed across hemispheres and normalized by intracranial volume (ICV). Figure 2. Volumetric Regions of Interest (ROIs). Bilateral hippocampus (purple) and bilateral amyg-dala control region (gold). Brain image and ROI visualization were generated using MRIcroGL [51]. 2.10. Analytic Strategy Analyses were conducted using Stata software (v. 15.1; StataCorp, College Station, TX, USA). Demographic and biometric characteristics for the high- and low-fitness (HRRM) groups were assessed using independent samples t-tests or nonparametric equal-ity-of-means tests for continuous variables and Chi-square or Fisher exact tests for cate-gorical variables. As a precursor to hypothesis testing, exploratory analyses were conducted to exam-ine the distribution within each variable and identify potential outliers. Prior to model fitting, other statistical assumptions of linear regression were evaluated using standard Figure 2. Volumetric Regions of Interest (ROIs). Bilateral hippocampus (purple) and bilateral amyg-dala control region (gold). Brain image and ROI visualization were generated using MRIcroGL [51].2.10. Analytic StrategyAnalyses were conducted using Stata software (v. 15.1; StataCorp, College Station, TX,USA). Demographic and biometric characteristics for the high- and low-fitness (HRRM)groups were assessed using independent samples t-tests or nonparametric equality-of-means tests for continuous variables and Chi-square or Fisher exact tests for categoricalvariables.As a precursor to hypothesis testing, exploratory analyses were conducted to exam-ine the distribution within each variable and identify potential outliers. Prior to modelfitting, other statistical assumptions of linear regression were evaluated using standardtechniques [52]. Ordinary least squares (OLS) linear regression was used to evaluaterelations between the actigraphic sleep parameters and regional brain volume; robustregression (i.e., weighted and reweighted OLS) was used to test the associations with globalcognitive performance and to confirm the results when outlier influence was suspected (i.e.,values > 2.5 standard deviations from the mean). Separate models were estimated for eachcombination of sleep parameters and (a) regional volume or (b) global cognition (MMSE).All models controlled for potential nuisance variance in age and employed z-transformedcontinuous variables. Missing baseline HR data for one participant were imputed usingthe sample mean. This approach enabled us to rigorously assess the potential interactiveBrain Sci. 2022, 12, 1360 7 of 15effects of sleep and cardiorespiratory fitness (HRRM) on bilateral hippocampal volume. Incases where the interaction was not significant, the interaction term was removed and theanalysis was repeated without HRRM in the model. Parallel analyses were performed forthe amygdala control region. The same approach enabled us to test the potential interactiveconsequences of sleep and fitness (HRRM) on global cognitive function (MMSE). To controlfor alpha inflation, the Benjamini, Krieger, and Yekutieli adaptive step-up false-discoveryrate (FDR) method was used to compute the sharpened q-values for both families of analy-ses (i.e., (a) sleep and hippocampal volume, (b) sleep and global cognition [53]). Analysesinvolving the amygdala control region were not corrected for multiple comparisons. Wedid not adjust for or specifically evaluate the effects of biological sex for several reasons:(1) the raw volume-to-ICV fraction accounted for variance in brain volume by sex [20];(2) we wanted to minimize the total number of statistical comparisons; (3) there was anequal number of men and women in both fitness groups; and (4) we did not hypothesizesex differences.3. Results3.1. Participant CharacteristicsBy design, individuals with high cardiorespiratory fitness performed exercise at alower intensity as a percentage of HRReserve (67.1 ± 18.2%) compared to individuals withlow fitness (80.4 ± 21.6%) (x2(1) = 4.80, p = 0.028) and had a lower resting HR followingthe acute bout of exercise (84.9 ± 10.7 beats per minute; bpm) compared to those withlow fitness (100.7 ± 11.3 bpm) (t(28) = −3.91, p = 0.001). Individuals with high fitness alsoslept less at night (6.2 ± 1.1 h) compared to individuals with low fitness (7.3 ± 1.0 h) (TST:t(28) = −2.85, p = 0.008). The high- and low-fitness groups did not significantly differ inany of the other measured variables (all p-values (ps) ≥ 0.062) (Table 1).3.2. Cardiorespiratory Fitness Moderated the Negative Association between Longer Wake BoutLength and Hippocampal VolumeAs expected, variations in cardiorespiratory fitness (HRRM) significantly moderatedrelations between wake bout length (WBL) and hippocampal volume (β = −1.36, 95%confidence interval [CI] = −2.45, −0.28, FDR-corrected p = 0.037). As shown in Figure 3a,stratified analyses demonstrated that a greater WBL was associated with smaller hippocam-pal volume among less-fit individuals (β = −1.42, 95% CI = −2.71, −0.14, p = 0.032) but notamong more-fit individuals (β = −0.17, 95% CI = −0.59, 0.25, p = 0.396). The same patternwas evident using robust regression (not shown). As detailed in Table 2, the sleep × fitnesseffects were not evident for the remaining sleep metrics (TST and