On account of the complexities of the symptoms and the significant individual variations in Parkinson's disease (PD), it becomes very crucial to develop clinically relevant digital features for the objective assessment of the motor symptoms and the levels of severity of the disease. For the quantification of the resting tremor of the lower limb, gait impairment data, and bradykinesia data through the use of wearable insole sensors, new features are extracted through design, that deliver appropriate medical interpretability. XGBoost regression models combined with SHAP analysis are employed towards the evaluation of the performance of gait features and the selection of key interpretable features for gait impairment. A total of 55 PD patients with various severity symptoms, and 44 healthy participants, were recruited for the validation of the analysis. The results illustrate that the proposed tremor quantification feature methodology achieves an effect size of 1.89 and a correlation coefficient of 0.42. Gait impairment and bradykinesia are accurately quantified by regression models (R = 0.79) and characterized using interpretable features reflecting various manifestations, including gait amplitude, frequency, and cycle variability. The proposed features offer potential tools for clinical objective assessment of PD.

BackgroundAlzheimer’s disease (AD) and sleep deprivation (SD), two common conditions in the elderly, share complex molecular connections and may mutually influence each other’s pathogenesis. Current drugs only relieve symptoms with limited efficacy, making it urgent to explore the shared pathological mechanisms and potential intervention targets of the two conditions. This study used bioinformatics: first screening AD-related genes associated with SD and N4-acetylcytidine (ac4C) from relevant data; then identifying key genes via Mendelian randomization (MR) analysis and machine learning; finally screening AD-related key cells with single-cell RNA sequencing (scRNA-seq) data, to provide a basis for revealing the molecular and cellular regulatory mechanisms of AD-SD comorbidity.MethodsThis study integrated bulk RNA sequencing (RNA-Seq) and scRNA-seq data from the Gene Expression Omnibus (GEO) database to identify AD-related key genes associated with SD and ac4C. Machine learning algorithms, including MR, were applied to screen these key genes. Additionally, gene set enrichment analysis (GSEA) was conducted to explore the pathways associated with the key genes, while ssGSEA was used to assess differences in immune cell infiltration. For the scRNA-seq data, key cells involved in AD pathology were further identified. Subsequently, the differential expression of the two key genes was validated using peripheral blood samples collected from AD and SD patients.ResultsThrough MR analysis, machine learning algorithms, and other analytical approaches, FLOT1 and EEF1D were identified as key genes. GSEA revealed that these key genes were enriched in multiple pathways, including the lysosome pathway, chemokine signaling pathway, and leukocyte transendothelial migration. Immune cell infiltration analysis suggested that myeloid-derived suppressor cells (MDSCs) might serve as key immune cells. Additionally, scRNA-seq analysis identified microglia, CD4 + T cells, CD8 + T cells, and natural killer (NK) cells as key cell types involved in AD pathogenesis. Critically, these key genes were successfully validated in peripheral blood samples from AD and SD patients, aligning with the above analysis.ConclusionOverall, FLOT1 and EEF1D were identified as key genes associated with SD and ac4C in AD. This finding provided new grounds for the clinical diagnosis and treatment of AD.

Background Early detection of Alzheimer's disease (AD) is critical for timely intervention. Subjective cognitive decline (SCD), defined as self-perceived cognitive worsening while objective performance on standardized tests remains normal, when accompanied by neurodegenerative changes on brain imaging (e.g., hippocampal atrophy), can be classified as SCD with neurodegeneration of AD form (SCD-NDAD). This phenotype may represent an early stage of AD. Objective Investigate the prevalence and clinical characteristics of SCD-NDAD in general population. Methods: This multicenter, community-based cross-sectional study was conducted from 2013 to 2019 across 31 communities in eight major cities of northern, eastern, southern, and western China. Community-dwelling adults aged 50 years and older were recruited through cluster sampling. Participants underwent standardized interviews, neuropsychological assessments, and magnetic resonance imaging, on the basis of which SCD-NDAD was identified. The prevalence of SCD-NDAD was estimated with age- and sex-standardized weights. Results Of 5054 participants (mean age 69.4 years, 60.6% women), 2886 completed MRI. In participants aged ≥50 years, the prevalence of SCD-NDAD was 4.9% (95% confidence interval: 4.1% to 5.8%). In participants aged 65 years and older, prevalence increased to 6.5% (95% confidence interval: 5.5% to 7.7%). While these individuals exhibited preserved cognitive function across all domains, they demonstrated significant hippocampal atrophy, a key marker of AD-related neurodegeneration. Conclusions SCD-NDAD is common among older adults in China, with an estimated prevalence affecting 12.4 million individuals aged ≥65 years. Identifying this cohort may offer a critical window for early intervention and holds significant implications for public health strategies aimed at dementia prevention.

