Association of Sleep Duration with Cognitive Performance in the Elderly: A Cross-Sectional Study from National Health and Nutrition Examination Survey (NHANES)

doi:10.21203/rs.3.rs-3972564/v1

Background

The incidence of cognitive dysfunction and nervous system diseases is increasing with the aging trend, which has become an essential challenge in the global health field. This study aims to explore the relationship between sleep duration and cognitive performance, paying particular attention to the influence of sleep duration in different populations.

Methods

The National Health and Nutrition Survey data from 2011 to 2014 were used to evaluate the relationship between sleep duration and cognitive performance. Cognitive performance was assessed by three tests: Alzheimer's Disease Registration Association (CERAD), Animal Fluency (AF), and Digital Symbol Substitution Test (DSST). The weighted multivariate logistic regression model verified the relationship between sleep duration and cognitive performance. A restricted cubic spline model was used to determine the nonlinear relationship between sleep duration and cognitive performance.

Results

Of a total of 19931 patients, 2582 patients were included. Our study found a significant positive correlation between prolonged sleep (more than 9 hours per night) and cognitive decline (multivariate-adjusted OR = 1.50,95%CI:1.10 ~ 2.05). In contrast, short periods of sleep (less than seven hours per night) showed no significant correlation in cognitive function. Participants who slept moderately (7–9 hours per night) performed better on cognitive tests, with higher CERAD, AFT, and DSST scores. The association findings highlight the possible effects of sleep on cognitive health, especially in older people.

Conclusion

This study emphasizes that long sleep may be related to the decline of cognitive function in the elderly. Future research should explore the physiological mechanism and potential causes of sleep and cognitive function to support the maintenance of cognitive health in the elderly more scientifically.

Sleep duration

Cognitive performance

NHANES survey

Neurological diseases

Cognitive dysfunction

With the deepening trend of population aging, the incidence of cognitive dysfunction and nervous system diseases has increased significantly in recent years[1–3]. This trend raises deep concerns about the health of the elderly, as society will face a more significant burden on the elderly population with the extension of life expectancy. According to the statistics of the World Health Organization (WHO), the proportion of people over the age of 60 in the total global population is increasing rapidly[4], and the number of older adults suffering from cognitive impairment and neurological diseases is also on the rise[5]. This global challenge has a far-reaching impact on individual health and poses significant challenges to the socio-economic and health systems. Cognitive impairment and neurological diseases not only cause a heavy burden on patients and their families but also put pressure on medical resources and social support systems [5, 6]. Therefore, understanding the causes of the increase in the incidence of cognitive impairment and nervous system diseases in the elderly and finding effective interventions have become essential problems to solve in medicine and public health. Sleep, a vital part of daily life, is widely considered closely related to cognitive function[7, 8]. However, the complex relationship between sleep and cognitive performance still needs further study. Over the years, much literature has emerged on the relationship between sleep and cognitive function[7–13]. However, the result is controversial. In previous studies, some scholars pointed out that short and long sleep may be related to cognitive decline[12–15], while others put forward the opposite view[9–11]. One of the studies on diabetic patients pointed out that the length of sleep may play a unique role in this group[16]. This has aroused our deep concern about the relationship between sleep duration and cognitive function in different groups.

This study intends to deeply understand the relationship between sleep and cognitive function, paying particular attention to the influence of sleep duration in different groups. In past research, many inconsistent findings about the relationship between sleep and cognitive function exist, especially in diabetic patients. Therefore, our research is devoted to filling the knowledge gap in this research field, and by comprehensively investigating the relationship between different sleep duration and cognitive performance, we hope to provide a more comprehensive understanding of the maintenance of cognitive health and promote a comprehensive understanding of the relationship between sleep and cognitive function, and thus better serve the cognitive health needs of society.

2.1. Study Population

Ethical approval

for the National Health and Nutrition Examination Survey (NHANES) protocols was granted by the National Center for Health Statistics ethics review board, ensuring all participants provided written informed consent. Consistent with policies of the National Institutes of Health, analyses involving anonymized data, which did not entail direct contact with participants, were exempt from being classified as human subjects research, thereby not requiring institutional review board oversight.

NHANES are representative cross-sectional surveys designed by the National Center for Health Statistics (NCHS)[17]. The data on sleep duration and cognitive performance test scores were collected from two NHANES cycles: 2011–2012 and 2013–2014. We excluded participants without cognitive performance test scores, sleep duration information, and other covariates information. Finally, we included 2582 patients for analysis, as shown in Fig. 1.

