Sleep quality, APOE ε4, and Alzheimer’s disease: associations from two prospective cohort studies and mechanisms by plasma proteomic analysis

Liang-Yu Huang; Lan Tan; Chen-Chen Tan; Xin-Yu Zhang; Xu Lin; Juan-Juan Zou; Yu-Jing Lin; Yu-Ying Zhang; Quan Hao; Zi-Qi Zhang; for the Alzheimer’s Disease Neuroimaging Initiative; Wei Xu

doi:10.1186/s12916-025-04255-z

. 2025 Aug 6;23:462. doi: 10.1186/s12916-025-04255-z

Sleep quality, APOE ε4, and Alzheimer’s disease: associations from two prospective cohort studies and mechanisms by plasma proteomic analysis

Liang-Yu Huang ^1,^2,^#, Lan Tan ^1,^2,^#, Chen-Chen Tan ^1,², Xin-Yu Zhang ³, Xu Lin ⁴, Juan-Juan Zou ⁵, Yu-Jing Lin ⁶, Yu-Ying Zhang ¹, Quan Hao ¹, Zi-Qi Zhang ⁶; for the Alzheimer’s Disease Neuroimaging Initiative, Wei Xu ^1,^2,^✉

PMCID: PMC12329939 PMID: 40770318

Abstract

Background

The relationship between sleep quality and Alzheimer's disease (AD), and its interaction with genetic susceptibility, remains unclear. Our study explores the complex association between sleep quality and AD risk, focusing on the moderating role of the APOE ε4 allele.

Methods

Linear regression models, linear mixed-effects models, and Cox proportional hazard models were conducted in 321,905 non-demented participants from UK Biobank (UKB, mean age = 56.49, mean follow-up: 12.3 years) and 1,598 non-demented participants (mean age = 73.19, mean follow-up: 3.90 years) from Alzheimer's Disease Neuroimaging Initiative (ADNI). The interaction terms of sleep by APOE ε4 status were added in all analyses and stratified analyses were further performed. Proteomic and bioinformatic analyses were conducted to explore the biological mechanisms by which sleep and its interaction with APOE ε4 influence the development of AD.

Results

Poor sleep quality was significantly associated with worse cognition, faster hippocampal atrophy, and increased AD risk (HR = 1.05 in UKB and HR = 1.37 in ADNI). Notably, these associations were intensified in APOE ε4 carriers. Proteomic analyses identified eleven proteins linked to both poor sleep and AD risk (P < 1.72 × 10^–5). These proteins were enriched mainly in inflammatory and metabolic pathways. Growth differentiation factor 15 was identified as the bridge linking poor sleep and AD risk specifically in APOE ε4 carriers.

Conclusions

Poor sleep is associated with increased risk of AD, possibly by dysregulating peripheral inflammatory responses and metabolic pathways. The interaction between poor sleep and APOE ε4 may further enhance AD risk. Future studies are warranted to test whether this interaction was driven by neuroinflammation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12916-025-04255-z.

Keywords: Sleep quality index, Cognition, Hippocampal volume, Alzheimer’s disease

Background

Dementia represents one of the most rapidly escalating public health issues, impacting an estimated 30-40 million individuals globally. Alzheimer’s disease (AD) is the most prevalent form of dementia, influenced by a combination of genetic and environmental factors [1]. APOE ε4 was the strongest genetic risk factor contributing to the development of AD [2]. Recent studies have reported the critical roles of gene–environment interactions in AD occurrence [3]. For example, our previous study found that APOE-specific AD risk might be offset to some extent by adhering to a healthy dietary pattern [4]. Recently, unhealthy sleep behaviors, including short or long sleep duration, insomnia, snoring, late chronotype, and excessive daytime sleepiness, have been identified as significant risk factors for incident dementia and AD [5, 6]. However, the evidence regarding this association, especially the roles of sleep-APOE interaction in the development of AD is less robust and inconsistent. Emerging evidence suggested that the APOE ε4 genotype plays a crucial role in modifying the risk of developing AD in relation to insomnia and circadian misalignment [7, 8]. Conversely, other studies detected significant associations between sleep behaviors and AD risk only in the absence of APOE ɛ4 [9, 10]. More importantly, the mechanisms underpinning the potential interaction are unclear.

In addition, other issues remain unresolved in this field. First, sleep behaviors are typically correlated and may affect health conditions in a concerted manner [11]. However, most previous studies have failed to consider the complexity and correlations among sleep behaviors. It remains largely unknown whether and how the sleep quality index (SQI), which integrates important sleep behaviors [11, 12], moderates the risk of AD. Second, existing studies tend to ignore dynamic exposure to sleep problems during follow-up and instead adopt a single measurement of sleep behavior at baseline, which could introduce measurement bias. Third, although poor sleep quality is associated with increase in white matter hyperintensities (WMH) and poorer white matter microstructure in specific tracts [13], it is unclear whether sleep quality is associated with neurodegeneration of hippocampus, a featured neuroimaging marker of AD.

Herein, we generated a comprehensive SQI by jointly evaluating multiple sleep behaviors in the UK Biobank (UKB), and a dynamic SQI using the longitudinal data on sleep quality from Alzheimer's Disease Neuroimaging Initiative (ADNI). We aimed to examined (1) the associations of sleep quality with cognitive function, hippocampal volume, and AD risk, (2) the moderating roles of sleep in influencing cognitive decline, hippocampal atrophy, and AD risk associated with APOE ε4 status, and (3) the biological mechanisms for the associations of sleep and sleep-by-APOE ε4 interaction with AD.