Background: Patient safety critically influences both quality of life and disease progression in older adults with cognitive impairment, yet large-scale multicenter data remain scarce. This study aims to systematically analyze the types of safety incidents experienced by patients with Alzheimer's disease and related cognitive impairments, and explore the network associations of different safety incidents. Methods: Initiated by the Alzheimer's Disease China (ADC), this survey recruited 1057 older individuals with Alzheimer's and related cognitive impairments, along with their families, across 31 provinces, autonomous regions, and municipalities. The safety incidents evaluated in this study included falls, getting lost, medication errors, verbal aggression, physical aggression, household fire, aspiration, and choking. Incidence rates for overall and specific safety incidents were calculated. Correlation analyses and network analysis were performed to examine relationships between safety incidents. Results: A high proportion (73.5%) of participants reported at least one safety incident in the past year, with over one-third (36.0%) experiencing three or more concurrent incidents. Medication errors (55.9%) and verbal aggression (39.6%) were most frequent, followed by falls (32.5%) and physical aggression (22.7%). Incidence rates varied significantly by cognitive impairment stage, care setting, and geographic region. Network analysis highlighted medication errors and getting lost as central nodes bridging other incidents. Conclusions: This study reveals an alarmingly high incidence of safety incidents among cognitively impaired patients, affecting their physical, psychological, and familial well-being. A collaborative, multidisciplinary effort involving healthcare professionals, family caregivers, fire and police emergency responders, and public health policymakers is essential to develop individualized safety strategies aligned with patient needs and contextual considerations.

The relationship between neural activities and hemodynamic responses was referred to as neurovascular coupling (NVC). Although several methodologies were proposed for the NVC study, these approaches may be constrained in extracting event-related neural activities (ERNA) and lacking the characterization of NVC. To optimize the extraction of ERNA, the modified connectivity-related neural activity (CRNA) was extracted by combining the absolute value of neural oscillation with non-negative matrix factorization (NNMF). Then, cross-correlation between modified CRNA and hemoglobin oxygenation (HbO) was computed to represent NVC, and temporal and intensity parameters of NVC were extracted, respectively. A digit-Sternberg task was designed with three different levels for investigating effects of working memory load (WML) on NVC. Electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS)were recorded simultaneously in 30 healthy elderly adults. We observed that as WML increased, NVC intensity at prefrontal and parietal regions exhibited a notable enhancement, while NVC lag exhibited a significant decrease. Furthermore, a significant negative correlation was found between NVC intensity and reaction time (RT). The proposed NVC analysis method could effectively characterize the influence of WML changes on NVC.

Parkinson's disease (PD), with its rising global prevalence, poses severe risks from falls and motor impairments. Current fall risk assessments rely heavily on subjective clinical evaluations, underscoring the need for quantitative methods. In this exploratory study, wearable inertial and photoelectric sensors attached to the limbs and trunk were used to objectively collect biomechanical movement data during standardized MDS-UPDRS motor assessments. Leveraging the clinically validated correlation between Hoehn-Yahr (H-Y) staging and fall risk, we propose a data-driven framework to quantify risk. Mutual information (MI) analysis links biomechanical features to H-Y stages, generating a weighted Fall FRS (FRS). Machine learning validation was further performed to preliminarily evaluate the discriminative capability of the proposed FRS in stratifying patients by risk severity. Based on a cohort of 92 PD patients, experimental results on the independent test set showed that incorporation of the FRS improved classification accuracy from 50.00% to 82.14%, while the macro-average AUC increased from 0.698 to 0.907. These findings suggest that wearable sensor-based biomechanical assessment may provide useful quantitative information for exploratory fall-risk stratification in PD patients.

