AD: The Shen Yong team of the First Affiliated Hospital of the University of Science and Technology of China proposed a new standard for the diagnosis of Alzheimer's disease blood suitable for Chinese

*For medical professionals only

Alzheimer's disease (AD) is the most common neurodegenerative disease in middle-aged and elderly people, mainly manifested as progressive cognitive decline, and there is no effective early diagnosis tool and specific treatment widely used in clinical practice
.

With the acceleration of population aging, AD has become a worldwide problem
that endangers human health.
According to the data of the seventh population census, China's population aged 60 and above accounted for 18.
70%, an increase of 5.
44% compared with 2010, indicating that China's aging problem is becoming increasingly serious [1].

The latest research shows that there are nearly 10 million elderly AD patients in China, with a prevalence of 3.
9% [2], and it can be expected that the burden of disease brought by AD will also increase
among individuals, families and society.

Currently recognized pathologic changes in AD include: β-amyloid aggregation (A), tau protein hyperphosphorylation (T), neurodegenerative changes, or neuronal damage (N).

Based on these three biomarkers, scientists proposed an A/T/N diagnostic framework, which provides new ideas
for AD diagnosis and treatment.

Clinical studies based on the A/T/N framework have been conducted in multiple countries, and researchers have developed cut-off values that can be used for clinical diagnosis and validated in multicenter, large sample populations [3].

It is worth mentioning that in January 2018, the FDA recommended the A/T/N framework as the diagnostic criteria for AD [3], which is an important breakthrough
in the standardization of AD marker combinations to guide clinical early intervention.

Previously, some Chinese scholars have studied the core markers of Chinese AD, which has promoted the development of AD pathology [4,5].

Unfortunately, comprehensive studies based on the A/T/N framework have rarely been conducted in China, so it is unclear whether the A/T/N model reflects pathological changes
in the brain of Chinese patients.
Due to the advanced equipment used in Aβ-PET scanning, which can be carried out in only a few tertiary hospitals in China, there is an urgent need for simple, cost-effective non-invasive tools for early diagnosis of AD to enable early intervention and delay or even reverse the occurrence and development
of the disease.

Recently, a research team led by Shen Yong of the First Affiliated Hospital of the University of Science and Technology of China published important research results in the famous journal Alzheimer's & Dementia [6].

Their findings confirmed that AD patients had elevated CSF and plasma p-tau levels and reduced Aβ42/Aβ40
.
In addition, CSF Aβ42/Aβ40, p-tau and plasma p-tau had high consistency in distinguishing AD and non-AD dementia and healthy elderly controls.

What's more, they also found that machine learning models that combined plasma p-tau, apolipoprotein E (APOE) genotype, and MRI brain atrophy measurements could accurately predict Aβ PET deposition
.

The above results show that the A/T/N framework can be used in the clinical work of Chinese group AD diagnosis, and Shen Yong's team also established an optimal diagnostic model
for AD combining low cost and non-invasiveness.
It is worth mentioning that this is also the first time to use the clinical cohort to systematically construct an AD diagnostic model based on the A/T/N framework in the Chinese middle-aged and elderly population, which fills the gap in China's A/T/N framework research and provides a potential diagnostic alternative model
for AD clinical diagnosis.

Screenshot of the first page of the paper

Next, let's take a look at how this research
unfolds.

The study was based on the Chinese Aging and Neurodegenerative Diseases Research Cohort (CANDI), in which researchers screened 411 subjects from CANDI, including 96 cognitively normal subjects (CN, control group), 94 patients with mild cognitive impairment (MCI), 107 patients with early-onset AD (EOAD), 66 patients with late-onset AD (LOAD), and 48 patients
with non-AD dementia (non-ADD).

First, the comparison between groups in different diagnostic groups showed that p-tau and t-tau in the AD group were higher than those in the control group and the non-AD dementia group
, both in CSF and plasma.
Conversely, plasma and CSF levels of Aβ42 were significantly reduced in the AD group, and plasma Aβ40 did not differ significantly between groups, however, CSF Aβ40 was significantly reduced
in the EOAD and non-AD dementia groups compared to the control group.

Biomarker concentrations or ratios for different groups

Using Spearmen correlation analysis, the researchers found high
agreement between plasma and CSF Aβ42/Aβ40, t-tau, and p-tau levels.
Subjects were divided into four quadrants based on cut-off values of CSF and plasma biomarkers (A−; A+； T−； T+), interestingly, CSF p-tau and Aβ42/Aβ40 had 88% agreement in distinguishing positive and negative individuals (A+T+: 51.
5%; A−T−：36.
5%）； CSF and plasma p-tau had 81.
4% agreement
.

In addition, most AD patients are located in the A+T+ quadrant, while non-AD dementia patients and healthy controls are mostly in the A−T− quadrant, which shows that plasma and CSF p-tau have similar and high diagnostic ability
.
Therefore, plasma p-tau can be used as a non-invasive monitoring method for AD, which can improve the limitations of the current diagnostic tools such as invasiveness, poor availability, and high price
.

