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Background: High-density lipoprotein cholesterol (HDL-C) is widely recognized for its protective effects against cognitive decline. However, recent studies have presented conflicting results, with some suggesting no significant cognitive benefits or even an increased risk of dementia associated with high HDL-C levels. For those who suffer from depression, the cognitive benefits of HDL-C may be diminished or reversed. The purpose of this study is to investigate the associations between HDL-C, cognitive ability, and depressive symptoms in middle-aged and older Chinese adults., Methods: The datasets utilized were sourced from the China Health and Retirement Longitudinal Study (CHARLS) for the years 2011 and 2015, comprising 4,302 participants. Cross-lagged models were employed to explore the temporal sequence between cognitive performance and HDL-C levels, and to examine the interplay among depression, cognition, and HDL-C. Confounding factors such as sociodemographic characteristics, sleep conditions, and history of chronic diseases were controlled for., Results: The analysis revealed unidirectional effects of baseline impaired cognition and greater severity of depression on increased HDL-C levels at follow-up (β =  - 0.036 and β = 0.028, respectively, P < 0.05). However, higher baseline HDL-C levels did not significantly predict cognitive performance or depression 4 years later (β =  - 0.008 and β = 0.023, respectively, P > 0.05). Depressive symptoms and cognition were found to have a significant bidirectional association (β =  - 0.026 and β =  - 0.053, respectively, P < 0.05)., Conclusions: Cognitive impairment and depression are associated with higher HDL-C levels, whereas higher HDL-C levels do not appear to protect against cognitive decline or depressive symptoms. These findings underscore the importance of preserving cognitive and mental health, which may lower the likelihood of cardiovascular disease and dementia. Future studies should validate these findings and develop targeted interventions tailored to specific populations., (© 2024. The Author(s).)

Background: Mental disorders and cognitive impairment are common in older patients with arthritis. While it is recognized that mental conditions may play a role in the connection between arthritis and cognitive impairment, the precise underlying relationship remains uncertain., Methods: The data was derived from the baseline survey of the Guangdong Mental Health Survey in South China, involving a sample of 3,764 citizens aged 65 and older. An array of aspects were explored, including socio-demographics, lifestyle behaviors, self-reported chronic conditions, depression, anxiety, and cognitive impairment. Logistic regression analyses examined the association between arthritis and cognitive impairment after adjustment for potential confounders. Serial mediation models were used to examine whether depression or anxiety played a mediating role in the arthritis-cognitive impairment linkage., Results: The prevalence rates of cognitive impairment and arthritis of the older adults were 28.9% and 12.1%, respectively. Compared to those without arthritis, participants with arthritis were at a higher risk of cognitive impairment (OR = 1.322, 95%CI: 1.022-1.709) after adjustment for socio-demographics, lifestyle behaviors, and mental health conditions. Serial mediation analyses indicated that depressive and anxiety symptoms co-played a serial mediating role in the association between arthritis and cognitive impairment (B 1 = 0.025, 95%CI: 0.005-0.052; B 2 = 0.050, 95%CI: 0.021-0.086)., Conclusions: Arthritis may heighten cognitive impairment risk in Chinese older adults, and the relationship was potentially mediated by depressive and anxiety symptoms. Future interventions should be considered, integrating mental health assessments into arthritis care frameworks and being alert to possible cognitive impairment., Competing Interests: DISCLOSURES The study was supported by theNational Social Science Foundation of China(grant number:19ZDA360), theMedical Scientific Research Foundation of Guangdong Province of China(Grant No.C2022010, C2021006), andGuangdong Key Disciplines Project(grant number:2022ZDJS139). The authors declare that they have no competing interests., (Copyright © 2024 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.)