METHODS

Study population

The UKB (www.ukbiobank.ac.uk) was a large-scale longitudinal cohort study which recorded the baseline phenotypic and genetic data of over 500,000 participants of 22 assessment centers in England, Wales, and Scotland from 2006 to 2010 [14]. Participants were followed up until the earliest occurrence of either first diagnosis, death, loss to follow-up, or the final date with available data. Participants with dementia and major neuropsychiatric diseases at baseline were excluded from the present study. The UKB received approval from the North West Multicenter Research Ethics Committee, and all participants provided written informed consent.

To validate the findings from UKB and to strengthen the causal relationships, we further conducted data analyses of participants who were free of dementia and major neuropsychiatric diseases from the ADNI database (adni.loni.usc.edu). The multicenter ADNI was established to evaluate clinical, imaging, genetic, and biochemical biomarkers of AD, with participants aged 55 to 90 years recruited from the United States and Canada. Each ADNI participant underwent systemic neuropsychological examinations, as well as neurological and physical assessments at baseline and at annual follow-up. The ADNI received approval from the institutional review boards of all participating institutions, and written informed consent was obtained from all participants or their guardians in accordance with the Declaration of Helsinki.

Assessments of sleep behavior and classification of sleep quality

In UKB cohort, we utilized an algorithm derived from self-reported sleep quality data, initially introduced in 2020 [11], due to the lack of specialized sleep questionnaires during the baseline survey. This algorithm was used to create a SQI with five sleep-related items, including snoring, chronotype, daytime sleepiness, sleep duration, and insomnia [11], demonstrating its effectiveness as an alternative method for assessing the sleep quality. To further enhance the evaluation of sleep quality in the UKB, we incorporated six self-reported sleep behaviors: items previously used in the 5-item sleep score, along with the “difficulties in getting up in the morning” trait, which has been linked to reduced health-span [15] and accelerated biological aging [12]. This additional component was included to provide a more comprehensive measure of sleep quality, reflecting a broader range of behaviors that are predictive of long-term health outcomes. Low-risk sleep behaviors were defined as follows: early chronotype ('morning' or 'more morning than evening'), sleeping 7–8 h per day, reporting never or rarely experiencing insomnia symptoms, no self-reported snoring, no frequent daytime sleepiness ('never/rarely' or 'sometimes'), and finding it easy to get up in the morning ('fairly easy' or 'very easy'). Each sleep component was scored as 0 if the participant was classified as low risk for that factor, and 1 if considered high risk. The scores for all components were then summed to generate a SQI ranging from 0 (best) to 6 (worst), with higher scores reflecting poorer sleep quality. We then classified the overall sleep patterns as ‘good sleep quality’ (SQI = 0–1, as reference), ‘general sleep quality’ (SQI = 2–3), and ‘poor sleep quality’ (SQI = 4–6) based on the continuous term of SQI.

The SQI in ADNI was calculated based on longitudinal data of sleep item in the Neuropsychiatric Inventory (NPI) scale. The informants of participants were asked “does the participant have difficulty falling asleep, awaken you during the night, rise too early in the morning, or sleep excessively during the day?”; “if yes, rate the severity: 1—mild (noticeable, but not a significant change).; 2—moderate (significant, but not a dramatic change).; 3—severe (very marked or prominent)”. Participants were interviewed annually for these sleep-related questions. Those with less than 1-year follow-up sleep data were excluded. Longitudinal data of sleep were extracted for further analyses only if they were in parallel with longitudinal measurements of cognitive or biomarker. The “exposure (answering yes to the question) intensity” [ratio] was calculated by dividing “exposure” times by total interview times. The SQI was calculated by multiplying ratio with the average severity score. The participants were further categorized into three groups according to tertiles of SQI: good sleep (0 ≤ SQI ≤ 0.1, as reference), general sleep (0.1 < SQI ≤ 0.5), and poor sleep quality (SQI > 0.5).

Cognitive measures

In UKB, five cognitive domains were assessed at baseline, including visuospatial memory (VM), measured by the average number of incorrect matched pairs; processing speed, indicated by mean reaction time (RT); prospective memory (PM), based on the total number of times an intention was forgotten in the PM task; fluid intelligence (FI), reflected by total scores on a set of cognitive tasks; and working memory (WM), assessed by the total number of digits correctly recalled. Higher scores in VM, RT, and PM indicated poorer cognitive performance, while higher scores in FI and WM reflected better performance. In ADNI, global cognition was evaluated by the Alzheimer’s Disease Assessment Scale (ADAS). Cognitive domains were assessed by administering the neuropsychological battery which included components indicative of memory function (MEM) and executive function (EF) [16]. Cognitive assessments in ADNI were carried out at both baseline and follow-up.

Hippocampal volume measurement

Hippocampal volumes in both UKB and ADNI were derived from magnetic resonance imaging (MRI), of which the T1-weighted and T2-FLAIR structural images were obtained in a straight sagittal orientation and underwent central preprocessing to derive the hippocampal volume. Image processing details in UKB and ADNI could be found elsewhere [17, 18]. In the UKB cohort, hippocampal volume represents the absolute volume at a single time point. In the ADNI cohort, hippocampal volume is measured at each visit, and the analysis is based on the annual rate of change in hippocampal volume derived from longitudinal MRI scans.

AD diagnoses

AD diagnose in UKB was defined as code 331.0 in ICD-9 and codes F00 and G30 in ICD-10 over a follow-up period from 2007 to 2020, using data from hospital inpatient records, death certificates, primary care records, and self-reports. In ADNI, AD was diagnosed according to the National Institute of Neurological Disorders and Stroke–Alzheimer Disease and Related Disorders criteria [19], whereas mild cognitive impairment was diagnosed according to the Mayo Clinic criteria [20].

