INS NYC 2024 Program

Poster	Poster Session 11 Program Schedule 02/17/2024 10:45 am - 12:00 pm Room: Shubert Complex (Posters 1-60) Poster Session 11: Cultural Neuropsychology \| Education/Training \| Professional Practice Issues Final Abstract #31 Sleep and Cognition in American Indians: Data from the Strong Heart Study Luciana Fonseca, Washington State University, Spokane, United States Myles Finlay, Washington State University, Spokane, United States Steven Verney, University of New Mexico, Albuquerque, United States Naomi Chaytor, Washington State University, Spokane, United States Dedra Buchwald, Washington State University, Seattle, United States Hans Van Dongen, Washington State University, Spokane, United States Stuart Quan, Harvard Medical School, Boston, United States Astrid Suchy-Dicey, Washington State University, Seattle, United States Category: Aging Keyword 1: aging disorders Keyword 2: diversity Keyword 3: sleep Objective: Sleep disorders may increase the risk of neurodegenerative diseases and dementia. A growing body of evidence shows that racial/ethnic disparities exist in sleep quality. American Indians (AIs) have a high prevalence of both sleep disorders and dementia, but whether these phenomena are related is unknown. Longitudinal studies of the effects of sleep and sleep disorders on cognition in older racial minority groups may shed light on this issue. Here, in a sample of aging AIs assessed longitudinally, we investigated whether previously measured sleep features are predictive of cognitive performance impairments assessed ~15 years later. Participants and Methods: This study analyzes data collected in two multisite ancillary studies from the Strong Heart Study: the Sleep Heart Health Study, which includes polysomnography and self-reported sleep quality, and the Cerebrovascular Disease and its Consequences in American Indians Study, which includes neuropsychological assessments. The overlapping sample comprises 160 AIs (69.4% females; mean age at baseline 62.6, SD 5.4). Sleep measures include sleep timing and duration, sleep stages, sleep apnea indices, respiratory disturbance index (RDI), oxygen saturation, as well as self-perception of daytime sleepiness and sleep onset delay. Cognitive performance was assessed using the Modified Mini Mental State Test (3MSE), the Wechsler Adult Intelligence Scale 4^th edition digit symbol coding test (WAIS-IV), the California Verbal Learning Test short form (CVLT-SF), and the Controlled Oral Word Association test (COWA). Correlation analyses with correction for multiple comparisons were performed to identify sleep and performance variable pairs of interest. These were then included in separate linear regressions with cognitive tests as dependent variables and measures of sleep as predictor variables, including total sleep time, sleep latency, respiratory disturbance index and oxygen saturation. Additionally, multivariate logistic regressions were performed to analyze the degree to which cognitive impairment measured by more than 1.5 standard deviations below the average score on the 3MSE is predicted by sleep variables. All regression models were adjusted for age, sex, years of education, BMI, study site, scores of depressive symptoms measured by the Center for Epidemiologic Studies Depression Scale, and APOE e4 phenotype from isoelectric focusing. Results: Using polysomnography data, 64 (40%) participants had a moderate to severe respiratory disturbance index (RDI ≥ 15 events per hour). After the linear regression analysis, longer sleep latency was associated with worse performance in the COWA test (verbal phonemic fluency and executive function); higher oxygen saturation was associated with worse performance in multiple CVLT-SF indices (episodic verbal memory) and WAIS-IV digit symbol coding test (executive function and processing speed); and more time at an oxygen saturation below 90% with better CVLT short-delay recall (P<0.05). In addition, longer sleep latency was associated with higher likelihood of being classified in the cognitively impaired group (Odds Ratio 1.037, P=0.004). Conclusions: In our sample of aging AIs, sleep characteristics derived from polysomnography, but not subjective sleep quality, are predictive of cognitive performance assessed ~15 years later. Sleep disorders may be modifiable risk factors for cognitive impairment and dementia, and suitable candidates for interventions aimed at preventing neurodegenerative disease development and progression.

