Abstract
Background
Abnormal sleep duration represents a significant contributor to dementia; however, this association has not been explored among the elderly Chinese population. Therefore, this study investigated the relationship between sleep duration and dementia among the Chinese elderly population.
Methods
Data were obtained from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). This study included 7,680 participants aged 65 years and older at baseline in 2011. Cox proportional hazards models were performed to determine the relationship between sleep duration and dementia risk in this patient population.
Results
Over a total follow-up period of 28,147 person-years, 398 dementia cases were identified. The mean age of participants was 85.55 ± 11.09 years, with female predominance (54.71%). Participants with optimal sleep duration (6–8 h) had a significantly lower risk of dementia than those with excessive sleep duration (> 10 h) (P < 0.001, log-rank test). In the fully adjusted Model 3, participants with excessive sleep durations at baseline were associated with an 82% higher risk of dementia (HR = 1.82, 95% CI = 1.25–2.65) compared with the reference group. Tests for nonlinearity between sleep duration and dementia were not significant (P = 0.06). No significant interactions were found between sleep duration and sex, age, residence, or marital status. Finally, the sensitivity analysis demonstrated the stability of these findings.
Conclusions
Overall, the present study findings demonstrate that excessive sleep duration (over 10 h) is an independent predictor of increased risk of dementia among older Chinese adults. Conversely, maintaining a moderate sleep duration of 6 to 8 h appears to be a protective factor, correlating with a lower incidence of the condition.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-026-07159-6.
Keywords: Dementia, Sleep duration, Aged, Cohort study
Background
Dementia is typically caused by cerebrovascular disease and neurodegenerative brain disorders [1]. Characterized by deficits in memory, language, and executive function, dementia significantly hinders the ability of affected individuals to manage routine daily tasks [2, 3]. Dementia not only constitutes a common and serious global public health issue but also imposes a substantial financial burden [4, 5]. Importantly, the global dementia population is projected to reach 152 million by 2050 [6]. Unfortunately, there is no effective cure to control the progression of dementia; thus, it is critical to determine risk factors for the development of early prevention strategies [7, 8].
Sleep has also been identified as a modifiable risk factor for dementia [9]. Despite extensive research into the relationship between sleep duration and dementia risk, the results are inconsistent, and a consensus has yet to be established. Some studies have emphasized that short sleep duration is associated with an increased risk of dementia [10, 11], yet these studies failed to identify a significant link for long sleep duration. In contrast, a meta-analysis found a significant correlation between long sleep duration and dementia incidence [12]. This inconsistency in findings could be attributed to insufficient statistical power, often due to limited representation of individuals at the extremes of the sleep duration spectrum. Besides, other studies have provided evidence of a relationship between insufficient or excessive sleep duration and the onset of dementia [13, 14]. Therefore, the potential role of different sleep patterns in the development of dementia remains to be further elucidated.
Demographic projections indicate that China’s population will age at an unprecedented rate in the coming decades, a shift that will likely result in a substantial increase in the prevalence of dementias [15, 16]. While prior research has found that early sleep timing increases the risk of dementia [17], there is a lack of evidence regarding the association between sleep duration and dementia among Chinese adults aged over 65 years. To address this gap, this study utilized data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) examine the relationship between sleep duration and dementia in the elderly Chinese population. By categorizing sleep duration into specific groups, this study sought to identify potential modifiable risk factors for dementia.
Methods
Data source and participants
The data utilized in this study were derived from the CLHLS, a nationally representative survey of the Chinese population. The study design, protocol, and data collection methods of the CLHLS have been documented previously. Besides, the study received ethical approval from the Biomedical Ethics Committee of Peking University (ID: IRB00001052-13074) [18, 19]. All participants of CLHLS provided written informed consent. To mitigate the impact of higher mortality rates associated with long follow-up periods in the elderly population, this study utilized data from the 2011 CLHLS wave as a baseline [20, 21]. The study was conducted after the baseline assessment in 2011, with additional follow-ups in 2014 and during the 2018 wave, adhering to a three-year follow-up interval and concluding on 31 July 2019. This prospective cohort study recruited 9,765 participants aged 47–114 years between 2011 and 2018.