Resting heart rate (RHR) is an important risk factor for cardiovascular diseases (CVD) and has been linked to cognitive impairment (CI), but evidences remain inconsistent. Given that both cardiovascular regulation and cognitive function differ by sex and age, these factors may modify the RHR–CI association. This study aimed to examine this relationship and its potential sex- and age-specific differences in a general Chinese population. This population-based cross-sectional study included 1309 participants from rural areas of northwestern China. RHR was obtained from a standard 12-lead electrocardiogram. CI was diagnosed with Mini-Mental State Examination scores below the cutoff value. RHR data were fitted as restricted cubic spline to explore potential non-linear relationships. Stratified analyses by sex, age (50–69 years vs ≥ 70 years) and combined were performed. Two hundred twenty five (17.2

Plasma amyloid-β (Aβ) and phosphorylated tau (P-tau) are becoming biomarkers for Alzheimer’s disease (AD) diagnosis. However, the variation of plasma levels among different sampling times remains underexplored. In this study, we investigated the diurnal fluctuation of plasma biomarkers in healthy adults. Nineteen healthy adults (average age 29.95 ± 4.81 years, 47.4% female) underwent ethylenediamine tetraacetic acid (EDTA) plasma sampling around timepoints 08:00, 12:00 and 16:00—approximately 30 to 60 min before they consumed meals—on two consecutive days. All participants had normal sleep as usual during the experiment. Plasma Aβ42, Aβ40, P-tau181, P-tau217, glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) concentrations were measured using chemiluminescence immunoassay method. Linear mixed models assessed timepoint effects and associations with hours since wake. Plasma P-tau181 levels at 12:00 increased by 14.8% (mean difference = 0.239 pg/mL, 95% CI [0.027, 0.451], P = 0.022) compared to that at 08:00, and the levels at 16:00 increased by 25.1% (mean difference = 0.460 pg/mL, 95% CI [0.247, 0.673], P < 0.001) than that at 08:00. Plasma P-tau217 also exhibited significant daytime increase (16:00 vs. 08:00, + 12.8%, mean difference = 0.209 pg/mL, 95% CI [0.029, 0.389], P = 0.017). Meanwhile, plasma P-tau levels were positively correlated with wake duration (P-tau181: r = 0.3500, P < 0.001; P-tau217: r = 0.1946, P = 0.007). Plasma Aβ42, Aβ40, GFAP, NfL, and Aβ42/Aβ40 remained stable across all timepoints. Blood sampling time may warrant consideration when using plasma AD biomarkers in clinical research settings and longitudinal monitoring, pending validation in larger, independent cohorts.

Deposition of amyloid β-protein (Aβ) in the brain is a key pathogenesis and early change in Alzheimer's disease (AD). Clearing Aβ through anti-Aβ monoclonal antibody drugs is an important strategy for disease-modifying thrapy (DMT). Lecanemab is the first approved anti-Aβ monoclonal antibody durg for early therapy, and growing evidence has shown that Lecanemab could clear Aβ deposition and slow progress of AD. This article systematically summarizes the evidence for Lecanemab in treating AD, the results of the early-phase clinical studies, the phase Ⅲ clinical trials, open label extension (OLE) trials and long-term follow-up data, and ongoing clinical research, with the aim of providing clinical guidance on the safety, efficacy and applicability of AD treatment.

BackgroundSleep deprivation is a modifiable risk factor for Alzheimer’s disease (AD). Plasma amyloid-β (Aβ) and phosphorylated tau (P-tau) are closely related to cerebral AD pathology and serve as peripheral biomarkers. Our previous work showed that acute sleep deprivation elevated plasma Aβ40 in healthy adults. Here we conducted an exploratory analysis to examine the effects of sleep deprivation and subsequent recovery on plasma total tau (T-tau) and P-tau181 levels.MethodsTwenty healthy adults underwent 24 h of total sleep deprivation followed by daytime naps and a full night of recovery. Venous blood was drawn at 12 predefined time points. Plasma T-tau and P-tau181 were measured by enzyme-linked immunosorbent assay (ELISA).ResultsPlasma T-tau increased by 35.09% (P = 0.018) after 24 h of sleep deprivation, and decreased by 37.78% (P = 0.005) after sleep recovery. The rise in T-tau was positively correlated with wakefulness duration (β = 0.629, P < 0.001). In contrast, no significant changes in plasma P-tau181 were detected with sleep deprivation or recovery under the present experimental conditions.ConclusionUnder an experimental protocol of 24-h sleep deprivation followed by recovery sleep, transient elevation of plasma T-tau (but not P-tau181) was observed in healthy young adults in this exploratory study. These findings indicate that conditions involving short-term sleep loss are associated with fluctuations in a nonspecific plasma marker of neuronal activity.

