Plasma p tau 181 are associated with white matter microstructural changes in Alzheimer’s disease

Fardin Nabizadeh (  fardinnabizade1378@gmail.com ) Neuroscience Research Group (NRG), Universal Scienti c Education and Research Network (USERN), Tehran, Iran Seyed Behnamedin Jameie Neuroscience Research Center (NRC), Iran University of Medical Science, Tehran, Iran Saghar Khani Iran university of Medical science Aida Rezaei School of Medicine, Tehran University of Medical Science, Tehran, Iran Niloofar Deravi Student Research committee, School of medicine, Shahid Beheshti University of Medical Science, Tehran, Iran


Introduction
Alzheimer's disease (AD) is the course of dementia and memory de cits that affect millions of people and responsible for cognitive and functional decline, mostly in aged people (1,2). AD is characterized by cognitive impairments and memory di culties, which cause daily activities, personal and behavioural problems (1,3). AD is associated with the formation of neuro brillary tangles (NFTs), including hyperphosphorylated tau protein (p tau) and extracellular Amyloid β (Aβ) plaques in regions responsible for memory and other cognitive functions such as hippocampus (4,5). NFTs are typical brain lesions consist of aggregated and hyperphosphorylated forms of tau protein, which leads to loss of its ability to binding microtubules and assembled into paired helical laments. Intracellular NFTs in regions involved in cognitive functions are associated with cognitive decline by the disruption in axonal transport and neural loss (6,7).
Many researchers reported Aβ, total Tau and Tau phosphorylated at threonine 181 (p tau 181) in cerebrospinal uid (CSF), and Positron emission tomography (PET) as biomarkers for Alzheimer diagnoses. However, in recent years plasma biomarkers emerged as new diagnostic tools and showed su cient e cacy in detecting AD patients from healthy people (8). Blood-based measures showed that p tau 181 might be a reliable biomarker for Alzheimer's and disease progression (9)(10)(11). However, there is no evidence that shows the effect of plasma p tau 181 on white matter connections and neurodegeneration in the brain of AD patients. We hypothesized plasma p tau 181 level could predict structural brain connections changes in regions play a signi cant role in cognitive, learning and memory function. In the present research, we investigated the correlation between p tau 181 in serum and diffusion tensor imaging (DTI) values in an observational cross-sectional study to address this question.

Data Acquisition
Participants information acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI leading by Principal Investigator Michael W. Weiner, MD was launched in 2003 as a public-private partnership. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer's disease (AD). We extracted baseline visit data for whom demographic data, post-processed DTI, CSF Amyloid β, CSF p tau, CSF total, and plasma p tau 181 tau levels from baseline visit were available. We also downloaded results of white matter hyperintensity analysis and brief volumetric data for better understanding. Our cross-sectional study consisted of 21 patients with AD, with their baseline plasma p tau 181, CSF Amyloid β, CSF total tau, and CSF p tau levels and their post-processed Diffusion tensor imaging (DTI).
Participants classi ed as AD patients if their mini-mental state examination (MMSE) was between 20 and 24, clinical dementia rating (CDR) score between 0.5 and 1, and met the National Institute of Neurologic and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association criteria (12).