Women are at a higher risk of cognitive impairments and Alzheimer's disease (AD), particularly after the menopause, when the estrous cycle becomes irregular and diminishes. Numerous studies have shown that estrogen deficiency, especially estradiol (E2) deficiency, plays a key role in this phenomenon. Recently, a novel polymeric drug, hyaluronic acid-17β-estradiol conjugate (HA-E2), has been introduced for the delivery of E2 to brain tissues. Studies have indicated that HA-E2 crosses the blood-brain barrier (BBB) and facilitates a prolonged E2 release profile while lowering the risk of estrogen-supplement-related side effects. In this study, we used ovariohysterectomy (OHE) rats, a postmenopausal cognitive deficit model, to explore the effect of a 2-week HA-E2 treatment (210 ng/kg body weight, twice a week) on the cholinergic septo-hippocampal innervation system, synaptic transmission in hippocampal pyramidal neurons and cognitive improvements. Our study revealed an 11% rise in choline acetyltransferase (ChAT) expression in both the medial septal nucleus (MS nucleus) and the hippocampus, along with a 14-18% increase in dendritic spine density in hippocampal pyramidal neurons, following HA-E2 treatment in OHE rats. These enhancements prompted the recovery of cognitive functions such as spatial learning and memory. These findings suggest that HA-E2 may prevent and improve estrogen-deficiency-induced cognitive impairment and AD.

The high prevalence rate of Alzheimer's disease (AD) and mild cognitive impairment (MCI) has been a serious public health threat to the modern society. Recently, many studies have demonstrated the potential of using non-invasive electroencephalography (EEG) and machine learning to assist the diagnosis of AD/MCI. However, the majority of these research recorded EEG signals from a single center, leading to significant concerns regarding the generalizability of the findings in clinical settings. The current study aims to reevaluate the effectiveness of EEG-based machine learning model for the detection of AD/MCI in the case of a relatively large and diverse data set. We collected resting-state EEG data from 150 participants across six hospitals and examined the classification performances of Linear Discriminative Analysis (LDA) classifiers on the phase lag index (PLI) feature. We also compared the performance of PLI over the other commonly-used EEG features and other classifiers. The model was first tested on a training set to select the feature subset and then further validated with an independent test set. The results demonstrate that PLI performs the best compared to other features. The LDA classifier trained with the optimal PLI features can provide 82.50% leave-one-participant-out cross-validation (LOPO-CV) accuracy on the training set and maintain a good enough performance with 75.00% accuracy on the test set. Our results suggest that PLI-based functional connectivity could be considered as a reliable bio-maker to detect AD/MCI in the real-world clinical settings.

Background: Subjective cognitive decline (SCD) may be the early screening signal to distinguish susceptible population with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Subjective cognitive complaints (SCCs) have been proved strongly associated with SCD. This study aimed to explore the association between sleep duration and SCCs in the Chinese elderly., Methods: We conducted a cross-sectional study involving 688 participants aged 60 years and older in Guangdong Province, China. SCCs were assessed by the Subjective Cognitive Decline questionnaire 9 (SCD-Q9), which contained 9 items with two dimensions, including the overall memory function and time comparison (OMTC) and daily activity ability (DAA). Restricted cubic splines and generalized additive model (GAM) were used to fit the association between sleep duration and SCD-Q9 score., Results: There were significant U-shaped associations between sleep duration and overall score of SCD-Q9 (EDF = 3.842, P < 0.001), as well as the OMTC dimension (EDF = 4.471, P < 0.001) in the age- and gender-adjusted GAM. The lowest points on the overall score of SCD-Q9 and OMTC score were observed in those sleeping 8 h per night. After further adjusting for other demographic characteristics, lifestyle behaviors, hypertension and diabetes, the U-shaped associations between sleep duration and the overall score of SCD-Q9 (EDF = 3.575, P = 0.004), sleep duration and the OMTC score (EDF = 4.478, P = 0.010) were still found. The daily activity ability (DAA) score was also non-linear associated with sleep duration both in the age- and gender-adjusted GAM (EDF = 2.314, P < 0.001) and further adjusted GAM (EDF = 2.080, P = 0.010)., Conclusions: Both longer sleep duration (> 8 h) and shorter duration (< 8 h) were linked to worse SCCs. Future studies should explore the protective effect of managing sleep duration on SCD and its progression to dementia., (© 2022. The Author(s).)