WSTP) (FDR-correctedps ≥ 0.108) or amygdala volume (ps ≥ 0.227).Table 2. Sleep × fitness (HRRM) interaction for regional brain volume and global cognition.Neurocognitive OutcomesParameter Hippocampal Volume AmygdalaVolumeGlobal Cognition(MMSE)β (95% CI) β (95% CI) β (95% CI)WBL — −0.09 (−0.50, 0.32) −0.40 (−0.73,−0.07) *WBL × Fitness −1.36 (−2.45,−0.28) * −0.74 (−1.98, 0.49) 0.51 (−0.46, 1.48)WSTP 0.52 (0.16, 0.89) * 0.31 (−0.10, 0.72) 0.33 (−0.06, 0.73)WSTP × Fitness 0.60 (−0.12, 1.33) 0.23 (−0.61, 1.08) −0.44 (−1.17, 0.29)TST 0.11 (−0.33, 0.55) 0.40 (−0.03, 0.82) —TST × Fitness −0.15 (−1.07, 0.77) −0.31 (−1.19, 0.56) −1.07 (−1.70,−0.43) *Note. Significant results are in bold and indicated by the following: * p < 0.05. Main effects were not evaluated formodels demonstrating a significant interaction, denoted by the dashed line (—). Abbreviations. TST—total sleeptime, WBL—average wake bout length, WSTP—wake-to-sleep transition probability, HRRM—modified heart raterecovery, MMSE—Mini-Mental State Exam, β—standardized beta coefficient, CI—confidence interval.Brain Sci. 2022, 12, 1360 8 of 15Brain Sci. 2022, 12, x FOR PEER REVIEW 8 of 15 Figure 3. Relations between sleep, fitness, and hippocampal volume in older adults. (a) Cardi-orespiratory fitness (HRRM) moderates the adverse association between wake bout length (WBL) and hippocampal volume. Bars depict standardized regression coefficients (β) for the sleep × fitness interaction (controlling for mean-centered age) for more-fit (blue) and less-fit (red) individuals. Among less-fit individuals (red), longer WBL is associated with smaller hippocampal volume. This association was not significant for more-fit individuals (blue). Error bars depict standard error. (b) Across fitness groups, greater sleep consolidation (WSTP) is associated with increased hippocampal volume (controlling for mean-centered age). X and Y axes reflect the standardized residual values for sleep consolidation (WSTP) and hippocampal volume, respectively. Dotted lines indicate the 95% confidence interval. Figure 3. Relations between sleep, fitness, and hippocampal volume in older adults. (a) Cardiores-piratory fitness (HRRM) moderates the adverse association between wake bout length (WBL) andhippocampal volume. Bars depict standardized regression coefficients (β) for the sleep × fitnessinteraction (controlli g for m an-centered age) for mor -fit (blue) and less-fit (red) individuals. Amongless-fit individuals (red), longer WBL is associated with smaller hippoc mpal volume. This associa-tion was not sig ificant for more-fit individuals (blue). Error bars depict standard error. (b) Acrossfitness groups, greater sleep consolidation (WSTP) is associated with increased hippocampal vol-ume (controlling for mean-centered age). X and Y axes reflect the standardized residual values forsleep consolidation (WSTP) and hippocampal volume, respectively. Dotted lines indicate the 95%confidence interval.3.3. Greater Sleep Consolidation Was Associated with Larger Hippocampal VolumeAcross the fitness groups, a greater sleep consolidation (WSTP) was associated witha larger hippocampus (β = 0.52, 95% CI = 0.16, 0.89, FDR-corrected p = 0.037) (Figure 3b).Simple bivariate sleep–brain relations were not evident for sleep duration (TST) (FDR-corrected ps ≥ 0.423) or amygdala volume (ps ≥ 0.064) (Table 2).3.4. Cardiorespiratory Fitness Moderated Relations between Sleep Duration and Global CognitionRegression analyses indicated that variations in cardiorespiratory fitness (HRRM) alsomoderated relations between sleep duration (TST) and global cognition (MMSE) (β =−1.07,95% CI = −1.70, −0.43, FDR-corrected p = 0.011). As shown in Figure 4, stratified analysesdemonstrated that a longer TST was associated with enhanced cognition among more-fitindividuals (β = 1.23, 95% CI = 0.45, 2.01, p = 0.005) but not less-fit individuals (β = 0.03, 95%CI = −0.44, 0.50, p = 0.891). As shown in Table 2, the sleep × fitness interaction effects werenot evident for the remaining sleep metrics (WBL and WSTP) (FDR-corrected ps ≥ 0.191).Brain Sci. 2022, 12, 1360 9 of 15Brain Sci. 2022, 12, x FOR PEER REVIEW 9 of 15 Table 2. Sleep × fitness (HRRM) interaction for regional brain volume and global cognition. Neurocognitive Outcomes Parameter Hippocampal Volume Amygdala Volume Global Cognition (MMSE) β (95% CI) β (95% CI) β (95% CI) WBL --- −0.09 (−0.50, 0.32) −0.40 (−0.73, −0.07) * WBL × Fitness −1.36 (−2.45, −0.28) * −0.74 (-1.98, 0.49) 0.51 (−0.46, 1.48) WSTP 0.52 (0.16, 0.89) * 0.31 (−0.10, 0.72) 0.33 (−0.06, 0.73) WSTP × Fitness 0.60 (−0.12, 1.33) 0.23 (−0.61, 1.08) −0.44 (−1.17, 0.29) TST 0.11 (−0.33, 0.55) 0.40 (−0.03, 0.82) --- TST × Fitness −0.15 (−1.07, 0.77) −0.31 (−1.19, 0.56) −1.07 (−1.70, −0.43) * Note. Significant results are in bold and indicated by the following: * p < 0.05. Main effects were not evaluated for models demonstrating a significant interaction, denoted by the dashed line (---). Abbreviations. TST—total sleep time, WBL—average wake bout length, WSTP—wake-to-sleep tran-sition probability, HRRM—modified heart rate recovery, MMSE—Mini-Mental State Exam, β—standardized beta coefficient, CI—confidence interval. 