Blood proteomics

For each participant in the UKB, blood samples were drawn into EDTA tubes and immediately centrifuged at 2,500 g for 10 min at 4 °C to separate the plasma. The plasma supernatant was then divided into aliquots and promptly stored at − 80 °C until further analysis. Using dual barcoded antibody technology on the Olink platform, the UKB conducted multiplexed proteomic assays on approximately 55,000 plasma samples, primarily collected at baseline. This approach produced specific and semiquantitative data for 2,923 protein assays in multiplex. Of the 2,923 protein assays available, we excluded proteins with more than 20% missing values (n = 12), resulting in a total of 2,911 protein assays retained for analyses.

Covariate assessments

We adjusted for the covariates, including age, sex, education, and APOE ε4 carrier status, smoking status, anxiety, depression, obesity, diabetes, hypertension, hyperlipidemia, stroke, and sleep medications, to lower potential confounding bias in both cohorts. APOE ε4 carrier status (rs7412 and rs429358) were determined by genetic information. Sociodemographic and behavioral confounders were collected by the questionnaire and information on comorbidities were ascertained based on self-reported information and medical records. Participants were dichotomized according to their self-reports on whether they took any sleep medication (including benzodiazepines, Z-drugs, and melatonin).

Statistical analyses

Baseline characteristics were summarized as mean and standard deviation (SD) for continuous variables, and as counts with percentages for categorical variables. We used χ² tests for categorical variables and the t-test or Mann–Whitney U test for continuous variables to assess intergroup differences in baseline characteristics. The values of dependent variables with skewed distribution in linear regression models were normalized using log-transformation method. The values greater or smaller than five fold SD from the mean value were considered as extreme outliers. We removed extreme outliers of hippocampal volume and then performed standardization by generating z-scores.

First, linear regressions were used to explore the cross-sectional relationships of SQI (the independent variable) with cognitive performance (dependent variables) in UKB cohort. Interaction effect between SQI and APOE ε4 status on cognition was tested and stratified analyses were further performed. Besides, the effects of SQI and its interaction with APOE ε4 status on longitudinal cognitive changes were examined using linear mixed-effects models in ADNI cohort. The linear mixed effects models were utilized due to their ability to manage unbalanced and censored data, as well as incorporate time as a continuous variable [21]. The overall significance of the three-way interaction term was evaluated using a likelihood ratio test, which compared the full model to a nested model without the three-way interaction term. Based on the APOE ε4 status and sleep quality, we further categorized the population as “good sleep and non-APOE ε4 carrier”, “good sleep and APOE ε4 carrier”, “poor sleep and non-APOE ε4 carrier”, “poor sleep and APOE ε4 carrier”. We fitted a linear mixed-effects model against the cognitive performance to further depict the combined genetic and sleep-related effects on cognition, in which Wald tests were adopted to compare the slope of each group. Next, we used similar approaches to test the association of sleep quality and its interaction by APOE ε4 with hippocampal volume (Fig. 1).

Fig. 1 — Study design and workflow. The cross-sectional associations of sleep quality with cognitive function and hippocampal volume were first explored in UK Biobank. Using the longitudinal data of sleep quality from ADNI cohort, we then examined the causal effects of sleep quality on cognitive decline and hippocampal atrophy. Further, the relationships between sleep quality and AD risk were explored in both UKB and ADNI cohorts. Interaction analyses by *APOE ε4* status were performed to investigate whether strata effects existed in all above three models. Proteomic and bioinformatic analyses were conducted to explore the biological mechanisms by which sleep and its interaction with *APOE* ε4 influence the development of AD. Abbreviations: AD, Alzheimer’s disease; ADAS, Alzheimer’s Disease Assessment Scale; MEM, memory function; EF, executive function

Then, the associations between sleep quality and incident probable AD in both UKB and ADNI cohorts were depicted using the time-dependent Cox proportional hazard models, in which results would be described as hazard ratios (HR) and 95% confidence intervals (95%CI). Cox proportional hazard models were conducted in longitudinal data when researchers focused on the time until a specific event [22]. Right censoring would be applied to account for scenarios where a subject does not develop AD before their last recorded observation or exits the study before its conclusion. The additive and multiplicative interactive effects of SQI and APOE ε4 status on the risk of incident AD were tested. Stratified analyses were performed to explore whether good sleep could attenuate APOE ε4-related AD risk. The proportional hazards assumption was checked using Schoenfeld’s global test to ensure that the proportional hazards assumption was not violated (P > 0.05). (Fig. 1).

Finally, comprehensive proteomic analyses combined with bioinformatic analyses were conducted to explore the biological mechanisms of poor sleep affecting AD risk. Cox proportional hazard models (for AD risk) and multiple logistic regression models (for poor versus good sleep) were employed to identify proteins associated with both sleep quality and incident AD risk in the total sample. Similar analyses were conducted among APOE ε4 carriers and among age- and sex-matched APOE ε4 non-carriers, respectively, to investigate the biological mechanisms underlying the interaction between sleep and APOE ε4 in AD development. Bonferroni corrections were applied to define the significance cutoff (P < 1.72 × 10^–5, number of proteins tested = 2,911). After screening out the proteins which were consistently positive or negative associated with incident AD and poor sleep, functional enrichment analyses were performed using the STRING database (http://string-db.org). (Fig. 1).

Considering that some discrepancies have been observed in how women report their sleep quality compared to men, we conducted sex-stratified analyses as a secondary analysis following the principal analysis described above. Specifically, we repeated all key models-including linear regressions, linear mixed-effects models, and Cox proportional hazard models-separately in female and male subsamples. This additional analysis provides a more nuanced understanding of how sleep quality may differently affect cognitive decline, hippocampal atrophy, and AD risk across genders.

