Alzheimer’s disease spectrum profiled by CSF and imaging biomarkers: Tau PET best predicts cognitive decline

The pathological relevance of naturally occurring antibodies to β-amyloid (NAbs-Aβ) in Alzheimer’s disease (AD) remains unclear. We aimed to investigate their levels and associations with Aβ burden and cognitive decline in AD in a cross-sectional cohort from China and a longitudinal cohort from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. NAbs-Aβ levels in plasma and cerebrospinal fluid (CSF) were tested according to their epitopes. Levels of NAbs targeting the amino terminus of Aβ increased, and those targeting the mid-domain of Aβ decreased in both CSF and plasma in AD patients. Higher plasma levels of NAbs targeting the amino terminus of Aβ and lower plasma levels of NAbs targeting the mid-domain of Aβ were associated with higher brain amyloidosis at baseline and faster cognitive decline during follow-up. Our findings suggest a dynamic response of the adaptive immune system in the progression of AD and are relevant to current passive immunotherapeutic strategies.

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Identification of Abnormal Lipid Profiles Promote Cognitive Decline in Alzheimer's Disease Spectrum via Large-Scale Brain Connectivity

SSRN Electronic Journal ◽

10.2139/ssrn.3904355 ◽

2021 ◽

Author(s):

Qing Wang ◽

Feifei Zang ◽

Cancan He ◽

Zhijun Zhang ◽

Chunming Xie

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Large Scale ◽

Brain Connectivity ◽

Lipid Profiles ◽

Disease Spectrum

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Amyloid and tau imaging biomarkers explain cognitive decline from late middle-age

Brain ◽

10.1093/brain/awz378 ◽

2019 ◽

Vol 143 (1) ◽

pp. 320-335 ◽

Cited By ~ 15

Author(s):

Tobey J Betthauser ◽

Rebecca L Koscik ◽

Erin M Jonaitis ◽

Samantha L Allison ◽

Karly A Cody ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Middle Age ◽

Neurofibrillary Tangle ◽

Imaging Biomarkers ◽

Cognitive Tests ◽

Preclinical Alzheimer’S Disease ◽

Significant Group ◽

Health Factors

Abstract This study investigated differences in retrospective cognitive trajectories between amyloid and tau PET biomarker stratified groups in initially cognitively unimpaired participants sampled from the Wisconsin Registry for Alzheimer’s Prevention. One hundred and sixty-seven initially unimpaired individuals (baseline age 59 ± 6 years; 115 females) were stratified by elevated amyloid-β and tau status based on 11C-Pittsburgh compound B (PiB) and 18F-MK-6240 PET imaging. Mixed effects models were used to determine if longitudinal cognitive trajectories based on a composite of cognitive tests including memory and executive function differed between biomarker groups. Secondary analyses investigated group differences for a variety of cross-sectional health and cognitive tests, and associations between 18F-MK-6240, 11C-PiB, and age. A significant group × age interaction was observed with post hoc comparisons indicating that the group with both elevated amyloid and tau pathophysiology were declining approximately three times faster in retrospective cognition compared to those with just one or no elevated biomarkers. This result was robust against various thresholds and medial temporal lobe regions defining elevated tau. Participants were relatively healthy and mostly did not differ between biomarker groups in health factors at the beginning or end of study, or most cognitive measures at study entry. Analyses investigating association between age, MK-6240 and PiB indicated weak associations between age and 18F-MK-6240 in tangle-associated regions, which were negligible after adjusting for 11C-PiB. Strong associations, particularly in entorhinal cortex, hippocampus and amygdala, were observed between 18F-MK-6240 and global 11C-PiB in regions associated with Braak neurofibrillary tangle stages I–VI. These results suggest that the combination of pathological amyloid and tau is detrimental to cognitive decline in preclinical Alzheimer’s disease during late middle-age. Within the Alzheimer’s disease continuum, middle-age health factors likely do not greatly influence preclinical cognitive decline. Future studies in a larger preclinical sample are needed to determine if and to what extent individual contributions of amyloid and tau affect cognitive decline. 18F-MK-6240 shows promise as a sensitive biomarker for detecting neurofibrillary tangles in preclinical Alzheimer’s disease.

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Abstract # 3156 Kynurenine pathway metabolites are longitudinally related to brain atrophy, cognitive decline, and negative affect across the Alzheimer’s disease spectrum

Brain Behavior and Immunity ◽

10.1016/j.bbi.2018.11.273 ◽

2019 ◽

Vol 76 ◽

pp. e31-e32

Author(s):

B.S. Klinedinst ◽

A. Collazo-Martinez ◽

R. Dantzer ◽

A.A. Willette

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Negative Affect ◽

Cognitive Decline ◽

Brain Atrophy ◽

Kynurenine Pathway ◽

Disease Spectrum

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P1-071: Genetic and imaging biomarkers predicting beta-amyloid amyloid-related cognitive decline using the Alzheimer's Disease Neuroimaging Initiative data

