Population Attributable Fraction of poor sleep quality and its association with dementia: a Brazilian population-based study.

doi:10.21203/rs.3.rs-3136779/v1

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Population Attributable Fraction of poor sleep quality and its association with dementia: a Brazilian population-based study.

https://doi.org/10.21203/rs.3.rs-3136779/v1

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The populational impact of sleep quality and the risk of dementia is unclear. We analyzed the Population Attributable Fraction (PAF) of sleep parameters in a large-scale Brazilian cohort (ELSI-Brazil). A subset of the ELSI-Brazil with complete responses to sleep quality was selected. This is a large representative sample of the Brazilian elderly population with an extensive assessment of sociodemographic and health risk variables. Prevalence was estimated according to the complex sample design, and its PAF was measured using a meta-analytic relative risk. A total of 6024 (out of 9412) individuals had complete responses. The prevalence of poor sleep quality was 24.9% (95%CI 23–26%), and the PAF of poor sleep quality including 10 modifiable risk factors of dementia was 52% in Brazil. Sleep parameters varied considerably according to age ranges, race, and gender. Poor sleep quality appears to be an important populational risk factor for dementia in Brazil. Targeted interventions may provide an important impact in preventing dementia in low- and middle-income countries.

Health sciences/Neurology

Health sciences/Risk factors

A growing body of evidence suggests that sleep disorders are associated with a cause-and-effect relationship to cognitive decline. A variety of sleep parameters, such as increased sleep latency ¹, poor sleep quality ², sleep loss ³, excessive daytime sleepiness ⁴, and sleep-disordered breathing ⁵ may increase cerebral amyloid-beta (Aβ) deposition in non-demented elderly. Sleep restriction could also have a detrimental synergic effect on other risk factors, such as physical exercise, on brain Aβ deposition ⁶. Thus, identifying and targeting sleep problems before cognitive decline might serve as a promising strategy for dementia prevention.

Observational studies have found an association between altered sleep parameters and the risk of cognitive decline ^1,6,7. Recently, a longitudinal study of 25-year follow-up confirmed the association between persistent short sleep duration and a higher risk of developing dementia ⁸. Self-reported sleep problems including poor sleep quality and daytime dysfunction were associated with a higher risk of cognitive impairment ⁹ and higher all-cause mortality ¹⁰. Besides, major depression and depressive symptoms may significantly interact with sleep parameters, ⁶ and should also be accounted for when evaluating sleep. Although the exact relationship is not yet fully understood and could reflect bidirectional or interrelated associations, sleep dysregulation may mediate the pathological process of dementia, wherein sleep improvements could promote cognitive abilities ^11,12. It is critical to mention that studies proposing that sleep dysregulation over the adult life course may shape the risk of dementia at older ages were conducted in high-income countries (HIC) ⁸. This association remains elusive in low- and middle-income countries (LMIC). Thus, it is imperative to investigate the impact of sleep problems in these countries, which may provide essential insights for the development of effective surveillance systems.

Brazil is a continental-size country with a large number of individuals with uncontrolled risk factors for dementia and low levels of education. This population has a high potential to reflect the community of LMIC. Thus, we aimed to investigate the Population Attributable Fraction (PAF) of poor sleep quality for dementia and the extent of the relationship between self-reported sleep parameters and clinical characteristics using population-level data. We also explored potential factors mediating this association, with special concern for the use of sleep drugs and depressive symptoms. Finally, sleep parameters and their association with clinical characteristics may provide important insights for policymakers and health strategies.

The ELSI-Brazil consisted of 9412 individuals (age range: 50–105) with an average of 63.55 (± 10.14) years of age and 5.33 (± 4.3) years of education (Table 1). A first epidemiological analysis was performed to measure the prevalence of sleep parameters, which was calculated after sample weighting for the whole cohort (n = 9412, Fig. 1).

