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  • 1
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    800 Similarities of Sleep Macrostructure in Cognitively Normal Elderly and Patients with Traumatic Brain Injury
    Oxford University Press (OUP) ; 2021
    In:  Sleep Vol. 44, No. Supplement_2 ( 2021-05-03), p. A311-A312
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    In: Sleep, Oxford University Press (OUP), Vol. 44, No. Supplement_2 ( 2021-05-03), p. A311-A312
    Abstract: The stability of sleep architecture and breathing across nights can depend on factors relating to the integrity of the nervous system. Traumatic brain injury (TBI) represents a sudden-onset dysfunction of the nervous system while normal aging is associated with more gradual changes to the nervous system. While normal aging and history of TBI are both associated with sleep complaints, less is known about the stability of sleep physiology variables in these populations. Therefore, the aims of our study are to determine which sleep variables have greater night-to-night stability in separate populations of individuals with TBI and in cognitively normal older individuals. Methods All volunteers completed 2 consecutive in-laboratory nocturnal polysomnograms (NPSG). The TBI sample (N=35) comprised 71% women and 26% men (average age of 47.3 years). The cognitively normal older sample (N=78) included 74% women and 25% men (average age of 66.4 years). Descriptive statistics and intra-class correlations (ICCs) were calculated for sleep macrostructure variables (total sleep time (TST), sleep efficiency (SE), arousal index (ArI), rapid eye movement (REM), non-REM 1 & 2 (N1, N2), slow-wave sleep (SWS)), and sleep apnea including stage-specific apneas (i.e., AHI4%, AHI3A). Results Among volunteers with TBI, ICCs for sleep architecture variables were: TST (0.68), SE (0.65), ArI (0.92), %SWS (0.77), %REM (0.50), %N1 (0.83), %N2 (0.62). ICC’s for sleep apnea variables were: AHI4% (0.86), AHI3A (0.86), REM AHI4% (0.63), REM AHI3A (0.65). Among cognitively normal older volunteers, ICCs for sleep architecture variables were: TST (0.26), SE (0.29), ArI (0.80), %SWS (0.68), %REM (0.39), %N1 (0.66), %N2 (0.49). ICC’s for sleep apnea variables were: AHI4% (0.91), AHI3A (0.92), REM AHI4% (0.85), REM AHI3A (0.83). All ICCs were statistically significant in both groups, except for %N1 among cognitively normal older volunteers. Conclusion In both populations, ICC’s for arousal index were greater than for TST or SE. Likewise, ICC’s were higher in %SWS and %N1 than for %N2 or %REM. Breathing variables were more stable than architecture variables. REM-specific breathing variables showed comparatively less consistency, possibly the product of lower ICC’s for %REM sleep versus other sleep stages. Support (if any) 5T32HL129953-04, R01AG056682, R01AG066870, R21AG059179, 1RF1NS115268-01, K24HL109156, P30AG059303, P30AG066512, AASM 231-BS-20, R25HL105444
    Type of Medium: Online Resource
    ISSN: 0161-8105 , 1550-9109
    URL: Article
    DOI: 10.1093/sleep/zsab072.797
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
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  • 2
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    0885 Alzheimer’s disease progression risk in Cognitive Normal Older Adults with Obstructive Sleep Apnea using NIA-AA Research Framework
    Oxford University Press (OUP) ; 2023
    In:  SLEEP Vol. 46, No. Supplement_1 ( 2023-05-29), p. A390-A390
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    In: SLEEP, Oxford University Press (OUP), Vol. 46, No. Supplement_1 ( 2023-05-29), p. A390-A390