All the models were calculated with adjustments for covariates, including age, sex, education, and APOE ε4 status (model 1). As for ADNI cohort, we additionally adjusted for cognitive status (mild cognitive impairment = 1, normal cognition = 0) and follow-up times (practice effect) before AD diagnosis (model 1). Sensitivity analyses were conducted by adding sleep medications and history of sleep apnea, hypertension, diabetes, stroke, smoking, hyperlipidemia, depression, anxiety, and obesity to the covariates in Model 1 (Model 2). A two-sided P value of < 0.05 was deemed significant unless otherwise specified. Statistical analyses and figure preparation were conducted using R software version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Characteristics of participants

Baseline characteristics of the participants stratified by the incident AD status are provided in Table 1. As for UKB cohort, a total of 321,905 individuals free of dementia and major psychological diseases were included in this study. Among those, the mean age was 56.49 (8.11) years, and 45.18% were male. During a median follow-up of 12.3 years, 2,690 incident AD events were recorded. As for ADNI, a total of 1,598 individuals free of dementia and major psychological diseases were included in this study. Among those, the mean age was 73.19 (6.99) years, and 46.13% were male. During a mean follow-up of 3.9 years, 293 incident AD events were recorded.

Table 1.

Baseline characteristics of participants

Characteristics		UKB				ADNI
Characteristics	Total (n = 321,905)	AD (n = 2,690)	Non-Demented (n = 319,215)	P	Total (1,598)	AD (n = 293)	Non-Demented (n = 1,305)	P
Age (years)	56.49 (8.11)	64.72 (4.19)	56.43 (8.10)	< 0.001	73.08 (6.99)	73.39 (6.86)	73.01 (7.01)	0.357
Male (%)	145,435 (45.18)	1,258 (46.77)	144,177 (45.17)	0.098	744 (46.56)	132 (45.05)	612 (46.90)	0.604
APOE ε4 status (%)	91,694 (28.48)	1,679 (62.42)	90,015 (28.20)	< 0.001	665 (41.61)	187 (63.82)	478 (36.63)	< 0.001
Educational level	15.20 (5.22)	13.30 (4.77)	15.21 (5.22)	< 0.001	16.24 (2.68)	16.11 (2.88)	16.28 (2.63)	0.571
TDI	−1.46 (3.01)	−1.36 (3.13)	−1.47 (3.01)	0.072	-	-	-
BMI (kg/m²)	27.30 (4.69)	27.34 (4.49)	27.30 (4.70)	0.636	27.10 (4.83)	27.12 (4.85)	27.15 (4.92)	0.932
Smoking status (%)	143,319 (44.52)	1,361 (50.59)	141,958 (44.47)	< 0.001	222 (13.89)	45 (15.36)	177 (13.56)	0.455
Anxiety (%)	4,308 (1.35)	43 (1.60)	4,265 (1.34)	0.236	102 (6.38)	27 (9.22)	75 (5.75)	0.034
Depression (%)	14,885 (4.62)	166 (6.17)	14,719 (4.61)	< 0.001	325 (20.34)	71 (24.23)	254 (19.46)	0.077
Obesity (%)	120,875 (37.55)	1,035 (38.48)	119,840 (37.54)	0.244	571 (35.73)	102 (34.81)	469 (35.94)	0.736
Diabetes (%)	15,057 (4.68)	293 (10.89)	14,764 (4.63)	< 0.001	129 (8.07)	26 (8.87)	103 (7.89)	0.555
Hypertension (%)	83,426 (25.92)	1,102 (40.97)	82,324 (25.79)	< 0.001	705 (44.12)	138 (47.10)	567 (43.45)	0.269
Hyperlipidemia (%)	38,375 (11.92)	629 (23.38)	37,745 (11.82)	< 0.001	320 (20.03)	59 (20.14)	261 (20.00)	0.949
Stroke (%)	3,926 (1.22)	78 (2.90)	3,848 (1.21)	< 0.001	56 (3.50)	15 (5.12)	41 (3.14)	0.112
Sleep medications (%)	2,882 (0.90)	47 (1.75)	2,835 (0.89)	< 0.001	176 (11.01)	25 (8.53)	151 (11.57)	0.148

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The t-test or Mann–Whitney U test (for continuous variables) and χ² tests (for categorical variables) were used to test the difference of baseline characteristics

Abbreviations AD Alzheimer’s disease, TDI Townsend deprivation index, BMI body mass index

Sleep quality, APOE ε4, and cognitive performance

In UKB samples, individuals with poor sleep quality had worse cognitive performance, as indicated by lower level of FI (P = 4.38 × 10^–5) and higher levels of RT (P = 1.45 × 10^–9) and VM (P = 2.28 × 10^–3). The associations were weakened after controlling for additional covariates in sensitivity analyses (Additional file 1: Table S1). Though no cross-sectional interaction by APOE ε4 status was found, the associations between sleep quality and cognitive measures tend to be more significant in APOE ε4 carriers (Fig. 2A-C, Additional file 1: Table S2). In APOE ε4 carrier subgroup, individuals with poor sleep had worse cognitive performance (β = 0.014, P = 8.43 × 10^–6 for RT; β = 0.056, P = 1.01 × 10^–3 for VM; β = −0.186, P = 1.04 × 10^–3 for FI) compared to those with good sleep. In APOE ε4 non-carriers, poor sleep was also associated with worse cognitive performance, with weakened effect sizes compared to the APOE ε4 carriers (β = 0.012, P = 5.44 × 10^–4 for RT; β = 0.030, P = 3.71 × 10^–3 for VM; β = −0.134, P = 1.53 × 10^–3 for FI). (Fig. 2A-C; Additional file 1: Table S3).