Table 1

Study sample demographics and clinical features.
	ELSI-Brazil Cohort (n = 9,412)	Study Sample (n = 6,024)
Sex (M)	4,098 (43.5%)	2,634 (43.7%)
Age (years)	63.6 (± 10.1)	62.8 (± 9.5)
Education, mean years (SD)	5.3 (± 4.3)	5.5 (± 4.4)
Region
Midwest	920 (9.8%)	612 (10.2%)
North	743 (7.9%)	488 (8.1%)
Northeast	2,549 (27.1%)	1,545 (25.7%)
South	1,278 (13.6%)	775 (12.9%)
Southeast	3,922 (41.7%)	2,604 (43.2%)
Races
White	3,589 (38.1%)	2,262 (37.6%)
Black	887 (9.4%)	567 (9.4%)
Brown	4,283 (45.5%)	2,765 (45.9%)
Asian	90 (1%)	54 (0.9%)
Indigenous	220 (2.3%)	143 (2.4%)
Risk factors for dementia
Less education	6,062 (64.8%)	3,781 (63.1%)
Physically inactive	4,494 (48.4%)	2,785 (46.8%)
Heavy drinking	366 (3.9%)	239 (4.0%)
Active smoking	1,604 (17.0%)	1,090 (18.1%)
Obesity	2,568 (28.6%)	1,701 (29%)
Hypertension	5,006 (53.3%)	3,140 (52.3%)
Diabetes	1,525 (16.3%)	969 (16.2%)
Depression	1,711 (18.2%)	1,055 (17.6%)
Social contact	1,928 (20.5%)	1,213 (20.1%)
Hearing loss	2,896 (30.8%)	1,620 (26.9%)

The prevalence of self-reported poor sleep quality in the whole sample was 24.9% (95%CI 23–26%), while the prevalence of non-restorative sleep was 50.7% (95%CI 49.1–52%), and the prevalence of frequent use of sleep drugs was 11.9% (95%CI 10.5–14%). The prevalence of positive screening for symptoms of depression was 33.7% (95%CI 32.1–35%). The PAF to develop dementia for self-reported poor sleep quality was 3.6% using the relative risk rate (RR = 1.49) from a previous meta-analysis ¹³. Previously measured weighted PAF was 50.5% when accounting for 10 risk factors of dementia ¹⁴, and it increased to 52.9% when including poor sleep quality in the calculation (Fig. 2 and Table 2).

Table 2

Population Attributable Fraction of 11 risk factors of dementia, including poor sleep quality, in the Brazilian population.
	RR for dementia	Overall prevalence calculated	CI 95%	Communality	PAF	Weighted PAF
Less education	1.6 (1.3-2)	51.1%	47.7–55	59.3%	23.5%	11.6%
Hearing loss	1.9 (1.4–2.7)	29.4%	28–31	32.0%	20.9%	16.6%
Hypertension	1.6 (1.2–2.2)	52.0%	50.1–54	56.4%	23.8%	10.6%
Alcohol (> 21 units/week)	1.2 (1.1–1.3)	4.3%	3.5–5	73.5%	0.9%	0.2%
Obesity	1.6 (1.3–1.9)	29.2%	27.9–31	57.6%	14.9%	7.3%
Smoking	1.6 (1.2–2.2)	17.1%	15.6–19	68.6%	9.3%	2.9%
Depression	1.9 (1.6–2.3)	18.1%	16.5–20	51.1%	14.1%	3.3%
Low social contact	1.6 (1.3–1.9)	5.1%	4.4–6	49.1%	3.0%	2.2%
Physical inactivity	1.4 (1.2–1.7)	47.2%	44–50	29.0%	15.9%	8.6%
Diabetes	1.5 (1.3–1.8)	15.7%	14.5–17	50.2%	7.2%	3.4%
Poor sleep quality	1.5 (1.2–1.9)	24.9%	23.4–26	75%	11%	3.6%
Overall Weighted PAF						52.9%