    Abstract: We determined risk profiles of strata-specific cognitive-normal (NL) older-adults with obstructive sleep apnea (OSA) characterized by their Aβ, P-Tau & T-tau (ATN) burden, on prospective AD stage-transition Methods Longitudinal study utilizing data from 167 community-dwelling NL older-adults participating in NYU studies on memory, sleep and aging. Subjects had baseline CSF AD-biomarker data and at least two follow-up clinical and neuropsychological data. OSA was defined using AHI4%. Using the NIA-AA Research Framework, data-driven, clinically relevant thresholds for CSF-Aβ42 (≤375pg/ml), P-tau (≥53.7pg/ml) and T-tau (≥367 pg/ml) indicated ATN status respectively. Twenty-four participants with suspected non-AD pathologic change defined as A-T+ were excluded leaving 143 for the analysis. Main outcome was AD stage-transition (i.e., change from Global Deterioration Scale (GDS) 1 or 2 [NL] at baseline to ≥3 [≥ MCI] during follow-up). Logistic mixed-effects models with random intercept and slope were used to assess associations between ATN characterized OSA subjects, and longitudinal AD stage transition, controlling for age-at-baseline, sex, APOE4-status, years-of-education, and their interactions with time. Results Of the 143 participants, 91 (63.8%) were women. The mean (SD) age was 69.6 (7.3) years and follow-up time was 4.73 (3.45) years. Sixteen (11.2%) were OSA+/A+/TN-, and 21 (14.7%) were OSA-/A+/TN-. Ninety-two (64.3%) had normal AD biomarkers (OSA+/A-/T- [n=45] and OSA-/A-/T- [N=47] ). To generate strata-specific risks, subjects were combined into groups: (i) OSA subjects with AD pathologic change OSA+/A+/TN [n=25] consisting of OSA+/A+/TN+ [n=9] plus OSA+/A+/TN- [n=16] (ii) non-OSA subjects with AD pathologic change OSA-/A+/TN [n=26] ) consisting of OSA-/A+/TN+ [n=5] and OSA-/A+/TN- [n=21] Fourteen subjects (9.8%) transitioned from NL to MCI (i.e., OSA+/A+/TN [6/25], OSA-/A+/TN [3/26] , OSA+/A-/TN- [4/45] and OSA-/A-/TN- [3/47] ). OSA+/A+/TN subjects were at higher risk of AD stage-transition relative to OSA-/A-/TN- (β = 1.31, 95%CI, 1.02, 1.62); OSA+/A-/TN- (β = 0.89, 95%CI, 0.42, 1.37); and OSA-/A+/TN subjects, (β = 0.71, 95%CI, 0.38, 1.04); P & lt; .01 for all. OSA+/A-/T- vs. OSA-/A-/T- participants did not show differences in cognitive change over time (β = 0.22, 95%CI, -0.15, 0.39, P =.17). Conclusion Among ATN characterized NL older-adults with OSA, those with evidence of AD pathologic change have the greatest risk of developing AD. Support (if any) AASMBTS#231-BS-20, NIAK23AG068534A, AARG-D- 21-848397, BFFA2022033S
    Type of Medium: Online Resource
    ISSN: 0161-8105 , 1550-9109
    URL: Article
    DOI: 10.1093/sleep/zsad077.0885
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
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  • 3
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    0275 Effect of acutely induced severe OSA on AD plasma biomarkers
    Oxford University Press (OUP) ; 2022
    In:  Sleep Vol. 45, No. Supplement_1 ( 2022-05-25), p. A124-A124
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    In: Sleep, Oxford University Press (OUP), Vol. 45, No. Supplement_1 ( 2022-05-25), p. A124-A124
    Abstract: Obstructive sleep apnea (OSA) has been associated with Alzheimer’s disease (AD) progression but a causal relationship is unclear. We hypothesized that OSA can influence (AD) biomarkers including beta-amyloid (Aβ) and tau, as well as neural filament light chain (NfL). Methods To test this hypothesis, we examined plasma tau, NfL, Aβ42, and Aβ40 in a randomized crossover study of OSA vs. 3-night CPAP withdrawal in 30 subjects with severe OSA adherent to CPAP. We compared the overnight change in evening to morning plasma samples across the untreated night (off) versus CPAP treated night (on). Paired t-tests were used to compare measures across sleep conditions while hierarchical linear regression with difference in the overnight change of each biomarker between conditions were set as dependent variables with age and sex as covariates. Results Of the 30 subjects, mean age was 52 years and 27% were women. As expected, CPAP withdrawal caused sleep disruption and recurrence of underlying OSA. Sleep architecture measures including %N3 (Off: 6.1% [3.7-8.5], On: 15.1% [10.6-19.6] , p & lt;0.001), %REM (Off: 11.8% [8.8-14.7], On: 20.6% [18.3-22.9] , p & lt;0.001), and measures of breathing such