Major risk factors for Alzheimer's disease: age and genetics

Advancing Alzheimer's disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study

OBJECTIVES

Alzheimer's disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Although existing AD risk prediction tools demonstrate high accuracy, their complexity and limited accessibility restrict practical application. This study aimed to develop a convenience, efficient prediction model for AD risk using machine learning techniques.

DESIGN AND SETTING

We conducted a cross-sectional study with participants aged 60 and older from the National Alzheimer's Coordinating Center. We selected personal characteristics, clinical data and psychosocial factors as baseline predictors for AD (March 2015 to December 2021). The study utilised Random Forest and Extreme Gradient Boosting (XGBoost) algorithms alongside traditional logistic regression for modelling. An oversampling method was applied to balance the data set.

INTERVENTIONS

This study has no interventions.

PARTICIPANTS

The study included 2379 participants, of whom 507 were diagnosed with AD.

PRIMARY AND SECONDARY OUTCOME MEASURES

Including accuracy, precision, recall, F1 score, etc. RESULTS: 11 variables were critical in the training phase, including educational level, depression, insomnia, age, Body Mass Index (BMI), medication count, gender, stenting, systolic blood pressure (sbp), neurosis and rapid eye movement. The XGBoost model exhibited superior performance compared with other models, achieving area under the curve of 0.915, sensitivity of 76.2% and specificity of 92.9%. The most influential predictors were educational level, total medication count, age, sbp and BMI.

CONCLUSIONS

The proposed classifier can help guide preclinical screening of AD in the elderly population.

Development and validation of the Florey Dementia Risk Score web-based tool to screen for Alzheimer's disease in primary care

Background

It is estimated that ∼60% of people with Alzheimer's disease (AD) are undetected or undiagnosed, with higher rates of underdiagnosis in low-to middle-income areas with limited medical resources. To promote health equity, we have developed a web-based tool that utilizes easy-to-collect clinical data to enhance AD detection rate in primary care settings.

Methods

This study was leveraged on the data collected from participants of the Australian Imaging, Biomarker & Lifestyle (AIBL) study and the Religious Orders Study and Memory and Aging Project (ROSMAP). The study included three phases: (1) constructing and evaluating a model on retrospective cohort data (1407 AIBL participants), (2) performing simulated trials to assess model accuracy (30 AIBL participants) and missing data tolerability (30 AIBL participants), and (3) external evaluation using a non-Australian dataset (500 ROSMAP participants). The auto-score machine learning algorithm was employed to develop the Florey Dementia Risk Score (FDRS). All the simulated trials and evaluation were performed using a web-based FDRS tool.

Findings

FDRS achieved an area under the curve (AUC) of approximately 0.82 [95% CI, 0.75-0.88], with a sensitivity of 0.74 [0.60-0.86] and a specificity of 0.73 [0.70-0.79]. The accuracy of the simulated pilot trial for 30 AIBL participants with complete record was 87% (26/30 correct), while it only slightly decreased (80.0-83.3%, depending on imputation methods) for another 30 AIBL participants with one or two missing data. FDRS achieved an AUC of 0.82 [0.77-0.86] of 500 ROSMAP participants.

Interpretation

The FDRS tool offers a potential low-cost solution to AD screening in primary care. The present study warrants future trials of FDRS for optimization and to confirm its generalizability across a more diverse population, especially people in low-income countries.

Funding

National Health and Medical Research Council, Australia (GNT2007912) and Alzheimer's Association, USA (23AARF-1020292).

A non-targeting magnetic metal-organic framework probe for highly specific peptide-mediated precise disease monitoring

Alzheimer's disease (AD) is a universal neurodegenerative disease in older adults with incurable and progressive properties, urging for precise monitoring to perform timely treatment to delay its progression. Herein, we introduced a non-targeting magnetic metal-organic framework probe coupled with high-throughput mass spectrometry, creating a rapid screening strategy for highly specific peptides associated with AD. Notably, an elution-free extraction process was proposed, significantly reducing sample preprocessing time while simultaneously ensuring the efficient detection of captured peptides. Using this elution-free extraction process, high-quality peptide profiles were rapidly extracted from the hundreds of samples from both diseased and healthy individuals. By integrating machine learning algorithms, LC-MS/MS, and Uniprot database searching, we identified three specific serum endogenous peptides (m/z = 4215.41, 2884.77 and 2704.61) closely associated with AD. Remarkably, with the use of any single specific peptide, the AUC (Area Under the Curve) values can reach approximately 0.9 during monitoring AD. Moreover, integrating three specific biomarkers provides a robust basis for machine learning algorithms to build monitoring models, with AUC value up to 1.000. This work represents a substantial advancement in the development of peptide-specific precise monitoring approaches for complex diseases, serving as a catalyst for increased dedication to the molecular detection field.