2.2. Cognitive Performance

The cognitive function data in the NHANES database were acquired using several assessment tools, including the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) Word Learning sub-test, which evaluates new verbal information learning capabilities; the Animal Fluency test, a measure of categorical verbal fluency as an aspect of executive function; and the Digit Symbol Substitution Test (DSST), which is designed to gauge processing speed, sustained attention, and working memory[18].

The CERAD assessment encompassed three tests for immediate memory recall and one for delayed recall. In the immediate recall segment, individuals were asked to remember and then immediately recount as many of the 10 non-related words presented to them as possible. The delayed recall component was conducted after the Animal Fluency test and the DSST. Each test in the CERAD battery had a maximum achievable score of 10, with the aggregate score being the total of the three immediate recall tests plus the delayed recall test. During the Animal Fluency test, participants aimed to name as many different animals as possible in 60 seconds, with their score being the tally of correctly named animals. In the DSST, participants were required to accurately match symbols to corresponding numbers in 133 boxes within a two-minute timeframe, with scores ranging from 0 to 133 based on the number of correct matches. Higher scores in all three assessments indicated superior cognitive functioning[18].

The CERAD test, Animal Fluency test, and DSST lacked recognized standards for defining low cognitive performance, and the study referred to the processing methods in the relevant published research[18, 19]. Additionally, the minimum quartile of these three test scores was used as the cutoff point, and these cutoff points are shown in Table 1. Individuals scoring at or below the established threshold levels were categorized as exhibiting low cognitive abilities. In contrast, those with scores surpassing these thresholds were deemed to demonstrate normal cognitive functioning.

Table 1. Study sample characteristics by sleep duration.

Variables	Sleep Duration(hours)
Variables	Total (n = 2582)	<7 (n = 893)	7-9 (n = 1429)	>9 (n = 260)		p
Age(y), Mean± SD	69.4 ± 6.7	68.3 ± 6.3	69.6 ± 6.8		71.6 ± 7.1	< 0.001
Sex, n (%)	0.676
Male	1251 (48.5)	422 (47.3)	701 (49.1)	128 (49.2)
Female	1331 (51.5)	471 (52.7)	728 (50.9)	132 (50.8)
Race, n (%)	< 0.001
Mexican American	213 (8.2)	85 (9.5)	111 (7.8)	17 (6.5)
Other Hispanic	254 (9.8)	113 (12.7)	130 (9.1)	11 (4.2)
Non-Hispanic white	1265 (49.0)	309 (34.6)	783 (54.8)	173 (66.5)
Non-Hispanic black	611 (23.7)	284 (31.8)	280 (19.6)	47 (18.1)
Others	239 (9.3)	102 (11.4)	125 (8.7)	12 (4.6)
Marital status, n (%)						0.001
Married	1422 (55.1)	457 (51.2)	839 (58.7)	126 (48.5)
Never married	144 (5.6)	55 (6.2)	70 (4.9)	19 (7.3)
Others	1016 (39.3)	381 (42.7)	520 (36.4)	115 (44.2)
Insurance, n (%)	0.216
No insurance	208 (8.1)	78 (8.7)	116 (8.1)	14 (5.4)
Any insurance	2374 (91.9)	815 (91.3)	1313 (91.9)	246 (94.6)
BMI (kg/m2), Mean± SD	29.1 ± 6.4	29.7 ± 7.1	28.7 ± 5.9	29.4 ± 6.2		< 0.001
Hypertension, n (%)	0.003
No	967 (37.5)	310 (34.7)	575 (40.2)	82 (31.5)
Yes	1615 (62.5)	583 (65.3)	854 (59.8)	178 (68.5)
Drinking, n (%)	0.236
No	809 (31.3)	293 (32.8)	445 (31.1)	71 (27.3)
Yes	1773 (68.7)	600 (67.2)	984 (68.9)	189 (72.7)
Smoking status, n (%)	0.882
Never	1260 (48.8)	433 (48.5)	703 (49.2)	124 (47.7)
Former	1322 (51.2)	460 (51.5)	726 (50.8)	136 (52.3)
Current	1260 (48.8)	433 (48.5)	703 (49.2)	124 (47.7)
Physical activity, n (%)						< 0.001
Sedentary	1465 (56.7)	550 (61.6)	749 (52.4)	166 (63.8)
Moderate	869 (33.7)	264 (29.6)	531 (37.2)	74 (28.5)
Vigorous	248 (9.6)	79 (8.8)	149 (10.4)	20 (7.7)
Depression, n (%)						< 0.001
Mild	2166 (83.9)	717 (80.3)	1238 (86.6)	211 (81.2)
Moderate	328 (12.7)	135 (15.1)	161 (11.3)	32 (12.3)
Severe	88 (3.4)	41 (4.6)	30 (2.1)	17 (6.5)
CAD, n (%)						0.158
No	2351 (91.1)	821 (91.9)	1301 (91)	229 (88.1)
Yes	231 (8.9)	72 (8.1)	128 (9)	31 (11.9)
Renal insufficiency, n (%)						0.004
No	2438 (94.4)	826 (92.5)	1368 (95.7)	244 (93.8)
Yes	144 (5.6)	67 (7.5)	61 (4.3)	16 (6.2)
Stroke, n (%)						0.065
No	2415 (93.5)	825 (92.4)	1351 (94.5)	239 (91.9)
Yes	167 (6.5)	68 (7.6)	78 (5.5)	21 (8.1)
HbA1C (%),Mean± SD	6.1 ± 1.1	6.1 ± 1.1	6.0 ± 1.1	6.0 ± 0.9		0.083
TC (mg/dl),Mean± SD	191.7 ± 42.4	189.8 ± 42.5	193.9 ± 42.5	185.9 ± 40.9		0.005
HDL-C (mg/dl), Mean ± SD	54.5 ± 16.0	53.3 ± 14.7	55.4 ± 16.6	53.4 ± 16.4		0.003
DSST, Mean ± SD	46.4 ± 17.1	45.8 ± 17.0	47.4 ± 17.3	43.1 ± 15.9		< 0.001
AFT, Mean ± SD	16.8 ± 5.5	16.5 ± 5.5	17.1 ± 5.5	15.8 ± 5.4		< 0.001
CERAD, Mean ± SD	25.0 ± 6.5	25.2 ± 6.4	25.2 ± 6.5	23.4 ± 6.7		< 0.001