3.3. Greater Sleep Consolidation Was Associated with Larger Hippocampal Volume Across the fitness groups, a greater sleep consolidation (WSTP) was associated with a larger hippocampus (β = 0.52, 95% CI = 0.16, 0.89, FDR-corrected p = 0.037) (Figure 3b). Simple bivariate sleep–brain relations were not evident for sleep duration (TST) (FDR-corrected ps ≥ 0.423) or amygdala volume (ps ≥ 0.064) (Table 2). 3.4. Cardiorespiratory Fitness Moderated Relations between Sleep Duration and Global Cognition Regression analyses indicated that variations in cardiorespiratory fitness (HRRM) also moderated relations between sleep duration (TST) and global cognition (MMSE) (β = -1.07, 95% CI = −1.70, −0.43, FDR-corrected p = 0.011). As shown in Figure 4, stratified anal-yses demonstrated that a longer TST was associated with enhanced cognition among more-fit individuals (β = 1.23, 95% CI = 0.45, 2.01, p = 0.005) but not less-fit individuals (β = 0.03, 95% CI = −0.44, 0.50, p = 0.891). As shown in Table 2, the sleep × fitness interaction effects were not evident for the remaining sleep metrics (WBL and WSTP) (FDR-corrected ps ≥ 0.191). Figure 4. Relations between sleep, fitness, and global cognition in older adults. Cardiorespiratoryfitness (HRRM) moderates the positive association between sleep duration and global cognition. Barsdepict standardized regression coefficients (β) for the sleep × fitness interaction (controlling formean-centered age) for more-fit (blue) and less-fit (red) individuals. Among more-fit individuals (blue),longer sleep duration is associated with better global cognition. This association was not significantfor less-fit individuals (red). Error bars depict standard error.3.5. Longer Wake Bout Length Was Associated with Lower Global CognitionAcross the fitness groups, a longer wake bout length (WBL) was associated withlower MMSE scores (β = −0.40, 95% CI = −0.73, −0.07, FDR-corrected p = 0.042). Simplebivariate sleep–cognition associations were not evident for sleep consolidation (WSTP)(FDR-corrected ps ≥ 0.107) (Table 2).4. DiscussionOlder adults with sleep problems are at increased risk of cognitive decline and demen-tia. Chronic aerobic exercise training, known to improve and maintain cardiorespiratoryfitness, represents a feasible intervention to improve both sleep and brain health [54]. In thisstudy of physically active, cognitively normal adults aged 55 and older, we addressed thepossible moderating effects of cardiorespiratory fitness on the relationships between sleepand regional brain volume and sleep and global cognitive performance in older adults. Ourfindings revealed that a longer WBL was associated with smaller hippocampal volumeamong those with lower (slower HRRM) but not higher fitness (faster HRRM). Our resultsalso demonstrated that higher fitness amplifies the benefits of longer sleep duration onglobal cognitive performance, suggesting that higher levels of cardiorespiratory fitness maysafeguard the hippocampus and cognition from sleep-related changes in older persons,among whom poor sleep is a common complaint. Further, regardless of fitness, individualswith more consolidated sleep (higher WSTP) exhibited larger hippocampal volume, andindividuals with a longer wake bout length (longer WBL) had poorer global cognitiveperformance, providing additional evidence for the neurocognitive consequences of poorsleep later in life.Our findings linking sleep disturbance with smaller hippocampal volume are broadlyconsistent with those from the Rush Memory and Aging Project study, which found thatgreater sleep fragmentation—measured by actigraphy—was associated with smaller totalcortical gray matter volume and smaller regional gray matter volume in the bilateralorbitofrontal cortices and inferior frontal gyri pars orbitalis [6]. A second longitudinalinvestigation of data in the Baltimore Longitudinal Study of Aging found that individualswith <7 h of self-reported sleep duration had higher rates of subsequent thinning in theleft superior temporal, inferior and middle frontal cortices, and right superior frontalcortex compared to those reporting 7 h of sleep, whereas those with >7 h had higherrates of thinning in the left superior and middle frontal cortices [7], suggesting that bothshorter and longer sleep durations may have negative neurocognitive consequences in olderpopulations. Our smaller sample size and limited range of sleep duration did not permitBrain Sci. 2022, 12, 1360 10 of 15us to adequately examine its potential nonlinear (inverted U-shaped) associations withhippocampal volume or global cognition. Notwithstanding, our findings are consistentwith the literature that suggests poor sleep quality may play an integral role in age-relatedneurocognitive degeneration [3,5,23]. The results from the current study revealed sleep-related differences in hippocampal volume based on hypothesis-driven analyses thatwere selected a priori based on the well-established neurotrophic effects of exercise on thehippocampus. We also demonstrated that these effects were specific to the hippocampusand did not generalize to the amygdala, another medial temporal lobe structure; we didnot interrogate the cortical gray matter.Our results regarding the fitness-related preservation of hippocampal volume inthe context of sleep disturbance are compelling