The Burden of Alzheimer's Disease Mortality in the United States, 1999-2018

Executive function measures of participants with mild cognitive impairment: Systematic review and meta-analysis of event-related potential studies

OBJECTIVE

Objective measurements of executive functions using event-related potential (ERP) may be used as markers for differentiating healthy controls (HC) from patients with mild cognitive impairment (MCI). ERP is non-invasive, cost-effective, and affordable. Older adults with MCI demonstrate deteriorated executive function, serving as a potentially valid neurophysiological marker for identifying MCI. We aimed to review published ERP studies on executive function in older adults with MCI and summarize the performance differences by component between healthy older adults and older adults with MCI.

METHODS

Eight electronic databases (Web of Science, PubMed, ScienceDirect, American Psychological Association PsycNet, Cochrane Library, Scopus, Embase, and Ovid) were searched for the study. Articles published from January 1 to December 31, 2022, were considered for this review. A random-effects meta-analysis and between-study heterogeneity analysis were conducted using Comprehensive Meta-Analysis V3.0 software.

RESULTS

We identified 7829 articles of which 28 met the full inclusion criteria and were included in the systematic review and analyses. Our pooled analysis suggested that participants with MCI can be differentiated from HC by significant P200, P300, and N200 latencies. The P100 and P300 amplitudes were significantly smaller in participants with MCI when compared with those in the HCs, and the patients with MCI showed increased N200 amplitudes. Our findings provide new insights into potential electrophysiological biomarkers for diagnosing MCI.

The efficacy and safety of anti-Aβ agents for delaying cognitive decline in Alzheimer's disease: a meta-analysis

Background

This meta-analysis evaluates the efficacy and safety of amyloid-β (Aβ) targeted therapies for delaying cognitive deterioration in Alzheimer's disease (AD).

Methods

PubMed, EMBASE, the Cochrane Library, and ClinicalTrials.gov were systematically searched to identify relevant studies published before January 18, 2023.

Results

We pooled 33,689 participants from 42 studies. The meta-analysis showed no difference between anti-Aβ drugs and placebo in the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), and anti-Aβ drugs were associated with a high risk of adverse events [ADAS-Cog: MDs = -0.08 (-0.32 to 0.15), p = 0.4785; AEs: RR = 1.07 (1.02 to 1.11), p = 0.0014]. Monoclonal antibodies outperformed the placebo in delaying cognitive deterioration as measured by ADAS-Cog, Clinical Dementia Rating-Sum of Boxes (CDR-SB), Mini-Mental State Examination (MMSE) and Alzheimer's Disease Cooperative Study-Activities of Daily Living (ADCS-ADL), without increasing the risk of adverse events [ADAS-Cog: MDs = -0.55 (-0.89 to 0.21), p = 0.001; CDR-SB: MDs = -0.19 (-0.29 to -0.10), p < 0.0001; MMSE: MDs = 0.19 (0.00 to 0.39), p = 0.05; ADCS-ADL: MDs = 1.26 (0.84 to 1.68), p < 0.00001]. Intravenous immunoglobulin and γ-secretase modulators (GSM) increased cognitive decline in CDR-SB [MDs = 0.45 (0.17 to 0.74), p = 0.002], but had acceptable safety profiles in AD patients. γ-secretase inhibitors (GSI) increased cognitive decline in ADAS-Cog, and also in MMSE and ADCS-ADL. BACE-1 inhibitors aggravated cognitive deterioration in the outcome of the Neuropsychiatric Inventory (NPI). GSI and BACE-1 inhibitors caused safety concerns. No evidence indicates active Aβ immunotherapy, MPAC, or tramiprosate have effects on cognitive function and tramiprosate is associated with serious adverse events.

Conclusion

Current evidence does not show that anti-Aβ drugs have an effect on cognitive performance in AD patients. However, monoclonal antibodies can delay cognitive decline in AD. Development of other types of anti-Aβ drugs should be cautious.

Systematic Review Registration

PROSPERO (https://www.crd.york.ac.uk/prospero/), identifier CRD42023391596.