2.3. Sleep duration

Participants self-reported sleep duration on their usual weekday or workday. In 2011–2014, sleep duration was created from a question of NHANES participants about how much sleep they routinely get: "How much sleep do you get (hours)?" Sleep duration was categorized as < 7 hours, 7 to 9 hours, and ≥ 9 hours.

2.4. Covariate assessment

When referring to previous research[20–22], we included several factors (age, gender, race, marital status, Insurance, body mass index, smoking, drinking status, hypertension, coronary artery disease diabetes, renal insufficiency, stroke, HbA1C, TC, and HDL-C) as covariates. In addition, depressive status affects cognitive functioning and should also be included as a covariate for analysis. In NHANES, self-reported race and ethnicity data was obtained from survey questions about race and Hispanic ethnicity. It was classified into non-Hispanic White, Black, Mexican-American, and other races. Anyone with any medical insurance or other type of health care plan (Including health insurance obtained through employment or purchased directly, as well as government programs like Medicare and Medicaid that provide medical care or help pay medical bills) is defined as having insurance. BMI was computed using a standardized technique based on weight and height. According to preceding literature definitions, smoking status was categorized as never smokers (smoked less than 100 cigarettes) and current smokers (smoked more than 100 cigarettes)[23]. Physical activity was classified as sedentary, moderate (In a typical week, at least 10 min of movement, resulting in a slight increase in breathing or heart rate), and vigorous (In a typical week, at least 10 min of movement, resulting in significant increases in breathing or heart rate). Hypertension and diabetes were defined by a combination of the physician’s history of diagnosis in the questionnaire and laboratory measurements of systolic/diastolic blood pressure and glycated hemoglobin. We determined whether a participant has CAD/Stroke/ renal insufficiency based on their replies to the question in the portion of the miscellaneous pain questionnaire: “Has a doctor or other health professional ever told you had CAD/Stroke/ weak or failing kidneys?” Other Covariates, including HbA1C, TC, and HDL-C, were collected from laboratory tests. All the variables mentioned above were outlined on the NHANES website, which is available for access at https://wwwn.cdc.gov/Nchs/Nhanes/continuousnhanes.

2.5. Statistical Analysis

The Kolmogorov-Smirnov normality test was applied to assess the normality of continuous variables. Their characteristics were summarized using the mean ± standard deviation (SD) for variables following a normal distribution. In contrast, variables not adhering to normal distribution were described using the median (interquartile range). When a variable was confirmed to be normally distributed, differences in mean values between the group with lower cognitive performance and the group with typical cognitive performance were evaluated using the student’s t-test. Alternatively, the Mann-Whitney U test was employed for variables without a normal distribution. The chi-square test was utilized to examine the differences in the proportions of categorical variables across various groups.