as several prior studies have identifieda salubrious impact of exercise and/or improvements in fitness on brain volume andstructural integrity in later life [13,25,26,28–30]. One such study found that higher aerobicfitness was associated with larger hippocampal volume [24], whereas another study foundthat higher fitness was linked to reduced age-related brain atrophy in the frontal, parietal,and temporal cortices [27]; and a 12-week exercise intervention in a sample comprisedof both older adults with normal cognition (n = 16) and individuals with mild cognitiveimpairment (n = 14) demonstrated that increased cardiorespiratory fitness was associatedwith greater cortical thickness after exercise training in the bilateral insula, precuneus,posterior cingulate, precentral gyri, and inferior and superior frontal cortices [55]. However,the answer to the question of how greater fitness would protect brain tissue volume fromthe deleterious effects of poor sleep remains unclear. Our study also does not addressthe possibility that better sleep leads to an opportunity to achieve higher fitness [56]. Toour knowledge, however, only two other studies have examined the interactive effects ofsleep and fitness on the markers of neurodegeneration. Using data from 74 older adults inthe Wisconsin Registry of Alzheimer’s Prevention, an interaction was reported betweenself-reported sleep quality—measured by the Medical Outcomes Study Sleep Scale—andaerobic fitness on CSF levels of total tau (t-tau), phosphorylated tau (p-tau), and the p-tau/β-amyloid42 ratio, in which poor sleep was associated with greater neurodegenerationamong older individuals with lower but not higher cardiorespiratory fitness [21]. The otherstudy, which was conducted using data from the Wisconsin Sleep Cohort (and currentlyonly published in abstract form), demonstrated an adverse relationship between sleepapnea severity and total gray matter volume among less-fit (but not more-fit) cognitivelynormal older individuals [8]. Although our study was different in both measurement anddesign, our results demonstrating the modifying effects of HRRM on hippocampal volumein disturbed sleepers are congruent with the findings from this report.4.1. Potential MechanismsThe neurophysiological mechanisms related to the association between sleep and brainvolume may also help account for the neuroprotective effects of higher cardiorespiratoryfitness in the face of poor sleep including growth factors, pro-inflammatory cytokines,and clearance of metabolic waste. For example, insomnia and abnormal sleep durationhave been associated with lower circulating levels of BDNF [57], which protects againstage-related brain atrophy [58], whereas both endurance exercise training and higher car-diorespiratory fitness have been shown to increase levels of BDNF [59]. Similarly, a shortersleep duration and sleep fragmentation have been tied to pro-inflammatory cytokines,such as C-reactive protein (CRP) and interleukin-6 (IL-6) [60], which have been inverselyassociated with hippocampal volume [61], whereas aerobic exercise training and fitnessimprovements have been shown to reduce levels of CRP and IL-6 [62]. Additionally, theglymphatic system, which is more active during deep non-rapid eye movement (NREM)sleep, facilitates the clearance of metabolic waste [63] including both amyloid-β [64] andtau protein [65]. It is also possible that sleep disturbances (i.e., longer WBL) result inlower levels of slow-wave activity during NREM sleep and may thereby inhibit glym-phatic function, thus hindering the clearance of neurotoxic substances (e.g., β-amyloid,Brain Sci. 2022, 12, 1360 11 of 15tau, α-synuclein) and ultimately promoting neurodegeneration. Although more research iswarranted, recent evidence indicates that aerobic exercise training improves sleep [66] andmay even enhance glymphatic system function [67], highlighting the pleiotropic natureof exercise and yet another pathway by which exercise training may improve cognitionand slow the progression of neurodegenerative disease. Conversely, it is also plausible thatgood sleep increases next-day physical activity [56] and/or protects against the negativeeffects of low fitness; both of which underscore the need for future studies in this area.4.2. Future ChallengesThe strengths of this study include the sample of physically active, cognitively normalolder adults, who completed wrist actigraphy (~8 days), a single bout of moderate-intensityexercise from which HRRM was quantified, and an MRI scan, which allowed us to evaluatethe influence of objectively measured sleep and HRRM on regional brain volume. Further,by combining data from repeated MRI scans, we improved image reconstruction and theidentification of tissue-specific and regional boundaries. Despite these strengths, the presentstudy is not without limitations. Although we used wrist actigraphy to measure sleep, wehad no objective indices of sleep stages or sleep-disordered breathing, which limited thescope of our sleep analyses. Similarly, although we used HRRM as a proxy for aerobic fitness,a more direct measure of fitness (e.g., maximal or sub-maximal graded exercise tests) shouldbe used in future studies. Nevertheless, a faster HRRM (higher fitness) was