Influence of Gender on Tau Precipitation in Alzheimer's Disease According to ATN Research Framework

Impact of diabetes on the progression of Alzheimer's disease via trajectories of amyloid-tau-neurodegeneration (ATN) biomarkers

BACKGROUND

Alzheimer's disease (AD) is characterized by the accumulation of abnormal proteins, such as β-amyloid and tau, in the brain, which precedes cognitive impairment. Although diabetes mellitus (DM) is a well-established risk factor for AD, few studies have investigated how the presence of DM affects the sequential pathogenesis of AD, specifically within the amyloid-tau-neurodegeneration (ATN) and cognition framework.

OBJECTIVES

This study aims to investigate the trajectories of ATN biomarkers in relation to the presence of DM in the preclinical and prodromal stages of AD.

DESIGN

Participants with normal cognition (CN) or mild cognitive impairment (MCI) at baseline were included. Subjects were followed for 12-192 months, with neuroimaging and cognitive assessments conducted at every 12 or 24 months.

SETTING

This study utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

PARTICIPANTS

A total of 603 participants aged 55-90 years were included, comprising 284 CN (25 with DM, 259 without DM) and 319 MCI (39 with DM, 280 without DM) individuals.

MEASUREMENTS

ATN biomarkers were identified using florbetapir positron emission tomography (PET), flortaucipir PET, and magnetic resonance imaging (MRI), respectively. Cognition was assessed using the Clinical Dementia Rating-Sum of Boxes (CDR-SB) and Mini-Mental State Examination (MMSE). Moderation analysis was conducted to investigate the effect of DM on the association between ATN biomarkers of AD.

RESULTS

Elevated amyloid standardized uptake value ratios (SUVRs) were associated with increased tau levels in the hippocampus, and this association was significantly enhanced by the presence of DM in MCI participants (p = 0.021). DM also strengthened the association between increased tau SUVR levels and neurodegeneration (indicated by decreased entorhinal cortical volumes; p = 0.005) in those with MCI. Furthermore, DM enhanced the association of decreased entorhinal (p = 0.012) and middle temporal cortex (p = 0.031) volumes with increased (worsened) CDR-SB scores in MCI participants. However, DM did not predict significant longitudinal changes in ATN pathology or cognitive decline in CN participants.

CONCLUSIONS

Our study suggests that DM may increase the risk of AD by accelerating each step of the A-T-N cascade in the prodromal stage of AD, underscoring the importance of DM management in preventing the MCI conversion to AD.

HOPE: Hybrid-Granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment

Mild cognitive impairment (MCI) is often at high risk of progression to Alzheimer's disease (AD). Existing works to identify the progressive MCI (pMCI) typically require MCI subtype labels, pMCI vs. stable MCI (sMCI), determined by whether or not an MCI patient will progress to AD after a long follow-up. However, prospectively acquiring MCI subtype data is time-consuming and resource-intensive; the resultant small datasets could lead to severe overfitting and difficulty in extracting discriminative information. Inspired by that various longitudinal biomarkers and cognitive measurements present an ordinal pathway on AD progression, we propose a novel Hybrid-granularity Ordinal PrototypE learning (HOPE) method to characterize AD ordinal progression for MCI progression prediction. First, HOPE learns an ordinal metric space that enables progression prediction by prototype comparison. Second, HOPE leverages a novel hybrid-granularity ordinal loss to learn the ordinal nature of AD via effectively integrating instance-to-instance ordinality, instance-to-class compactness, and class-to-class separation. Third, to make the prototype learning more stable, HOPE employs an exponential moving average strategy to learn the global prototypes of NC and AD dynamically. Experimental results on the internal ADNI and the external NACC datasets demonstrate the superiority of the proposed HOPE over existing state-of-the-art methods as well as its interpretability.

Potential therapeutic natural compounds for the treatment of Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a complicated neurodegenerative disease with cognitive impairment occurring in the older people, in which extracellular accumulation of β-amyloid and intracellular aggregation of hyperphosphorylated tau are regarded as the prevailing theories. However, the exact AD mechanism has not been determined. Moreover, there is no effective treatment available in phase III trials to eradicate AD, which is imperative to explore novel treatments.

PURPOSE

A number of up-to-date pre-clinical studies on cognitive impairment is beneficial to clarify the pathology of AD. This review recapitulates several advances in AD pathobiology and discusses the neuroprotective effects of natural compounds, such as phenolic compounds, natural polysaccharides and oligosaccharides, peptide, and lipids, underscoring the therapeutic potential for AD.

METHODS

Electronic databases involving PubMed, Web of Science, and Google Scholar were searched up to October 2023. Articles were conducted using the keywords like Alzheimer's disease, pathogenic mechanisms, natural compounds, and neuroprotection.