Binary logistic regression analyses were used to explore whether there were associations between sleep duration and cognitive performance. Age, gender, race, marital status, and insurance were adjusted for in model 1, and the other covariates were further adjusted for in model 2. In addition, restricted cubic spline was used to explore the furtherdose–response relationships between sleep duration and different cognitive performance test scores, which located 5 percent, 50 percent, and 95 percent of the exposure distribution in the logistic regression after adjusting all covariates[18]. The test size was 0.05, and the results were considered statistically significant when the bilateral p-value ≤ 0.05.

All analyses were performed using the statistical software packages R 3.3.2 (http://www.R-project.org, The R Foundation, Shanghai, China) (accessed on 20 November 2023) and Free Statistics software version 1.9. A descriptive study was conducted on all participants. By a two-tailed testing, a p-value of < 0.05 was declared significant.

3.1. Characteristics of the study population by diabetes status

There was no difference in the distribution of gender, drinking, smoking status, CAD, and stroke according to sleep duration grouping. Patients in the 7–9 hours of sleep duration were more likely to be non-Hispanic white, married, with hypertension, sedentary, without depression, without renal insufficiency, and have a lower BMI and higher cholesterol and HDL. Compared with the short and long sleep duration category, the mid-range sleep duration category has higher scores of CERAD, AFT, and DSST (Table 1).

3.2. Association between sleep duration and Cognition

Table 2 presents the association between sleep duration to the CERAD test, Animal Fluency test, and DSST. While comparing to the mid-range sleep duration category, the multivariate-adjusted ORs (95% CIs) of the long duration category was 1.5(1.1 ~ 2.05) for the CREAD score and 1.48(1.07 ~ 2.03) for the Animal Fluency test score, respectively. After adjustment for age, gender, race, marital status, and insurance only, the long-duration category was associated with an increased risk of poor cognitive performance as assessed by the DSST score. Compared to the mid-range sleep duration category, the multivariate-adjusted ORs (95% CIs) of the short duration category were 0.9 (0.72 ~ 1.12) for the CREAD score, 1.1 (0.89 ~ 1.37) for the Animal Fluency test score, and 0.83 (0.66 ~ 1.04) for the DSST score, all the p-vale >0.05.

Table 2. association between sleep duration to CERAD test, Animal Fluency test, DSST

Sleep duration（hours）
		2-12	<7	7-9	>9
CREAD Test	No.	2582	893	1429	260
	Crude	1.1 (1.03~1.17) *	0.92 (0.75~1.12)	1.00 (Ref.)	1.71 (1.28~2.27) *
	Model1	1.08 (1.01~1.16) *	0.94 (0.76~1.16)	1.00 (Ref.)	1.59 (1.17~2.15) *
	Model2	1.09 (1.01~1.16) *	0.9 (0.72~1.12)	1.00 (Ref.)	1.5 (1.1~2.05) *
Animal Fluency Test	No.	2582	893	1429	260
	Crude	0.97 (0.91~1.04)	1.36 (1.12~1.66) *	1.00 (Ref.)	1.58 (1.17~2.13) *
	Model1	1.02 (0.95~1.09)	1.21 (0.98~1.49)	1.00 (Ref.)	1.63 (1.2~2.23) *
	Model2	1.03 (0.96~1.1)	1.1 (0.89~1.37)	1.00 (Ref.)	1.48 (1.07~2.03) *
Digit Symbol Test	No.	2582	893	1429	260
	Crude	0.99 (0.93~1.06)	1.11 (0.91~1.35)	1.00 (Ref.)	1.37 (1.02~1.84) *
	Model1	1.05 (0.98~1.13)	0.93 (0.74~1.16)	1.00 (Ref.)	1.44 (1.04~2) *
	Model2	1.08 (1~1.16) *	0.83 (0.66~1.04)	1.00 (Ref.)	1.28 (0.91~1.8)

Reference (Ref.); Model 1 adjusted for age, sex, race, marital status, and insurance; Model 2 adjusted for Model 1+body mass index (BMI), physical activity level, drinking status, smoking status, depression, coronary artery disease (CAD), renal insufficiency, hypertension, stroke, HbA1C, high density lipoprotein cholesterol (HDL-C) and total cholesterol (TC). * p-value ≤ 0.05.

When sleep duration was used as a continuous variable, the associations persisted after adjustment for all covariates, with ORs (95% CIs) of 1.09 (1.01 ~ 1.16) for the CREAD score and 1.08 (1 ~ 1.16) for the DSST score. However, the ORs (95% CIs) for the Animal Fluency test score were 1.03 (0.96–1.1), with a p-value > 0.05.

