Tau burden is associated with cross‐sectional and longitudinal neurodegeneration in the medial temporal lobe in cognitively normal individuals

BackgroundNeurofibrillary tangle pathology is thought to drive neurodegeneration in beta‐amyloid positive (A+) cognitively normal (CN) individuals, i.e., preclinical Alzheimer’s disease (AD). However, in beta‐amyloid negative (A‐) CN, the contribution of tau pathology [primary age‐related tauopathy (PART)] to neurodegeneration remains uncertain. We investigate the correlation between tau burden measured by PET in the medial temporal lobe (MTL) and MRI‐derived cross‐sectional and longitudinal structural atrophy in these cohorts.Methods420 CN (A‐/A+: 294/101, Table 1) individuals from ADNI with AV1451 PET and T1‐weighted MRI acquired within one year were included. Bilateral anterior/posterior hippocampal volume and thickness of entorhinal cortex (ERC), Brodmann areas 35/36 (BA35/BA36) and parahipocampal cortex (PHC) were obtained from baseline MRI scans. Bilateral MTL tau burden was computed as AV1451 uptake across ERC and BA35. Beta‐amyloid status was determined with PET by standard cut‐offs (Florbetapir: 1.11; Florbetaben: 1.08). In a subset of participants with prospective longitudinal MRI scans (up to 4.5 years), annualized volume change rate of each MTL subregion was estimated. Partial Pearson’s correlation, controlling for age, sex, and MRI‐PET date difference, was performed between MTL tau burden and structural atrophy measurements. Intracranial volume and MRI follow‐up time were additional covariates for cross‐sectional and longitudinal analysis respectively. We performed the analysis separately for each hemisphere in the whole CN cohort and its A+ and A‐ subgroups.ResultsTau burden was significantly associated with cross‐sectional left BA35/36 thickness in the whole cohort and bilateral posterior hippocampus volume in both A+ CN and the whole cohort (Table 2, Figure 1), but not in in A‐ CN. Stronger correlations between MTL tau burden and longitudinal atrophy, despite smaller sample size, was observed in almost all the MTL subregions regardless of amyloid status (Table 3, Figure 1). In general, effects from the left hemisphere were stronger than those from the right hemisphere. All significant correlations were maintained when corrected for beta‐amyloid PET SUVR.ConclusionsThe results demonstrated that elevated tau predicts subsequent neurodegeneration in early Braak regions in CN subjects regardless of amyloid status. This indicates that PART may be an important driver of neurodegeneration already during normal ageing in cognitively normal individuals.

Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

A neuroinflammatory reaction in Alzheimer disease (AD) brains involves reactive astrocytes that overexpress monoamine oxidase-B (MAO-B). 18F-(S)-(2-methylpyrid-5-yl)-6-[(3-fluoro-2-hydroxy)propoxy]quinoline (18F-SMBT-1) is a novel 18F PET tracer highly selective for MAO-B. We characterized the clinical performance of 18F-SMBT-1 PET across the AD continuum as a potential surrogate marker of reactive astrogliosis. Methods: We assessed 18F-SMBT-1 PET regional binding in 77 volunteers (76 ± 5.5 y old; 41 women, 36 men) across the AD continuum: 57 who were cognitively normal (CN) (44 amyloid-β [Aβ]-negative [Aβ-] and 13 Aβ-positive [Aβ+]), 12 who had mild cognitive impairment (9 Aβ- and 3 Aβ+), and 8 who had AD dementia (6 Aβ+ and 2 Aβ-). All participants also underwent Aβ and tau PET imaging, 3-T MRI, and neuropsychologic evaluation. Tau imaging results were expressed in SUV ratios using the cerebellar cortex as a reference region, whereas Aβ burden was expressed in centiloids. 18F-SMBT-1 outcomes were expressed as SUV ratio using the subcortical white matter as a reference region. Results: 18F-SMBT-1 yielded high-contrast images at steady state (60-80 min after injection). When compared with the Aβ- CN group, there were no significant differences in 18F-SMBT-1 binding in the group with Aβ- mild cognitive impairment. Conversely, 18F-SMBT-1 binding was significantly higher in several cortical regions in the Aβ+ AD group but also was significantly lower in the mesial temporal lobe and basal ganglia. Most importantly, 18F-SMBT-1 binding was significantly higher in the same regions in the Aβ+ CN group as in the Aβ- CN group. When all clinical groups were considered together, 18F-SMBT-1 correlated strongly with Aβ burden and much less with tau burden. Although in most cortical regions 18F-SMBT-1 did not correlate with brain volumetrics, regions known for high MAO-B concentrations presented a direct association with hippocampal and gray matter volumes, whereas the occipital lobe was directly associated with white matter hyperintensity. 18F-SMBT-1 binding was inversely correlated with Mini Mental State Examination and the Australian Imaging Biomarkers and Lifestyle's Preclinical Alzheimer Cognitive Composite in some neocortical regions such as the frontal cortex, lateral temporal lobe, and supramarginal gyrus. Conclusion: Cross-sectional human PET studies with 18F-SMBT-1 showed that Aβ+ AD patients, but most importantly, Aβ+ CN individuals, had significantly higher regional 18F-SMBT-1 binding than Aβ- CN individuals. Moreover, in several regions in the brain, 18F-SMBT-1 retention was highly associated with Aβ load. These findings suggest that increased 18F-SMBT-1 binding is detectable at the preclinical stages of Aβ accumulation, providing strong support for its use as a surrogate marker of astrogliosis in the AD continuum.