Participants were excluded based on the following criteria: loss to follow-up (n = 820), individuals under 65 years of age at baseline (n = 57), and those with missing sleep duration data (n = 84). Besides, we excluded participants with a prior dementia diagnosis or unknown dementia status at baseline (n = 641), as well as those with unspecified dementia status at the end of the follow-up period (n = 410). Finally, participants with missing or abnormal follow-up durations were removed from the analysis (n = 73) [22, 23]. Overall, 7,680 participants were included in the present study to evaluate the association between sleep duration and dementia incidence. The criteria for patient inclusion and exclusion are depicted in Figure S1.
Sleep duration
Sleep duration was ascertained via self-report in response to the question, “How long do you sleep normally?” [24]. Participants were subsequently divided into four groups based on sleep duration quartiles (≤ 6 h, 6 to 8 h, 8 to 10 h, > 10 h). In the multivariate analysis, sleep duration was treated as both a categorical and a continuous variable to evaluate its association with dementia incidence and to calculate corresponding hazard ratios (HRs).
Incidence of dementia
The study outcome was defined as a self-reported, physician-diagnosed case of dementia. Participants were only classified as positive if they reported having received a formal dementia diagnosis from a healthcare provider [25]. For participants who died during the follow-up period, dementia status was ascertained via informant reports. Information was obtained from the CLHLS “Questionnaire for Deceased Elderly Individuals” (2018 wave), which was administered to family members, neighbors, or community workers [26]. These informants, primarily spouses, children, or children-in-law, were selected based on their history of co-residence or regular contact with the deceased. The questionnaire explicitly recorded the informant’s relationship to the deceased to ensure the reliability of proxy-reported dementia diagnosis.
Given that CLHLS does not provide specific dates for the onset of dementia, the follow-up duration was determined in accordance with established literature [27, 28]. For surviving participants, the time to incident dementia was defined as the interval between the baseline assessment and the first follow-up interview at which dementia was identified. For participants who died during the study period, follow-up time was calculated as the time from baseline to the date of death. Censored observations were defined as participants without documented dementia or who had died from another disease at the termination of the follow-up. The censoring period extended from the baseline to the final follow-up, the date of death, or the conclusion of the study.
Covariate
To mitigate potential confounding factors, covariates such as age, sex, smoking status, drinking status, physical activity, marital status, education attainment, residence and household income were collected via self-reported responses to the questionnaire and incorporated into our analyses [25, 29]. Age was dichotomized into two categories: individuals under 80 years of age and those 80 years and older. Sex was categorized as female and male. Residence status was divided into urban and rural categories. Smoking status was categorized into current smokers, former smokers and never smokers. Drinking status was categorized into current drinker, former drinker and never drinker. Physical activity was defined as engagement in purposeful fitness activities, including running, playing ball, etc. The responses of participants were recorded as yes or no [30].
Marital status was categorized into married (currently married or living with spouse) and other (widowed, separated, divorced, or never married or married but not living with spouse). Educational attainment was stratified based on years of formal schooling into three levels: low (0 years), moderate (1–6 years), and high (> 6 years). Household income was classified into four groups (< 4000 China Yuan (RMB), 4000 RMB–9,999 RMB, 10,000 RMB–19,999 RMB, and ≥20,000 RMB). Body mass index (BMI) was derived from measured data and participants were categorized as underweight (< 18.5 kg/m2), normal weight (18.5 to < 24 kg/m2), overweight (24 to < 28 kg/m2), and obese (≥ 28 kg/m2) according to the formula: body weight (kg) / [height (m)]2. The prevalence of heart disease, diabetes and stroke was determined via patient self-reports and treated as binary variables (“yes” or “no”).
Statistical analysis
Continuous variables were expressed as means and standard deviations (SD) and were compared using a one-way ANOVA. Categorical variables were presented as frequencies and percentages and were compared using chi-square tests. Baseline characteristics were stratified by predefined sleep duration categories. The relationship between the categorical variable (sleep duration) and the incidence of dementia was evaluated using Cox proportional hazards regression models, with sleep duration of 6 h or less as the reference category. Before performing the Cox regression analysis, the proportional hazards assumption was verified; tests indicated no violations. We constructed three hierarchical models to assess the association: Model 1 adjusted for age and sex; Model 2 further adjusted for residence, smoking and drinking status, physical activity, marital status, educational attainment, household income, and BMI category; and Model 3, representing the fully adjusted analysis, included all variables from Model 2 with additional adjustments for heart disease, diabetes, and stroke.