Pain as a Protective Factor for Alzheimer Disease in Patients with Cancer

OBJECTIVE

Alzheimer disease (AD) and cancer have been reported to be inversely correlated in incidence, but the mechanism remains elusive.

METHODS

A case-control study was conducted, based on the SEER (Surveillance, Epidemiology, and End Results) Research Plus data, to evaluate 12 factors in patients with cancer.

RESULTS

Severe pain was related to reduced AD risk, while older age at cancer diagnosis, female, longer survival years after tumor diagnosis, more benign/borderline tumors, less cancer-directed surgery, and more chemotherapy were associated with higher AD risk. In addition, patients of different races or with different cancer sites were associated with different risks of getting AD. Cases had a higher prevalence of severe pain than controls in all race and cancer site subgroups, except for in digestive cancer, where the result was the opposite.

CONCLUSIONS

This study indicated pain as a novel protective factor for AD in patients with cancer. The mechanism behind it may provide new perspective on AD pathogenesis and AD-cancer association, which we discussed in our own hypothesis of the mechanism of pain action. In addition, digestive cancer pain had an opposite impact on AD risk from other cancer pains, which suggests the uniqueness of digestive system in interacting with the central nervous system.

Protective effect of Monarda didymaL. essential oil and its main component thymol on learning and memory impairment in aging mice

The aging process of human beings is accompanied by the decline of learning and memory ability and progressive decline of brain function, which induces Alzheimer’s Disease (AD) in serious cases and seriously affects the quality of patient’s life. In recent years, more and more studies have found that natural plant antioxidants can help to improve the learning and memory impairment, reduce oxidative stress injury and aging lesions in tissues. This study aimed to investigate the effect of Monarda didymaL. essential oil and its main component thymol on learning and memory impairment in D-galactose-induced aging mice and its molecular mechanism. The composition of Monarda didymaL. essential oil was analyzed by Gas Chromatography-Mass Spectrometer (GC-MS). A mouse aging model was established by the subcutaneous injection of D-galactose in mice. The behavior changes of the mice were observed by feeding the model mice with essential oil, thymol and donepezil, and the histopathological changes of the hippocampus were observed by HE staining. And the changes of acetylcholinesterase (AchE), superoxide dismutase (SOD) and glutathione peroxidase (GSH-PX) activities, and the content of malondialdehyde (MDA) in hippocampal tissues were detected by corresponding kits. The expression of mitogen activated protein kinase (MAPK) and nuclear factor E2 related factor 2 (Nrf2) pathways related proteins were detected by western blot. Animal experimental results showed that compared with model group, the above indexes in Monarda didymaL. essential oil and thymol groups improved significantly in a dose-dependent manner. Monarda didymaL. essential oil and its main active component thymol can improve the learning and memory impairment of aging mice to some extent, and Nrf2 and MAPK pathways may be involved in its action process.

Risk factors and a nomogram for frailty in Chinese older patients with Alzheimer's disease: A single-center cross-sectional study

This study aimed to determine the risk factors of frailty in Chinese older patients with Alzheimer's disease (AD) and then construct a nomogram for frailty in this population. A total of 205 eligible older AD patients were recruited. Patients' general demographic characteristics were collected through a self-designed questionnaire. A nomogram was constructed for frailty based on the risk factors identified from the multivariate analysis. The discrimination and calibration capabilities of this nomogram were assessed with the C-index and calibration curve, respectively. The results showed that older age, no regular exercise habit, severe cognitive decline, and low social support were identified as important risk factors of frailty in AD patients. The C-index of the nomogram was 0.884 by bootstrapping validation, and the calibration curve of the nomogram showed high coherence between the predicted and actual probabilities of frailty. In conclusion, this nomogram was validated to have favorable discrimination and calibration capabilities.