as AHI4% (Off: 63/hr [54-72], On: 3/hr [2-4] , p & lt;0.001), SpO2 below 90% (Off: 20 min [14-26], On: 1 min [0-3] , p & lt;0.001), and SpO2 min (Off: 77% [74-80], On: 88% [86-90] , p & lt;0.001) were all significantly different in the untreated versus CPAP treated nights. Compared to CPAP treatment, the overnight change in NfL was increased relative to CPAP withdrawal while the overnight change in Aβ40 was decreased relative to CPAP withdrawal. No change was observed with tau or Aβ42. We found that difference in %N3 between the on- and off-CPAP conditions significantly explained an additional 15.7% of the variance beyond a base model including age and sex alone. No other difference in sleep architecture or apnea severity/hypoxemic burden metric significantly contributed to the variance in overnight change in Aβ40 between conditions, and we identified no significant predictors for variance in overnight change in NfL. Conclusion This study presents some of the first evidence for an effect of acute CPAP withdrawal on neurodegenerative and amyloid plasma biomarkers and implicates a role for N3 sleep in this effect. Support (If Any) AASMF Focus award, NIDDK P30DK072488, R01AG066870, K24HL109156
    Type of Medium: Online Resource
    ISSN: 0161-8105 , 1550-9109
    URL: Article
    DOI: 10.1093/sleep/zsac079.273
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
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  • 4
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    0308 The stability of slow wave sleep and EEG microstructure measures across two consecutive nights of laboratory polysomnography in cognitively normal older adults
    Oxford University Press (OUP) ; 2022
    In:  Sleep Vol. 45, No. Supplement_1 ( 2022-05-25), p. A138-A139
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    In: Sleep, Oxford University Press (OUP), Vol. 45, No. Supplement_1 ( 2022-05-25), p. A138-A139
    Abstract: Healthy and sleep disordered populations show high night-to-night variability of polysomnographic (PSG) macrostructure metrics, however there is evidence of stability in EEG microstructure. In-laboratory PSG is critical to gold standard measures of sleep physiology but multi-night investigations are resource heavy and burdensome to participants. Given the theoretical link between sleep and Alzheimer's disease (AD) pathology (tau and β-amyloid burden), we assessed the night-to-night reliability of sleep macrostructure and EEG microstructure in a group of cognitively normal elderly participating in aging and memory studies. Methods 107 participants (mean = 67±8 yrs., range [54-84 yrs.], 72% female) attended 2 consecutive nights PSG scored according to AASM guidelines for sleep staging, respiratory and leg movement events. Midline EEG (Fz, Cz and Pz referenced to average mastoid signals) were analyzed in 98 participants using an automatic algorithm (DETOKS) for detection of relative slow wave (0.5-4Hz) activity (SWA), NREM2 spindle and K-complexes (KC) densities. Differences between night 1 and 2 for total sleep time (TST), slow wave sleep (SWS), rapid eye movement (REM), stage 2 (%NREM 2), sleep efficiency (SE), apnea hypopnea (AHI) and periodic limb movement (PLM) indices, and EEG microstructure were assessed using t-tests and Wilcoxon rank sum tests where appropriate. Two-way intraclass correlations (ICC) for single unit and absolute agreement were used to determine variability between nights for all measures. Results Night 2 PSGs showed significantly greater TST (6.3 vs 6.8 hours, p & lt;0.001), %REM (17.5 vs 19.7, p & lt;0.001), SE (84.9 vs 87.4%, p & lt;0.02) and SWA (Fz:76.8 vs 78.0%, p & lt;0.01). There were no significant differences between nights for %SWS, %NREM2, AHI, PLMs, spindle and KC densities. ICC and 95% confidence interval estimates were low for TST(0.28), %REM(0.32) and SE(0.32), moderate for %SWS(0.67) and %NREM2(0.59), good for AHI(0.78), SWA(Fz:0.86) and KCs(Fz:0.85), and excellent for PLMs(0.91) and spindles (Pz:0.97). Conclusion SWS, SWA, spindles, KC’s, AHI and PLM indices show good to excellent intra-individual stability across two consecutive nights of PSG. Although there were differences in %REM, SE and SWA, these were numerically small and perhaps functionally or clinically less significant. One night of in-lab PSG is enough to provide reliable estimates of individuals’ SWS, SWA, spindles, KC’s and sleep disorders. Support (If Any) Funding NIH R21AG055002, K24HL109156, R01AG056682, R01AG056531