associated withperforming exercise at a lower percentage of maximal capacity, indicating that the overallmetabolic and cardiorespiratory strain associated with ‘Hard’ exercise was less in this group,and could thus be performed at a relatively lower percentage of maximal capacity (exerciseintensity as a percentage of HRReserve). Such findings, in association with the establishedliterature on HR return towards baseline after exercise [42–47], suggest that HRRM providesa reasonable proxy for cardiorespiratory fitness. Due to the rigor of our eligibility criteria,future studies using larger, more diverse samples—including separate studies focused onolder adults with cognitive impairments—would enhance the generalizability of the results.Because of our cross-sectional study design and the fact that we manipulated neitherfitness nor sleep, we cannot determine the causal directionality of or the driving factorsunderlying these relationships. Thus, another crucial direction for future research wouldbe to manipulate sleep (e.g., via acute, partial, and full deprivation and sleep-enhancementor extension sessions) and physical (in)activity (e.g., via exercise sessions or periods ofsedentary behavior/detraining) to clarify their mechanistic importance in the context ofbrain health among older adults. Finally, future larger-scale studies should (1) evaluatethe specific sub-regions of the hippocampus using similar experimental procedures todetermine whether distinct hippocampal regions (anterior/head, body, posterior/tail)exhibit a differential response to poor sleep; and (2) seek to determine whether the potentialprotective effects of fitness on sleep-related health outcomes differ by biological sex.5. ConclusionsWe found that cardiorespiratory fitness (HRRM) moderated the associations of sleepwith hippocampal volume and global cognition. Specifically, a longer wake bout length(longer WBL) was linked with smaller hippocampal volume among individuals withlower but not higher fitness, and a longer sleep duration was associated with better globalcognitive performance among individuals with higher but not lower fitness. Additionally,regardless of fitness level, our results indicated that more consolidated sleep (WTSP) waslinked to larger hippocampal volume and that more disrupted sleep (longer WBL) wasassociated with poorer global cognition. Overall, these findings suggest that sleep mayplay an important role in maintaining the structural integrity of brain regions critical tolearning and memory and that higher levels of fitness may protect against the impacts ofdisrupted sleep in old age, setting the stage for scalable interventions.Brain Sci. 2022, 12, 1360 12 of 15Author Contributions: J.C.S., A.J.A. and T.L.-A. developed the study idea. A.J.A. and J.W. managedparticipant recruitment and data collection. A.J.A., L.R.W. and J.W. collected data. L.R.W. oversawdata management. C.C.N. and A.J.A. scored actigraphy data. V.Z. helped develop and implementthe wake-to-sleep transition probability variable. J.W. processed and analyzed the imaging data.A.J.A., J.C.S., A.P.S., A.J.S. and V.Z. interpreted the data. J.C.S., A.J.A., A.P.S. and A.J.S. collectivelydeveloped the analytic strategy. A.J.A. drafted the paper and J.W., L.R.W., C.C.N., V.Z., A.P.S., T.L.-A.,A.J.S. and J.C.S. edited the paper. A.J.A. created the figures and tables. All authors have read andagreed to the published version of the manuscript.Funding: This study was partially supported by the National Institutes of Health (T32-AG027668,R01-AG050507, R01-MH125370, R01-MH121409, R01-MH107444, R01-MH131264), the AmericanAcademy of Sleep Medicine Foundation (#223-BS-19), and the Department of Kinesiology at theUniversity of Maryland, College Park. The content is solely the responsibility of the authors and doesnot necessarily represent the official views of the National Institutes of Health.Institutional Review Board Statement: This study was approved by the Institutional Review Boardof the University of Maryland in accordance with the Helsinki Declaration.Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.Data Availability Statement: The data reported in this paper are available upon request from anyqualified investigator for reanalysis and replication of results.Acknowledgments: The authors would like to thank the participants for their time and effort duringthis study. The authors would also like to thank Junrui Di for proposing the wake-to-sleep transitionprobability variable and for inspiring the use of this variable in the current analyses. An abstractdescribing this study was presented as a poster presentation at the 2019 Alzheimer’s AssociationInternational Conference, Los Angeles, CA.Conflicts of Interest: A.J.A. contributed to this article as a graduate student at the University ofMaryland and as an employee of the Johns Hopkins University. The views expressed are his own anddo not necessarily represent the views of the NIH or United States Government. L.R.W. is a consultantto Booze Allen. A.P.S. received payment for serving as a consultant for Merck and received honorariafrom Springer Nature Switzerland AG for Guest Editing Special Issues of Current Sleep MedicineReports. T.L.A. is a Canada Research Chair (Tier 2) in Physical Activity, Mobility, and CognitiveNeuroscience.References1. Lowsky, D.J.; Olshansky, S.J.; Bhattacharya, J.; Goldman, D.P. Heterogeneity in healthy aging. J. Gerontol. Ser. A Biomed. Sci. Med.Sci. 2014, 69, 640–649. [CrossRef] [PubMed]2. Alfini, A.J.; Tzuang, M.; Owusu, J.T.; Spira, A.P. Later-life sleep, cognition, and neuroimaging research: An update for 2020. Curr.Opin. Behav. Sci. 2020, 33, 72–77. [CrossRef]3. Siddarth, P.; Thana-Udom, K.; Ojha, R.; Merrill, D.; Dzierzewski, J.M.; Miller, K.; Small, G.W.; Ercoli, L. Sleep quality, neurocogni-tive performance, and memory self-appraisal in middle-aged and older adults with memory complaints. Int. Psychogeriatr. 