RESULT

This review summarized several AD pathologies and the neuroprotective effects of natural compounds such as natural polysaccharides and oligosaccharides, peptide, and lipids.

CONCLUSION

We have discussed the pathogenic mechanisms of AD and the effect natural products on neurodegenerative diseases particularly in treating AD. Specifically, we investigated the molecular pathways and links between natural compounds and Alzheimer's disease such as through NF-κB, Nrf2, and mTOR pathway. Further investigation is necessary in exploring the bioactivity and effectiveness of natural compounds in clinical trials, which may provide a promising treatment for AD patients.

Glucose metabolism in posterior cingulate cortex has supplementary value to predict the progression of cognitively unimpaired to dementia due to Alzheimer's disease: an exploratory study of 18F-FDG-PET

Amyloid-β (Aβ) and tau are important biomarkers to predict the progression of cognitively unimpaired (CU) to dementia due to Alzheimer's disease (AD), according to the diagnosis framework from the US National Institute on Aging and the Alzheimer's Association (NIA-AA). However, it is clinically difficult to predict those subjects who were already with Aβ positive (A +) or tau positive (T +). As a typical characteristic of neurodegeneration in the diagnosis framework, the hypometabolism of the posterior cingulate cortex (PCC) has significant clinical value in the early prediction and prevention of AD. In this paper, we proposed the glucose metabolism in the PCC as a biomarker supplement to Aβ and tau biomarkers. First, we calculated the standard uptake value ratio (SUVR) of PCC based on fluorodeoxyglucose positron emission computed tomography (FDG PET) imaging. Secondly, we performed Kaplan-Meier (KM) survival analyses to explore the predictive performance of PCC SUVR, and the hazard ratio (HR) was calculated. Finally, we performed Pearson correlation analyses to explore the physiological significance of PCC SUVR. As a result, the PCC SUVR showed a consistent downward trend along the AD continuum. KM analyses showed better predictive performance when we combined PCC SUVR with cerebro-spinal fluid (CSF) Aβ42 (from HR = 2.56 to 3.00 within 5 years; from HR = 2.76 to 4.20 within 10 years) and ptau-181 (from 2.83 to 3.91 within 5 years; from HR = 2.32 to 4.17 within 10 years). There was a slight correlation between Aβ42/Aβ40 and PCC SUVR (r = 0.14, p = 0.02). In addition, several cognition scales were also correlated to PCC SUVR (from r = -0.407 to 0.383, p < 0.05). Our results showed that glucose metabolism in PCC may be a potential biomarker supplement to the Aβ and tau biomarkers to predict the progression of CU to AD.

Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson's disease: a systematic review and meta-analysis

Artificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson's disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD. The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databases were systematically searched for related studies that developed an AI algorithm in PET imaging for diagnostic performance from PD and were published by August 17, 2023. Binary diagnostic accuracy data were extracted for meta-analysis to derive outcomes of interest: area under the curve (AUC). 23 eligible studies provided sufficient data to construct contingency tables that allowed the calculation of diagnostic accuracy. Specifically, 11 studies were identified that distinguished PD from normal control, with a pooled AUC of 0.96 (95% CI: 0.94-0.97) for presynaptic dopamine (DA) and 0.90 (95% CI: 0.87-0.93) for glucose metabolism (18F-FDG). 13 studies were identified that distinguished PD from the atypical parkinsonism (AP), with a pooled AUC of 0.93 (95% CI: 0.91 - 0.95) for presynaptic DA, 0.79 (95% CI: 0.75-0.82) for postsynaptic DA, and 0.97 (95% CI: 0.96-0.99) for 18F-FDG. Acceptable diagnostic performance of PD with AI algorithms-assisted PET imaging was highlighted across the subgroups. More rigorous reporting standards that take into account the unique challenges of AI research could improve future studies.

Improved interpretation of 18F-florzolotau PET in progressive supranuclear palsy using a normalization-free deep-learning classifier

While 18F-florzolotau tau PET is an emerging biomarker for progressive supranuclear palsy (PSP), its interpretation has been hindered by a lack of consensus on visual reading and potential biases in conventional semi-quantitative analysis. As clinical manifestations and regions of elevated 18F-florzolotau binding are highly overlapping in PSP and the Parkinsonian type of multiple system atrophy (MSA-P), developing a reliable discriminative classifier for 18F-florzolotau PET is urgently needed. Herein, we developed a normalization-free deep-learning (NFDL) model for 18F-florzolotau PET, which achieved significantly higher accuracy for both PSP and MSA-P compared to semi-quantitative classifiers. Regions driving the NFDL classifier's decision were consistent with disease-specific topographies. NFDL-guided radiomic features correlated with clinical severity of PSP. This suggests that the NFDL model has the potential for early and accurate differentiation of atypical parkinsonism and that it can be applied in various scenarios due to not requiring subjective interpretation, MR-dependent, and reference-based preprocessing.