A second analysis was performed using a subset of this population with complete sleep parameters, totaling 6024 individuals. Importantly, this subsample presented a similar sociodemographic and frequency of risk factors to the whole cohort (Table 1). Among the study subsample, 1540 (18.6%) reported poor sleep quality, 2747 (45.6%) reported non-restorative sleep, and 736 (12.2%) individuals reported frequent use of sleep drugs, which differed among races, sex, and genders (Fig. 3). Self-reported non-restorative sleep was reported in more than 30% of the whole sample, and women presented higher rates of poor sleep quality, non-restorative sleep, use of sleep drugs, and depressive symptoms than men (Fig. 3, p < 0.001). Black and indigenous women presented particularly worse sleep quality and restorative sleep, more depressive symptoms, and more frequent sleep drug use, independently of age when compared with their male peers.

Individuals presenting poor sleep quality had a distinct clinical profile when compared with individuals reporting good sleep quality. Individuals with poor sleep quality reported a higher frequency of physical inactivity and hearing loss (Table 3). Non-restorative sleep partially overlaps sleep quality, in which 1336 (22.2%) individuals reported non-restorative sleep and poor sleep quality concomitantly, while 3073 (51%) reported restorative sleep and good sleep quality.

Table 3

Frequency of risk factors of dementia according to sleep quality in the study sample.
	Good sleep quality (n = 4,484)	Poor sleep quality (n = 1,540)	p-value
Age (years)	63.0 (± 9.6)	62.3 (± 9.2)	0.036
Education, years (SD)	5.6 (± 4.4)	5.1 (± 4.3)	< 0.0001
Sex (M)	2,153 (48.0%)	481 (31.2%)	< 0.0001
Race			0.078
White	1,702 (38.0%)	561 (36.4%)
Black	404 (9.0%)	163 (10.6%)
Brown	2,056 (45.9%)	709 (46.0%)
Asian	34 (0.8%)	20 (1.3%)
Indigenous	105 (2.3%)	38 (2.5%)
Risk factors of dementia
Hypertension	2,233 (49.9%)	908 (59.2%)	< 0.0001
Diabetes	689 (15.4%)	280 (18.3%)	0.010
Social contact	880 (19.6%)	333 (21.6%)	0.097
Less education	2,755 (61.7%)	1,026 (67.1%)	0.0002
Depression	528 (11.8%)	527 (34.4%)	< 0.0001
Hearing loss	1,091 (24.4%)	530 (34.4%)	< 0.0001
Obesity	1,264 (28.9%)	437 (29.2%)	0.84
Active smoking	773 (17.2%)	317 (20.6%)	0.004
Physically inactive	1,992 (45.0%)	794 (52.2%)	< 0.0001
Heavy drinking	181 (4.0%)	58 (3.8%)	0.71

Discordant reports included 1411 (23.42%) individuals that reported good sleep quality but non-restorative sleep, while 204 (3.4%) reported poor sleep quality but restorative sleep (Fig. 4a). Total CES-D8 scores were significantly higher in those reporting poor sleep quality (4.46 ± 2.51 vs. 2.11 ± 2.2, p < 0.0001), non-restorative sleep (4.44 ± 2.28 vs. 1.25 ± 1.57, p < 0.0001), and frequent use of sleep drugs (4.28 ± 2.69 vs. 2.49 ± 2.39, p < 0.001) (Fig. 4b). Accordingly, age was not associated with sleep quality (Poor sleep 62.2 ± 9.15 vs. Good sleep 62.97 ± 9.63, p = 0.15), or frequency of sleep drug use (Eventual 62.7 ± 0.5 vs. Frequent 63.4 ± 9.6, p = 0.6).

This large-scale Brazilian study is a pioneering report on the PAF of poor sleep quality using a multiracial cohort. Firstly, the PAF of poor sleep quality estimates the percentage of cases of dementia potentially preventable in Brazil, and potentially other LMICs given the representative potential of this country. Secondly, we estimated the prevalence of poor sleep quality, non-restorative sleep, and frequent use of sleep drugs extracted from this epidemiological sample. Lastly, our findings highlight the potential impact of targeting better sleep quality using a multiracial cohort in the prevention of dementia, which was not attributed to depressive symptoms.

