Xi-Peng Cao

Clearance of Amyloid Beta and Tau in Alzheimer’s Disease: from Mechanisms to Therapy

Alzheimer’s disease (AD) is the most common neurodegenerative disease. Pathological proteins of AD mainly contain amyloid-beta (Aβ) and tau. Their deposition will lead to neuron damage by a series of pathways, and then induce memory and cognitive impairment. Thus, it is pivotal to understand the clearance pathways of Aβ and tau in order to delay or even halt AD. Aβ clearance mechanisms include ubiquitin–proteasome system, autophagy-lysosome, proteases, microglial phagocytosis, and transport from the brain to the blood via the blood-brain barrier (BBB), arachnoid villi and blood-CSF barrier, which can be named blood circulatory clearance. Recently, lymphatic clearance has been demonstrated to play a key role in transport of Aβ into cervical lymph nodes. The discovery of meningeal lymphatic vessels is another direct evidence for lymphatic clearance in the brain. Furthermore, periphery clearance also contributes to Aβ clearance. Tau clearance is almost the same as Aβ clearance. In this review, we will mainly introduce the clearance mechanisms of Aβ and tau proteins, and summarize corresponding targeted drug therapies for AD.

Genome-wide association study identifies two loci influencing plasma neurofilament light levels

BackgroundPlasma neurofilament light (NFL) is a promising biomarker for Alzheimer disease (AD), which increases in the early stage of AD and is associated with the progression of AD. We performed a genome-wide association study (GWAS) of plasma NFL in Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI-1) cohort to identify novel variants associated with AD.MethodsThis study included 179 cognitively healthy controls (HC), 176 patients with mild cognitive impairment (MCI), and 172 patients with AD. All subjects were restricted to non-Hispanic Caucasian derived from the ADNI cohort and met all quality control (QC) criteria. Association of plasma NFL with the genetic variants was assessed using PLINK with an additive genetic model, i.e.dose-dependent effect of the minor alleles. The influence of a genetic variant associated with plasma NFL (rs7943454) on brain structure was further assessed using PLINK with a linear regression model.ResultsThe minor allele (T) of rs7943454 in leucine zipper protein 2 gene (LUZP2) was associated with higher plasma NFL at suggestive levels (Pu2009=u20091.39u2009×u200910−u20096) in a dose-dependent fashion. In contrast, the minor allele (G) of rs640476 near GABRB2 was associated with lower plasma NFL at suggestive levels (Pu2009=u20096.71u2009×u200910−u20096) in a dose-dependent effect in all diagnostic groups except the MCI group. Furthermore, the minor allele (T) of rs7943454 within LUZP2 increased the onset risk of AD (odds ratiou2009=u20091.547, confidence interval 95%u2009=u20091.018–2.351) and was associated with atrophy of right middle temporal gyrus in the whole cohort in the longitudinal study (Pu2009=u20090.0234).ConclusionGWAS found the associations of two single nucleotide polymorphisms (rs7943454 and rs640476) with plasma NFL at suggestive levels. Rs7943454 in LUZP2 was associated with the onset risk of AD and atrophy of right middle temporal gyrusin the whole cohort. Using an endophenotype-based approach, we identified rs7943454 as a new AD risk locus.

Models for predicting risk of dementia: a systematic review

Background Information from well-established dementia risk models can guide targeted intervention to prevent dementia, in addition to the main purpose of quantifying the probability of developing dementia in the future. Methods We conducted a systematic review of published studies on existing dementia risk models. The models were assessed by sensitivity, specificity and area under the curve (AUC) from receiver operating characteristic analysis. Results Of 8462 studies reviewed, 61 articles describing dementia risk models were identified, with the majority of the articles modelling late life risk (n=39), followed by those modelling prediction of mild cognitive impairment to Alzheimer’s disease (n=15), mid-life risk (n=4) and patients with diabetes (n=3). Age, sex, education, Mini Mental State Examination, the Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological assessment battery, Alzheimer’s Disease Assessment Scale-cognitive subscale, body mass index, alcohol intake and genetic variables are the most common predictors included in the models. Most risk models had moderate-to-high predictive ability (AUC>0.70). The highest AUC value (0.932) was produced from a risk model developed for patients with mild cognitive impairment. Conclusion The predictive ability of existing dementia risk models is acceptable. Population-specific dementia risk models are necessary for populations and subpopulations with different characteristics.