The Influence of 24-h Ambulatory Blood Pressure on Cognitive Function and Neuropathological Biomarker in Patients With Alzheimer's Disease

Purpose

This study aimed to investigate the influence of 24-h ambulatory blood pressure (BP) on cognitive function and neuropathological biomarkers in patients with Alzheimer's disease (AD) at the stages of mild cognitive impairment (MCI) and dementia.

Methods

The patients with AD were divided into the MCI (AD-MCI) group and the dementia (AD-D) group. Notably, 24-h BP variables, including BP level, coefficient of variation (CV) of BP, and pulse pressure, were collected and compared between the two groups. The correlations between 24-h BP variables and the scores of cognitive domains were analyzed. The independent influencing factors of cognitive domains of patients with AD were investigated. The levels of neuropathological biomarkers of AD, including β amyloid (Aβ)1−42, phosphorylated tau (P-tau), and total tau (T-tau), in cerebrospinal fluid (CSF) were measured and compared between the two groups, and the correlations between 24-h BP variables and the levels of neuropathological biomarkers of AD were analyzed.

Results

Daytime CV of systolic BP (SBP) was significantly increased in the AD-D group compared to that in the AD-MCI group. The 24-h and daytime CV of SBP and ambulatory pulse pressure were significantly and negatively correlated with memory score. The average 24-h and average daytime SBP level and CV of SBP, daytime CV of diastolic BP (DBP), and 24-h, daytime, and night-time ambulatory pulse pressure were significantly and negatively correlated with language score. The average 24-h SBP level, daytime CV of SBP, and 24-h, daytime, and night-time ambulatory pulse pressure were significantly and negatively correlated with attention score. Further analysis indicated that daytime CV of SBP as well as age and course of disease were the independent influencing factors of language. Age was also the independent influencing factor of memory and attention of patients with AD. T-tau level in CSF in the AD-D group was significantly higher than that in the AD-MCI group, but the levels of Aβ1−42, P-tau, and T-tau in CSF were not correlated with 24-h ambulatory BP variables.

Conclusion

Daytime CV of SBP was the independent influencing factor of language in patients with AD. The AD-D patients had significantly severe neurodegeneration than AD-MCI patients, which was, however, not through the influence of 24-h ambulatory BP variables on neuropathological biomarkers of AD.

Physical activity attenuates the risk for dementia associated with aging in older adults with mild cognitive impairment. Findings from a population-based cohort study

From 2016 to 2040 the number of people with dementia in the United Kingdom is expected to increase by 57%, while 70% percent of it is due to a higher life expectancy. Thus, we analyzed the overall and age-stratified effect of physical activity on risk of dementia in participants with mild cognitive impairment (MCI) of the English Longitudinal Study of Ageing (ELSA). Participants of the ELSA, aged over 50 with MCI, were followed-up nine times between 2002 and 2019. Physical activity was assessed using a self-reported, validated questionnaire and participants were classified as inactive, low, or moderate-to-high active. Medical diagnosis of dementia was self-reported or determined using the Informant Questionnaire on Cognitive Decline in the Elderly. Data from 521 participants with MCI were analyzed (56% women; mean [SD] age, 68.7 [10.6]). Over 17-year follow-up, 20.5 (95%CI: 17.3 to 24.2)% were diagnosed with dementia. The risk of incident dementia was reduced in participants engaging in low (HR: 0.34; 95%CI: 0.22 to 0.54) or moderate-to-high (HR: 0.16; 95%CI: 0.08 to 0.33) levels of physical activity. Risk of dementia in adults aged 80 or more engaging in low or moderate-to-high levels of physical activity was not different from inactive adults aged between 50 and 69 years. Results were sustained after competing risk regression model and sensitivity analyses to reduce the impact of reverse causality. Physical activity appears to minimize the risk associated with aging in older adults with MCI.

Construction of a risk prediction model for Alzheimer's disease in the elderly population

BACKGROUND

Dementia is one of the greatest global health and social care challenges of the twenty-first century. The etiology and pathogenesis of Alzheimer's disease (AD) as the most common type of dementia remain unknown. In this study, a simple nomogram was drawn to predict the risk of AD in the elderly population.