DTI Imaging Processing
We extracted the results of DTI ROI analysis from ADNI. For each subject, All images were corrected, extracerebral tissue removed, and normalized.
Extraction Tool (BET) from FSL (13). To align data from different subjects into the same 3D coordinate space, each T1-weighted anatomical image was linearly aligned to a version of the Colins27 brain template (14) using FSL's irt (15) with 6 degrees of freedom to allow translations and rotations in 3D. The Colin27 brain was zero-padded to have a cubic isotropic image size 220x220x220 1mm^3) and then downsampled (110x110x110 2mm^3) to be more similar to the DWI resolution.
To correct for echo-planar imaging (EPI) induced susceptibility artifacts, which can cause distortions at tissue-uid interfaces, skull-stripped b0 images were linearly aligned to their respective T1-weighted structural scans using FSL's irt with 9 degrees of freedom and then elastically registered to their aligned T1 scans using an inverse consistent registration algorithm with a mutual information cost function (16) as described in (17). The resulting 3D deformation elds were then applied to the remaining 41 DWI volumes before mapping diffusion parameters. To account for linearly registering the average b0 from the DWI images to the structural T1-weighted scan, a corrected gradient table was calculated.
A single diffusion tensor was modeled at each voxel in the brain from the eddy-and EPI-corrected DWI scans using FSL's dti t command, and scalar anisotropy and diffusivity maps were obtained from the resulting diffusion tensor eigenvalues (λ1, λ2, λ3). Fractional anisotropy (FA) was calculated from the standard formula.
We registered the FA image from the JHU DTI atlas (18) to each subject using a previously described mutual information-based elastic registration algorithm (16). We then applied the deformation to the stereotaxic JHU "Eve" WM atlas labels (http://cmrm.med.jhmi.edu/cmrm/atlas/human_data/ le/Atlas Explanation2.htm) using nearest neighbour interpolation to avoid intermixing of labels.
This placed the atlas ROIs in the same coordinate space as our DTI maps. We were then able to calculate the average FA and MD within the boundaries of each of the ROI masks for each subject. Of the 56 WM ROIs, we excluded 4 ROIs, the left and right middle cerebellar peduncle, and the pontine crossing tract, as they often fall wholly or partially out of the eld of view (FOV). We note that this is also occasionally true of the left and right medial lemniscus, inferior and superior peduncles. We only included non-zero voxels within the FOV in our calculations of mean FA and MD. In addition to the 52 JHU labels, ve more ROIs were evaluated: the bilateral fornix, bilateral genu, bilateral body, and bilateral splenium of the corpus callosum and the full corpus callosum, to get full summary measures of the regions.
Tensor based spatial statistics (19) was also performed, and the mean FA in regions of interest along the skeleton was extracted. TBSS was performed according to protocols outlined by the ENIGMA-DTI group: http://enigma.loni.ucla.edu/wpcontent/uploads/2012/06/ENIGMA_TBSS_protocol.pdf In short, all subjects were registered to the ENIGMA-DTI template in ICBM space, and standard tbss steps were performed to project individual FA maps onto the skeletonized ENIGMA-DTI template. ROI extraction was also performed according to the following protocol to extract the mean FA in ROIs along with the skeleton: http://enigma.loni.ucla.edu/wpcontent/uploads/2012/06/ENIGMA_ROI_protocol.pdf.

Cognitive assessments
The patients' cognitive condition assessed by the Mini-Mental State Exam (MMSE) which is a common test of cognitive function among the elderly; it includes tests of orientation, attention, memory, language, and visual-spatial skills. MMSE scores were extracted for each patient from the ADNI Mini-Mental Examination.

Statistical Analyses
We used SPSS16 software for statistical analyses. First We tested normality of variables by Kolmogorov Smirnov and Shapiro Wilk tests. Then we log-transformed the nonnormal variables to normal distribution. We analyzed the difference between groups by one way ANOVA using Bonferroni correction for multiple comparisons. Then we used a Partial correlation adjusted for age, sex, and APOE for checking the relation between plasma p tau 181 and other demographic variables once among all participants and then within groups. We used the same model for investigating the association between plasma p tau 181 and DTI values in each ROI. The bootstrap method was used for addressing type I error due to multiple comparisons in correlation models.

Result Patient characteristic
In this study, baseline cohort information of 203 participants was entered. The mean age was 73, including 117 men and 86 women. Participants were all educated, and the average length of the education was 15.9 years. Among all 102 participants had at least one APOE ε4. the mean scores of the MMSE test were 27.25. details of demographic information for each group described in Table1.