2021,33, 703–713. [CrossRef] [PubMed]4. McSorley, V.E.; Bin, Y.S.; Lauderdale, D.S. Associations of sleep characteristics with cognitive function and decline among olderadults. Am. J. Epidemiol. 2019, 188, 1066–1075. [CrossRef]5. Qian, D.; Wong, J. Sleep disturbances, sleep-disordered breathing, and cognitive impairment in older adults: Perioperativeimplications. Int. Anesthesiol. Clin. 2022, 60, 20–26. [CrossRef] [PubMed]6. Lim, A.S.P.; Fleischman, D.A.; Dawe, R.J.; Yu, L.; Arfanakis, K.; Buchman, A.S.; Bennett, D.A. Regional neocortical gray matterstructure and sleep fragmentation in older adults. Sleep 2016, 39, 227–235. [CrossRef] [PubMed]7. Spira, A.P.; Gonzalez, C.E.; Venkatraman, V.K.; Wu, M.N.; Pacheco, J.; Simonsick, E.M.; Ferrucci, L.; Resnick, S.M. Sleep durationand subsequent cortical thinning in cognitively normal older adults. Sleep 2016, 39, 1121–1128. [CrossRef]8. Edmunds, K.; Hagen, E.; Gaitán, J.; Barnet, J.; Driscoll, I.; Lose, S.; Motovylyak, A.; Okonkwo, O.; Peppard, P. 244 Physical fitnessattenuates the deleterious association of sleep apnea with gray matter volume in the Wisconsin Sleep Cohort Study. Sleep 2021, 44,A98. [CrossRef]9. Bherer, L.; Erickson, K.I.; Liu-Ambrose, T. Physical exercise and brain functions in older adults. J. Aging Res. 2013, 2013, 197326.[CrossRef]10. Yogev-Seligmann, G.; Eisenstein, T.; Ash, E.; Giladi, N.; Sharon, H.; Nachman, S.; Bregman, N.; Kodesh, E.; Hendler, T.; Lerner, Y.Neurocognitive plasticity is associated with cardiorespiratory fitness following physical exercise in older adults with amnesticmild cognitive impairment. J. Alzheimers Dis. 2021, 81, 91–112. [CrossRef] [PubMed]Brain Sci. 2022, 12, 1360 13 of 1511. Markovic, S.J.; Fitzgerald, M.; Peiffer, J.J.; Scott, B.R.; Rainey-Smith, S.R.; Sohrabi, H.R.; Brown, B.M. The impact of exercise, sleep,and diet on neurocognitive recovery from mild traumatic brain injury in older adults: A narrative review. Ageing Res. Rev. 2021,68, 101322. [CrossRef]12. Rogers, M.A.; King, D.S.; Hagberg, J.M.; Ehsani, A.A.; Holloszy, J.O. Effect of 10 days of physical inactivity on glucose tolerancein master athletes. J. Appl. Physiol. 1990, 68, 1833–1837. [CrossRef] [PubMed]13. Tarumi, T.; Tomoto, T.; Repshas, J.; Wang, C.; Hynan, L.S.; Cullum, C.M.; Zhu, D.C.; Zhang, R. Midlife aerobic exercise and brainstructural integrity: Associations with age and cardiorespiratory fitness. Neuroimage 2021, 225, 117512. [CrossRef] [PubMed]14. Etnier, J.L.; Salazar, W.; Landers, D.M.; Petruzzello, S.J.; Han, M.; Nowell, P. The influence of physical fitness and exercise uponcognitive functioning: A meta-analysis. J. Sport Exerc. Psychol. 1997, 19, 249–277. [CrossRef]15. Hewston, P.; Kennedy, C.C.; Borhan, S.; Merom, D.; Santaguida, P.; Ioannidis, G.; Marr, S.; Santesso, N.; Thabane, L.; Bray, S.Effects of dance on cognitive function in older adults: A systematic review and meta-analysis. Age Ageing 2021, 50, 1084–1092.[CrossRef] [PubMed]16. Coelho-Junior, H.; Marzetti, E.; Calvani, R.; Picca, A.; Arai, H.; Uchida, M. Resistance training improves cognitive functionin older adults with different cognitive status: A systematic review and Meta-analysis. Aging Ment. Health 2022, 26, 213–224.[CrossRef]17. Tseng, B.Y.; Uh, J.; Rossetti, H.C.; Cullum, C.M.; Diaz-Arrastia, R.F.; Levine, B.D.; Lu, H.; Zhang, R. Masters athletes exhibit largerregional brain volume and better cognitive performance than sedentary older adults. J. Magn. Reson. Imaging 2013, 38, 1169–1176.[CrossRef] [PubMed]18. Saillant, K.; Intzandt, B.; Bérubé, B.; Sanami, S.; Gauthier, C.; Bherer, L. Mastering the relationship between the body and thebrain? The case of a female master athlete. Exp. Aging Res. 2022, 1–17. [CrossRef] [PubMed]19. Thomas, B.P.; Yezhuvath, U.S.; Tseng, B.Y.; Liu, P.; Levine, B.D.; Zhang, R.; Lu, H. Life-long aerobic exercise preserved baselinecerebral blood flow but reduced vascular reactivity to CO2. J. Magn. Reson. Imaging 2013, 38, 1177–1183. [CrossRef] [PubMed]20. Won, J.; Alfini, A.J.; Weiss, L.R.; Nyhuis, C.C.; Spira, A.P.; Callow, D.D.; Carson Smith, J. Caudate volume mediates the interactionbetween total sleep time and executive function after acute exercise in healthy older adults. Brain Plast. 