BackgroundObesity is a modifiable risk factor for cognitive impairment; however, body mass index fails to capture fat distribution. Waist-to-hip ratio (WHR), reflecting central adiposity, may better characterize obesity-related differences in cognitive impairment. Nevertheless, whether the association between WHR and cognitive impairment varies by sex and age remains unclear.MethodsThis cross-sectional study included 1,792 adults aged ≥40 years from rural China. Cognitive impairment was defined using education-adjusted cutoffs on the Chinese Mini-Mental Status. WHR and clinical characteristics were collected by trained investigators. Multivariable logistic regression, restricted cubic splines, stratified analyses, and interaction analyses were performed.ResultsIn the total population, WHR was positively associated with the odds of cognitive impairment (OR = 1.245 per SD, 95% CI: 1.022–1.517, P = 0.029), with borderline evidence of nonlinearity (Poverall = 0.019, Pnonlinear = 0.090). In sex-stratified analyses, the association in females showed evidence of nonlinearity, with the estimated odds remaining relatively stable below a WHR of 0.88 and increasing at higher levels (Poverall = 0.014, Pnonlinear = 0.049), whereas no significant association was observed in males. Age-stratified analyses demonstrated a significant association in the middle-aged group (40–59 years, Poverall = 0.016, Pnonlinear = 0.143) but not in those ≥60 years. Further interaction analyses showed that age appeared to modify the association between WHR and cognitive impairment in males (OR = 0.462 per SD, 95% CI: 0.258–0.828, Pinteraction = 0.010), but not in females. Specifically, the positive association between WHR and cognitive impairment was observed only in middle-aged males (OR = 1.652 per SD, 95% CI: 1.052–2.595, P = 0.029), but not in older males (OR = 0.804 per SD, 95% CI: 0.492–1.314, P = 0.384).ConclusionOur findings show that WHR is associated with screening-defined cognitive impairment, with patterns that may differ by sex and age. Females exhibited a possible nonlinear association, whereas in males the association was mainly observed in midlife. These findings highlight central adiposity as an important correlate of cognitive impairment. Given the rural single-region sample, larger longitudinal studies with clinically adjudicated outcomes are warranted to assess generalizability and clarify temporal associations.

AIMS:To investigate the association of concurrent diabetes mellitus and poor sleep with cognitive decline in a community-based population. METHODS:Cognitively normal adults aged 40 years or older were followed-up for 4 years. Cognitive decline was defined as a decrease of ≥ 4 points on the Chinese Mini-Mental Status from baseline. Sleep quality was assessed using the Pittsburgh Sleep Quality Index. Logistic regression was employed to evaluate associations of diabetes and poor sleep status with cognitive decline. RESULTS:Among 1218 participants, 30 (2.46%) developed cognitive decline during follow-up. Participants with concurrent diabetes and poor sleep had higher odds of cognitive decline (adjusted OR = 5.74, 95% CI: 1.58 - 18.81). The association of the concurrent presence of poor sleep and diabetes with cognitive decline was evident mainly when poor sleep was defined by poor subjective sleep quality, short sleep duration, low sleep efficiency, frequent sleep disturbances, or severe daytime dysfunction (adjusted ORs, 4.17-10.23; all P < 0.05), rather than prolonged sleep latency. The application of broader criteria for dysglycemia (adjusted OR = 4.10, 95% CI: 1.23 - 12.82) corroborated the robustness of these findings. CONCLUSIONS:The co-occurrence of diabetes and poor sleep was associated with higher odds of cognitive decline, suggesting the clinical potential of integrated interventions in preserving cognitive health.