Poster

02/17/2024
10:45 am - 12:00 pm
Room: Shubert Complex (Posters 1-60)

Poster Session 11: Cultural Neuropsychology | Education/Training | Professional Practice Issues

Final Abstract #31

Sleep and Cognition in American Indians: Data from the Strong Heart Study

Luciana Fonseca, Washington State University, Spokane, United States
Myles Finlay, Washington State University, Spokane, United States
Steven Verney, University of New Mexico, Albuquerque, United States
Naomi Chaytor, Washington State University, Spokane, United States
Dedra Buchwald, Washington State University, Seattle, United States
Hans Van Dongen, Washington State University, Spokane, United States
Stuart Quan, Harvard Medical School, Boston, United States
Astrid Suchy-Dicey, Washington State University, Seattle, United States

Category: Aging

Keyword 1: aging disorders
Keyword 2: diversity
Keyword 3: sleep

Objective:

Sleep disorders may increase the risk of neurodegenerative diseases and dementia. A growing body of evidence shows that racial/ethnic disparities exist in sleep quality. American Indians (AIs) have a high prevalence of both sleep disorders and dementia, but whether these phenomena are related is unknown. Longitudinal studies of the effects of sleep and sleep disorders on cognition in older racial minority groups may shed light on this issue. Here, in a sample of aging AIs assessed longitudinally, we investigated whether previously measured sleep features are predictive of cognitive performance impairments assessed ~15 years later.

Participants and Methods:

This study analyzes data collected in two multisite ancillary studies from the Strong Heart Study: the Sleep Heart Health Study, which includes polysomnography and self-reported sleep quality, and the Cerebrovascular Disease and its Consequences in American Indians Study, which includes neuropsychological assessments. The overlapping sample comprises 160 AIs (69.4% females; mean age at baseline 62.6, SD 5.4). Sleep measures include sleep timing and duration, sleep stages, sleep apnea indices, respiratory disturbance index (RDI), oxygen saturation, as well as self-perception of daytime sleepiness and sleep onset delay. Cognitive performance was assessed using the Modified Mini Mental State Test (3MSE), the Wechsler Adult Intelligence Scale 4^th edition digit symbol coding test (WAIS-IV), the California Verbal Learning Test short form (CVLT-SF), and the Controlled Oral Word Association test (COWA). Correlation analyses with correction for multiple comparisons were performed to identify sleep and performance variable pairs of interest. These were then included in separate linear regressions with cognitive tests as dependent variables and measures of sleep as predictor variables, including total sleep time, sleep latency, respiratory disturbance index and oxygen saturation. Additionally, multivariate logistic regressions were performed to analyze the degree to which cognitive impairment measured by more than 1.5 standard deviations below the average score on the 3MSE is predicted by sleep variables. All regression models were adjusted for age, sex, years of education, BMI, study site, scores of depressive symptoms measured by the Center for Epidemiologic Studies Depression Scale, and APOE e4 phenotype from isoelectric focusing.

Results:

Using polysomnography data, 64 (40%) participants had a moderate to severe respiratory disturbance index (RDI ≥ 15 events per hour). After the linear regression analysis, longer sleep latency was associated with worse performance in the COWA test (verbal phonemic fluency and executive function); higher oxygen saturation was associated with worse performance in multiple CVLT-SF indices (episodic verbal memory) and WAIS-IV digit symbol coding test (executive function and processing speed); and more time at an oxygen saturation below 90% with better CVLT short-delay recall (P<0.05). In addition, longer sleep latency was associated with higher likelihood of being classified in the cognitively impaired group (Odds Ratio 1.037, P=0.004).

Conclusions:

In our sample of aging AIs, sleep characteristics derived from polysomnography, but not subjective sleep quality, are predictive of cognitive performance assessed ~15 years later. Sleep disorders may be modifiable risk factors for cognitive impairment and dementia, and suitable candidates for interventions aimed at preventing neurodegenerative disease development and progression.