The PAF provides important guidance for policymakers and stakeholders aimed to decrease the burden of dementia. A meta-analysis found that 15% of Alzheimer’s disease may be attributed to sleep problems in high-income countries ¹³, while we found that poor sleep quality alone had a PAF of 8%. This discrepancy between prevalences comes essentially from different definitions of sleep problems and consequently distinct estimated prevalences to calculate the PAF. Although heterogeneity in the assessment of outcomes and sleep problems may contribute to the overestimation of sleep complaints, the Brazilian or Latin American population was not represented in a previous meta-analysis ¹³. The high populational impact of poor sleep quality in dementia is consistent with the hypothesis that modifiable risk factors of cognitive decline play an important impact in LMIC. It may reflect poor healthcare assistance for cardiovascular diseases, lower educational levels, and sociodemographic vulnerabilities. Twelve potentially modifiable risk factors together have been associated with 40% of the PAF of dementia worldwide ¹⁵, while in Brazil this accounts for nearly half of cases of dementia ¹⁶. Importantly, the majority of individuals with dementia are living in LMIC, which is particularly uncovered by epidemiological data ¹⁷. Although there is a relative absence of specific evidence of the impact of risk factors on dementia risk in low and middle-income countries, particularly from Africa and Latin America ¹⁸, calculations considering the country-specific prevalence (not including Brazil) of nine potentially modifiable risk factors estimated overall PAF of 56% in Latin America ^19,20. This whole scenario plays a massive role in mitigating the impact of dementia, especially in resource-limited countries.

A quarter of the sample reported poor sleep quality, while the prevalence of reported non-restorative sleep was around 50%. The prevalence of poor sleep quality, non-restorative sleep, and frequent use of sleep drugs were higher than described in other countries ^21–23. Importantly, a cross-sectional analysis from National Health and Nutrition Survey including 4073 patients, mostly a healthy nonclinical sample, found that non-restorative sleep was reported in the majority of the study population, specifically 57.47% ²⁴. Consistent with our findings, non-restorative sleep was associated with a myriad of characteristics shared with vulnerable populations in LMIC, such as black/African American ethnicity, lower educational levels, no private insurance, and very low environmental security ²⁴. Sleep health is considered an important indicator of overall health, and it may reflect a self-perception of health ²⁵, while high estimates of sleep problems may ultimately indicate worse health quality in this population. The high prevalence of subjective sleep disorders reported in this study highlight the potentially high impact of improving this modifiable risk factor of dementia in Brazil.²⁶

Remarkably, disparities in sleep parameters were highly variable according to age range, race, and gender. Overall, black adult women and indigenous older women showed the highest rates of poor sleep quality, non-restorative sleep, and frequent use of sleep drugs. These subgroups also presented higher rates of symptoms of depression. According to racial identities, indigenous individuals presented a high frequency of poor sleep quality and non-restorative sleep when compared to other racial groups. A gender-specific analysis pointed out that women presented increased rates of poor sleep quality, non-restorative sleep, frequent use of sleep drugs, and symptoms of depression. Large population-based studies have also highlighted the higher prevalence of poor sleep quality in women ²⁷. Besides, black and Hispanic individuals have been associated with worse sleep patterns when compared with Caucasian women ^28–30. These findings are potentially associated with high social vulnerability and lack of emotional support ³¹. Sleep behavior and sleep disorders have a distinct pattern in women ³², which also have potential implications for the increased risk of dementia in this population. Potential reasons for the disparities aforementioned include genetic influence ³³, complex socio-environmental factors, and structural racism ^34,35. In primary care specifically, poor sleep quality may reflect the need for an individualized assessment, and it may arise as a target for clinical interventions ²⁵.