Parkinson's disease (PD) is a neurodegenerative disorder that affects both motor and cognitive functions. An objective and easily measurable digital marker is crucial for improving the diagnosis and monitoring of PD. Since gait is a complex activity that requires both motor control and cognitive input, this study assumes that kinetic parameters of the foot sensitive to the cognitive load (dual-tasking) for healthy adults can be used to diagnose PD. In this study, walking with a cognitive task has been conducted on healthy subjects, the kinetic parameters have been calculated with algorithms of inverse dynamics in Opensim. Subsequently, the moment-related variables, including the bend and force of the plantar surface, were collected from 13 patients with PD and 32 healthy controls using the wearable system. Statistical analysis of the focused kinetic parameters indicates that the moment of the metatarsophalangeal joint has a significant difference between dual-task walking and single walking. The experimental results demonstrate that features extracted from the bend and force signal of the plantar surface can diagnose PD with an average accuracy of 95.55% with 5-fold cross validation. It demonstrates that kinetic data from the foot captured by wearable sensors can serve as an objective digital marker for PD.

Background Infusion-related reactions (IRRs) represent the most common adverse events associated with lecanemab. However, real-world data on IRR characteristics and risk factors in Asian populations, particularly Chinese, remain scarce. Methods In a multicenter prospective registry, 139 patients with early Alzheimer’s disease (AD) receiving lecanemab were included. IRRs were physician-confirmed. Multivariable logistic regression identified independent predictors. Results The cumulative IRR incidence was 12.36 %, highest at the first infusion (17.3 %) and decreased significantly thereafter (P < 0.001). Fever (54.2 %) and dizziness (16.7 %) were the most common symptoms. 45.8 % of IRRs occurred 2–24 hours after infusion. All IRRs were mild (Grade 1) and self-limited. Hypertension (OR = 5.017, P = 0.007) and higher Fazekas score (OR = 2.734, P = 0.017) were independently associated with IRR. Discussion In this Chinese real‑world cohort, lecanemab‑associated IRRs were less frequent, mild, and delayed. Hypertension and white‑matter hyperintensity severity emerged as key risk factors, underscoring the potential role of cerebrovascular health in IRR susceptibility.

IntroductionParkinson's disease (PD) is a typical neurodegenerative disorder characterized by progressive motor impairments. Gait analysis offers a promising avenue for non-invasive PD diagnosis, yet extracting discriminative features from gait signals remains challenging.MethodsThis study proposed an intelligent diagnostic framework for PD based on quantitative analysis of recurrence plots derived from plantar pressure gait signals. Gait data were collected from 61 PD patients and 48 healthy subjects using a wearable gait acquisition system. Various recurrence plots representations, including thresholded, non-thresholded, basic, cross, and joint recurrence plots were constructed. From these recurrence plots, traditional recurrence quantification analysis (RQA) features and novel recurrence plot entropy features derived from compressed one-dimensional sequences of non-thresholded recurrence plots were extracted.ResultsStatistical analysis revealed that recurrence plot entropy features particularly from cross recurrence plots exhibited superior discriminability. Pressure signals from the heel and toe positions showed the highest specificity. For classification, an integrated feature set combining temporal, pressure, and recurrence plot features achieved the best diagnostic performance using a Cubic Support Vector Machine (CSVM) model, yielding a maximum accuracy of 92.71% in distinguishing PD patients from healthy controls (HC).DiscussionThe results demonstrated that the proposed quantitative recurrence plots analysis framework provides a highly effective and automated approach for intelligent PD diagnosis based on gait dynamics.

BACKGROUND:Alzheimer's disease (AD) dementia and Parkinson's disease dementia (PDD) both frequently involve sleep disturbances, but the characteristics of sleep disruption differ between the two patient groups, and the extent of these differences remains unclear. METHODS:We consecutively enrolled 105 patients with AD and 104 with PDD from a memory clinic. All participants underwent assessments of global cognition (MMSE, MoCA), daily living (ADL), anxiety and depression (HAMA, HAMD), and sleep characteristics (PSQI, RBD, daytime nap questionnaire, Epworth Sleepiness Scale). Multivariable regression analyses were conducted to examine whether sleep characteristics differed independently between the two groups. ROC curves evaluated discriminative performance. RESULTS:Global PSQI scores did not differ between the two groups. In univariate analyses, PDD patients reported worse subjective sleep quality (the first PSQI component), more sleep disturbances, greater use of hypnotics, higher daytime nap frequency and duration, and a much higher prevalence of RBD. After full adjustment, PDD remained independently associated with poorer sleep quality (B = 0.355, 95% CI: 0.092-0.619, p = 0.008), higher daytime nap frequency (B = 2.124, 95% CI: 1.327-2.920, p < 0.001), and RBD (OR = 18.482, 95% CI: 3.297-103.603, p = 0.001). A combination of sleep items (sleep quality, nap frequency, RBD) distinguished PDD from AD with an AUC of 0.763. CONCLUSIONS:Despite a comparable total PSQI score, PDD patients showed significantly poorer sleep quality (the first PSQI component), more frequent daytime napping, and a much higher prevalence of RBD compared with AD patients. Subjective sleep profiling may help to understand the differential sleep burden in these common dementias.