Alzheimer s & Dementia ◽

10.1016/j.jalz.2013.05.292 ◽

2013 ◽

Vol 9 ◽

pp. P178-P178 ◽

Cited By ~ 3

Author(s):

Sheng Feng ◽

Jeff Sevigny ◽

Ajay Verma ◽

Donald Bennett ◽

Yen Ying Lim ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Beta Amyloid ◽

Imaging Biomarkers

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Predictors of rapid cognitive decline in Alzheimer's disease: results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing

International Psychogeriatrics ◽

10.1017/s1041610211001335 ◽

2011 ◽

Vol 24 (2) ◽

pp. 197-204 ◽

Cited By ~ 22

Author(s):

Alessandro Sona ◽

Ping Zhang ◽

David Ames ◽

Ashley I. Bush ◽

Nicola T. Lautenschlager ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Rating Scale ◽

Cognitive Status ◽

Imaging Biomarkers ◽

Clinical Dementia Rating ◽

Factors Associated ◽

Biological Variables ◽

Rapid Cognitive Decline

ABSTRACTBackground: The AIBL study, which commenced in November 2006, is a two-center prospective study of a cohort of 1112 volunteers aged 60+. The cohort includes 211 patients meeting NINCDS-ADRDA criteria for Alzheimer's disease (AD) (180 probable and 31 possible). We aimed to identify factors associated with rapid cognitive decline over 18 months in this cohort of AD patients.Methods: We defined rapid cognitive decline as a drop of 6 points or more on the Mini-Mental State Examination (MMSE) between baseline and 18-month follow-up. Analyses were also conducted with a threshold of 4, 5, 7 and 8 points, as well as with and without subjects who had died or were too severely affected to be interviewed at 18 months and after, both including and excluding subjects whose AD diagnosis was “possible” AD. We sought correlations between rapid cognitive decline and demographic, clinical and biological variables.Results: Of the 211 AD patients recruited at baseline, we had available data for 156 (73.9%) patients at 18 months. Fifty-one patients were considered rapid cognitive decliners (32.7%). A higher Clinical Dementia Rating scale (CDR) and higher CDR “sum of boxes” score at baseline were the major predictors of rapid cognitive decline in this population. Furthermore, using logistic regression model analysis, patients treated with a cholinesterase inhibitor (CheI) had a higher risk of being rapid cognitive decliners, as did males and those of younger age.Conclusions: Almost one third of patients satisfying established research criteria for AD experienced rapid cognitive decline. Worse baseline functional and cognitive status and treatment with a CheI were the major factors associated with rapid cognitive decline over 18 months in this population.

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A Strategy for Identifying the Risk of Developing Alzheimer's Disease From Healthy Controls: Distance to Novelty Detection Boundary

10.21203/rs.3.rs-129454/v1 ◽

2020 ◽

Author(s):

Jiangbing Mao ◽

Qinyong Ye ◽

Hongqing Yang ◽

Magda Bucholc ◽

Shuo Liu ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Cognitive Decline ◽

Early Stage ◽

Novelty Detection ◽

Imaging Biomarkers ◽

Support Vector ◽

Support Vector Data Description ◽

Healthy Controls

Abstract Background:Machine learning (ML) techniques are expected to tackle the problem of the high prevalence of Alzheimer’s disease (AD) we are facing worldwide. However, few studies of novelty detection (ND), a typical ML technique for safety-critical systems especially in healthcare, were engaged for identifying the risk of developing cognitive impairment from healthy controls (HC) population.Materials and Methods: Two independent datasets were used for this study, including the Australian Imaging Biomarkers and Lifestyle Study of Ageing (AIBL) and the Fujian Medical University Union Hospital (FMUUH), China datasets. Multiple feature selection methods were applied to identify the most relevant features for predicting the severity of AD. Four easily interpretable ND algorithms, including k nearest neighbor, Mixture of Gaussian (MoG), KMEANS, and support vector data description were used to construct predictive models. The models were visualized by drawing their decision boundaries tightly surrounding the HC data. A distance to boundary (DtB) strategy was proposed to differentiate individuals with mild cognitive impairment (MCI) and AD from HC. Results: The best overall MCI&AD detection performance in both AIBL and FMUUH was obtained on the cognitive and functional assessments (CFA) modality only using MoG-based ND with AUC of 0.8757 and 0.9443, respectively. The highest sensitivity of MCI was presented by using a combination of CFA and brain imaging modality. The DTB value reflects the risk of developing cognitive impairment for HC and the dementia severity of MCI/AD.Conclusions: Our findings suggest that applying some non-invasive and cost-effective features can significantly detect cognitive decline in an early stage. The visualized decision boundary and the proposed DtB strategy illustrated the severity of cognitive decline of potential MCI&AD patients in an early stage. The results would help inform future guidelines for developing a clinical decision-making support system aiming at an early diagnosis and prognosis of MCI&AD.

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