Interaction effects for age, sex, residence, and marital status were examined by including multiplicative interaction terms within the models. To explore potential non-linear relationships between sleep duration and dementia, we employed restricted cubic spline (RCS) analysis. This analysis evaluated the continuous change in hazard ratios (HRs) for sleep duration in the fully adjusted model. Restricted cubic splines were applied with four knots at the 5th, 35th, 65th, and 95th percentiles. The median of sleep duration was used as the reference value for the RCS. Besides, two sensitivity analyses were conducted. First, we explored whether extreme sleep durations affect the relationship between sleep patterns and dementia. To this end, participants with sleep duration exceeding 15 h per day were excluded. Finally, to rule out reverse causality, we excluded participants with dementia from re-analysis during the year following their baseline visit. All analyses were conducted using R (version 4.2.2). Two-sided P values less than 0.05 were considered statistically significant.
Results
Table 1 illustrates the characteristics of the subjects in the CLHLS by sleep duration. Participants exhibited a mean age of 85.55±11.09 years, and were predominantly female (54.71%) and rural-dwelling (89.79%). Besides, 41.60% reported primary school or higher educational attainment. About 18.47% and 17.65% of participants were current smokers and drinkers, respectively. The mean BMI was 22.45 (SD: 44.08) kg/m2. Among the 7753 participants, 2379 (30.68%) had less than 6 h of sleep duration, while 744 (9.60%) participants had more than 10 h of sleep duration. Notable differences between sleep duration groups were observed for all baseline characteristics, except residence status (P = 0.659), history of diabetes (P = 0.83), and history of stroke (P = 0.069). Furthermore, sleep durations of more than 10 h were more prevalent among adults aged 80 years or older, those who did not participate in physical activity, and those with a marital status of “other” (P < 0.001).
Table 1.
Baseline characteristics of CLHLS older people by sleep duration groups
| Variables | level | Overall | ≤ 6 h | 6-≤8 h | 8-≤10 h | > 10 h | P | |
|---|---|---|---|---|---|---|---|---|
| 7753 | 2379 | 2810 | 1820 | 744 | ||||
| Age (mean (SD)) | 85.55 (11.09) | 84.66 (10.53) | 83.31 (11.05) | 87.40 (11.03) | 92.30 (9.63) | < 0.001 | ||
| Age group (%) | ≤ 80 years old | 2803 (36.15) | 902 (37.92) | 1259 (44.80) | 542 (29.78) | 100 (13.44) | < 0.001 | |
| > 80 years old | 4950 (63.85) | 1477 (62.08) | 1551 (55.20) | 1278 (70.22) | 644 (86.56) | |||
| Sex (%) | Male | 3511 (45.29) | 941 (39.55) | 1416 (50.39) | 851 (46.76) | 303 (40.73) | < 0.001 | |
| Female | 4242 (54.71) | 1438 (60.45) | 1394 (49.61) | 969 (53.24) | 441 (59.27) | |||
| Residence status (%) | urban | 724 (10.21) | 221 (10.35) | 265 (10.51) | 173 (10.14) | 65 (8.93) | 0.659 | |
| rural | 6368 (89.79) | 1915 (89.65) | 2257 (89.49) | 1533 (89.86) | 663 (91.07) | |||
| Smoking (%) | Now smoker | 1420 (18.47) | 394 (16.69) | 570 (20.44) | 340 (18.86) | 116 (15.80) | 0.001 | |
| Former smoker | 1236 (16.08) | 364 (15.42) | 461 (16.53) | 305 (16.92) | 106 (14.44) | |||