Objective This study aims to explore the association between Life's Essential 8 (LE8) and stroke and all-cause mortality, and compare whether it has an advantage over Life's Simple 7 (LS7). Methods This study investigated data from NHANES spanning from 1999 to 2018. The LE8 was categorized as low, moderate and high cardiovascular health (CVH). LS7 score was categorized as inadequate, average, or optimal. Weighted logistic regression and restricted cubic spline (RCS) were used to examin correlations. Receiver operating characteristic (ROC) curves were employed to detect the accuracy in predicting stroke. The stratified and sensitivity analyses were conducted along with mediation analysis. In addition, a longitudinal cohort was constructed by combining the mortality data, and Cox regression models were utilized to determine the association between CVH and the mortality rate. Results : For LE8, compared to low CVH, moderate CVH was associated with a 41% lower risk of stroke, and high CVH was associated with a 71% lower prevalence of stroke. For LS7, compared to inadequate CVH, average CVH was associated with a 24% lower prevalence of stroke, and optimal CVH was associated with a 39% lower prevalence of stroke. RCS showed inverse dose-response relationships of both LE8 and LS7 with stroke. In unweighted ROC, LE8 (AUC=0.702, 95% CI: 0.685-0.718, P<0.001) has a stronger ability to discriminate stroke than LS7 (0.677, 95% CI: 0.658-0.696, P<0.001) (PDeLong=0.046). Sensitivity analyses demonstrated robustness of LE8 in predicting stroke. GGT and WBC mediated 4.92% and 4.58% of the association, respectively. Cox regression showed neither LE8 nor LS7 were predictive of mortality risk among stroke survivor. Conclusions : LE8 outperformed LS7 in classifying stroke. Oxidative stress and inflammation mediated the association between LE8 and stroke.

BackgroundEssential tremor (ET) is the most common neurological movement disorder with few treatments and limited therapeutic efficacy, research into noninvasive and effective treatments is critical. Abnormal cerebello-thalamo-cortical (CTC) loop function are thought to be significant pathogenic causes of ET, with the cerebellum and cortex are common targets for ET treatment. In recent years, transcranial magnetic stimulation (TMS) has been recognized as a promising brain research technique owing to its noninvasive nature and safety. In this study, we will use left M1 cortex continuous theta-burst stimulation (cTBS) combined with right cerebellar hemisphere 1 Hz repetitive transcranial magnetic stimulation (rTMS) dual-target stimulation to explore the Safety, feasibility and efficiency of this dual-target stimulation mode, and the mechanism of its therapeutic effect.MethodsTwenty-four patients with ET will be randomly assigned to three groups: dual-target stimulation, single-target stimulation, or sham stimulation. The single-target stimulation group will receive stimulation of the right cerebellar hemisphere for 10 days, whereas the dual-target stimulation group will be given stimulation of both the left M1 cortex and the right cerebellar hemisphere. The sham stimulation group will be given sham stimulation for 10 days. Tremor will be assessed using both the subjective The Essential Tremor Rating Assessment Scale (TETRAS) and objective accelerometer-based tremor analysis. at baseline (before stimulation), after the first, fifth, tenth days of treatment (D1, 5, 10), 24 h after 10 days of treatment (D10-24 h), and 1, 2, 3, and 4 weeks after stimulation (W1, 2, 3, 4).DiscussionThis is a Phase 2 randomized, controlled, patient-assessor blinded clinical trial. The goal of this study is to investigate the Safety, feasibility and efficiency of TMS for the treatment of ET.

