Preventing Alzheimer's disease within reach by 2025: Targeted-risk-AD-prevention (TRAP) strategy

Multisystem failure, tipping points, and risk of Alzheimer's disease

INTRODUCTION

Medical conditions including obesity, diabetes, hyperlipidemia, and depression significantly increased risk of Alzheimer's disease (AD). However, effect of their duration, influenced by non-modifiable factors like chromosomal sex and apolipoprotein E (APOE) genotype, remains unclear.

METHODS

Data from 5644 UKBiobank participants were analyzed using Cox regression model to identify critical tipping points based on age of onset, risk factor (RF) duration and their interaction with sex and APOE genotype.

RESULTS

Hypertension or diabetes before age 62 exerted greater AD risk than APOEε4 alone. Obesity before age 62 increased AD risk by 54%, with the risk nearly tripling between ages 62-72. Hyperlipidemia and depression were associated with age-independent risk increases of 33% and 69%, respectively. After age 72, APOEε4 became the dominant RF.

DISCUSSION

Duration of AD-risk-factors can have a greater impact than APOEε4. Identification of critical age-related tipping points highlights temporal dynamics of AD progression and role of multisystem failure in AD progression.

HIGHLIGHTS

AD risk factors impact AD onset, especially diagnosed between ages 62 and 72. Later diagnoses of hypertension, diabetes, and obesity delayed AD onset. Hyperlipidemia and depression increased AD risk by 33% and 69%, age-independent. APOEε4 carriers regardless of sex exhibited a higher risk increasing with age. Trajectories differed between APOEε4 carriers and non-carriers across sex.

Combination therapy targeting Alzheimer's disease risk factors is associated with a significant delay in Alzheimer's disease-related cognitive decline

BACKGROUND

Alzheimer's disease (AD) cognitive decline can be a major contributor to loss of independent living. Therapeutic strategies that alter the course of cognitive deterioration have the potential to sustain activities of daily living, promote quality of life, and delay transition to nursing-home care.

METHODS

We performed longitudinal linear regression analysis of National Alzheimer's Coordinating Center (NACC) cognitive data from 7653 mild dementia AD participants at baseline with at least one medication for diabetes (DBMD), lipid-lowering (LIPL), anti-hypertensive (AHTN), and non-steroidal anti-inflammatory (NSD) medications or any combination in 5684 (74%) participants and in 1969 (26%) participants with no study-relevant prescriptions over 10 years. Change in cognitive function was determined by Mini-Mental State Examination (MMSE) and CDR® Dementia Staging Instrument Sum of Boxes (CDR-SB) scores relative to non-treated participants stratified by sex and apolipoprotein E (APOE) genotype. Validation analysis was performed using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.

RESULTS

Combination of DBMD+LIPL+AHTN+NSD (QuadRx) resulted in a significant 46% MMSE and 32% CDR-SB delay in cognitive decline at 5 years, which was sustained at 10 years with a delay in decline of 47% MMSE and 33% CDR-SB. QuadRx was equally effective for the delay of cognitive decline in both females and males at 5 and 10 years. QuadRx mitigated the impact of the APOE ε4 genotype. Findings were validated in ADNI AD participants in which QuadRx was associated with a significant 60% MMSE delay in cognitive decline at 1 and 2 years.

CONCLUSIONS

Combination therapy was associated with a significant delay in cognitive decline in NACC AD participants at a magnitude comparable to or greater than amyloid beta immunomodulators. Further, the delay in decline was sustained for 10 years. The impact of QuadRx to delay cognitive decline was validated in deeply characterized ADNI participants. These data support combination therapy in persons with AD risk factors to alter the course of AD that persists for a decade, enabling cognitive function at a magnitude associated with independent living.

Highlights

QuadRx slowed Alzheimer's disease (AD) cognitive decline by 47% in the National Alzheimer's Coordinating Center NACC and 60% in Alzheimer's Disease Neuroimaging Initiative ADNI participants.Combination therapy exhibited additive and synergistic slowing of cognitive decline.QuadRx was equally effective in females and males at 5 and 10 years.QuadRx mitigated the impact of the apolipoprotein E ε4 genotype.QuadRx was effective in AD participants reporting drug use for their AD risk factor.

Increased plasma DYRK1A with aging may protect against neurodegenerative diseases

Early markers are needed for more effective prevention of Alzheimer's disease. We previously showed that individuals with Alzheimer's disease have decreased plasma DYRK1A levels compared to controls. We assessed DYRK1A in the plasma of cognitively healthy elderly volunteers, individuals with either Alzheimer's disease (AD), tauopathies or Down syndrome (DS), and in lymphoblastoids from individuals with DS. DYRK1A levels were inversely correlated with brain amyloid β burden in asymptomatic elderly individuals and AD patients. Low DYRK1A levels were also detected in patients with tauopathies. Individuals with DS had higher DYRK1A levels than controls, although levels were lower in individuals with DS and with dementia. These data suggest that plasma DYRK1A levels could be used for early detection of at risk individuals of AD and for early detection of AD. We hypothesize that lack of increase of DYRK1A at middle age (40-50 years) could be a warning before the cognitive decline, reflecting increased risk for AD.

Network pharmacology analysis reveals neuroprotective effects of the Qin-Zhi-Zhu-Dan Formula in Alzheimer's disease

There is yet no effective drug for Alzheimer's disease (AD) which is one of the world's most common neurodegenerative diseases. The Qin-Zhi-Zhu-Dan Formula (QZZD) is derived from a widely used Chinese patent drug-Qing-Kai-Ling Injection. It consists of Radix Scutellariae, Fructus Gardeniae, and Pulvis Fellis Suis. Recent study showed that QZZD and its effective components played important roles in anti-inflammation, antioxidative stress and preventing brain injury. It was noted that QZZD had protective effects on the brain, but the mechanism remained unclear. This study aims to investigate the mechanism of QZZD in the treatment of AD combining network pharmacology approach with experimental validation. In the network pharmacology analysis, a total of 15 active compounds of QZZD and 135 putative targets against AD were first obtained. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were then applied to clarify the biological mechanism. The anti-inflammatory mechanism of QZZD was proved, and a synthetic pathway-TNFR1-ERK1/2-NF-κBp65 signaling pathway was obtained. On the basis of the above discoveries, we further validated the protective effects QZZD on neurons with an APP/PS1 double transgenic mouse model. Weight change of the mice was monitored to assess QZZD's influence on the digestive system; water maze experiment was used for evaluating the effects on spatial learning and memory; Western blotting and immunohistochemistry analysis were used to detect the predicted key proteins in network pharmacology analysis, including Aβ, IL-6, NF-κBp65, TNFR1, p-ERK1/2, and ERK1/2. We proved that QZZD could improve neuroinflammation and attenuate neuronal death without influencing the digestive system in APP/PS1 double transgenic mice with dementia. Combining animal pharmacodynamic experiments with network pharmacology analysis, we confirmed the importance of inflammation in pathogenesis of AD, clarified the pharmacodynamic characteristics of QZZD in treating AD, and proved its neuroprotective effects through the regulation of TNFR1-ERK1/2-NF-κBp65 signaling pathway, which might provide reference for studies on treatment of AD in the future.

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Alzheimer’s disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer’s Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.