| Never smoker | 5031 (65.45) | 1603 (67.89) | 1758 (63.03) | 1158 (64.23) | 512 (69.75) | |||
| Drinking (%) | now | 1350 (17.65) | 353 (15.01) | 525 (18.95) | 342 (19.06) | 130 (17.69) | < 0.001 | |
| former | 1117 (14.60) | 320 (13.61) | 410 (14.80) | 284 (15.83) | 103 (14.01) | |||
| never | 5183 (67.75) | 1678 (71.37) | 1835 (66.25) | 1168 (65.11) | 502 (68.30) | |||
| Physical activity (%) | Yes | 2631 (34.39) | 774 (32.91) | 1036 (37.50) | 599 (33.26) | 222 (30.25) | < 0.001 | |
| No | 5019 (65.61) | 1578 (67.09) | 1727 (62.50) | 1202 (66.74) | 512 (69.75) | |||
| Marital status (%) | married | 2838 (36.87) | 868 (36.70) | 1232 (44.25) | 596 (32.93) | 142 (19.22) | < 0.001 | |
| Other | 4860 (63.13) | 1497 (63.30) | 1552 (55.75) | 1214 (67.07) | 597 (80.78) | |||
| Education attainment(%) | low (0 years) | 4511 (58.40) | 1419 (59.97) | 1503 (53.64) | 1098 (60.50) | 491 (66.26) | < 0.001 | |
| moderate (1–6 years) | 2387 (30.90) | 708 (29.92) | 928 (33.12) | 549 (30.25) | 202 (27.26) | |||
| high (> 6 years) | 826 (10.70) | 239 (10.10) | 371 (13.24) | 168 (9.26) | 48 (6.48) | |||
| BMI (mean (SD)) | 22.45 (44.08) | 22.35 (36.06) | 22.66 (50.37) | 20.29 (32.40) | 26.93 (61.84) | 0.015 | ||
| BMI group (%) | low weight | 2241 (37.90) | 645 (36.59) | 646 (32.20) | 628 (42.43) | 322 (48.49) | < 0.001 | |
| Normal weight | 2604 (44.04) | 809 (45.89) | 933 (46.51) | 620 (41.89) | 242 (36.45) | |||
| Overweight | 737 (12.46) | 220 (12.48) | 294 (14.66) | 165 (11.15) | 58 (8.73) | |||
| Obese | 331 (5.60) | 89 (5.05) | 133 (6.63) | 67 (4.53) | 42 (6.33) | |||
| Income (%) | < 4000 | 1347 (19.04) | 514 (23.64) | 465 (18.02) | 277 (16.82) | 91 (13.52) | < 0.001 | |
| 4000 ~ 10,000 | 1141 (16.13) | 365 (16.79) | 420 (16.28) | 247 (15.00) | 109 (16.20) | |||
| 10,000 ~ 20,000 | 1222 (17.27) | 367 (16.88) | 438 (16.98) | 293 (17.79) | 124 (18.42) | |||
| ≥ 20,000 | 3364 (47.55) | 928 (42.69) | 1257 (48.72) | 830 (50.39) | 349 (51.86) | |||
| Diabetes (%) | Yes | 319 (4.20) | 101 (4.33) | 115 (4.17) | 77 (4.32) | 26 (3.57) | 0.83 | |
| No | 7283 (95.80) | 2234 (95.67) | 2643 (95.83) | 1704 (95.68) | 702 (96.43) | |||
| Heart disease (%) | Yes | 943 (12.35) | 343 (14.66) | 339 (12.23) | 195 (10.88) | 66 (9.04) | < 0.001 | |
| No | 6692 (87.65) | 1996 (85.34) | 2434 (87.77) | 1598 (89.12) | 664 (90.96) | |||
| stroke (%) | Yes | 599 (7.81) | 196 (8.32) | 204 (7.34) | 127 (7.07) | 72 (9.80) | 0.069 | |
| No | 7070 (92.19) | 2161 (91.68) | 2576 (92.66) | 1670 (92.93) | 663 (90.20) | |||
During a mean follow-up period of 3.67 years, 398 participants were identified with incident dementia. Probability curves showing the likelihood of not developing dementia for different sleep duration groups are presented in Fig. 1. Compared with participants with sleep duration of more than 10 h, those with sleep duration of 6–8 h had a lower risk of dementia (P < 0.001 for the log-rank test). Compared with participants who slept less than 6 h, those with sleep duration exceeding 10 h had an 82% higher risk of dementia (HR = 1.82, 95% CI = 1.25–2.65) in the fully adjusted model (model 3; Table 2).
Fig. 1.
Probability curves of not developing dementia for different sleep duration groups
Table 2.