Background: Subjective cognitive decline (SCD) may be the first symptomatic manifestation of Alzheimer's disease, but information on its health correlates is still sparse in Chinese older adults. This study aimed to estimate SCD symptoms and its association with socio-demographic characteristics, common chronic diseases among southern Chinese older adults., Methods: Participants aged 60 years and older from 7 communities and 2 nursing homes in Guangzhou were recruited and interviewed with standardized assessment tools. Pittsburgh Sleep Quality Index (PSQI), Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) were used to measure poor sleep quality, depression symptoms and anxiety symptoms. The SCD symptoms were measured by SCD questionnaire 9 (SCD-Q9) which ranged from 0 to 9 points, with a higher score indicating increased severity of the SCD. Participants were divided into low score group (SCD-Q9 score ≤ 3) and higher score group (SCD-Q9 score > 3). Chi-square tests and multivariate logistic regression analysis were used for exploring the influences of different characteristics of socio-demographic and lifestyle factors on SCD symptoms. Univariate and multivariate logistic regression analysis were applied to explore the association between SCD symptoms with common chronic diseases., Results: A total of 688 participants were included in our analysis with a mean age of 73.79 (SD = 8.28, range: 60-101), while 62.4% of the participants were females. The mean score of the SCD-Q9 was 3.81 ± 2.42 in the whole sample. A total of 286 participants (41.6%) were defined as the low score group (≤3 points), while 402 participants (58.4%) were the high score group (> 3 points). Multivariate logistic regression analysis revealed that female (OR = 1.99, 95%CI: 1.35-2.93), primary or lower education level (OR = 2.58, 95%CI: 1.38-4.83), nursing home (OR = 1.90, 95%CI: 1.18-3.05), napping habits (OR = 1.59, 95%CI: 1.06-2.40), urolithiasis (OR = 2.72, 95%CI: 1.15-6.40), gout (OR = 2.12, 95%CI: 1.14-3.93), poor sleep quality (OR = 1.93, 95%CI: 1.38-2.71), depression symptoms (OR = 3.01, 95%CI: 1.70-5.34) and anxiety symptoms (OR = 3.11, 95%CI: 1.29-7.46) were independent positive related to high SCD-Q9 score. On the other hand, tea-drinking habits (OR = 0.64, 95%CI: 0.45-0.92), current smoking (OR = 0.46, 95%CI: 0.24-0.90) were independent negative related to high SCD-Q9 score., Conclusions: Worse SCD symptoms were closely related to common chronic diseases and socio-demographic characteristics. Disease managers should pay more attention to those factors to early intervention and management for SCD symptoms among southern Chinese older adults., (© 2022. The Author(s).)

Background: Poor sleep quality exacerbates subjective cognitive decline (SCD). Mental health may play an important role in the relationship, but the potential underlying mechanisms remain unknown., Methods: A cross-sectional study was conducted on older adult residents in Guangdong province, south China from November 2020 to March 2021. A total of 717 adults aged 60 years and over were recruited for a face-to-face investigation on a range of socio-demographic factors, lifestyle behaviors, sleep quality, SCD, depressive and anxiety symptoms. Parallel, serial and moderated mediation models were used to examine whether depression and anxiety mediated in the poor sleep quality-SCD linkage., Results: Both direct and indirect effects of poor sleep quality on SCD symptoms were found. Anxiety and depressive symptoms mediated the relationship between poor sleep quality and SCD symptoms(β Anxiety  = 0.123, 95%CI: 0.079 to 0.173; β Depression =0.274, 95%CI: 0.200 to 0.348), respectively. Serial mediation analyses indicated that depressive and anxiety symptoms co-play a serial mediating role in the association of poor sleep quality and SCD symptoms (β a =0.052, 95% CI: 0.026 to 0.084; β b =0.077, 95% CI: 0.033 to 0.128). Moderated mediation model revealed that the mediation of depressive symptoms on the relationship between poor sleep quality and SCD symptoms was moderated by anxiety symptoms(β=0.318, 95% CI: 0.164 to 0.472)., Limitations: The study is the cross-sectional design, which limits the causal inference ability., Conclusions: These findings provided new insights into possible avenues for prevention and intervention on SCD through sleep-based treatments with a multi-faceted approach on psychiatric disorders., (Copyright © 2021. Published by Elsevier B.V.)