Fig. 2 — Cross-sectional and longitudinal associations of sleep quality with cognitive function and hippocampal volume. A-C Poor sleep quality was associated with worse cognitive function in UK Biobank. The associations between sleep quality and cognitive measures tend to be more significant in both *APOE* ε4 carriers. D-F Poor sleep quality was associated with faster cognitive decline in ADNI. Interaction effects by *APOE* ε4 status were found (P < 0.05), for which the association between poor sleep (compared to those of good sleep) and faster cognitive decline was stronger in *APOE* ε4 carriers than in non-carriers. G Poor sleep quality was associated with smaller hippocampal volume in UK Biobank. The associations between sleep quality and hippocampal volume tend to be more significant in both *APOE* ε4 carriers. H Poor sleep quality was associated with faster hippocampal atrophy with aging in ADNI. The association between poor sleep (compared to those of good sleep) and faster hippocampal atrophy was significant only in *APOE* ε4 carriers. Abbreviations: ADAS, Alzheimer’s Disease Assessment Scale; MEM, memory function; EF, executive function. P’, p for Likelihood ratio test. *, p for linear regression analyses < 0.05 in UKB or p for Wald test < 0.05 in ADNI

In ADNI samples, poor sleep quality was associated with faster cognitive decline with aging, as indicated by the larger absolute value of slope for EF (P < 2.00 × 10^–16), MEM (P < 2.00 × 10^–16) and ADAS (P < 2.00 × 10^–16). The associations remained significant after controlling for more covariates in sensitivity analysis (Additional file 1: Table S1). Interaction effects by APOE ε4 status were found (Likelihood ratio tests, P = 0.013 for EF; P = 0.034 for MEM; P = 9.36 × 10^–3 for ADAS), for which the association between poorer sleep quality and faster cognitive decline was stronger in APOE ε4 carriers than in non-carriers (Additional file 1: Table S2). In APOE ε4 carriers, individuals of poor sleep had faster cognitive decline compared to those of good sleep (β = −0.073, P for Wald test = 3.80 × 10^–5 for EF; β = −0.068, P for Wald test = 4.70 × 10^–6 for MEM; β = 0.101, P for Wald test = 4.10 × 10^–3 for ADAS). In APOE ε4 non-carriers, poor sleep was also linked to faster cognitive decline but with weakened effects than in APOE ε4 carriers (β = −0.049, P for Wald test = 1.20 × 10^–4 for EF; β = −0.042, P for Wald test = 5.30 × 10^–5 for MEM; β = 0.098, P for Wald test = 1.70 × 10^–3 for ADAS). (Fig. 2D-F; Additional file 1: Table S3).

Sleep quality, APOE ε4, and hippocampus

In UKB samples, individuals with poor sleep quality had smaller hippocampal volume (P = 0.018). However, the significance disappeared when adding more covariates in Model 2 (Additional file 1: Table S4). No potential interaction effect due to APOE ε4 status was found (P = 0.744). Individuals with poor sleep had smaller hippocampal volume over those with good sleep (β = −0.098, P = 0.035) in APOE ε4 carriers only, which hinted an attenuation effect of good sleep on the APOE ε4 related risk for hippocampal atrophy. (Fig. 2G; Additional file 1: Table S5).

In ADNI samples, poor sleep quality was associated with a faster rate of hippocampal atrophy (P = 0.001). The association remained significant after controlling more covariates (Additional file 1: Table S4). Interaction effect due to APOE ε4 status was found (Likelihood ratio tests, P = 7.82 × 10^–3), for which the association between poorer sleep quality and faster hippocampal atrophy was significant only in APOE ε4 carriers (P = 0.007). In APOE ε4 carriers, individuals of poor sleep experienced a faster rate of hippocampal atrophy compared to those of good sleep (β = −0.033, P for Wald test = 0.029). (Fig. 2H; Additional file 1: Table S5).

Sleep quality, APOE ε4, and incident AD risk

In UKB cohort, analyses based on Model 1 showed that per unit of increment in SQI was associated with a substantially 5% increased AD risk (95%CI = 1.01–1.08, P = 0.010). Compared with participants of good sleep, those with poor sleep were associated with an average of 28% increased risk of developing AD (HR = 1.28, 95%CI = 1.08–1.52, P = 0.005). However, the significance became non-significant in Model 2 (Additional file 1: Table S6). Significant multiplicative interactions by APOE ε4 status was found (P = 9.36 × 10^–3), but no significant additive interactions were observed (Additional file 1: Table S7). Subgroup analyses showed that poor sleep was associated with 28% increased AD risk (HR = 1.28, 95%CI = 1.05–1.59, P = 0.025) only in APOE ε4 carriers while no significant association was found in APOE ε4 non-carriers (HR = 1.29, 95% CI = 0.978–1.72, P = 0.077). (Fig. 3; Additional file 1: Table S8).

Fig. 3 — Associations between sleep quality and the risk of Alzheimer’s disease. In the UKB cohort, per unit of increment in SQI was associated with a substantially 5% increased AD risk. Compared with participants of good sleep, those with poor sleep were associated with an average of 28% increased risk of developing AD. Poor sleep was associated with 28% increased AD risk only in *APOE* ε4 carriers while no significant association was found in non-*APOE* ε4 carriers. In ADNI cohort, the association between SQI and AD risk was validated. The association between SQI and AD risk was pronounced in *APOE* ε4 carriers (P = 0.041) than in non-carriers (P = 0.088). Subgroup analyses showed that participants of poor sleep had a 59% increased AD risk in *APOE* ε4 carriers. Squares represent hazard ratios; horizontal lines indicate corresponding 95% confidence intervals around hazard ratios. Hazard ratios were calculated using Cox-proportional hazards regression analysis after adjustments for age, sex, educational level, *APOE*-ε4 carrier status. Abbreviations: HR, hazard ratio; CI, confidence interval. *, statistical significance, p < 0.05

In ADNI cohort, the association between SQI and AD risk was validated (HR = 1.37, 95%CI = 1.07–1.75, P = 0.013) and the significance remained after adjusting more covariates in Model 2 (P = 0.020) (Additional file 1: Table S6). While no potential interaction effect due to APOE ε4 status was found (P = 0.325), the association between SQI and AD risk was significant only in APOE ε4 carriers (P = 0.041) but not in non-carriers (P = 0.088). Additionally, no significant additive interaction effect was observed (Additional file 1: Table S7). Subgroup analyses showed that participants with poor sleep had a 59% increased AD risk (HR = 1.59, 95% CI = 1.04–2.43, P = 0.034) in APOE ε4 carriers. (Fig. 3; Additional file 1: Table S8).