BackgroundThe medial temporal lobe's (MTL) early involvement in tau pathology makes it a key focus in the development of preclinical Alzheimer’s disease (AD) biomarkers. ROI analyses in prior studies reported significant MTL structural differences in cognitively normal individuals with and without ß‐amyloid (A+/‐CN). Pointwise analysis, offering spatial information of early neurodegeneration, has potential to pinpoint “signature regions” of pathological change, but has been underexplored in the MTL. This study employs a specialized pointwise analysis pipeline to examine the spatial pattern of MTL structural change in subgroups dichotomized by both ß‐amyloid and tau status in a large cohort of CN individuals.MethodsA dataset of 3036 CN (A‐/A+: 1270/1558, Table 1) individuals from ADNI, HABS, A4 and ABC were analyzed. We extracted MTL regional thickness maps from MRI using tailored pipelines, ASHS‐T1 and CRASHS. For participants with prospective longitudinal MRI (five years follow‐up), regional maps of longitudinal atrophy rate were extracted using SkelDBM. Subjects with cross‐sectional tau PET available (N=563) were further divided into A and T subgroups by tracer uptake. General linear modeling was performed on each surface point to investigate cross‐sectional and longitudinal MTL structural group differences (detailed in Figure 1) and their correlation with MTL tau burden in All/A+/A‐ CN. Age and sex were covariates and cluster‐level multiple comparison correction was performed.ResultsA+CN demonstrated a significantly faster atrophy rate than A‐CN across the whole MTL, primarily driven by A+T+CN individuals (Figure 1‐b). Notably, A‐T+CN showed significantly faster atrophy rate in the entorhinal cortex (ERC) and Brodmann area 35 (BA35), the earliest sites of tau pathology (Figure 1‐b, second column). Figure 2‐b displays an MTL‐wise significant correlation between atrophy rate and tau in All/A+/A‐ CN. In both analyses, cross‐sectional effects are consistently weaker than longitudinal ones, but have some significant clusters in ERC and BA35.ConclusionsPointwise analysis revealed extensive tau‐associated accelerated neurodegeneration in the MTL in preclinical AD. Furthermore, accelerated atrophy was observed in early Braak regions in A‐CN with evidence of tau pathology, potentially driven by primary age‐related tauopathy (PART). These pointwise longitudinal MTL measures provide sensitive measures that may allow for disease monitoring in preclinical AD.