3.3. Dose-Response Relationships

As shown in Fig. 2, nonlinearitydose–response relationships were found between sleep duration and the risk of low cognitive performance (CREAD, P for nonlinearity = 0.008, Animal Fluency test, P for nonlinearity = 0.024; DSST, P for nonlinearity = 0.04).

In Fig. 2, the solid line indicates the estimated risk of poor cognitive performanc. The dotted lines represent a a point-wise 95% confidence interval adjusted for age, gender, race, marital status, Insurance, body mass index, smoking, drinking status, hypertension, coronary artery disease, diabetes, renal insufficiency, stroke, HbA1C, TC, and HDL-C.

This study investigated the associations between sleep duration and cognitive performance in older adults. The study found that long sleep may be related to the decline of cognitive function in the elderly, and participants who slept moderately (7–9 hours per night) performed better on cognitive tests, with higher CERAD, AFT, and DSST scores.

Previous research has extensively explored the relationship between sleep duration and cognitive performance. Zijlmans J et al.[14] conducted a population-based study, suggesting longer sleep onset latency is mainly associated with lower cognitive reserve; Wang et al.[9] found that sleep duration was significantly negatively correlated with cognitive performance. A study published by Li et al. [24] from Fudan University in the Nature sub-journal found that the optimal length of sleep was 7 hours, which was positively associated with cognitive function and mental health. However, when sleep was insufficient or more than 7 hours, it was associated with cognitive decline and poor mental health[24]. Through the European Longitudinal cohort study on the Prevention of Alzheimer's dementia (EDPA-LAC), Blackman et al. [25] found that sleep of fewer than 7 hours was associated with an increased pathological burden of Alzheimer's disease in people without cognitive impairment.

Compared to these previous studies, our research delves deeper into the association between sleep duration and cognitive performance among the elderly. While prior research has provided valuable insights into this relationship, our study offers several novel contributions. Firstly, we utilized data from the National Health and Nutrition Survey from 2011 to 2014, encompassing a large and diverse sample of participants. This allows for a robust analysis of the relationship between sleep duration and cognitive function. Secondly, our study employed a weighted multivariate logistic regression model, adjusting for various covariates to minimize confounding effects. Furthermore, we utilized a restricted cubic spline model to explore the nonlinear relationship between sleep duration and cognitive performance, providing a more nuanced understanding of this association.

Despite these advancements, our study shares some similarities with prior research. Like previous studies, we found a significant positive correlation between prolonged sleep duration (more than 9 hours per night) and cognitive decline. This aligns with the findings of Zijlmans J et al. [14] and Wang et al. [9], highlighting the detrimental effects of excessive sleep on cognitive health in the elderly. However, unlike previous studies, we did not observe a significant correlation between short sleep duration (less than seven hours per night) and cognitive function. Instead, participants who slept moderately (7–9 hours per night) demonstrated better cognitive performance across various tests, consistent with the findings of Li et al. [24] and Blackman et al. [25].

In summary, while our study builds upon prior research, offering novel insights into the relationship between sleep duration and cognitive performance in the elderly, it also reinforces some existing findings. By leveraging a robust methodology and a large dataset, our study contributes to a deeper understanding of this crucial association with implications for cognitive health interventions in aging populations.

Our results clearly show that sleep duration is highly correlated with cognitive functioning. Long sleep may be related to the decline of cognitive function in the elderly, particularly in the CERAD and Animal Fluency tests. This association may reflect the effect of sleep on cognitive processes in the brain. However, the association with DSST testing is weak, and more in-depth research may be needed to explain the mechanisms behind it. We believe that the following three mechanisms could explain the current findings. First, long-term sleep may have adverse effects on cognitive function through a variety of physiological pathways. One of them may involve the regulation of the biological clock[26]. An extended sleep period may disrupt the normal circadian rhythm, resulting in abnormal levels of neurotransmitters in the brain during a specific period, thus affecting cognitive functions such as memory and language[27–29]. Second, the effect of sleep on brain neurotransmitters excessive sleep may lead to disorders in the level of neurotransmitters in the brain, especially those closely related to cognitive function, such as acetylcholine[30]. This may hinder the everyday communication of nerve cells and eventually affect the execution of cognitive function. Third, the relationship between sleep duration and brain structure. Long sleep may be related to changes in brain structure[31]. Previous studies have shown that long periods of sleep may reduce the volume of gray matter, especially in brain regions associated with cognitive function[32]. This may be one of the potential mechanisms for the correlation between sleep duration and cognitive function. Finally, although we focused on the differences in long sleep in different groups of people with diabetes, our results showed no significant correlation between sleep duration and cognitive function in this group. This may be related to the effect of diabetes on cognitive function and the intervention of other factors[33]. Future research can dig deeper into this relationship.