Poor sleep quality was pointed out as an indicator of early cognitive impairment for elderly people in a Chinese cohort ³⁶. Two meta-analyses found that sleep problems were associated with a higher risk of AD, cognitive impairment, and preclinical AD ^13,37. Subjective sleep complaints were frequently associated with an increased risk of dementia ³⁷, but studies on sleep quality are scarce. Poor sleep quality may reflect underlying pathological processes that arise from dysfunctions of circadian rhythms or disturbed sleep, and it presents an increased risk of overall cognitive impairment ³⁸. Sleep deprivation, among other sleep-related factors, increases the accumulation of amyloid-beta proteins in the brain, in a potential bidirectional relationship ³⁹. This study provides evidence that evaluating sleep quality through a single question, a proxy to a myriad of sleep problems may provide important information on the odds of cognitive decline. Strategies that include a brief evaluation of sleep quality in primary care may help identify those individuals at higher risk of dementia.

Strengths of our study include a complex large representative sample of the Brazilian elderly population and an extensive assessment of sociodemographic and health risk variables, enabling us to perform a comprehensive adjustment for potential confounders. Additionally, the sleep parameter used is very simple, making it easily translatable into daily clinical practice as a screening tool. Despite our efforts, this study also presented limitations. Sleep measurements were collected through self-report without including neuropsychological assessments, which can overestimate their real prevalence due to recall and social desirability bias ⁴⁰. Also, assessment of sleep medication was limited to self-report of use, without further information about the type or dose of sleep medication, nor the indications for its prescription. Moreover, sleep quality was collected in both adults and older adults, which may present a risk or a consequence of neurodegenerative processes. As mentioned previously, sleep parameters may reflect an underdiagnosed depressive syndrome, with unclear cause or consequence in the context of dementia. Thus, further investigations may focus on biomarkers of poor sleep quality and measure objective sleep parameters accounting for depressive symptoms.

Finally, this study provides evidence for the high prevalence of poor sleep quality in a population representative of LMIC. Poor sleep quality is an important modifiable risk factor for dementia identified in this study. Poor sleep quality, non-restorative sleep, and frequent use of sleep drugs varied considerably between age ranges, sex, and gender. Our findings indicate that tailored public health strategies may greatly impact cognitive outcomes in the population. Further studies are warranted to examine the role of sleep management in benefiting cognition and preventing dementia.

Study design and sampling

The ELSI-Brazil is a population-based cohort designed to study determinants of aging. The baseline evaluation was conducted between 2015–2016 and included individuals 50 years or older. A complex sampling design was used based on the size of municipalities to allow small, medium, and large cities of urban and rural areas to be included. All participants signed the informed consent form before enrollment. The ELSI-Brazil was approved by the Fundação Oswaldo Cruz (FIOCRUZ) ethics committee, Minas Gerais, Brazil (protocol number 34649814.3.0000.5091). The study was conducted in strict adherence to applicable guidelines and regulations, ensuring compliance throughout the entire process. Face-to-face interviews were conducted by trained interviewers using an electronic registry system through a tablet.

Sample weights were derived to account for differential probability of selection and differential nonresponse. Data acquisition was performed according to a standardized protocol and included: (1) a clinical interview about general household characteristics and socioeconomic conditions; (2) an individual consultation to acquire physical and mental health and other relevant aspects; (3) blood pressure measurements, anthropometric measures and physical functioning assessment and (4) blood collection. Further details can be found elsewhere⁴¹. Sociodemographic and clinical features were collected from the original dataset as described in Table 1.

Definition of sleep parameters

Three parameters were collected in this study. Sleep quality, sleep drug use, and restorative sleep. Sleep quality was evaluated through a single self-reported question, similar to that described in the Pittsburgh Sleep Quality Index (PSQI, Panel 1). This PSQI question presented a good discriminative performance to identify sleep quality in previous studies ⁴².

Sleep quality was dichotomized into “Poor sleep quality”, when individuals responded “Very poor” or “Poor”, and “Good sleep quality” when they responded “Good” and “Very good” to the first question of panel 1. Frequent use of sleep drugs was defined when individuals reported the use of sleep drugs “3 or more per week” to the second question of panel 1. Restorative sleep was defined when individuals responded “No” to the third question (Panel 1).