    Type of Medium: Online Resource
    ISSN: 0161-8105 , 1550-9109
    URL: Article
    DOI: 10.1093/sleep/zsac079.306
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
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  • 5
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    0221 Effect of Acutely Induced OSA During SWS on AD Plasma Biomarkers
    Oxford University Press (OUP) ; 2024
    In:  SLEEP Vol. 47, No. Supplement_1 ( 2024-04-20), p. A95-A95
    Details
    In: SLEEP, Oxford University Press (OUP), Vol. 47, No. Supplement_1 ( 2024-04-20), p. A95-A95
    Abstract: OSA induces both sleep fragmentation and intermittent hypoxemia and has been associated with AD progression. We hypothesized that SWS-specific OSA can influence plasma AD biomarkers. Methods We developed a model of SWS-specific CPAP-withdrawal in subjects with AHI4% ≥ 20/hour to create 3 polysomnologically (PSG)-verified conditions per subject: 1) stable-SWS on CPAP, 2) SWS-fragmentation with intermittent hypoxemia (OSAsws), and 3) SWS-fragmentation with reduced hypoxemia (OSAsws+O2). We examined post-PSG morning plasma Aβ42 and Aβ40 by mass-spectrometry and plasma T-tau, P-tau181, NfL and GFAP by SIMOA in a study of 34 patients. Wilcoxon signed rank and Kruskal Wallace tests were used to compare SWS-specific OSA metrics and plasma measures across PSG conditions. Results In 34 patients (57 years, 32% female) CPAP withdrawal caused sleep disruption and recurrence of underlying OSA such that the OSAsws and OSAsws+O2 conditions caused significant increases in AHI4 and arousals during SWS [CPAP: 0 ±0, OSAsws: 16.1 ±15.7, OSAsws+O2: 16.2 ±12.0 in evts/hr, p & lt; 0.0001], and arousal index during SWS [CPAP: 0.9 ±2.0, OSAsws: 11.7 ±14.3, OSAsws+O2: 11.7 ±15.5 in evts/hr, p & lt; 0.0001] compared to CPAP treatment. Furthermore, the minimum SpO2 desaturation level of OSAsws was lower than OSAsws+O2 (OSAsws: 89.9 ±4.0, OSAsws+O2: 90.1 ±5.9 in % SpO2, p= 0.007). No change was observed between PSG conditions in Aβ42 (CPAP: 22.9 ±6.2, OSAsws: 21.3 ±6.2, OSAsws+O2: 21.2 ±6.8 in pg/mL), Aβ40 (CPAP: 232.3 ±39.9, OSAsws: 233.2 ±89.6, OSAsws+O2: 197.7 ±114.6), the ratio of Aβ42 to Aβ40 (CPAP: 0.10 ±0.01, OSAsws: 0.10 ±0.02, OSAsws+O2: 0.10 ±0.01), T-tau (CPAP: 3.1 ±0.3, OSAsws: 3.3 ±1.1, OSAsws+O2: 2.9 ±0.8 in pg/mL), P-tau181 (CPAP: 1.4 ±1.0, OSAsws: 1.5 ±0.5, OSAsws+O2: 1.6 ±0.9 in pg/mL), NfL (CPAP: 8.7 ±4.4, OSAsws: 6.0 ±3.0, OSAsws+O2: 7.8 ±6.0 in pg/mL), and GFAP (CPAP: 42.9 ±33.2, OSAsws: 30.9 ±25.6, OSAsws+O2: 39.5 ±36.9 in pg/mL). Conclusion Although we were able to recapitulate OSA in SWS, the degree of OSA severity was less than that on subjects’ diagnostic studies, and breathing was normal in N1, N2, and REM sleep, factors which possibly account for the lack of significant differences in biomarkers between conditions. Support (if any) R01AG056682, R01AG066870, RF1AG083975, R01AG080609, R01AG082278, K24HL109156, T32HL160511, K23AG068534, K25HL151912
    Type of Medium: Online Resource
    ISSN: 0161-8105 , 1550-9109
    URL: Article
    DOI: 10.1093/sleep/zsae067.0221
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2024
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  • 6
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    0454 In Cognitively Normal Elderly, Obstructive Sleep Apnea Worsens Spatial Navigational Memory Performance
    Oxford University Press (OUP) ; 2024
    In:  SLEEP Vol. 47, No. Supplement_1 ( 2024-04-20), p. A195-A196
    Details
    In: SLEEP, Oxford University Press (OUP), Vol. 47, No. Supplement_1 ( 2024-04-20), p. A195-A196