BackgroundPredicting brain age from neuroimaging data is an emerging field. The age gap (AG), the difference between chronological age (CA) and brain age (BA), is crucial for indicating individual neuroanatomical aging. Previous deep learning models faced challenges in generalizability and neuroanatomical interpretability. We incorporated patients with different dementia types, including dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD), alongside mild cognitive impairment (MCI) and cognitive normal (CN) individuals. This inclusive strategy is essential for comprehensive mapping of neurocognitive trajectories and understanding distinct aging patterns across various cognitive conditions.MethodUtilizing T1‐weighted MRI images of n = 3,859 subjects (Table 1) from the CamCAN, NACC, and ADNI databases, this study aimed to predict brain age in four groups (CN, MCI, AD, and DLB). Structural MRI data were spatial normalized and skull‐striped. Then a 3D Convolutional Neural Network (CNN) based on the skull‐striped data was used for age prediction. The model’s architecture includes three convolutional layers with ReLU activation, max‐pooling, batch normalization, and dropout for regularization, ending with global average pooling and dense layers. The model was trained and validated on CN subjects. The trained model was used to predict age in MCI, DLB, and AD patients as well as the test set of CN subjects.ResultThe 3D CNN model accurately predicted brain age in the CN test set with an AG of 0.64 ± 2.74 years and an absolute AG of 1.86 ± 2.11 years (Figure 1 and Table 1). In DLB and AD patients, the average AG was 3.81 and 2.90 years, respectively, and significantly larger than 0 (P < 10‐5), indicating accelerated aging patterns in these groups. The average AG of MCI was 0.09 years which was significantly smaller than that of both DLB and AD (P < 10‐3), indicating the early stage of impairment in MCI patients.ConclusionOur 3D CNN model accurately predicted brain age in cognitively normal individuals and identified accelerated aging in DLB and AD patients. The model's precision highlights its potential for early detection and understanding of neurocognitive trajectories, contributing to advancements in neurological research and clinical diagnostics.

A challenge in developing informative neuroimaging biomarkers for early diagnosis of Alzheimer's disease is the need to identify biomarkers that are evident before the onset of clinical symptoms, and which have sufficient sensitivity and specificity on an individual patient basis. Recent literature suggests that spatial patterns of brain atrophy discriminate amongst Alzheimer's disease, mild cognitive impairment (MCI) and cognitively normal (CN) older adults with high accuracy on an individual basis, thereby offering promise that subtle brain changes can be detected during prodromal Alzheimer's disease stages. Here, we investigate whether these spatial patterns of brain atrophy can be detected in CN and MCI individuals and whether they are associated with cognitive decline. Images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to construct a pattern classifier that recognizes spatial patterns of brain atrophy which best distinguish Alzheimer's disease patients from CN on an individual person basis. This classifier was subsequently applied to longitudinal magnetic resonance imaging scans of CN and MCI participants in the Baltimore Longitudinal Study of Aging (BLSA) neuroimaging study. The degree to which Alzheimer's disease-like patterns were present in CN and MCI subjects was evaluated longitudinally in relation to cognitive performance. The oldest BLSA CN individuals showed progressively increasing Alzheimer's disease-like patterns of atrophy, and individuals with these patterns had reduced cognitive performance. MCI was associated with steeper longitudinal increases of Alzheimer's disease-like patterns of atrophy, which separated them from CN (receiver operating characteristic area under the curve equal to 0.89). Our results suggest that imaging-based spatial patterns of brain atrophy of Alzheimer's disease, evaluated with sophisticated pattern analysis and recognition methods, may be useful in discriminating among CN individuals who are likely to be stable versus those who will show cognitive decline. Future prospective studies will elucidate the temporal dynamics of spatial atrophy patterns and the emergence of clinical symptoms.

A major focus of Alzheimer's disease (AD) research has been finding sensitive outcome measures to disease progression in preclinical AD, as intervention studies begin to target this population. We hypothesize that tailored measures of longitudinal change of the medial temporal lobe (MTL) subregions (the sites of earliest cortical tangle pathology) are more sensitive to disease progression in preclinical AD compared to standard cognitive and plasma NfL measures. Longitudinal T1‐weighted MRI of 337 participants were included, divided into amyloid‐β negative (Aβ−) controls, cerebral spinal fluid p‐tau positive (T+) and negative (T−) preclinical AD (Aβ+ controls), and early prodromal AD. Anterior/posterior hippocampus, entorhinal cortex, Brodmann areas (BA) 35 and 36, and parahippocampal cortex were segmented in baseline MRI using a novel pipeline. Unbiased change rates of subregions were estimated using MRI scans within a 2‐year‐follow‐up period. Experimental results showed that longitudinal atrophy rates of all MTL subregions were significantly higher for T+ preclinical AD and early prodromal AD than controls, but not for T− preclinical AD. Posterior hippocampus and BA35 demonstrated the largest group differences among hippocampus and MTL cortex respectively. None of the cross‐sectional MTL measures, longitudinal cognitive measures (PACC, ADAS‐Cog) and cross‐sectional or longitudinal plasma NfL reached significance in preclinical AD. In conclusion, longitudinal atrophy measurements reflect active neurodegeneration and thus are more directly linked to active disease progression than cross‐sectional measurements. Moreover, accelerated atrophy in preclinical AD seems to occur only in the presence of concomitant tau pathology. The proposed longitudinal measurements may serve as efficient outcome measures in clinical trials.