METHODS

Nine variables affecting the risk of AD were obtained from 1099 elderly people through clinical data and questionnaires. Least Absolute Shrinkage Selection Operator (LASSO) regression analysis was used to select the best predictor variables, and multivariate logistic regression analysis was used to construct the prediction model. In this study, a graphic tool including 9 predictor variables (nomogram-see precise definition in the text) was drawn to predict the risk of AD in the elderly population. In addition, calibration diagram, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to verify the model.

RESULTS

Six predictors namely sex, age, economic status, health status, lifestyle and genetic risk were identified by LASSO regression analysis of nine variables (body mass index, marital status and education level were excluded). The area under the ROC curve in the training set was 0.822, while that in the validation set was 0.801, suggesting that the model built with these 6 predictors showed moderate predictive ability. The DCA curve indicated that a nomogram could be applied clinically if the risk threshold was between 30 and 40% (30 to 42% in the validation set).

CONCLUSION

The inclusion of sex, age, economic status, health status, lifestyle and genetic risk into the risk prediction nomogram could improve the ability of the prediction model to predict AD risk in the elderly patients.

Andrographolide ameliorates neuroinflammation in APP/PS1 transgenic mice

Alzheimer's disease is a devastating neurodegenerative disorder, with no disease-modifying treatment available yet. There is increasing evidence that neuroinflammation plays a critical role in the pathogenesis of AD. Andrographolide (Andro), a labdane diterpene extracted from the herb Andrographis paniculata, has been reported to exhibit neuroprotective property in central nervous system diseases. However, its effects on Aβ and Aβ-induced neuroinflammation have not yet been studied. In the present study, we found that Andro administration significantly alleviated cognitive impairments, reduced amyloid-β deposition, inhibited microglial activation, and decreased the secretion of proinflammatory factors in APP/PS1 mice. Furthermore, transcriptome sequencing analysis revealed that Andro could significantly decrease the expression of Itgax, TLR2, CD14, CCL3, CCL4, TLR1, and C3ar1 in APP/PS1 mice, which was further validated by qRT-PCR. Our results suggest that Andro might be a potential therapeutic drug for AD by regulating neuroinflammation.

Comprehensive Management of Daily Living Activities, behavioral and Psychological Symptoms, and Cognitive Function in Patients with Alzheimer's Disease: A Chinese Consensus on the Comprehensive Management of Alzheimer's Disease

Alzheimer's disease (AD) is the most common cognitive disorder in the elderly. Its main clinical manifestations are cognitive decline (C), behavioral and psychological symptoms (B), and a decline in the activities of daily living (A), also known as ABC symptoms. Early identification and evaluation of ABC symptoms are helpful for establishing the accurate diagnosis, comprehensive treatment, and prognosis of AD. To guide Chinese clinical practice for optimization of the comprehensive management of AD, in 2018, The Academy of Cognitive Disorder of China gathered 22 neurologists and gerontologists in China to build a consensus on the comprehensive management of AD. Based on a review of the evidence, the consensus summarizes the pathogenesis, pathological changes, clinical manifestations, evaluation, diagnosis, drug and non-drug treatment, and patient care for AD. Focus group discussion was used to establish a flowchart of comprehensive ABC management for AD patients. The new consensus provides a feasible AD management process for clinicians.

Molecular identification of protein kinase C beta in Alzheimer's disease

The purpose of this study was to investigate the potential roles of protein kinase C beta (PRKCB) in the pathogenesis of Alzheimer’s disease (AD). We identified 2,254 differentially expressed genes from 19,245 background genes in AD versus control as well as PRKCB-low versus high group. Five co-expression modules were constructed by weight gene correlation network analysis. Among them, the 1,222 genes of the turquoise module had the strongest relation to AD and those with low PRKCB expression, which were enriched in apoptosis, axon guidance, gap junction, Fc gamma receptor (FcγR)-mediated phagocytosis, mitogen-activated protein kinase (MAPK) and vascular endothelial growth factor (VEGF) signaling pathways. The intersection pathways of PRKCB in AD were determined, including gap junction, FcγR-mediated phagocytosis, MAPK and VEGF signaling pathways. Based on the performance evaluation of the area under the curve of 75.3%, PRKCB could accurately predict the onset of AD. Therefore, low expressions of PRKCB was a potential causative factor of AD, which might be involved in gap junction, FcγR-mediated phagocytosis, MAPK and VEGF signaling pathways.