Several unhealthy lifestyles have been identified as potential risk factors for cognitive impairment, however the effect of combinations of lifestyles are ambiguous. In the present study, we examined the association of lifestyles with cognitive decline among cognitively normal people aged 40 years and older. This was a communicate population based cohort study, using a cluster random sampling to select a population of 2 villages in Xi'an, and cognitively normal subjects were followed up for 4 years. A comprehensive score of lifestyle was calculated based on the factors including smoking, drinking, exercise, and diet collected at the baseline. The Mini-Mental State Examination (MMSE) was used to evaluate global cognitive function at both baseline and follow-up, and a drop of ≥ 4 points in MMSE score from baseline was defined as significant cognitive decline. Multivariable logistic regression, propensity score correction and propensity score matching were used to investigate the relationship between lifestyle and cognitive decline. 1348 participants were ultimately enrolled and 56 (4.2%) met the criteria for significant cognitive decline (ΔMMSE ≥ 4 points). 304 (22.6%) met the definition of the unhealthy lifestyle (comprehensive score <6). Multivariable logistic regression analysis showed that unhealthy lifestyle was positively associated with significant cognitive decline (OR=2.780, 95% CI 1.345-5.748, p  = 0.006). Propensity-score adjusted model yielded similar result (OR=2.786, 95% CI 1.371-5.661, p  = 0.005) (Table 1). Propensity score matching was performed to balance the differences in covariates between the two groups (Figure 1). Multivariate logistic regression analysis conducted in the matched population revealed the risk of significant cognitive decline was still higher for those with unhealthy lifestyle (OR=3.994, 95% CI 1.582-12.176, p  = 0.006). Stratified analysis found an association between lifestyle and cognitive decline in participants who were aged ≤60 years (OR=2.630, 95% CI 1.026-6.745, p  = 0.006), male (OR=5.541, 95% CI 1.792-17.131, p  = 0.003), and who had school education ≤6 years (OR=2.691, 95% CI 1.040-6.963, p  = 0.041) or stroke history (OR=2.673, 95% CI 1.208-5.915, p  = 0.015) (Table 2). Unhealthy lifestyle is associated with an increased risk of cognitive decline in people aged 40 years and older, particularly in the male, middle-aged, low-educated and with a history of stroke.

BACKGROUND:To analyze the relationship between APOE genotype and cognitive impairment among individuals aged 40 and above in rural Xi'an, and to explore the potential influence of education on this relationship. METHOD:All permanent residents aged 40 and above from two villages in Huyi District, Xi'an City were selected as research subjects, employing a cross-sectional survey approach. The Mini-Mental State Examination (MMSE) was utilized to assess overall cognitive function, with MMSE scores below the threshold values (illiterate ≤17, primary school ≤20, junior high and above ≤24) considered as cognitive impairment. Fasting elbow venous blood was drawn in the morning, and the apolipoprotein E (APOE) genotype was determined. The population was divided into low education (LE; ≤9 years) and high education (HE; >9 years) groups based on educational level. Univariate and multivariate analysis were applied to explore the association between APOE genotype and cognitive impairment, as well as MMSE scores in both the total and stratified populations. RESULT:Out of 1692 participants, there were 263 APOEε4 carriers (E2/4, E3/4, E4/4) (15.3%), and 205 individuals met the criteria for cognitive impairment (12.1%). Binary logistic regression and multiple linear regression analyses revealed that, in both the total population and the LE population, compared to APOEε4 non-carriers (E2/2, E2/3, E3/3), APOEε4 carriers exhibited a significantly higher risk of cognitive impairments (total population: OR=1.509, p = 0.035; LE: OR=1.604, p = 0.019) (Table 1, Model2), and their MMSE scores were significantly lower (total population: β=-0.053, p = 0.006; LE: β=-0.052, p = 0.013) (Table 2, Model2). However, in the HE population, there was no significant difference in the prevalence of cognitive impairment (OR=1.883, p = 0.536) (Table 1, Model2) and MMSE scores (β=0.001, p = 0.992) (Table 2, Model2) between APOEε4 carriers and non-carriers. CONCLUSION:The APOEε4 allele was associated with an increased risk of cognitive impairment in individuals aged 40 and above in rural areas of Xi'an, while higher educational attainment may offer protective effects against cognitive impairment in APOEε4 carriers.