Objective To investigate the correlation and influencing factors between mild cognitive impairment （MCI） and the changes of hippocampal and entorhinal cortex volume and to evaluate the value of using MRI volumetric measurement of hippocampus and entorhinal cortex to identify MCI and normal cognition （NC）. Methods Twenty-one subjects selected from physical examinations were divided into an MCI group and 18 subjects were divided into an NC group. The general assessment, laboratory tests and neuropsychological scale evaluation were conducted. The MRI volumetric measurement of hippocampus and entorhinal cortex was used and its correlation with memory function was analyzed, The specificity and sensibility of diagnosing MCI of hippocampal and entorhinal cortex volume were analyzed by non-conditional Logistic regression. The influencing factors of hippocampal and entorhinal cortex volume were analyzed by multiple linear regressions. Results The hippocampal and entorhinal cortex volume in subjects with MCI was 6. 19 ± 0. 74 and 2.66 ± 0. 17 cm^3, respectively; they were significantly smaller than 6.80 ±0.79 and 3.03 ±0. 12 cm^3 in the NC group （P 〈0.05）. The hippocampal volume was significantly correlated with the entorhinal cortex volume （r = 0. 566, P 〈0. 001 ）; the hippocampal volume was significantly correlated with the total score of Clinical Memory Scale （r = 0. 430, P = 0. 04）, and the entorhinal cortex volume was significantly correlated with the total score of Clinical Memory Scale （r =0.722, P 〈0. 001）. The specificity and sensitivity of using hippocampal volume to differentiate MCI and NC were 66.7% and 76.2%, respectively. The specificity and sensitivity of using entorhinal cortex volume to differentiate MCI and NC were 88.9% and 90. 5%, respectively. In addition, There was a negative correlation between the hippocampal volume and 2 h postprandial blood glucose （P 〈 0.05）, systolic blood pressure （P 〈 0.05 ）, diastolic blood pressure （P 〈 0.05 ）, and plasma total cholesterol levels （P 〈0.01）, while the entorhinal cortex volume was not affected by the above factors. Conelusiom The atrophy of entorhinal cortex and hippocampal volume are closely associated with the memory disorders. The entorhinal cortex and hippocampal volume measured by MRI have some values for the identification of MCI and NC, and the specificity and sensitivity of entorhinal cortex volume are superior to hippocampal volume. The increased blood pressure, blood glucose, and serum lipid levels may accelerate the transformation of MCI to Alzheimer＇s disease by affecting the hippocampal volume. Key words: Cognition disorders; Hippocampus; Entorhinal cortex; Atrophy; Magnetic resonance imaging

Longitudinal Evolution of Financial Capacity and Cerebral Tau and Amyloid Burden in Older Adults with Normal Cognition or Mild Cognitive Impairment

BackgroundNeuroinflammation is a key process in initiating and propagating Alzheimer’s disease (AD). Even though it is widely known that the deposit of amyloid plaques and CSF levels of amyloid distinguishes patients with AD or mild cognitive impairment (MCI) from cognitively normal (CN) individuals, little is known about the role of amyloid‐specific immune response in cognitive decline.MethodUsing a polyfunctionality assay typically used for detecting virus‐specific T cell responses, we tested participants from the Epidemiology of Mild Cognitive Impairment in Taiwan study (EMCIT) and the Taiwan Precision Medicine Initiative of Cognitive impairment and dementia (TPMIC) study to compare the amyloid‐specific T cell responses between CN and MCI individuals. The abilities of T cell response parameters and plasma p‐Tau181 to distinguish MCI from CN were tested.ResultResults from both cohorts showed an enhanced amyloid‐specific T‐cell response in individuals with MCI. In the EMCIT cohort, the individual’s amyloid‐specific CD4+ response frequency of total CD4+ cells was significantly larger in MCI (n = 69, 0.93%) than in CN (n = 69, 0.51%, p < 0.001). CD4+ T cell response discriminated MCI versus CN (area under curve [AUC], 0.72 [0.64‐0.81]) with significantly higher accuracy than p‐Tau181 (AUC: 0.59 [0.5‐0.69], p < 0.01). In the TPMIC cohort, both CD4+ and CD8+ response frequencies were higher in MCI individuals (n = 21, CD4: 1.2%, CD8: 2.02%) than in CN (n = 30, CD4: 0.14%, CD8:0.27%; both p < 0.001). CD4+ T cell response frequency and CD8+ response frequency also outperform p‐Tau181 in their discriminative accuracy of MCI versus NC (CD4+ AUC, 0.97, [0.94‐1.01]; CD8+ AUC, 0.96, [0.92‐1.01]; p‐Tau181 AUC, 0.83, [0.69‐0.96]; both p < 0.05).ConclusionOur study validates the amyloid hypothesis by showing that amyloid‐associated neuroinflammation is involved in the process of neurodegeneration and demonstrated the accuracy of using amyloid‐specific T cell response to discriminate MCI from CN individuals. The TPMIC cohort is an ongoing longitudinal study that includes amyloid PET results and thus we will investigate the prognostic value of amyloid‐T cell response in the future.