Panel 1. Sleep parameters extracted in the original dataset, their corresponding questions, and answer options.

Self-reported sleep quality (PSQI question)

“How do you evaluate the quality of your sleep?”

“Very poor”, “Poor”, “Regular”, “Good” and “Very good”.

Sleep drugs use

“During the last month, did you take a drug to sleep?”

“Not”, “Less than once a week”, “1 to 2 times per week”, “3 or more per week”.

Restorative sleep

“During the last week, for the majority of time, did you feel that your sleep was not restorative, or did you wake without feeling rested?”

“No”, “Yes”.

Variables analyzed

Sociodemographic factors and clinical symptoms were collected in the original ELSI-Brazil dataset. Neuropsychiatric variables were also collected, such as the presence of depressive symptoms with the Center for Epidemiological Studies - Depression 8 scale questionnaire (CES-D8).

We also analyzed 10 risk factors for dementia, namely low education, hearing loss, hypertension, abuse of alcohol, obesity, smoking, depression, social isolation, physical inactivity, and diabetes ¹⁵. The definition of each risk factor in the ELSI-Brasil database was detailed elsewhere ¹⁶, but also described in detail in Supplemental table 2.

Statistical analysis

Summary demographic statistics were calculated accounting for complex survey analysis. Prevalences were estimated considering the sample weight due to the complex sample, for both the whole sample and regionally, with 95% confidence intervals. Categorical variables were compared using the Chi-squared test and continuous variables were compared using independent t-tests. We performed Bonferroni's correction for post hoc multiple comparisons and described continuous variables as mean ± standard deviation.

PAF for poor sleep quality, similar to the question used in the PSQI, was calculated using previously described methods ²⁰. The PAF estimates the percentage of dementia that could be prevented in the described population if the exposure was controlled. The formula to calculate PAF required the relative risk of dementia for a certain risk factor and the prevalence of the same factor within the studied population. The relative risk for poor sleep quality was acquired in a previous meta-analysis ¹³, and communality was accounted for in the overall weighted calculation. A multivariate regression analysis was conducted to identify predictors of poor sleep quality. Variables that exhibited a significant difference in univariate analysis were included in the multivariate regression model. All analyses were performed using R (V4.1.0) using epiR and psych packages.

DATA AVAILABILITY STATEMENT

The data supporting the reported results in the article are available in the public release data files of ELSI-Brazil, accessible at https://elsi.cpqrr.fiocruz.br/data-access/.

Competing Interests Statement

The authors declare they have no conflict of interest.

Sponsor’s Role

ELSI-Brazil was supported by the Brazilian Ministry of Health: DECIT/SCTIE (Grants: 404965/2012-1 and TED 28/2017); COPID/DECIV/SAPS (Grants: 20836, 22566, 23700, 25560, 25552, and 27510).

RMC has received funding from the Alzheimer’s Association (AARG-D 21-846545).

ERZ has received funding from the Alzheimer’s Association (ALZ-NAN-22-928381).

WVB has received funding from the Alzheimer’s Association (AACSFD-22-928689).

Author contributions

WVB designed, drafted figures, performed the statistical analysis, and wrote the first and final versions of the manuscript. GN wrote the first draft of this manuscript, coordinated the data analysis. ACT wrote the first and final version of the manuscript, and submitted its final version. VBL designed the concept of data analysis, and reviewed the final version of the manuscript. ERZ designed and reviewed the final draft of the manuscript. MLFC designed and reviewed the final draft of the manuscript. MLFC reviewed the final draft of the manuscript. RMC designed the concept and analysis and reviewed the final draft of the manuscript.

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No competing interests reported.

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Population Attributable Fraction of poor sleep quality and its association with dementia: a Brazilian population-based study.

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Abstract

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INTRODUCTION

RESULTS

DISCUSSION

METHODS

Study design and sampling

Definition of sleep parameters

Variables analyzed

Statistical analysis

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References

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