There are some limitations in our study. First, the data used in our study came from cross-sectional surveys, which limited the establishment of causality. Longitudinal studies are more suitable to clarify the temporal sequence and causal direction between sleep duration and cognitive function. Secondly, the assessment of sleep duration depends on self-reported data, which is affected by recall bias and inaccuracy. Objective sleep measurement methods, such as behavioral recorders or polysomnograms, can provide more reliable sleep duration and quality data. Thirdly, although we have adjusted various confounding factors in the analysis, there may still be residual confounding factors that have not been considered. Sleep disorders, drug use, and environmental factors may affect sleep duration and cognitive function, but our analysis does not include them. Fourthly, our research focuses on the elderly, and the results may not apply to young people or individuals with specific health conditions. Future studies should explore the relationship between sleep duration and cognitive function in different age groups and clinical populations.

Furthermore, the cognitive performance tests used in our study may not comprehensively capture all aspects of cognitive functioning. Including a broader range of cognitive assessments may provide a more nuanced understanding of the relationship between sleep duration and cognitive performance. Furthermore, our study did not explore the mechanisms underlying the observed associations. Future studies should explore biological, neuroimaging, and molecular pathways to elucidate potential mechanisms between sleep duration and cognitive function in older adults. Finally, as with any observational study, unmeasured confounding factors and deviations may affect the results. Caution should be taken in interpreting the findings, and further research is needed to confirm our observations and address these limitations.

Our findings suggested that long sleep may be related to the decline of cognitive function in the elderly. Future research should explore the physiological mechanism and potential causes of sleep and cognitive function to support the maintenance of cognitive health in the elderly more scientifically. This study provides practical significance for the adjustment of sleep habits and the improvement of cognitive function.

Conflict of Interest

The authors declare that they have no conflict of interest.

Author Contribution

Min Yuan designed the study. Yue Wu and Min Yuan conducted data analysis and wrote the manuscript. Yue Wu ran data collection and interpretation; Min Yuan and Yue Wu had full access to the raw data and took responsibility for the data's integrity and the accuracy of the data analysis. All authors critically revised the manuscript for important intellectual content and approved the final version.

Funding

The present study was funded by the Science and Technology Program of Jiangxi Provincial Health Committee (NO. 202110003), the General Project of Jiangxi Provincial Department of Education (NO. GJJ218910), and the General Project of Jiangxi Provincial Administration of Traditional Chinese Medicine (NO. 2022B741) for Min Yuan. The authors thank the participants of this study for their significant contributions.

Ethical Statement

The survey protocol for the NHANES was approved by the CDC’s National Center for Health Statistics Institutional Research Ethics Review Board. All participants provided written informed consent, and the study was approved by the NCHS Research Ethics Review Board (https:// wwwn. Cdc. Gov/ nchs/ nhanes/ default.aspx).