Disease-modifying vs symptomatic treatments: Splitting over lumping

Clinical trials of putative disease-modifying therapies in neurodegeneration have obeyed the century-old principle of convergence, or lumping, whereby any feature of a clinicopathologic disease entity is considered relevant to most of those affected. While this convergent approach has resulted in important successes in trials of symptomatic therapies, largely aimed at correcting common neurotransmitter deficiencies (e.g., cholinergic deficiency in Alzheimer's disease or dopaminergic deficiency in Parkinson's disease), it has been consistently futile in trials of neuroprotective or disease-modifying interventions. As individuals affected by the same neurodegenerative disorder do not share the same biological drivers, splitting such disease into small molecular/biological subtypes, to match people to therapies most likely to benefit them, is vital in the pursuit of disease modification. We here discuss three paths toward the splitting needed for future successes in precision medicine: (1) encourage the development of aging cohorts agnostic to phenotype in order to enact a biology-to-phenotype direction of biomarker development and validate divergence biomarkers (present in some, absent in most); (2) demand bioassay-based recruitment of subjects into disease-modifying trials of putative neuroprotective interventions in order to match the right therapies to the right recipients; and (3) evaluate promising epidemiologic leads of presumed pathogenetic potential using Mendelian randomization studies before designing the corresponding clinical trials. The reconfiguration of disease-modifying efforts for patients with neurodegenerative disorders will require a paradigm shift from lumping to splitting and from proteinopathy to proteinopenia.

The Allosteric Antagonist of the Sigma-2 Receptors-Elayta (CT1812) as a Therapeutic Candidate for Mild to Moderate Alzheimer's Disease: A Scoping Systematic Review

Nearly 35 million people worldwide live with Alzheimer's disease (AD). The prevalence of the disease is expected to rise two-fold by 2050. With only symptomatic treatment options available, it is essential to understand the developments and existing evidence that aims to target brain pathology and dementia outcomes. This scoping systematic review aimed to collate existing evidence of CT1812 for use in patients with AD and summarize the methodologies of ongoing trials. Adhering to PRISMA Statement 2020 guidelines, PubMed/MEDLINE, Embase, Cochrane, and ClinicalTrials.gov were systematically searched through up to 15 November 2022 by applying the following keywords: CT1812, Alzheimer's disease, dementia, and/or sigma-2 receptor. Three completed clinical trials were included along with three ongoing records of clinical trials. The three completed trials were in Phases I and II of testing. The sample size across all three trials was 135. CT1812 reached endpoints across the trials and obtained a maximum concentration in the cerebrospinal fluid with 97-98% receptor occupancy. The findings of this systematic review must be used with caution as the results, while mostly favorable so far, must be replicated in higher-powered, placebo-controlled Phase II-III trials.

Trend dynamics of gout prevalence among the Chinese population, 1990-2019: A joinpoint and age-period-cohort analysis

Background

The burden of gout is increasing worldwide, which places a heavy burden on society and healthcare systems. This study investigates the independent effects of age, period, and cohort on the gout prevalence from 1990 to 2019 in China, compares these effects by gender and then predicts the future burden of gout over the next decade.

Methods

The data were obtained from the Global Burden of Disease (GBD) study in 2019. Joinpoint regression model was employed to calculate the annual percentage change (APC) in gout prevalence, and the age-period-cohort analysis was utilized to estimate the independent effects of age, period, and cohort. ARIMA model was extended to predict the gout epidemic in 2020-2029.

Results

In 2019, there were 16.2 million cases of gout in China, with an age-standardized prevalence rate (ASPR) of 12.3‰ and 3.9‰ in men and women, respectively. During 1990-2019, the ASPR of gout was increasing significantly, with an average APC of 0.9%. The periods of 2014-2017 and 2001-2005 were "joinpoint" for men and women (APC: 6.3 and 5.6%). The age-period-cohort analyses revealed that the relative risk (RR) of developing gout increased with age, peaking at 70-74 years in men (RR_age(70-74) = 162.9) and 75-79 years in women (RR_age(75-79)=142.3). The period effect trended upward, with a more rapid increase in women (RR_period(2019) = 2.31) than men (RR_period(2019) = 2.23). The cohort effect generally peaked in the earlier cohort born in 1905-1909 for both sexes. Gout prevalence showed a strong positive correlation with the consumption of meat and aquatic products (r_meat = 0.966, r_{aquaticproducts} = 0.953). Within 2029, the ASPR of gout was projected to be 11.7‰ and 4.0‰ in men and women, respectively.