The association between sleep duration and risk of developing dementia in the CLHLS study
| Variable | Model 1 | Model 2 | Model 3 | |
|---|---|---|---|---|
| Sleep duration | ≤ 6 h | 1(Reference) | 1(Reference) | 1(Reference) |
| 6-≤8 h | 0.94[0.73,1.2] | 0.85[0.63,1.15] | 0.85[0.62,1.16] | |
| 8-≤10 h | 1.10[0.84,1.43] | 1.15[0.85,1.57] | 1.18[0.87,1.61] | |
| > 10 h | 1.67[1.21,2.32] | 1.81[1.25,2.61] | 1.82[1.25,2.65] |
Model 1: Estimates were adjusted for age and sex
Model 2: Building on Model 1, additional adjustments were made for residence status, smoking, drinking, physical activity, marital status, educational attainment, household income and BMI group
Model 3: Building on Model 2, additional adjustments were made for heart disease, diabetes and stroke
In the final model 3, sleep duration was a continuous variable and was associated with increased risk of dementia incidence (HR = 1.06, 95% CI = 1.01–1.11, P = 0.009). The result revealed no non-linear relationship between sleep duration and the risk of dementia (P for non-linearity = 0.06; Fig. 2). We found no evidence of interaction between sleep duration and sex, age, residence, or marital status (Table 3). The probability curves for not developing dementia for different sleep duration groups by age, sex, residence status and marital status are shown in Supplementary Materials Figures S2-S5.
Fig. 2.
RCS curve depicting the relationship between sleep duration and dementia incidence
Table 3.
Relationships between sleep duration and dementia by subgroup of subjects in the CLHLS study
| Groups | HR(95%CI) | P for interaction | ||
|---|---|---|---|---|
| Age group | ≤ 80 years old | > 80 years old | ||
| ≤ 6 h | 1(Reference) | 1(Reference) | ||
| 6-≤8 h | 0.87[0.46,1.64] | 0.84[0.59,1.2] | 0.93 | |
| 8-≤10 h | 1.16[0.58,2.35] | 1.16[0.82,1.64] | 0.87 | |
| > 10 h | 0.95[0.22,4.16] | 1.87[1.26,2.79] | 0.33 | |
| Sex | Male | Female | ||
| ≤ 6 h | 1(Reference) | 1(Reference) | ||
| 6-≤8 h | 0.81[0.49,1.36] | 0.86[0.58,1.27] | 0.96 | |
| 8-≤10 h | 1.28[0.76,2.15] | 1.11[0.75,1.64] | 0.66 | |
| > 10 h | 1.35[0.66,2.77] | 2.02[1.29,3.16] | 0.33 | |
| Residence | Urban | Rural | ||
| ≤ 6 h | 1(Reference) | 1(Reference) | ||
| 6-≤8 h | 1.15[0.52,2.55] | 0.80[0.57,1.13] | 0.30 | |
| 8-≤10 h | 1.35[0.57,3.16] | 1.12[0.80,1.57] | 0.45 | |
| > 10 h | 2.56[0.77,8.48] | 1.76[1.18,2.62] | 0.66 | |
| Marital status | Married | Other | ||
| ≤ 6 h | 1(Reference) | 1(Reference) | ||
| 6-≤8 h | 0.85[0.48,1.5] | 0.88[0.61,1.27] | 0.79 | |
| 8-≤10 h | 1.11[0.61,2.02] | 1.18[0.82,1.7] | 0.90 | |
| > 10 h | 1.57[0.66,3.75] | 1.88[1.23,2.87] | 0.84 | |
In the sensitivity assessment of sleep duration and dementia risk, we obtained similar results (Supplementary Material, Table S1). After excluding subjects who developed dementia within 365 days of their baseline visit, the results remained consistent with the primary analysis. After excluding participants with a sleep duration exceeding 15 h per day, the association between sleep duration and dementia risk remained consistent with the primary analysis.
Discussion
In this prospective cohort survey, we found that excessive sleep duration increased the risk of dementia at 7-years of follow-up. Participants with sleep durations exceeding 10 h demonstrated an enhanced risk of dementia compared to those in the reference (shortest sleep duration) group. The test for non-linearity in the association between sleep duration and dementia did not yield significant non-linear relationships. Given that the CLHLS study included only an older Chinese population, the generalizability of these findings to other populations requires further investigation.