2019, 5, 69–82. [CrossRef][PubMed]21. Law, L.L.; Sprecher, K.E.; Dougherty, R.J.; Edwards, D.F.; Koscik, R.L.; Gallagher, C.L.; Carlsson, C.M.; Zetterberg, H.; Blennow, K.;Asthana, S. Cardiorespiratory fitness modifies influence of sleep problems on cerebrospinal fluid biomarkers in an at-risk cohort.J. Alzheimers Dis. 2019, 69, 111–121. [CrossRef] [PubMed]22. Joo, E.Y.; Kim, H.; Suh, S.; Hong, S.B. Hippocampal substructural vulnerability to sleep disturbance and cognitive impairmentin patients with chronic primary insomnia: Magnetic resonance imaging morphometry. Sleep 2014, 37, 1189–1198. [CrossRef][PubMed]23. Chen, L.; Shao, Z.; Lei, W.; Xu, Y.; Liu, S.; Zhang, M.; Wang, S.; Wen, X.; Yu, D.; Yuan, K. Abnormal hippocampal substructurevolume in insomnia disorder. Brain Imaging Behav. 2022, 16, 672–679. [CrossRef] [PubMed]24. Erickson, K.I.; Voss, M.W.; Prakash, R.S.; Basak, C.; Szabo, A.; Chaddock, L.; Kim, J.S.; Heo, S.; Alves, H.; White, S.M.; et al.Exercise training increases size of hippocampus and improves memory. Proc. Natl. Acad. Sci. USA 2011, 108, 3017–3022.[CrossRef] [PubMed]25. Firth, J.; Stubbs, B.; Vancampfort, D.; Schuch, F.; Lagopoulos, J.; Rosenbaum, S.; Ward, P.B. Effect of aerobic exercise onhippocampal volume in humans: A systematic review and meta-analysis. Neuroimage 2018, 166, 230–238. [CrossRef] [PubMed]26. Feter, N.; Penny, J.C.; Freitas, M.P.; Rombaldi, A.J. Effect of physical exercise on hippocampal volume in adults: Systematic reviewand meta-analysis. Sci. Sports 2018, 33, 327–338. [CrossRef]27. Colcombe, S.J.; Erickson, K.I.; Scalf, P.E.; Kim, J.S.; Prakash, R.; McAuley, E.; Elavsky, S.; Marquez, D.X.; Hu, L.; Kramer, A.F.Aerobic exercise training increases brain volume in aging humans. J. Gerontol. Ser. A: Biol. Sci. Med. Sci. 2006, 61, 1166–1170.[CrossRef]28. d’Arbeloff, T. Cardiovascular fitness and structural brain integrity: An update on current evidence. GeroScience 2020, 42, 1285–1306.[CrossRef]29. d’Arbeloff, T.; Cooke, M.; Knodt, A.R.; Sison, M.; Melzer, T.R.; Ireland, D.; Poulton, R.; Ramrakha, S.; Moffitt, T.E.; Caspi, A. Iscardiovascular fitness associated with structural brain integrity in midlife? Evidence from a population-representative birthcohort study. Aging 2020, 12, 20888. [CrossRef]30. Fraser, M.A.; Walsh, E.I.; Shaw, M.E.; Anstey, K.J.; Cherbuin, N. Longitudinal effects of physical activity change on hippocampalvolumes over up to 12 years in middle and older age community-dwelling individuals. Cerebral Cortex 2022, 32, 2705–2716.[CrossRef] [PubMed]31. Alfini, A.J.; Weiss, L.R.; Leitner, B.P.; Smith, T.J.; Hagberg, J.M.; Smith, J.C. Hippocampal and cerebral blood flow after exercisecessation in master athletes. Front. Aging Neurosci. 2016, 8, 184. [CrossRef] [PubMed]32. Folstein, M.F.; Folstein, S.E.; McHugh, P.R. “Mini-mental state”. A practical method for grading the cognitive state of patients forthe clinician. J. Psychiatr. Res. 1975, 12, 189–198. [CrossRef]33. Alfini, A.J.; Won, J.; Weiss, L.R.; Nyhuis, C.C.; Shackman, A.J.; Spira, A.P.; Smith, J.C. Impact of exercise on older adults’ mood ismoderated by sleep and mediated by altered brain connectivity. Soc. Cogn. Affect. Neurosci. 2020, 15, 1238–1251. [CrossRef]34. Ancoli-Israel, S.; Martin, J.L.; Blackwell, T.; Buenaver, L.; Liu, L.; Meltzer, L.J.; Sadeh, A.; Spira, A.P.; Taylor, D.J. The SBSM guideto actigraphy monitoring: Clinical and research applications. Behav. Sleep Med. 2015, 13 (Suppl. S1), S4–S38. [CrossRef] [PubMed]Brain Sci. 2022, 12, 1360 14 of 1535. Schrack, J.A.; Kuo, P.-L.; Wanigatunga, A.A.; Di, J.; Simonsick, E.M.; Spira, A.P.; Ferrucci, L.; Zipunnikov, V. Active-to-sedentarybehavior transitions, fatigability, and physical functioning in older adults. J. Gerontol. Ser. A 2018, 74, 560–567. [CrossRef][PubMed]36. Borg, G. Psychophysical scaling with applications in physical work and the perception of exertion. Scand. J. Work. Environ. Health1990, 16 (Suppl. S1), 55–58. [CrossRef]37. Sage, M.; Middleton, L.E.; Tang, A.; Sibley, K.M.; Brooks, D.; McIlroy, W. Validity of rating of perceived exertion ranges inindividuals in the subacute stage of stroke recovery. Top. Stroke Rehabil. 2013, 20, 519–527. [CrossRef]38. Dishman, R.K. Prescribing exercise intensity for healthy adults using perceived exertion. Med. Sci. Sports Exerc. 1994, 26,1087–1094. [CrossRef]39. Tanaka, H.; Monahan, K.D.; Seals, D.R. Age-predicted maximal heart rate revisited. J. Am. Coll. Cardiol. 2001, 37, 153–156.[CrossRef]40. Nanas, S.; Anastasiou-Nana, M.; Dimopoulos, S.; Sakellariou, D.; Alexopoulos, G.; Kapsimalakou, S.; Papazoglou, P.; Tsolakis, E.;Papazachou, O.; Roussos, C.; et al. Early heart rate recovery after exercise predicts mortality in patients with chronic heart failure.Cardiovasc. Dis. 2006, 110, 393–400. [CrossRef]41. Vivekananthan, D.P.; Blackstone, E.H.; Pothier, C.E.; Lauer, M.S. Heart rate recovery after exercise is apredictor of mortality,independent of the angiographic severity of coronary disease. J. Am. Coll. Cardiol. 2003, 42, 831–838. [CrossRef]42. Hagberg, J.M.; Hickson, R.C.; McLane, J.A.; Ehsani, A.A.; Winder, W.W. Disappearance of norepinephrine from the circulationfollowing strenuous exercise. J. Appl. Physiol. 1979, 47, 1311–1314. [CrossRef] [PubMed]43. Hagberg, J.M.; Hickson, R.C.; Ehsani, A.A.; Holloszy, J.O. Faster adjustment to and recovery from submaximal exercise in thetrained state. J. Appl. Physiol. 1980, 48, 218–224. [CrossRef]44. Imai, K.; Sato, H.; Hori, M.; Kusuoka, H.; Ozaki, H.; Yokoyama, H.; Takeda, H.; Inoue, M.; Kamada, T. Vagally mediated heartrate recovery after exercise is accelerated in athletes but blunted in patients with chronic heart failure. J. Am. Coll. Cardiol. 