Head-to-head comparison of plasma and PET imaging ATN markers in subjects with cognitive complaints

BACKGROUND

Gaining more information about the reciprocal associations between different biomarkers within the ATN (Amyloid/Tau/Neurodegeneration) framework across the Alzheimer's disease (AD) spectrum is clinically relevant. We aimed to conduct a comprehensive head-to-head comparison of plasma and positron emission tomography (PET) ATN biomarkers in subjects with cognitive complaints.

METHODS

A hospital-based cohort of subjects with cognitive complaints with a concurrent blood draw and ATN PET imaging (18F-florbetapir for A, 18F-Florzolotau for T, and 18F-fluorodeoxyglucose [18F-FDG] for N) was enrolled (n = 137). The β-amyloid (Aβ) status (positive versus negative) and the severity of cognitive impairment served as the main outcome measures for assessing biomarker performances.

RESULTS

Plasma phosphorylated tau 181 (p-tau181) level was found to be associated with PET imaging of ATN biomarkers in the entire cohort. Plasma p-tau181 level and PET standardized uptake value ratios of AT biomarkers showed a similarly excellent diagnostic performance for distinguishing between Aβ+ and Aβ- subjects. An increased tau burden and glucose hypometabolism were significantly associated with the severity of cognitive impairment in Aβ+ subjects. Additionally, glucose hypometabolism - along with elevated plasma neurofilament light chain level - was related to more severe cognitive impairment in Aβ- subjects.

CONCLUSION

Plasma p-tau181, as well as 18F-florbetapir and 18F-Florzolotau PET imaging can be considered as interchangeable biomarkers in the assessment of Aβ status in symptomatic stages of AD. 18F-Florzolotau and 18F-FDG PET imaging could serve as biomarkers for the severity of cognitive impairment. Our findings have implications for establishing a roadmap to identifying the most suitable ATN biomarkers for clinical use.

Neuroimaging modalities in the detection of Alzheimer's disease-associated biomarkers

Alzheimer's disease (AD) is the most common cause of dementia. Neuropathological changes in AD patients occur up to 10-20 years before the emergence of clinical symptoms. Specific diagnosis and appropriate intervention strategies are crucial during the phase of mild cognitive impairment (MCI) and AD. The detection of biomarkers has emerged as a promising tool for tracking the efficacy of potential therapies, making an early disease diagnosis, and prejudging treatment prognosis. Specifically, multiple neuroimaging modalities, including magnetic resonance imaging (MRI), positron emission tomography, optical imaging, and single photon emission-computed tomography, have provided a few potential biomarkers for clinical application. The MRI modalities described in this review include structural MRI, functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy, and arterial spin labelling. These techniques allow the detection of presymptomatic diagnostic biomarkers in the brains of cognitively normal elderly people and might also be used to monitor AD disease progression after the onset of clinical symptoms. This review highlights potential biomarkers, merits, and demerits of different neuroimaging modalities and their clinical value in MCI and AD patients. Further studies are necessary to explore more biomarkers and overcome the limitations of multiple neuroimaging modalities for inclusion in diagnostic criteria for AD.

PARP16-Mediated Stabilization of Amyloid Precursor Protein mRNA Exacerbates Alzheimer's Disease Pathogenesis

The accumulation and deposition of beta-amyloid (Aβ) are key neuropathological hallmarks of Alzheimer's disease (AD). PARP16, a Poly(ADP-ribose) polymerase, is a known tail-anchored endoplasmic reticulum (ER) transmembrane protein that transduces ER stress during pathological processes. Here, we found that PARP16 was significantly increased in the hippocampi and cortices of APPswe/PS1dE9 (APP/PS1) mice and hippocampal neuronal HT22 cells exposed to Aβ, suggesting a positive correlation between the progression of AD pathology and the overexpression of PARP16. To define the effect of PARP16 on AD progression, adeno-associated virus mediated-PARP16 knockdown was used in APP/PS1 mice to investigate the role of PARP16 in spatial memory, amyloid burden, and neuroinflammation. Knockdown of PARP16 partly attenuated impaired spatial memory, as indicated by the Morris water maze test, and decreased amyloid deposition, neuronal apoptosis, and the production of inflammatory cytokines in the brains of APP/PS1 mice. In vitro experiments demonstrated that the knockdown of PARP16 expression rescued neuronal damage and ER stress triggered by Aβ. Furthermore, we discovered that intracellular PARP16 acts as an RNA-binding protein that regulates the mRNA stability of amyloid precursor protein (APP) and protects targeted APP from degradation, thereby increasing APP levels and AD pathology. Our findings revealed an unanticipated role of PARP16 in the pathogenesis of AD, and at least in part, its association with increased APP mRNA stability.