Our findings were consistent with the literature, which indicated that prolonged sleep duration and longer sleep are linked to an increased risk of dementia [11, 31–33]. Besides, prolonged sleep duration suggests the presence of a potential sleep disorder related to fragmented sleep, for instance, obstructive sleep apnea syndrome [26]. It is now understood that prolonged sleep duration acts on dementia indirectly through a debilitated state or depression [27]. Previous studies have suggested that prolonged sleep duration is a prevalent phenomenon in the early phases of neurodegeneration [22].
It is well-established that prolonged sleep duration and earlier bedtime can indicate circadian rhythm desynchronization, especially in older adults. These circadian disruptions may affect cerebral blood flow, melatonin secretion, beta-amyloid clearance, the lymphatic system, metabolism, and inflammatory processes, which may further raise the risk of dementia [28–30]. The mechanism underlying the association between prolonged sleep duration and dementia may be attributed to neurodegenerative changes within brain regions regulating sleep-wake cycles, specifically the suprachiasmatic nucleus (SCN) and its pathways involving the pineal gland and retina [31]. Phases of the wake-promoting neuropeptide hypothalamic secretin-1 and hypothalamic secretin-1 neurons and their associated neurons have also been reported to be reduced [32], which represents one of the mechanisms contributing to the long sleep duration in Alzheimer’s disease patients. Moreover, since the preclinical stage of dementia typically persists for at least a decade [33], prolonged sleep duration may represent a critical modifiable risk factor or a target for early clinical intervention.
Accordingly, the significant association between excessive sleep duration and dementia risk identifies a potential clinical awareness. Family physicians and healthcare professionals could consider comprehensive neurological assessments for patients reporting prolonged sleep patterns [34]. Besides, the effective policies of sleep health may show benefits in older adults with dementia who are at a high risk [10]. Building upon the above findings, promoting optimal sleep duration should be a key public health policy objective to improve population health, with emphasis on older populations.
The limitations of this study should be acknowledged. First, sleep duration was determined based on participants’ self-reports rather than objective measurements obtained through wearable or other smart devices, which could introduce bias. Nonetheless, some studies have demonstrated that subjectively and objectively evaluated sleep may reflect multifaceted features of sleep [35, 36]. Secondly, given that specific cohort characteristics may have influenced the study outcomes, we performed stratified analyses and sensitivity tests to verify the robustness and internal validity of our findings. Finally, certain covariates may have been broadly defined; for instance, physical activity was assessed as a categorical variable, lacking comprehensive data on its exact duration or intensity. Furthermore, the potential for unmeasured or residual confounding cannot be entirely excluded. Addressing these latent variables remains a primary objective for future research.
Conclusion
This nationwide prospective cohort study demonstrates a significant association between sleep duration and the incidence of dementia. This study indicated that prolonged sleep duration is a potential risk factor for dementia among older Chinese adults. The mechanistic role of sleep duration in the onset and progression of dementia warrants further investigation.
Supplementary Information
Acknowledgements
The authors acknowledge the CLHLS team for making the data available. We are grateful to the researchers and participants of the CLHLS.
Abbreviations
- BMI
Body mass index
- CLHLS
Chinese longitudinal healthy longevity survey
- HR
Hazard ratios
- RMB
China Yuan
- SD
Standard deviation
Authors’ contributions
ZF assessed the literature and drafted the manuscript; XL and XL generated the figures, analyzed and interpreted the findings; MZ and YW assessed the literature and drafted the manuscript and were responsible for the revision; all authors reviewed the manuscript.
Funding
This work was funded by the Project of NINGBO Leading Medical & Health Discipline (Grant No. 2022-B12 and 2026-A25); Ningbo Natural Science Foundation (Grant No.202003N4248) and Medical Scientific Research Foundation of Zhejiang Province (Grant No. 2025KY1404).
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The CLHLS study protocol was approved by the Biomedical Ethics Committee of Peking University (IRB00001052-13074). Consent was obtained from the study subjects. All procedures involving human participants in the study were conducted in accordance with the Declaration of Helsinki.