Conclusion

The prevalence of gout is increasing at an alarming rate in China; thus, it is necessary to provide targeted health education, regular screening, and accessible urate-lowering therapy healthcare to prevent and protect against gout in China, particularly in older women.

Temporal trends in prevalence and mortality for chronic kidney disease in China from 1990 to 2019: an analysis of the Global Burden of Disease Study 2019

Background

This study aimed to characterize the temporal trends of chronic kidney disease (CKD) burden in China during 1990-2019, evaluate their age, period and cohort effects, and predict the disease burden for the next 10 years.

Methods

Data were obtained from the Global Burden of Disease (GBD) 2019 study. Join-point regression model was used to estimate the average annual percentage change (AAPC) of CKD prevalence and mortality, and the age-period-cohort analysis was used to estimate the age, period and cohort effects. We extended the autoregressive integrated moving average (ARIMA) model to predict the disease burden of CKD in 2020-2029.

Results

In 2019, there were 150.5 million cases of (10.6%) and 196 726 deaths from (13.8 per 100 000 general population) CKD in China. Between 1990 and 2019, the prevalence and mortality rate of CKD increased significantly from 6.7% to 10.6%, and from 8.3/100 000 to 13.8/100 000. The AAPC was estimated as 1.6% and 1.8%, respectively. Females had a higher CKD prevalence of CKD but a lower mortality rate. Setting the mean level of age, period and cohort as reference groups, the risk of developing CKD increased with age [RR_age(15-19) = 0.18 to RR_age(85-89) = 2.45]. The cohort risk was significantly higher in the early birth cohort [RR_{cohort(1905-1909)} = 1.56]. In contrast, the increase in age-specific CKD mortality rate after 60-64 years was exponential [RR_age(60-64) = 1.24]. The cohort-based mortality risk remained high prior to the 1945-1949 birth cohorts (RR_cohort ranging from 1.69 to 1.89) and then declined in the 2000-2004 birth cohort [RR_{cohort(2000-2004)} = 0.22]. The CKD prevalence and mortality are projected to rise to 11.7% and 17.1 per 100 000, respectively, by 2029.

Conclusions

To reduce the disease burden of CKD, a comprehensive strategy that includes risk factors prevention at the primary care level, CKD screening among the elderly and high-risk population, and access to high-quality medical services is required.

Stem cells from human exfoliated deciduous teeth relieves Alzheimer's disease symptoms in SAMP8 mice by up-regulating the PPARγ pathway

The pathology of Alzheimer's disease (AD) is complex and heterogeneous, and there are currently no drugs that can stop its progression. The failure of traditional chemical small-molecule drug development showed the weakness of single target and made researchers look to cell therapy with multiple regulatory effects. Stem cells from human exfoliated deciduous teeth (SHED) are a kind of neural crest-derived mesenchymal stem cells which have broad prospects in the treatment of neurodegenerative diseases. In this study, we demonstrated the therapeutic effects of SHED in AD mice, including behavioral improvement, neuronal protection, and alleviation of neuroinflammation. Tracking experiments on SHED showed that some of the transplanted cells could enter the brain. To elucidate the role played by the majority of cells transplanted into veins, blood proteomic assays were performed. Data are available via ProteomeXchange with identifier PXD030313. Among the altered proteins, the PPAR pathway related to energy metabolism was considered to be an important signaling pathway involved in regulation through gene ontology analysis and pathway analysis. Western blot showed that the transplantation of SHED improved the glucose metabolism in AD mice by increasing the PPARγ signaling pathway. These results suggested that SHED have a potential in relieving AD pathological symptoms and improving behavioral cognition. The therapeutic mechanism of SHED is related to up-regulating PPARγ signaling pathway and reducing neuronal damage.

Active Compounds and Targets of Yuanzhi Powder in Treating Alzheimer's Disease and Its Relationship with Immune Infiltration Based on HPLC Fingerprint and Network Pharmacology

Background

Yuanzhi powder (YZP) has been extensively investigated as a natural prescription with therapeutic benefits for Alzheimer's disease (AD). However, its active compounds and underlying immune mechanism for treating AD are still unclear. This study aimed to investigate the immune mechanism of YZP against AD through high-performance liquid chromatography (HPLC)-based network pharmacology and gene chip technology.