    Abstract: Normal sleep has a favorable effect on the consolidation of spatial navigational memory. Previous work suggests normal overnight sleep enhances spatial navigation performance in a hippocampus-dependent manner. Our group has previously demonstrated post-sleep spatial navigational performance may be affected by obstructive sleep apnea (OSA). Here we aim to replicate these findings in a much larger sample size. Methods Using a virtual 3D Maze, 162 subjects (66.8 ± 6.5 years, 88 female) completed spatial navigational encoding and recall across their nocturnal polysomnography visit. Participants were instructed to find the maze exit within a ten-minute period and were given three trials pre- and post-sleep. We compared overnight changes and trial-by trial completion times between participants with and without OSA (AHI4% & gt; 5) using Wilcoxon signed-rank test. Results Of the 162 participants, 76 had OSA (10.5± 16.12 AHI4). There was no statistically significant difference in overnight change in completion time (%) between OSA/non-OSA groups (OSA: 2.5 ± 58.3, non-OSA: 9.9 ± 52.1). However, the average completion time worsened with each successive trial during encoding (pre-sleep) in OSA [T1: 305 ± 18, T2: 328 ± 21, T3: 345 ± 23 seconds] as compared to non-OSA [T1: 369 ± 20, T2: 324 ± 19, T3: 320 ± 20 seconds] . Further, we observed that the average completion time worsened with each successive trial during recall (post-sleep) in the OSA group [T4: 306 ± 20, T5: 296 ± 20, T6: 332 ± 20 seconds] as compared to the non-OSA group [T4: 331 ± 19, T5: 312 ± 20, T6: 277 ± 18 seconds] , such that, in the final post-sleep trial (T6), OSA patients had significantly slower completion times than the those without OSA (p = 0.034). Conclusion Despite no difference in overnight change in completion time between non-OSA and OSA, the pattern of performance during encoding and recall was different; These observations suggest OSA may be creating a working memory deficiency more strongly than an offline processing deficiency. Future analyses evaluating PVT performance in the OSA and non-OSA groups may be instructive to probe whether a different domain of executive is also impacted and its relationship to subjective sleepiness. Support (if any) R01AG066870,R01AG056682,R01HL118624,R01AG056531,R01AG056031,U01OH011852
    Type of Medium: Online Resource
    ISSN: 0161-8105 , 1550-9109
    URL: Article
    DOI: 10.1093/sleep/zsae067.0454
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2024
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  • 7
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    791 Association of Obstructive Sleep Apnea Severity and Novel Plasma Biomarkers of Alzheimer’s Disease Pathology
    Oxford University Press (OUP) ; 2021
    In:  Sleep Vol. 44, No. Supplement_2 ( 2021-05-03), p. A308-A308
    Details
    In: Sleep, Oxford University Press (OUP), Vol. 44, No. Supplement_2 ( 2021-05-03), p. A308-A308
    Abstract: Recent evidence suggests novel plasma Alzheimer’s Disease (AD) pathology biomarkers have high potential for AD risk prediction. We determined whether obstructive sleep apnea (OSA) severity is associated with plasma levels of Aβ40, Aβ42, Aβ42/Aβ40, Tau, tau/Aβ42 and NfL and whether this relationship is dependent of amyloid burden. Methods Cross-sectional analysis of baseline data from 120 community-dwelling, cognitively normal older-adults, selected from ongoing NYU prospective longitudinal studies on memory, sleep and aging. Of the 120 participants, 70 had baseline CSF-Aβ42 (measured using ELISA). OSA-severity was defined using AHI4% criteria. Levels of plasma Aβ40, Aβ42, Tau and NfL were determined using single molecule array technology ultra-sensitive assays. Associations of OSA-severity and plasma AD-biomarker levels (n=120) were assessed using Pearson correlation analysis. The association of OSA-severity and AD plasma biomarkers dependent on CSF-Aβ42 levels (n=70) was assessed using generalized