Consent for publication
All authors approved the final manuscript and the submission to this journal.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Greenberg SM, Bacskai BJ, Hernandez-Guillamon M, Pruzin J, Sperling R, van Veluw SJ. Cerebral amyloid angiopathy and alzheimer disease - one peptide, two pathways. Nat Rev Neurol. 2020;16(1):30–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Iadecola C, Duering M, Hachinski V, Joutel A, Pendlebury ST, Schneider JA, et al. Vascular cognitive impairment and dementia: JACC scientific expert panel. J Am Coll Cardiol. 2019;73(25):3326–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Ravindranath V, Sundarakumar JS. Changing demography and the challenge of dementia in India. Nat Rev Neurol. 2021;17(12):747–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Feng S, Gao D, Liao F, Zhou F, Wang X. The health effects of ambient PM2.5 and potential mechanisms. Ecotoxicol Environ Saf. 2016;128:67–74. [DOI] [PubMed] [Google Scholar]
- 5.de Rojas I, Moreno-Grau S, Tesi N, Grenier-Boley B, Andrade V, Jansen IE, et al. Common variants in alzheimer’s disease and risk stratification by polygenic risk scores. Nat Commun. 2021;12(1):3417. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chételat G, Teunissen CE, et al. Alzheimer’s disease. Lancet (London England). 2021;397(10284):1577–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Zhang Y, Chen S-D, Deng Y-T, You J, He X-Y, Wu X-R, et al. Identifying modifiable factors and their joint effect on dementia risk in the UK biobank. Nat Hum Behav. 2023;7(7):1185–95. [DOI] [PubMed] [Google Scholar]
- 8.Wang J, Jiao D, Zhao X, Tian Y, Li H, Li X, et al. Childhood loneliness and cognitive decline and dementia risk in Middle-Aged and older adults. JAMA Netw Open. 2025;8(9):e2531493. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the lancet commission. Lancet (London England). 2020;396(10248):413–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Sabia S, Fayosse A, Dumurgier J, van Hees VT, Paquet C, Sommerlad A, et al. Association of sleep duration in middle and old age with incidence of dementia. Nat Commun. 2021;12(1):2289. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Lutsey PL, Misialek JR, Mosley TH, Gottesman RF, Punjabi NM, Shahar E, et al. Sleep characteristics and risk of dementia and alzheimer’s disease: the atherosclerosis risk in communities study. Alzheimers Dement. 2018;14(2):157–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Fan L, Xu W, Cai Y, Hu Y, Wu C. Sleep duration and the risk of dementia: A systematic review and Meta-analysis of prospective cohort studies. J Am Med Dir Assoc. 2019;20(12):1480–e14875. [DOI] [PubMed] [Google Scholar]
- 13.Xu W, Tan C-C, Zou J-J, Cao X-P, Tan L. Sleep problems and risk of all-cause cognitive decline or dementia: an updated systematic review and meta-analysis. J Neurol Neurosurg Psychiatry. 2020;91(3):236–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Huang S-Y, Li Y-Z, Zhang Y-R, Huang Y-Y, Wu B-S, Zhang W, et al. Sleep, physical activity, sedentary behavior, and risk of incident dementia: a prospective cohort study of 431,924 UK biobank participants. Mol Psychiatry. 2022;27(10):4343–54. [DOI] [PubMed] [Google Scholar]
- 15.Sun R, Cao H, Zhu X, Liu J-P, Dong E. Current aging research in China. Protein Cell. 2015;6(5):314–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Sommerlad A, Sabia S, Singh-Manoux A, Lewis G, Livingston G. Association of social contact with dementia and cognition: 28-year follow-up of the Whitehall II cohort study. PLoS Med. 2019;16(8):e1002862. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Liu R, Ren Y, Hou T, Liang X, Dong Y, Wang Y, et al. Associations of sleep timing and time in bed with dementia and cognitive decline among Chinese older adults: A cohort study. J Am Geriatr Soc. 2022;70(11):3138–51. [DOI] [PubMed] [Google Scholar]
- 18.Hao Z, Ruggiano N, Li Q, Guo Y, Pan X. Disparities in depression among Chinese older adults with neurodegenerative diseases. Aging Ment Health. 2022;26(3):632–8. [DOI] [PubMed] [Google Scholar]
- 19.Wu S, Lv X, Shen J, Chen H, Ma Y, Jin X, et al. Association between body mass index, its change and cognitive impairment among Chinese older adults: a community-based, 9-year prospective cohort study. Eur J Epidemiol. 2021;36(10):1043–54. [DOI] [PubMed] [Google Scholar]