Proteins linking poor sleep to AD risk

After Bonferroni correction (P < 1.72 × 10^–5), we identified 21 proteins associated with incident AD risk and 576 proteins linked to poor sleep (Fig. 4A-B; Additional file 1: Table S9). Among these, 11 proteins showed significant correlations with both sleep and AD, with consistently positive or negative association estimates. Functional enrichment analyses showed significant enrichment in immune response, metabolic regulation, extracellular matrix organization, and neural processes (Fig. 4C). Accordingly, these proteins can be categorized into four functional groups: signal transduction and cell communication; immune regulation and inflammation; nervous system development and functional regulation; growth factors and metabolic regulation (Fig. 4D; Additional file 1: Table S10).

Proteins underpinning the interaction between poor sleep and APOE ε4 in AD development

After Bonferroni correction (P < 1.72 × 10^–5), we uncovered eight proteins associated with incident AD risk in APOE ε4 carriers while only two proteins in APOE ε4 non-carriers (Fig. 5A-B). Similarly, we also found 87 and 45 differentially expressed proteins associated with poor sleep quality among APOE ε4 carriers and APOE ε4 non-carriers, respectively (Fig. 5C-D). A protein named “GDF15” (growth differentiation factor 15) was highlighted as the bridge connecting poor sleep and AD risk specifically in APOE ε4 carriers (Fig. 5E). Higher level of GDF15 was significantly associated with poor sleep quality (P = 3.24 × 10^–7) and increased risk of incident AD (P = 5.03 × 10^–8) in APOE ε4 non-carriers. No such protein was observed to connect sleep and AD risk in APOE ε4 non-carriers. (Additional file 1: Table S11-12).

The GDF15-enriched networks (P = 0.007) are organized into three distinct clusters of proteins (Fig. 5F). These clusters were related to signaling receptor binding, tissue homeostasis, and response to stimuli. Notably, the GDF15 was majorly clustered into cluster 1, where the Gene Ontology (GO) items mainly involve glial cell-derived neurotrophic factor receptor signaling pathway (P = 5.41 × 10^–5), enzyme-linked receptor protein signaling pathway (P = 0.002), and transforming growth factor beta receptor signaling pathway (P = 0.011). (Fig. 5G). (Additional file 1: Table S13).

Sex-specific effects of sleep quality

In UKB samples, individuals with poor sleep exhibited worse cognitive performance, as indicated by lower level of FI (P = 4.04 × 10^–5 for female; P = 0.003 for male) and higher levels of RT (P = 3.04 × 10^–4 for female; P = 1.82 × 10^–9 for male) and VM (P = 0.001 for female; P = 0.008 for male), compared to those with good sleep in both female and male subsamples (Additional file 1: Table S14). In ADNI samples, compared to good sleep, the association between poor sleep and cognitive decline was stronger in females than in males, as indicated by weakened effects in males than in females (Additional file 1: Table S14). Similar results were obtained for the association between poor sleep and the rate of hippocampal atrophy (Additional file 1: Table S15). In both UKB and ADNI, poor sleep was associated with increased AD risks (HR = 1.34, P = 0.011 in UKB; HR = 2.06, P = 0.001 in ADNI) only in females, while no significant association was found in male (Additional file 1: Table S16).

Discussion

This study indicated that poor sleep quality accelerates cognitive decline, hippocampal atrophy, and the occurrence of AD. Interaction and stratified analyses uncovered that the association of sleep quality with AD was more pronounced in APOE ε4 carriers. Proteomic analyses identified inflammatory and metabolic pathways as key links between poor sleep and AD, and uncovered GDF15-enriched pathways to be an important mechanism underpinning the interaction between poor sleep and APOE ε4. These findings underscored the roles of sleep quality and its interaction by APOE ε4 in AD occurrence.

Our results are in line with those of previous studies reporting that sleep behaviors (including short and long sleep duration, insomnia symptoms, sleep apnea, and excessive daytime napping) are associated with increased risks of cognitive decline and dementia [23–25]. However, these studies have typically assessed sleep behaviors through a single baseline measurement as individual factors, without considering the complexity and correlations of various sleep behaviors in free-living individuals. In contrast, our study addresses this limitation by using a composite measure of multiple sleep indicators in the UKB cohort, which reflects the overall sleep patterns. Additionally, the present study further incorporates longitudinal data on sleep behavior in the ADNI cohort, allowing for a more comprehensive evaluation of long-term sleep quality. One key difference in comparison to the current research results is that a study involving 2,386 middle-aged men with a median follow-up of 21.9 years found no joint effects between sleep disturbance and APOE ε4 on incident dementia risk [26]. Heterogeneity across studies has been observed, potentially to differing definitions of sleep quality. Moreover, differences in population structure may have contributed to the inconsistent findings, since subgroup analysis by gender further suggests that sleep has a more significant impact on AD in women.

As the abovementioned sleep behaviors are typically clustered, they may exert synergic effect. These disturbances can cause endocrine or metabolic imbalances, increase sympathetic nervous activity, and/or activate inflammatory pathways [27], further contributing to brain aging. Our proteomic analyses also revealed the role of inflammatory and metabolic pathways in bridging the association between poor sleep and AD. However, the existing literature is inconsistent regarding how sleep behaviors are associated with AD risk [9, 28], perhaps because the interplay between sleep and the APOE genotype has been ignored.