Data Availability Statement

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

Kraft, P.; Hausler, K.G. [Stroke-Related Cognitive Dysfunction]. Fortschr Neurol Psychiatr. 2023, 91, 503-509.
van Sloten, T.T.; Sedaghat, S.; Carnethon, M.R.; Launer, L.J.; Stehouwer, C. Cerebral microvascular complications of type 2 diabetes: stroke, cognitive dysfunction, and depression. Lancet Diabetes Endocrinol. 2020, 8, 325-336.
Zhou, L.; Kong, J.; Li, X.; Ren, Q. Sex differences in the effects of sleep disorders on cognitive dysfunction. Neurosci Biobehav Rev. 2023, 146, 105067.
Karna, E.; Aavikko, L.; Rohner, R.; Gallistl, V.; Pihlainen, K.; Muller, C.; Ehlers, A.; Bevilacqua, R.; Strano, S.; Maranesi, E.; Cerna, K.; Hengl, L.; Kolland, F.; Waldenberger, F.; Naegele, G.; Park, S.; Hess, M.; Reuter, V.; Frewer-Graumann, S.; Korjonen-Kuusipuro, K. A Multilevel Model of Older Adults' Appropriation of ICT and Acquisition of Digital Literacy. Int J Environ Res Public Health. 2022, 19.
Mukhopadhyay, A.; Sundar, U.; Adwani, S.; Pandit, D. Prevalence of stroke and post-stroke cognitive impairment in the elderly in Dharavi, Mumbai. J Assoc Physicians India. 2012, 60, 29-32.
Jacus, J.P.; Dupont, M.P.; Herades, Y.; Pelix, C.; Large, H.; Baud, M. [Awareness disorders in Alzheimer's disease and in mild cognitive impairment]. Encephale. 2014, 40, 180-187.
Perez-Carbonell, L.; Iranzo, A. Sleep-Related Changes Prior to Cognitive Dysfunction. Curr Neurol Neurosci Rep. 2023, 23, 177-183.
Hollinger, A.; Rust, C.A.; Riegger, H.; Gysi, B.; Tran, F.; Brugger, J.; Huber, J.; Toft, K.; Surbeck, M.; Schmid, H.R.; Rentsch, K.; Steiner, L.; Siegemund, M. Ketamine vs. haloperidol for prevention of cognitive dysfunction and postoperative delirium: A phase IV multicentre randomised placebo-controlled double-blind clinical trial. J. Clin. Anesth. 2021, 68, 110099.
Wang, Q.; Xu, S.; Liu, F.; Liu, Y.; Chen, K.; Huang, L.; Xu, F.; Liu, Y. Causal relationship between sleep traits and cognitive impairment: A Mendelian randomization study. J Evid Based Med. 2023, 16, 485-494.
Zhou, J.; Zhu, L.; Teng, Y.; Tong, J.; Gao, G.; Yan, S.; Tao, F.; Huang, K. Early sleep duration trajectories and children's cognitive development: a prospective cohort study. Eur. J. Pediatr. 2023, 182, 5353-5365.
Overton, M.; Skoog, J.; Laukka, E.J.; Bodin, T.H.; Darin, M.A.; Sjoberg, L.; Hofer, S.M.; Johansson, L.; Kulmala, J.; Kivipelto, M.; Solomon, A.; Skoog, I.; Kareholt, I.; Sindi, S. Sleep disturbances and change in multiple cognitive domains among older adults: A multicentre study of five Nordic cohorts. Sleep. 2023.
Zhang, Y.; Elgart, M.; Granot-Hershkovitz, E.; Wang, H.; Tarraf, W.; Ramos, A.R.; Stickel, A.M.; Zeng, D.; Garcia, T.P.; Testai, F.D.; Wassertheil-Smoller, S.; Isasi, C.R.; Daviglus, M.L.; Kaplan, R.; Fornage, M.; DeCarli, C.; Redline, S.; Gonzalez, H.M.; Sofer, T. Genetic associations between sleep traits and cognitive ageing outcomes in the Hispanic Community Health Study/Study of Latinos. EBioMedicine. 2023, 87, 104393.
Suh, S.W.; Han, J.W.; Lee, J.R.; Byun, S.; Kwon, S.J.; Oh, S.H.; Lee, K.H.; Han, G.; Hong, J.W.; Kwak, K.P.; Kim, B.J.; Kim, S.G.; Kim, J.L.; Kim, T.H.; Ryu, S.H.; Moon, S.W.; Park, J.H.; Seo, J.; Youn, J.C.; Lee, D.Y.; Lee, D.W.; Lee, S.B.; Lee, J.J.; Jhoo, J.H.; Kim, K.W. Sleep and cognitive decline: A prospective nondemented elderly cohort study. Ann. Neurol. 2018, 83, 472-482.
Zijlmans, J.L.; Riemens, M.S.; Vernooij, M.W.; Ikram, M.A.; Luik, A.I. Sleep, 24-Hour Activity Rhythms, and Cognitive Reserve: A Population-Based Study. J. Alzheimers Dis. 2023, 91, 663-672.
Lo, J.C.; Ong, J.L.; Leong, R.L.; Gooley, J.J.; Chee, M.W. Cognitive Performance, Sleepiness, and Mood in Partially Sleep Deprived Adolescents: The Need for Sleep Study. Sleep. 2016, 39, 687-698.
Huang, C.Y.; Lin, Y.C.; Lu, Y.C.; Chen, C.I. Application of Grey Relational Analysis to Predict Dementia Tendency by Cognitive Function, Sleep Disturbances, and Health Conditions of Diabetic Patients. Brain Sci. 2022, 12.
He, L.; Xie, X.; Xue, J.; Xie, H.; Zhang, Y. Association of the systemic immune-inflammation index with all-cause mortality in patients with arteriosclerotic cardiovascular disease. Front Cardiovasc Med. 2022, 9, 952953.