- 20.Hou X-Z, Wu Q, Lv Q-Y, Yang Y-T, Li L-L, Ye X-J, et al. Increasing the frequency of plant-based food intake in daily diets reduces the risk of cardiovascular disease among elderly chinese: a cohort study. Front Nutr. 2024;11:1440025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Wang J, Li S, Hu Y, Ren L, Jiang Y, Yu M, et al. The bi-directional relationships between diversified leisure activity participation and cognitive function in older adults in china: separating between-person effects from within-person effects. BMC Geriatr. 2024;24(1):426. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Biggs ML, Mukamal KJ, Luchsinger JA, Ix JH, Carnethon MR, Newman AB, et al. Association between adiposity in midlife and older age and risk of diabetes in older adults. JAMA. 2010;303(24):2504–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Zhang M, Lv X, Chen Y, Tu L, Fan Z, Yao Y, et al. Excessive sleep increased the risk of incidence of cognitive impairment among older Chinese adults: a cohort study based on the Chinese longitudinal healthy longevity survey (CLHLS). Int Psychogeriatr. 2022;34(8):725–34. [DOI] [PubMed] [Google Scholar]
- 24.Pu L, Zhang R, Wang H, Zhao T, Zeng J, Yang H, et al. Association between sleep pattern and incidence of hypertension: A prospective cohort study of older adult participants in the Chinese longitudinal healthy longevity survey. Arch Gerontol Geriatr. 2024;119:105314. [DOI] [PubMed] [Google Scholar]
- 25.Tian G, Shuai J, Li R, Zhou T, Shi Y, Cheng G, et al. Association between playing cards/mahjong and risk of incident dementia among the Chinese older adults: a prospective cohort study. Front Aging Neurosci. 2022;14:966647. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Zeng Y, Feng Q, Hesketh T, Christensen K, Vaupel JW. Survival, disabilities in activities of daily living, and physical and cognitive functioning among the oldest-old in china: a cohort study. Lancet (London England). 2017;389(10079):1619–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Deng Y, Gao Q, Yang D, Hua H, Wang N, Ou F, et al. Association between biomass fuel use and risk of hypertension among Chinese older people: A cohort study. Environ Int. 2020;138:105620. [DOI] [PubMed] [Google Scholar]
- 28.Du M, Tao L, Zhu L, Liu J. Association between biomass fuel use and the risk of cognitive impairment among older populations in china: a population-based cohort study. Environ Health. 2021;20(1):21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Jia B, Dao J, Han J, Huang Z, Sun X, Zheng X, et al. LINC00958 promotes the proliferation of TSCC via miR-211-5p/CENPK axis and activating the JAK/STAT3 signaling pathway. Cancer Cell Int. 2021;21(1):147. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Chinese Longitudinal Healthy Longevity Survey (CLHLS), Cross-Sectional Data (2011). Center for Healthy Aging and Development Studies, Peking University. 2015. https://opendata.pku.edu.cn/dataset.xhtml?persistentId=doi:10.18170/DVN/WBO7LK.
- 31.Westwood AJ, Beiser A, Jain N, Himali JJ, DeCarli C, Auerbach SH, et al. Prolonged sleep duration as a marker of early neurodegeneration predicting incident dementia. Neurology. 2017;88(12):1172–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Ohara T, Honda T, Hata J, Yoshida D, Mukai N, Hirakawa Y, et al. Association between daily sleep duration and risk of dementia and mortality in a Japanese community. J Am Geriatr Soc. 2018;66(10):1911–8. [DOI] [PubMed] [Google Scholar]
- 33.Larsson SC, Wolk A. The role of lifestyle factors and sleep duration for Late-Onset dementia: A cohort study. J Alzheimers Dis. 2018;66(2):579–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Wall J, Xie H, Wang X. Interaction of sleep and cortical structural maintenance from an individual person microlongitudinal perspective and implications for precision medicine research. Front Neurosci. 2020;14:769. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Winer JR, Deters KD, Kennedy G, Jin M, Goldstein-Piekarski A, Poston KL, et al. Association of short and long sleep duration with Amyloid-β burden and cognition in aging. JAMA Neurol. 2021;78(10):1187–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Lauderdale DS, Knutson KL, Yan LL, Liu K, Rathouz PJ. Self-reported and measured sleep duration: how similar are they? Epidemiology. 2008;19(6):838–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.