Correlation and consistency of Aβ and Tau

Next, the researchers used MRI to determine the thickness of the cerebral cortex and the volume of the hippocampus and amygdala, and found that the cortical thickness and the relative volume of the hippocampus and amygdala were significantly smaller in the AD group than in the control and MCI groups
.
Notably, there was no significant difference
in brain structure changes between the AD and non-AD dementia groups.

Shen Yong's team also carried out the correlation analysis of body fluids and image markers based on locally weighted linear regression (LOWESS) model, and found that hippocampal and amygdala volume had a strong correlation with the p-tau concentration of body fluids in the early stage of AD, and tended to be stable in the later stage.
The correlation with the Aβ42/Aβ40 ratio is divided into two opposite stages, with an upward trend in the early stage and a decline
in the later stage.
Based on the results of different analyses, plasma p-tau protein may be a more effective biomarker reflecting early brain atrophy in AD
.

Correlation between humoral biomarkers and MRI measures of brain atrophy

They also used a secondary model to observe a significant increase in CSF and plasma p-tau during the occurrence of brain atrophy, and continued to increase to the stage of AD pathological development
.
It is worth noting that there is no significant difference
in the slope of CSF and plasma p-tau during the progression of brain atrophy.

However, in the progression of amygdala and hippocampal atrophy, CSF has a greater slope than plasma Aβ42/Aβ40, which indicates that plasma Aβ42/Aβ40 has no strong correlation
with brain atrophy indicators compared with CSF Aβ42/Aβ40.
Therefore, plasma p-tau is a reliable blood biomarker for identifying and monitoring cerebral cortical atrophy and can be used as a potential alternative tool
for patients with contraindications to MRI.

Trajectories of AD humoral biomarkers during continuous changes in brain atrophy

Next, Shen's team used a single-factor receiver operating characteristic curve (ROC) model to analyze
the diagnostic capabilities of each plasma biomarker.

The results showed that plasma p-tau and p-tau/t-tau ratios performed well in distinguishing AD from non-AD dementia and predicting Aβ deposition in the brain (area under the curve [AUC]=0.
847 and 0.
853).

In addition, plasma p-tau has strong predictive power
in distinguishing CSF Aβ (AUC = 0.
856) and Aβ PET (AUC=0.
849) states.

Finally, Shen Yong's team combined plasma with imaging markers to evaluate whether multifactorial markers could further improve the accuracy of
predicting Aβ in the brain.

They designed a machine learning model based on Aβ deposition, screened out the model with the lowest Akachi Information Criterion (AIC), and gradually removed all plasma biomarkers and MRI indicators to evaluate its contribution
to the model.

They found that the best models included plasma p-tau, Aβ42/Aβ40, cortical thickness (middle temporal gyrus, inferior temporal gyrus), and brain volume measurements (hippocampus and amygdala) with an ROC curve accuracy of 0.
864
.
Based on the aforementioned model, they built a model that added APOE ε4 carrier state, and the results showed that the model using only APOE ε4 carrier state and plasma p-tau was similar to the previous model (AUC=0.
877).

Machine learning models incorporating plasma p-tau, APOE ε4, and cortical thickness (cingulate gyrus and middle temporal gyrus) had significantly improved accuracy (AUC=0.
909), which was similar to the accuracy of the best models (plasma p-tau, Aβ42/Aβ40, cortical thickness, brain volume measurement, and APOE ε4 carrier status) (AUC=0.
914).

Therefore, the combination of plasma p-tau, APOE ε4 carrier status and cerebral cortex thickness has the best diagnostic validity in Aβ-PET-positive patients, providing a potential diagnostic alternative model
for clinical diagnosis of the disease.

The choice of stepwise regression model variables is determined by the model of the area under the maximum curve of ROC

In general, based on the comprehensive AD clinical biomarker longitudinal cohort of A/T/N framework, Shen Yong's team systematically evaluated the level of core biomarkers in China's middle-aged and elderly AD population, screened out a variety of markers through the combination of clinical prediction model and machine learning, and established an AD biomarker diagnostic model, which greatly improved the AD diagnosis ability and provided strong support
for the early diagnosis and treatment of AD and precision treatment.

The establishment of this diagnostic model also lays a foundation
for global multi-center cohort collaborative research and interpretation of the similarities and differences of AD biomarkers in different populations.
However, the sample size of this study is insufficient, and it is still necessary to include more patients, especially non-AD dementia patients, to improve the ability
to distinguish AD from other dementia pathological changes.
In addition, the emerging A/T/(X)/N model incorporates more recognized diagnostic markers of AD, and future studies can introduce biomarkers related to synaptic loss, glial activation or cerebrovascular damage, reflecting the neuropathological and cognitive injury processes
of AD in many aspects.

References

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6.
Gao F, Lv X, Dai L, et al.
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