An important finding of the present study was that sleep quality might be differentially associated with AD risk depending on the APOE genotype. Consistent with previous evidence [7, 28, 29], the present associations of sleep quality with cognitive function and AD risk were more pronounced in APOE ε4 carriers. First, poor sleep might synergize with APOE ε4 to impair the clearance of amyloid plaques, finally resulting in AD. Sleep deprivation could promote microglial activation around plaques [30]. In the presence of the APOE ε4 allele, but not APOE ε3, activated microglia disrupted the coordinated activity of neuronal ensembles by impairing their response to neuronal activity, leading to a diminished response to amyloid deposition [31]. The APOE ε4 allele, functions as the strongest genetic risk factor for sporadic AD, with carriers exhibiting an increased amyloid burden [32]. As sleep loss could also induce increased amyloid deposition [33], we hypothesized that poor sleep might synergize with APOE ε4 to accelerate Aβ plaque accumulation. Second, poor sleep might also synergize with APOE ε4 to induce increased risk of AD through impairing hippocampal function. The presence of APOE ε4 would exacerbate the loss of γ-aminobutyric acid (GABA) interneurons in the hippocampal dentate gyrus, leading to disruptions in slow gamma oscillations during hippocampal sharp-wave ripples and subsequently impairing cognitive function [34]. Sleep deprivation resulted in a reduction of spine density in the hippocampus [35], which was accompanied by a marked shortening of dendrite length [36]. Therefore, the loss and weakening of neuronal connections due to poor sleep and the presence of APOE ε4 might compromise effective information processing in the hippocampus, ultimately resulting in AD.

Interestingly, GDF15 was identified as the bridge linking poor sleep and AD risk in APOE ε4 carriers, while no such protein was found in APOE ε4 non-carriers. It was thus postulated that poor sleep could synergize with APOE ε4 to increase AD risk through GDF15-enriched pathways. As previously depicted, GDF15 is induced in response to cellular stress, mitochondrial dysfunction and inflammation to maintain cellular and tissue homeostasis [37]. Moreover, both chronic sleep restriction and APOE ε4 carriers exhibit a pro-inflammatory brain profile, such as elevated levels of interleukins, and increased susceptibility to AD [38, 39]. Therefore, one biologically plausible mechanism that bridge the interaction between poor sleep and APOE ε4 in the pathogenesis of incident AD may be activation of the neuroinflammation, which is thought to be an early event associated with the onset of AD [40]. Hence, targeting inflammation on preclinical stages of AD may block the effect of poor sleep and APOE ε4 synergies on AD risk.

However, the role of GDF15 in the inflammatory process remains unclear. Some studies suggest it has anti-inflammatory effects, while others indicate it may promote inflammation [41]. Therefore, future research should aim to clarify the role of GDF15 in inflammation to facilitate the development of anti-inflammatory drugs targeting GDF15 or its enriched pathways. Additionally, our plasma proteomic analyses hinted that inflammatory and metabolic pathways might bridge the association between poor sleep and AD. Accordingly, additional studies are required to gain a deeper understanding of the communications between the periphery and the central nervous system, and to inform preventive and therapeutic approaches. Moreover, around 25–66% of individuals with AD experience sleep disturbances, which can also manifest during the preclinical stage of the disease [42]. Therefore, additional translational studies are needed to investigate whether enhancing sleep quality in APOE ε4 carriers can slow the progression of AD pathology during the preclinical and early clinical stages of the disease.

The major strengths of this study include the use of two cohorts, enabling us to replicate the findings from one cohort to another. However, it also had some potential limitations. First, the observational design of our study restricted us from establishing a causal relationship between sleep quality and brain aging, therefore, we could not rule out the possibility that sleep disturbances result from AD-related pathology starting decades before the onset of clinical dementia. Second, the current SQI does not include all sleep behaviors, such as sleep apnea, which could cause cognitive decline [43]. Third, the methodological discrepancy in sleep quality definition could introduce heterogeneity in the association estimates between the two cohorts. Nevertheless, as the assessed sleep behaviors (such as insomnia, excessive daytime sleepiness, and sleep duration) are often interrelated [11], the SQI in both cohorts may still reflect similar underlying sleep constructs. Future cohort studies with repeated assessments of comprehensive sleep behaviors are needed to investigate the effect of sleep quality changes on AD. Fourth, the potential bias might be introduced by including individuals in preclinical or prodromal AD stages, which may confound the observed interaction between APOE ε4 and poor sleep. Stratifying analyses by biomarker-confirmed AD status could help clarify this issue.

Conclusions

In conclusion, the present study indicated that poor sleep could not only elevate the risk of AD by disrupting inflammatory responses and metabolic pathways, but also interact with APOE ε4 to increase AD risk, possibly via GDF15-enriched pathways. Future studies are needed to test whether targeting GDF15-enriched pathways could be potential strategies for mitigating AD risk, particularly in APOE ε4 carriers with poor sleep quality.