1994,24, 1529–1535. [CrossRef]45. Rosenwinkel, E.T.; Bloomfield, D.M.; Arwady, M.A.; Goldsmith, R.L. Exercise and autonomic function in health and cardiovasculardisease. Cardiol. Clin. 2001, 19, 369–387. [CrossRef]46. Fu, Q.; Levine, B.D. Exercise and the autonomic nervous system. Handb. Clin. Neurol. 2013, 117, 147–160.47. Brown, S.P.; Li, H.; Chitwood, L.F.; Anderson, E.R.; Boatwright, D. Blood pressure, hemodynamic, and thermal responses aftercycling exercise. J. Appl. Physiol. 1993, 75, 240–245. [CrossRef]48. Berryman, N.; Bherer, L.; Nadeau, S.; Lauzière, S.; Lehr, L.; Bobeuf, F.; Kergoat, M.J.; Vu, T.T.M.; Bosquet, L. Executive functions,physical fitness and mobility in well-functioning older adults. Exp. Gerontol. 2013, 48, 1402–1409. [CrossRef]49. Mekari, S.; Dupuy, O.; Martins, R.; Evans, K.; Kimmerly, D.S.; Fraser, S.; Neyedli, H.F. The effects of cardiorespiratory fitness onexecutive function and prefrontal oxygenation in older adults. GeroScience 2019, 41, 681–690. [CrossRef]50. Fischl, B. FreeSurfer. Neuroimage 2012, 62, 774–781. [CrossRef] [PubMed]51. Rorden, C.; Brett, M. Stereotaxic display of brain lesions. Behav. Neurol. 2000, 12, 191–200. [CrossRef] [PubMed]52. Tukey, J.W. Exploratory Data Analysis; Addison-Wesley: Boston, MA, USA, 1997.53. Benjamini, Y.; Krieger, A.M.; Yekutieli, D. Adaptive linear step-up procedures that control the false discovery rate. Biometrika2006, 93, 491–507. [CrossRef]54. Landry, G.J.; Liu-Ambrose, T. Buying time: A rationale for examining the use of circadian rhythm and sleep interventions todelay progression of mild cognitive impairment to Alzheimer’s disease. Front. Aging Neurosci. 2014, 6, 325. [CrossRef] [PubMed]55. Reiter, K.; Nielson, K.A.; Smith, T.J.; Weiss, L.R.; Alfini, A.J.; Smith, J.C. Improved cardiorespiratory fitness is associated withincreased cortical thickness in mild cognitive impairment. J. Int. Neuropsychol. Soc. 2015, 21, 757–767. [CrossRef] [PubMed]56. Dzierzewski, J.M.; Buman, M.P.; Giacobbi, P.R., Jr.; Roberts, B.L.; Aiken-Morgan, A.T.; Marsiske, M.; McCrae, C.S. Exerciseand sleep in community-dwelling older adults: Evidence for a reciprocal relationship. J. Sleep Res. 2014, 23, 61–68. [CrossRef][PubMed]57. Furihata, R.; Saitoh, K.; Otsuki, R.; Murata, S.; Suzuki, M.; Jike, M.; Kaneita, Y.; Ohida, T.; Uchiyama, M. Association betweenreduced serum BDNF levels and insomnia with short sleep duration among female hospital nurses. Sleep Med. 2020, 68, 167–172.[CrossRef]58. Erickson, K.I.; Miller, D.L.; Roecklein, K.A. The aging hippocampus: Interactions between exercise, depression, and BDNF.Neurosci. 2012, 18, 82–97. [CrossRef]59. Dinoff, A.; Herrmann, N.; Swardfager, W.; Liu, C.S.; Sherman, C.; Chan, S.; Lanctôt, K.L. The effect of exercise training onresting concentrations of peripheral brain-derived neurotrophic factor (BDNF): A meta-analysis. PLoS ONE 2016, 11, e0163037.[CrossRef]60. Irwin, M.R.; Olmstead, R.; Carroll, J.E. Sleep disturbance, sleep duration, and inflammation: A systematic review and meta-analysis of cohort studies and experimental sleep deprivation. Biol. Psychiatry 2016, 80, 40–52. [CrossRef] [PubMed]61. Schmidt, M.F.; Freeman, K.B.; Windham, B.G.; Griswold, M.E.; Kullo, I.J.; Turner, S.T.; Mosley, T.H., Jr. Associations betweenserum inflammatory markers and hippocampal volume in a community sample. J. Am. Geriatr. Soc. 2016, 64, 1823–1829.[CrossRef]Brain Sci. 2022, 12, 1360 15 of 1562. Kohut, M.L.; McCann, D.A.; Russell, D.W.; Konopka, D.N.; Cunnick, J.E.; Franke, W.D.; Castillo, M.C.; Reighard, A.E.; Vanderah,E. Aerobic exercise, but not flexibility/resistance exercise, reduces serum IL-18, CRP, and IL-6 independent of β-blockers, BMI,and psychosocial factors in older adults. Brain Behav. Immun. 2006, 20, 201–209. [CrossRef]63. Jessen, N.A.; Munk, A.S.F.; Lundgaard, I.; Nedergaard, M. The Glymphatic System: A Beginner’s Guide. Neurochem. Res. 2015,40, 2583–2599. [CrossRef] [PubMed]64. Xie, L.; Kang, H.; Xu, Q.; Chen, M.J.; Liao, Y.; Thiyagarajan, M.; O’Donnell, J.; Christensen, D.J.; Nicholson, C.; Iliff, J.J.; et al. Sleepdrives metabolite clearance from the adult brain. Science 2013, 342, 373–377. [CrossRef] [PubMed]65. Iliff, J.J.; Chen, M.J.; Plog, B.A.; Zeppenfeld, D.M.; Soltero, M.; Yang, L.; Singh, I.; Deane, R.; Nedergaard, M. Impairment ofglymphatic pathway function promotes tau pathology after traumatic brain injury. J. Neurosci. 2014, 34, 16180–16193. [CrossRef]66. King, A.C.; Oman, R.F.; Brassington, G.S.; Bliwise, D.L.; Haskell, W.L. Moderate-intensity exercise and self-rated quality of sleepin older adults. A randomized controlled trial. JAMA 1997, 277, 32–37. [CrossRef] [PubMed]67. He, X.; Liu, D.; Zhang, Q.; Liang, F.; Dai, G.; Zeng, J.; Pei, Z.; Xu, G.; Lan, Y. Voluntary exercise promotes glymphatic clearance ofamyloid beta and reduces the activation of astrocytes and microglia in aged mice. Front. Mol. Neurosci. 2017, 10, 144. [CrossRef]
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Cardiorespiratory Fitness as a Moderator of Sleep-Related Associations with Hippocampal Volume and Cognition
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