linear models. Analyses were adjusted for age, sex, BMI, race, education and APOE4. Results Of the 120 participants, 80 (67%) were women. Mean (SD) age was 69.1 (7.2) years. Mean (SD) AHI was 14.3/hr. (16.3) {48 (40%) had AHI & lt;5, 30 (25%) had AHI: 5 to ≤ 15, 18 (15%) had AHI: 15 to ≤30, and 22 (18%) had AHI & gt;30}. Independent of amyloid-burden, OSA-severity was associated with higher levels of plasma Aβ40 (r=.21, p-value=.02), plasma Aβ42 (r=.26, p-value=.01), plasma Aβ42/Aβ40 (r=.20, p-value=.05), but not plasma Tau, plasma tau/Aβ42 or plasma NfL. The association of OSA-severity and plasma levels of Tau, Tau/Aβ42 or NfL dependent on CSF-Aβ42 levels revealed significant interactions between CSF-Aβ42 levels and AHI (p-value & lt;.05 for all), with β-estimates suggesting that with combined increases in AHI and decreases in CSF-Aβ42 levels, there were corresponding increases in plasma levels of Tau, plasma Tau/Aβ42 or plasma NfL. The analysis was not powered for generating dichotomized strata specific (i.e. OSA+/Aβ+, OSA+/Aβ-, OSA-/Aβ+ and OSA-/Aβ-) estimates. Conclusion In this sample of cognitively-normal older- adults, OSA-severity was associated with levels of plasma Aβ40, Aβ42, Aβ42/Aβ40 and showed a synergistic effect with CSF Aβ42 on plasma levels of tau and NfL. Larger cohorts are necessary to delineate mechanisms and examine for OSA/Aβ strata-specific estimates. Support (if any) NIH/NIA/NHLBI (L30-AG064670, CIRAD-P30AG059303-Pilot, NYU-ADRC-P30AG066512-Developmental-Grant, AASM#231-BS-20)
    Type of Medium: Online Resource
    ISSN: 0161-8105 , 1550-9109
    URL: Article
    DOI: 10.1093/sleep/zsab072.788
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
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    0645 Associations of Objective Sleep Parameters and Gray Matter Microstructure in community dwelling cognitive normal older adults
    Oxford University Press (OUP) ; 2022
    In:  Sleep Vol. 45, No. Supplement_1 ( 2022-05-25), p. A283-A284
    Details
    In: Sleep, Oxford University Press (OUP), Vol. 45, No. Supplement_1 ( 2022-05-25), p. A283-A284
    Abstract: Disturbed sleep measures differentially alter white-matter microstructure. We examined whether obstructive sleep apnea (OSA)-severity, sleep fragmentation and duration measures were associated with gray-matter diffusion tensor-imaging metrics (DTI) (i.e., fractional anisotropy [FA], mean diffusivities [MD] and kurtoses [MK]) in community-dwelling cognitive-normal older-adults. Methods Gray-matter DTI metrics from MRI including mean FA, MD and MK measures of the hippocampus, thalamus, medial prefrontal-cortex (mPC) and Alzheimer’s Disease (AD) vulnerable regions (temporal [inferior, middle, and superior], parietal [inferior and superior] , entorhinal cortex, and precuneus) were determined from 85 subjects. OSA-severity measures included AHI3a and AHI4%. Other sleep measures included sleep efficiency (SE), non-rapid eye movement (NREM) slow wave sleep (SWS) duration, percent time spent in SWS (%SWS), slow wave activity (SWA), total sleep time (TST), and wake after sleep onset (WASO). To analyze the data, first, we utilized factor analysis using varimax rotation to account for the DTI metrics as multivariate outcomes. Using factor loadings, we anticipated a two or three-factor model was sufficient to explain the variance of the DTI metrics. Second, we investigated predictive associations between the sleep parameters and the loaded DTI factors, and explored age, sex, BMI, education and APOE4 as covariates underlying any differences. Results Of the 85 participants, 60 (70.6%) were women, 67 (78.8%) were non-Hispanic Whites. Mean (SD) age, BMI and education was 66.7 (5.3) years, 27.9 (6.1) kg/m2 and 16.8 (2.4) years respectively. We selected the two primary loadings’ factor model based on AIC criteria. FA metrics of the investigated brain regions except thalamus, loaded on one factor, conceptualized as manifest indicators for FA. MD and MK metrics of all the investigated brain regions loaded