Supplementary Information

12916_2025_4255_MOESM1_ESM.docx^{(166.6KB, docx)}

Additional file 1: Detailed results on the associations of sleep quality with cognition, hippocampal volume and AD risk. Table S1. The associations between sleep quality and cognitive function. Table S2. The multiplicative interaction effects of poor sleep and APOE ε4 status on cognitive function. Table S3. APOE ε4 status-stratified associations between sleep quality and cognitive function. Table S4. The associations between sleep quality and hippocampal volume. Table S5. APOE ε4 status-stratified associations between sleep quality and hippocampal volume. Table S6. The associations between sleep quality and AD risk. Table S7. The additive interaction effects of poor sleep and APOE ε4 status on AD risk. Table S8. APOE ε4 status-stratified associations between sleep quality and AD risk. Table S9. Proteins significantly associated with both poor sleep and incident AD. Table S10. Bioinformatics information of the proteins associated with both poor sleep and AD risk. Table S11. Proteins significantly associated with incident AD among APOE ε4 carriers and APOE ε4 non-carriers. Table S12. Proteins significantly associated with poor sleep among APOE ε4 carriers and APOE ε4 non-carriers. Table S13. Bioinformatics information underpinning the GDF15-enriched networks. Table S14. Sex-stratified associations between sleep quality and cognitive function. Table S15. Sex-stratified associations between sleep quality and hippocampal volume. Table S16. Sex-stratified associations between sleep quality and AD risk.

Acknowledgements

The authors thank contributors, including the staff at Alzheimer’s Disease Centers who collected samples used in this study, patients, and their families whose help and participation made this work possible. Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

The ADNI cohort data used in preparation for this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at:

http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Abbreviations

AD: Alzheimer’s disease
SQI: Sleep quality index
UKB: UK Biobank
ADNI: Alzheimer's Disease Neuroimaging Initiative
WMH: White matter hyperintensity
VM: Visuospatial memory
RT: Reaction time
PM: Prospective memory
FI: Fluid intelligence
WM: Working memory
ADAS: The Alzheimer’s Disease Assessment Scale
MEM: Memory function
EF: Executive function
MRI: Magnetic resonance imaging
SD: Standard deviation
HR: Hazard ratios
CI: Confidence intervals
GDF15: Growth differentiation factor 15
GO: Gene Ontology
KEGG: Kyoto Encyclopedia of Genes and Genomes
GABA: γ-Aminobutyric acid

Authors’ contributions

LYH: analysis of the data, drafting and revision of the manuscript, and prepared all the figures. LT: drafting and revision of the manuscript. CCT, XYZ, XL, and JJZ: revision of the manuscript. YJL, YYZ, QH, and ZQZ: analysis of the data. Prof. Wei Xu: conceptualization and design of the study, analysis of the data, drafting and revision of the manuscript. All authors read and approved the final manuscript.

Funding

This study was supported by grants from the Taishan Scholar Project (NO.tsqn202211375).

Data availability

This research has been conducted using the UK Biobank resource under application number 108930. All data are available upon reasonable request or can be obtained from the UKB (https://biobank.ctsu.ox.ac.uk/) and Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu).

Declarations

Ethics approval and consent to participate

The UK Biobank received ethical approval from the National Health Service National Research Ethics Service (11/NW/0382; 16/NW/0274). The ADNI was approved by the Institutional Review Boards of all participating centers. Written informed consent was obtained from all participants or authorized representatives according to the 1975 Declaration of Helsinki.

Consent for publication

Not applicable.

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.

Liang-Yu Huang and Lan Tan contributed equally to this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12916_2025_4255_MOESM1_ESM.docx^{(166.6KB, docx)}

Data Availability Statement

[CR1] 1.Scheltens P, Blennow K, Breteler MM, de Strooper B, Frisoni GB, Salloway S, et al. Alzheimer’s disease. Lancet. 2016;388(10043):505–17. 10.1016/S0140-6736(15)01124-1. [DOI] [PubMed] [Google Scholar]

[CR2] 2.Wang YY, Ge YJ, Tan CC, Cao XP, Tan L, Xu W. The Proportion of APOE4 carriers among non-demented individuals: a pooled analysis of 389,000 Community-Dwellers. J Alzheimers Dis. 2021;81(3):1331–9. 10.3233/JAD-201606. [DOI] [PubMed] [Google Scholar]

[CR3] 3.Dunn AR, O’Connell KMS, Kaczorowski CC. Gene-by-environment interactions in Alzheimer’s disease and Parkinson’s disease. Neurosci Biobehav Rev. 2019;103:73–80. 10.1016/j.neubiorev.2019.06.018. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR4] 4.Li L, Xu W, Tan CC, Cao XP, Wei BZ, Dong CW, et al. A gene-environment interplay between omega-3 supplementation and APOE epsilon4 provides insights for Alzheimer’s disease precise prevention amongst high-genetic-risk population. Eur J Neurol. 2022;29(2):422–31. 10.1111/ene.15160. [DOI] [PubMed] [Google Scholar]

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PERMALINK

Sleep quality, APOE ε4, and Alzheimer’s disease: associations from two prospective cohort studies and mechanisms by plasma proteomic analysis

Liang-Yu Huang

Lan Tan

Chen-Chen Tan

Xin-Yu Zhang

Xu Lin

Juan-Juan Zou

Yu-Jing Lin

Yu-Ying Zhang

Quan Hao

Zi-Qi Zhang

Wei Xu

Abstract

Background

Methods

Results

Conclusions

Supplementary Information

Background

METHODS

Study population

Assessments of sleep behavior and classification of sleep quality

Cognitive measures

Hippocampal volume measurement

AD diagnoses

Blood proteomics

Covariate assessments

Statistical analyses

Fig. 1.

Results

Characteristics of participants

Table 1.

Sleep quality, APOE ε4, and cognitive performance

Fig. 2.

Sleep quality, APOE ε4, and hippocampus

Sleep quality, APOE ε4, and incident AD risk

Fig. 3.

Proteins linking poor sleep to AD risk

Fig. 4.

Proteins underpinning the interaction between poor sleep and APOE ε4 in AD development

Fig. 5.

Sex-specific effects of sleep quality

Discussion

Conclusions

Supplementary Information

Acknowledgements

Abbreviations

Authors’ contributions

Funding

Data availability

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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