Discussion
In a cross-sectional study based on the ADNI cohort, we concluded that the plasma level of p tau 181 independently predicts microstructural changes in the brain of Alzheimer's patients. We used a step-bystep strategy; rst, we investigate the difference between participants demographic, including age, glucose uptake, MMSE scores, education, APOE genotype, sex, and plasma p tau 181. Then we narrowed the study to examine the correlation between p tau 181 with demographic variables among all participants and within groups separately. Then we used a partial correlation model controlled for age, APOE and sex to investigate the relation between p tau 181 in plasma and changes in white matter. Baseline plasma p tau 181 levels are associated with widespread white matter changes in all participants in the disease's pathological signatures areas, including the hippocampal cingulum, Splenium of corpus callosum, Tapatum, Posterior corona radiate, Sagittal stratum, Uncinate fasciculus, Retrolenticular part of internal capsule, cerebellar peduncles, and Medial lemniscus. overall, according to observations, a decrease in FA and increase in MD re ect demyelination and axonal loss (20). this is the rst study that investigated the relationship between plasma p tau 181 and white matter changes.
In the onset of dementia and cognitive decline, many areas seem to change. The internal capsule, corona radiate, Uncinate fasciculus, cerebellar peduncles, Medial lemniscus, and hippocampal cingulum in AD people change compared to healthy people without cognitive problems (21). In our study, plasma p tau 181 levels were signi cantly associated with these areas, indicating that our ndings are in line with previous studies investigating microstructural changes related to Alzheimer's. As results of our analysis, plasma p tau 181 predicted structural changes in the left hippocampal cingulum, which indicate the importance of the cingulum as an important area in the pathological course of the disease, and there is also evidence of a link between CSF p tau and Aβ with a change of MD in the cingulum region (22). Moreover, the research results provide compelling evidence in support of our ndings and state that brain connectivity in the posterior cingulum can be a good predictor for cognitive decline in Alzheimer's disease (23). The cingulum bundle is an important white matter tract that connects the frontal, parietal, and medial temporal, linking the subcortical nucleus to the cingulate gyrus and extending into the hippocampal and parahippocampal regions, and for that damage in areas close to the hippocampus in the cingulum causes cognitive problems in many domains such as language, memory and executive control (24).
Previously CSF biomarkers revealed as a predictor for changes in the Uncinate fasciculus (25) and these regions involved in language processing and damage to uncinate fasciculus may cause language impairments (26) and as well as there is a signi cant correlation between plasma p tau 181 and changes in this region from our results. Similarly, X Li et al. Found that pathological levels of Aβ42 and CSF total tau in people with Alzheimer's-related cognitive impairments correlated with decreased FA and increased MD in the white matter pathway (27).
Biomarkers in CSF may be active several years before the onset of symptoms, and Aβ and Tau are most critical (28). Many studies have emphasized the diagnostic role of t tau and p tau in CSF and state that they can predict the progression to dementia (29)(30)(31)(32). On the other hand, some studies present different ndings (33,34). Despite this, in predicting AD by each of these biomarkers alone, p-tau preferred in terms of speci city and sensitivity (28).
Studies have been conducted on blood-based biomarkers in recent years as a noninvasive and accessible marker for monitoring people with risk of developing Alzheimer. Evidence revealed that plasma p tau 181 and t tau levels have a high diagnostic value, and their levels are much higher in AD subjects than in MCI and healthy control (35). However, studies have described p tau 181 better than t tau (9,10). The largest plasma p tau 181 study in the diagnosis of Alzheimer's, which included the results of four independent cohorts, states that plasma p tau 181 has a high performance in identifying the clinical diagnosis of Alzheimer's patients with an unknown amyloid status and was able to differentiate Alzheimer's disease from other neurodegenerative diseases and with Aβ PET can detect Alzheimer's in the early stages (11).
In conclusion, our study results show that plasma p tau 181 levels are associated with neurodegeneration in pathogenesis regions of Alzheimer's disease, which enhance this biomarker's diagnostic status and support the application of blood-based biomarkers as an early detector for white matter damages. Due to the increasing population with Alzheimer's and the resulting social costs and considering that AD's pathogenesis exists several years before its clinical signs, achieving a reliable biomarker with adequate sensitivity and speci city is necessary. Although plasma p tau 181 is superior to CSF biomarkers and imaging techniques in terms of availability, low cost, and non-invasiveness, More efforts should be made to standardize biomarkers' measurement and de ne their pathological threshold. Longitudinal studies are also necessary to prove the e cacy of these biomarkers predicting role in structural changes. Declarations *Data used in the preparation of this article were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: Partial correlation coe cient of DTI metrics value of the brain regions and plasma p tau 181 levels controlled for age, APOE, and sex.