on the second factor, conceptualized as manifest indicators for MD/MK. SWS, %SWS, TST were predictors of FA (P ≤0.01 for all). AHI3a, AHI4%, and SWA were predictors of MD/MK (P ≤0.05 for all). Additionally, sex, age, APOE4 and education were predictors of FA (P ≤0.01 for all). Conclusion In this limited sample of cognitively-normal older-adults, sleep duration measures predicted gray-matter FA, OSA-severity measures predicted gray matter MD and MK. Demographic, genetic and SES factors explained the differences in these relationships. Support (If Any) AASM 231-BS-20, AARGD-21-8488397, NIH/NIA/NHLBI (K23AG068534, L30-AG064670, CIRAD P30AG059303 Pilot, NYU ADRC P30AG066512 Developmental Grant, R25HL105444, SRG, R01AG12101, R01AG022374, R01AG13616, RF1AG057570, R01HL118624, R01AG056031)
    Type of Medium: Online Resource
    ISSN: 0161-8105 , 1550-9109
    URL: Article
    DOI: 10.1093/sleep/zsac079.642
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
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    0887 Divergent Slow wave sleep and Alzheimer's disease Plasma Biomarkers’ Associations in Black and White Cognitive Normal Older-Adults
    Oxford University Press (OUP) ; 2023
    In:  SLEEP Vol. 46, No. Supplement_1 ( 2023-05-29), p. A390-A391
    Details
    In: SLEEP, Oxford University Press (OUP), Vol. 46, No. Supplement_1 ( 2023-05-29), p. A390-A391
    Abstract: We determined whether slow wave sleep is associated with plasma levels of Aβ40, Aβ42, Aβ42/Aβ40, Tau, tau/Aβ42 and NfL and whether this relationship differed between Blacks/African-Americans and non-Hispanic Whites. Methods This was a cross-sectional analysis of baseline data from 171 community-dwelling cognitively normal older-adults, participating in ongoing NYU studies on memory, sleep and aging. Non-rapid eye movement sleep (NREM) slow wave sleep (SWS) duration was calculated from 2 nights of in-lab NPSGs. Plasma Aβ40, Aβ42, Tau and NfL were determined using single molecule array (SIMOA). Associations of NREM SWS duration and plasma AD biomarker levels were assessed using adjusted generalized linear models and Pearson correlation analysis after data normalization. Analyses were adjusted for age, sex, BMI, race, and education. Results Of the 171 subjects (128 non-Hispanic Whites and 43 Blacks/African-Americans), 112 (65.5%) were females, and mean (SD) age was 68.6 (6.6) years, BMI was 27.6 (6.1) kg/m**2, and education was 16.9 (2.1). There were no racial differences in age, sex, BMI, NREM SWS and AHI4%. Compared to non-Hispanic Whites, Blacks/African-Americans had significantly lower years of education (14.2 vs. 17.2, p & lt;.01), plasma Aβ40 (248.3 vs. 262.5 pg/ml) and NfL levels (11.4 vs. 15.2 pg/ml) p & lt;.05 for both. There were no significant racial differences in levels of plasma Aβ42, Aβ42/Aβ40, Tau, Tau/Aβ40 and Tau/Aβ42 (p & gt;.05 for all). NREM SWS duration was not associated with plasma Aβ42, Aβ40 or tau in the overall sample (p & gt;.05 for all). However, in non-Hispanic Whites, NREM SWS negatively correlated with plasma Aβ42 (r=-0.28, p & lt;=0.05), plasma Aβ40 (r=-0.087, p=0.72), though not significant, and plasma Tau (r=-0.153, p=0.27), though not significant. In Black/African-Americans, NREM SWS positively correlated with plasma Aβ42 (r=0.48, p=0.05), plasma Aβ40 levels (r=0.32, p=0.04), and plasma Tau levels (r=0.52, p=0.04). NREM SWS was not associated with plasma tau/Aβ42, plasma tau/Aβ40 or plasma NfL in the overall sample and across racial subgroups. Conclusion Race-specific divergent associations between NREM SWS and plasma Aβ42, Aβ40 & Tau may suggest differences in SDOH factors and mechanisms that could influence sleep and AD-risk in older-adults. Support (if any) AASMBTS#231-BS-20, NIAK23AG068534A, AARG-D- 21-848397, BFFA2022033S
    Type of Medium: Online Resource
    ISSN: 0161-8105 , 1550-9109
    URL: Article
    DOI: 10.1093/sleep/zsad077.0887
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2056761-3
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