Methods

Active components of YZP were obtained from HPLC and public databases. Subsequently, GSE5281, GSE28146, GSE29378, and GSE97760 from the Gene Expression Omnibus (GEO) database were downloaded to extract AD difference genes (DEGs). The active components-targets network and protein interaction network were then constructed by Cytoscape. The biological processes and signaling pathways, which implicate the targets of YZP for AD, were analyzed using the ClueGo Cytoscape plug-in. Molecular docking experiments were performed to verify the affinity of targets and ligands. Ultimately, the link between the hub genes and immune cell infiltration was assessed via CIBERSORT.

Results

83 YZP active compounds and 641 DEGs associated with AD, including quercetin, berberine, 3,6′-disinapoylsucrose, coptisine, and palmatine, were evaluated. We showed that FOS, CCL2, and GJA1 were the core targets and that the gap junction is an essential signaling pathway in YZP for AD. Furthermore, the AD group had a higher infiltration level of naïve B cells and resting CD4 memory T cells, as determined by the CIBERSORT. Notably, the immune cells-targets network demonstrates that GJA1 and GRM1 are intimately related to naïve B cells and plasma cells.

Conclusions

YZP may help treat AD by targeting proteins with key active compounds to regulate naïve B cells and plasma cells. Our results demonstrate a new immune mechanism for treating AD with YZP.

Classification of Alzheimer’s Disease Using Dual-Phase 18F-Florbetaben Image with Rank-Based Feature Selection and Machine Learning

18F-florbetaben (FBB) positron emission tomography is a representative imaging test that observes amyloid deposition in the brain. Compared to delay-phase FBB (dFBB), early-phase FBB shows patterns related to glucose metabolism in 18F-fluorodeoxyglucose perfusion images. The purpose of this study is to prove that classification accuracy is higher when using dual-phase FBB (dual FBB) versus dFBB quantitative analysis by using machine learning and to find an optimal machine learning model suitable for dual FBB quantitative analysis data. The key features of our method are (1) a feature ranking method for each phase of FBB with a cross-validated F1 score and (2) a quantitative diagnostic model based on machine learning methods. We compared four classification models: support vector machine, naïve Bayes, logistic regression, and random forest (RF). In composite standardized uptake value ratio, RF achieved the best performance (F1: 78.06%) with dual FBB, which was 4.83% higher than the result with dFBB. In conclusion, regardless of the two quantitative analysis methods, using the dual FBB has a higher classification accuracy than using the dFBB. The RF model is the machine learning model that best classifies a dual FBB. The regions that have the greatest influence on the classification of dual FBB are the frontal and temporal lobes.

Recent Advances in Progresses and Prospects of IL-37 in Central Nervous System Diseases

Interleukin-37 (IL-37) is an effective anti-inflammatory factor and acts through intracellular and extracellular pathways, inhibiting the effects of other inflammatory cytokines, such as IL-1β, IL-6, and tumor necrosis factor-α (TNF-α), thereby exerting powerful anti-inflammatory effects. In numerous recent studies, the anti-inflammatory effects of IL-37 have been described in many autoimmune diseases, colitis, and tumors. However, the current research on IL-37 in the field of the central nervous system (CNS) is not only less, but mainly for clinical research and little discussion of the mechanism. In this review, the role of IL-37 and its associated inflammatory factors in common CNS diseases are summarized, and their therapeutic potential in CNS diseases identified.

Predicting Diabetes and Estimating Its Economic Burden in China Using Autoregressive Integrated Moving Average Model

Objectives: To predict the number of people with diabetes and estimate the economic burden in China.

Methods: Data from natural logarithmic transformation of the number of people with diabetes in China from 2000 to 2018 were selected to fit the autoregressive integrated moving average (ARIMA) model, and 2019 data were used to test it. The bottom-up and human capital approaches were chosen to estimate the direct and indirect economic burden of diabetes respectively.

Results: The number of people with diabetes in China would increase in the future. The ARIMA model fitted and predicted well. The number of people with diabetes from 2020 to 2025 would be about 94, 96, 97, 98, 99 and 100 m respectively. The economic burden of diabetes from 2019 to 2025 would be about $156b, $160b, $163b, $165b, $167b, $169b and $170b respectively.

Conclusion: The situation of diabetes in China is serious. The ARIMA model can be used to predict the number of people with diabetes. We should allocate health resources in a rational manner to improve the prevention and control of diabetes.