Wang, R.; Wang, W.; Hu, P.; Zhang, R.; Dong, X.; Zhang, D. Association of Dietary Vitamin D Intake, Serum 25(OH)D(3), 25(OH)D(2) with Cognitive Performance in the Elderly. Nutrients. 2021, 13.
Huang, G.; Ren, G. Interaction between omega-6 fatty acids intake and blood cadmium on the risk of low cognitive performance in older adults from National Health and Nutrition Examination Survey (NHANES) 2011-2014. Bmc Geriatr. 2022, 22, 292.
Peeri, N.C.; Egan, K.M.; Chai, W.; Tao, M.H. Association of magnesium intake and vitamin D status with cognitive function in older adults: an analysis of US National Health and Nutrition Examination Survey (NHANES) 2011 to 2014. Eur. J. Nutr. 2021, 60, 465-474.
Casagrande, S.S.; Lee, C.; Stoeckel, L.E.; Menke, A.; Cowie, C.C. Cognitive function among older adults with diabetes and prediabetes, NHANES 2011-2014. Diabetes Res Clin Pract. 2021, 178, 108939.
Dong, X.; Li, S.; Sun, J.; Li, Y.; Zhang, D. Association of Coffee, Decaffeinated Coffee and Caffeine Intake from Coffee with Cognitive Performance in Older Adults: National Health and Nutrition Examination Survey (NHANES) 2011-2014. Nutrients. 2020, 12.
Xiao, Y.; Xiao, Z. Association between Serum Klotho and Kidney Stones in US Middle-Aged and Older Individuals with Diabetes Mellitus: Results from 2007 to 2016 National Health and Nutrition Survey. Am. J. Nephrol. 2023, 54, 224-233.
Li, Y.; Sahakian, B.J.; Kang, J.; Langley, C.; Zhang, W.; Xie, C.; Xiang, S.; Yu, J.; Cheng, W.; Feng, J. The brain structure and genetic mechanisms underlying the nonlinear association between sleep duration, cognition and mental health. Nat Aging. 2022, 2, 425-437.
Blackman, J.; Stankeviciute, L.; Arenaza-Urquijo, E.M.; Suarez-Calvet, M.; Sanchez-Benavides, G.; Vilor-Tejedor, N.; Iranzo, A.; Molinuevo, J.L.; Gispert, J.D.; Coulthard, E.; Grau-Rivera, O. Cross-sectional and longitudinal association of sleep and Alzheimer biomarkers in cognitively unimpaired adults. Brain Commun. 2022, 4, c257.
Valentine, G.W.; Sanacora, G. Targeting glial physiology and glutamate cycling in the treatment of depression. Biochem. Pharmacol. 2009, 78, 431-439.
Hozer, C.; Pifferi, F. Relationships between endogenous circadian period, physiological and cognitive parameters and sex in aged gray mouse lemurs (Microcebus murinus). Chronobiol. Int. 2022, 39, 363-373.
Hozer, C.; Pifferi, F. Physiological and cognitive consequences of a daily 26 h photoperiod in a primate: exploring the underlying mechanisms of the circadian resonance theory. Proc Biol Sci. 2020, 287, 20201079.
Chougule, P.S.; Najjar, R.P.; Finkelstein, M.T.; Kandiah, N.; Milea, D. Light-Induced Pupillary Responses in Alzheimer's Disease. Front Neurol. 2019, 10, 360.
Zhang, Z.; Jin, X.; Wan, Z.; Zhu, M.; Wu, S. A Feasibility Study on Smart Mattresses to Improve Sleep Quality. J. Healthc. Eng. 2021, 2021, 6127894.
Brooks, S.J.; Katz, E.S.; Stamoulis, C. Shorter Duration and Lower Quality Sleep Have Widespread Detrimental Effects on Developing Functional Brain Networks in Early Adolescence. Cereb Cortex Commun. 2022, 3, b62.
Ramos, A.R.; Tarraf, W.; Wu, B.; Redline, S.; Cai, J.; Daviglus, M.L.; Gallo, L.; Mossavar-Rahmani, Y.; Perreira, K.M.; Zee, P.; Zeng, D.; Gonzalez, H.M. Sleep and neurocognitive decline in the Hispanic Community Health Study/Study of Latinos. Alzheimers Dement. 2020, 16, 305-315.
Umegaki, H.; Kawamura, T.; Kawano, N.; Umemura, T.; Kanai, A.; Sano, T. Factors associated with cognitive decline in elderly diabetics. Dement Geriatr Cogn Dis Extra. 2011, 1, 1-9.

No competing interests reported.

Association of Sleep Duration with Cognitive Performance in the Elderly: A Cross-Sectional Study from National Health and Nutrition Examination Survey (NHANES)

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Materials and Methods

2.1. Study Population

2.2. Cognitive Performance

2.3. Sleep duration

2.4. Covariate assessment

2.5. Statistical Analysis

3. Results

3.1. Characteristics of the study population by diabetes status

3.2. Association between sleep duration and Cognition

3.3. Dose-Response Relationships

4. Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1