Atrophy of medial temporal lobe (MTL) subregions is an early biomarker of Alzheimer’s disease (AD). This study aimed to examine the relationship between MTL subregion volumes and cognitive performance in patients across the AD continuum. We analyzed data from 276 participants using the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including 74 cognitively normal (CN), 110 subjective memory complaints (SMC), 37 early mild cognitive impairment (EMCI), 35 late mild cognitive impairment (LMCI), and 20 AD participants. MTL subregions volumes were measusing high-resolution T2-weighted MRI, and analyses were adjusted for age, education, APOE ε4 status, and intracranial volume (ICV). Significant atrophy in regions such as the cornu ammonis (CA), dentate gyrus (DG), subiculum (SUB), entorhinal cortex (ERC), and Brodmann area 35 (BA35) was found in AD participants compared with other groups. In AD, poorer Alzheimer’s Disease Assessment Scale - Cognitive Subscale 13 (ADAS-13) performance was associated with reduced CA, DG, BA35, and parahippocampal cortex (PHC) volumes. In LMCI, lower Mini-Mental State Examination (MMSE) scores were associated with atrophy in CA and SUB. Diminished Montreal Cognitive Assessment (MoCA) scores were linked to reduced ERC volumes in CN, as well as with atrophy in BA35, ERC and CA subfields among AD patients. In LMCI, poorer Trail Making Test, Part B performance (i.e., longer completion time) was related to smaller Brodmann area 36 (BA36), collateral sulcus (CS), and PHC subregion volumes, whereas in the AD, it was related to BA36 only. Poorer immediate memory recall in AD was associated with atrophy in CA, DG, while in early stages of MCI, poorer verbal learning scores correlated with atrophy in the CA, DG, BA35, SUB, and CS regions. Moreover, diminished Logical Memory Delayed Recall was associated with atrophy in the CA, BA35, and PHC subfields among AD subjects. These findings support the value of atrophy in MTL subregions as potential imaging markers for detecting and monitoring cognitive decline across the AD continuum.

The pathological effects of amyloid β oligomers (Aβo) may be mediated through the metabotropic glutamate receptor subtype 5 (mGluR5), leading to synaptic loss in Alzheimer's disease (AD). Positron emission tomography (PET) studies of mGluR5 using [18F]FPEB indicate a reduction of receptor binding that is focused in the medial temporal lobe in AD. Synaptic loss due to AD measured through synaptic vesicle glycoprotein 2A (SV2A) quantification with [11C]UCB-J PET is also focused in the medial temporal lobe, but with clear widespread reductions is commonly AD-affected neocortical regions. In this study, we used [18F]FPEB and [11C]UCB-J PET to investigate the relationship between mGluR5 and synaptic density in early AD. Fifteen amyloid positive participants with early AD and 12 amyloid negative, cognitively normal (CN) participants underwent PET scans with both [18F]FPEB to measure mGluR5 and [11C]UCB-J to measure synaptic density. Parametric DVR images using equilibrium methods were generated from dynamic. For [18F]FPEB PET, DVR was calculated using equilibrium methods and a cerebellum reference region. For [11C]UCB-J PET, DVR was calculated with a simplified reference tissue model - 2 and a whole cerebellum reference region.. A strong positive correlation between mGluR5 and synaptic density was present in the hippocampus for participants with AD (r = 0.81, p < 0.001) and in the CN group (r = 0.74, p = 0.005). In the entorhinal cortex, there was a strong positive correlation between mGluR5 and synaptic in the AD group (r = 0.85, p <0.001), but a weaker non-significant correlation in the CN group (r = 0.36, p = 0.245). Exploratory analyses within and between other brain regions suggested significant positive correlations between mGluR5 in the medial temporal lobe and synaptic density in a broader set of commonly AD-affected regions. Medial temporal loss of mGluR5 in AD is associated with synaptic loss in both medial temporal regions and more broadly in association cortical regions, indicating that mGluR5 mediated Aβo toxicity may lead to early synaptic loss more broadly in AD-affected networks. In CN individuals, an isolated strong association between lower mGluR5 and lower synaptic density may indicate non-AD related synaptic loss.

ASSOCIATION BETWEEN ODOR IDENTIFICATION AND REGIONAL GRAY MATTER IN EARLY PRECLINICAL ALZHEIMER'S DISEASE

To explore the atrophy rate of entorhinal cortex (ERC) in AD and normal aging and assess the value of rate measurement of ERC atrophy for classifying subjects with AD from cognitively normal (CN) control subjects. Twenty-one AD patients and 23 CN subjects had MRI scans and clinical evaluations twice within 1.8 +/- 0.6 years. ERC volumes were manually measured on volumetric T1-weighted MR images. Patients with AD had a greater annual percentage volume change of ERC than CN subjects on both sides (left: 6.8 +/- 4.3%/year for AD vs 1.4 +/- 2.5%/year for CN [F(1,42) = 25.6, p < 0.001]; right: 6.3 +/- 3.3%/year for AD vs 1.4 +/- 2.3%/year for CN [F(1,42) = 25.6, p < 0.001]). Furthermore, increased ERC atrophy rate was correlated (r = -0.56, p = 0.01) with decreased memory performance in AD. CN subjects had on average annual ERC atrophy rates greater than zero (p < 0.01). Baseline volume of ERC predicted atrophy rate of ERC (left: r = -0.53, p < 0.01; right: r = -0.42, p < 0.05) in CN subjects but not in AD subjects. Using ERC baseline volumes alone resulted in 77% overall correct classification (p < 0.01) between AD and CN subjects, with 76% sensitivity and 78% specificity and an area under receiver operator characteristic (ROC) curve of 0.83. Adding annual atrophy rate of ERC to the model accounted for most of the variance (p < 0.01), diminishing contributions from baseline volume and yielding 82% overall classification, with 76% sensitivity and 86% specificity and an area under the ROC curve of 0.93. ERC volume loss over time may be a better indicator for AD than cross-sectional measurements.

BackgroundAltered innate immunity has been long associated with late‐onset Alzheimer’s disease (AD) and its associated blood‐based biomarkers are important for AD diagnosis and prognosis.MethodWe collected 38 participants from the AIBL study, including 22 cognitive normal (CN) individuals with negative amyloid burden (Centiloid [CL] &lt; 15), 5 CN individuals with positive amyloid burden (CL &gt; 15), and 11 mild cognitive impairment (MCI) and AD cases. A total of 17 leukocyte surface antigens were examined by flow cytometry immunophenotyping and compared between healthy controls (CN Ab ‐ve), pre‐clinical patients (CN Ab +ve), and clinical cases (MCI &amp; AD), including CD36, MerTK, Clec7a, RAGE, Tyro3, CR1 (CD35), CX3CR1, CCR2, Axl, LILRB2, LILRB3, LILRB4, PILRA, and P2×7.ResultWe identified leukocyte surface markers differentially expressed between HC, pre‐clinical patients, and clinical cases. Mean fluorescence intensities of CD85d and CD85k were significantly reduced in pre‐clinical patients and cases compared with HC, implicating CD85d and CD85k downregulation in AD pathogenesis. Significant upregulation of RAGE, Tyro3, CCR2, CD85a, and PILRA was also noted in cases compared with HC. Leukocyte surface expressions of RAGE, CCR2, PILRA, and CD85k were significantly associated with the Preclinical Alzheimer’s Cognitive Composite (PACC), which is one of the most accurate estimates of cognition in AD diagnosis.ConclusionOur preliminary investigations into these leukocyte markers demonstrated their dysregulations in pre‐clinical stage of AD, implicating early deficits in innate immunity, as supported by genomic studies of AD. This project will continue collecting more AIBL participants up to 200 to evaluate leukocyte surface markers more comprehensively, which would benefit AD screening and diagnosis.