Diagnosing Alzheimer's disease—non‐clinicians and computerised algorithms together are as accurate as the best clinical practice

Foy, Catherine; Nicholas, Helen; Hollingworth, Paul; Boothby, Harry; Willams, Julie; Brown, Richard G.; Al‐Sarraj, Safa; Lovestone, Simon

doi:10.1002/gps.1810

Cited by 28 publications

(21 citation statements)

References 28 publications

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“…Standardized assessments include demographic and medical information, cognitive assessment including MMSE (both studies; all subjects), ADAS-Cog (AddNeuroMed only), CERAD battery, and scales to assess function, behavior and global levels of severity including the Clinical Dementia Rating (CDR). Cases with probable AD (NINCDS-ADRDA criteria) and amnestic MCI were identified as previously described7 and evaluated with a standardised assessment shown to have high diagnostic validity20. Cases with amnestic MCI were defined as having subjective memory complaints, CDR score <1 and evidence of objective memory impairment using the CERAD delayed word list recall (−1.5 SD cut off).…”

Section: Methodsmentioning

confidence: 99%

Association of Plasma Clusterin Concentration With Severity, Pathology, and Progression in Alzheimer Disease

Thambisetty¹,

Simmons²,

Velayudhan³

et al. 2010

Arch Gen Psychiatry

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362

332

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Context Blood-based analytes as indicators of pathological processes in Alzheimer's disease (AD). Objective Combined proteomic and neuroimaging approach to identify plasma proteins associated with AD pathology. Design Discovery-phase proteomic experiments to identify plasma proteins associated with correlates of AD pathology including evidence of atrophy using neuroimaging and more rapid clinical progression, followed by replication using quantitative immunoassay. Extension studies in older non-demented humans using 11C-PiB amyloid imaging and transgenic mice with amyloid pathology. Setting Multi-center European study, AddNeuroMed, and the Baltimore Longitudinal Study of Aging (BLSA) in United States. Participants AD patients, mild cognitive impairment (MCI) subjects and healthy controls with standardized clinical assessments and structural neuroimaging. Plasma samples from non-demented older BLSA participants with brain amyloid imaging by PET. Main outcome measures Association of plasma proteins with brain atrophy, disease severity and rate of clinical progression. Extension studies in man and transgenic mice tested association between plasma proteins and brain amyloid. Results Clusterin/apolipoprotein-J was associated with atrophy of the entorhinal cortex, baseline disease severity and rapid clinical progression in AD. Increased plasma concentration of clusterin was predictive of greater beta amyloid (Aβ) burden in the medial temporal lobe. Subjects with AD had increased clusterin mRNA in blood but there was no effect of SNPs in the gene encoding clusterin (CLU) with gene or protein expression. Finally, APP/PS1 transgenic mice showed increased plasma clusterin, age-dependent increase in brain clusterin and amyloid and clusterin co-localisation in plaques. Conclusions Clusterin/apolipoprotein-J is a known amyloid chaperone associated with Alzheimer's disease severity, pathology and progression. Increased plasma concentration of clusterin is also associated with greater burden of fibrillar Aβ in the brain. These results demonstrate an important role of clusterin in the pathogenesis of AD and suggest that alterations in amyloid chaperone proteins may be a biologically relevant peripheral signature of Alzheimer's disease.

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Section: Methodsmentioning

confidence: 99%

Association of Plasma Clusterin Concentration With Severity, Pathology, and Progression in Alzheimer Disease

Thambisetty¹,

Simmons²,

Velayudhan³

et al. 2010

Arch Gen Psychiatry

Self Cite

362

332

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“…Samples were collected from the ongoing cohort studies for biomarker discovery at the Institute of Psychiatry, KCL. These studies have been previously described [14], but in brief, a cohort of patients with AD, subjects with MCI and normal elderly controls were identified through primaryand secondary-care centres and assessed annually with valid and reliable diagnostic instruments [14]. See Table 1 for details on the 38 patients.…”

Section: Experimental Designmentioning

confidence: 99%

A proposed metabolic strategy for monitoring disease progression in Alzheimer's disease

et al. 2009

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A specific, sensitive and essentially non-invasive assay to diagnose and monitor Alzheimer's disease (AD) would be valuable to both clinicians and medical researchers. The aim of this study was to perform a metabonomic statistical analysis on plasma fingerprints. Objectives were to investigate novel biomarkers indicative of AD, to consider the role of bile acids as AD biomarkers and to consider whether mild cognitive impairment (MCI) is a separate disease from AD. Samples were analysed by ultraperformance liquid chromatography-MS and resulting data sets were interpreted using soft-independent modelling of class analogy statistical analysis methods. PCA models did not show any grouping of subjects by disease state. Partial least-squares discriminant analysis (PLS-DS) models yielded class separation for AD. However, as with earlier studies, model validation revealed a predictive power of Q(2)<0.5 and indicating their unsuitability for predicting disease state. Three bile acids were extracted from the data and quantified, up-regulation was observed for MCI and AD patients. PLS-DA did not support MCI being considered as a separate disease from AD with MCI patient metabolic profiles being significantly closer to AD patients than controls. This study suggested that further investigation into the lipid fraction of the metabolome may yield useful biomarkers for AD and metabolomic profiles could be used to predict disease state in a clinical setting.

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“…Within 2 years of the baseline visit, some MCI subjects progressed to a clinical diagnosis of AD (MCI c ), while others remained MCI (MCI nc ). Diagnoses were made by trained researchers following a previously validated protocol [54].…”

Section: Clinical Subjects and Samplesmentioning

confidence: 99%

A Blood Gene Expression Marker of Early Alzheimer's Disease

Lunnon

Sattlecker

Furney

et al. 2013

JAD

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A marker of Alzheimer's disease (AD) that can accurately diagnose disease at the earliest stage would significantly support efforts to develop treatments for early intervention. We have sought to determine the sensitivity and specificity of peripheral blood gene expression as a diagnostic marker of AD using data generated on HT-12v3 BeadChips. We first developed an AD diagnostic classifier in a training cohort of 78 AD and 78 control blood samples and then tested its performance in a validation group of 26 AD and 26 control and 118 mild cognitive impairment (MCI) subjects who were likely to have an AD-endpoint. A 48 gene classifier achieved an accuracy of 75% in the AD and control validation group. Comparisons were made with a classifier developed using structural MRI measures, where both measures were available in the same individuals. In AD and control subjects, the gene expression classifier achieved an accuracy of 70% compared to 85% using MRI. Bootstrapping validation produced expression and MRI classifiers with mean accuracies of 76% and 82%, respectively, demonstrating better concordance between these two classifiers than achieved in a single validation population. We conclude there is potential for blood expression to be a marker for AD. The classifier also predicts a large number of people with MCI, who are likely to develop AD, are more AD-like than normal with 76% of subjects classified as AD rather than control. Many of these people do not have overt brain atrophy, which is known to emerge around the time of AD diagnosis, suggesting the expression classifier may detect AD earlier in the prodromal phase. However, we accept these results could also represent a marker of diseases sharing common etiology.

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Diagnosing Alzheimer's disease—non‐clinicians and computerised algorithms together are as accurate as the best clinical practice

Abstract: A combination of non-clinical researchers, a structured interview and a computerised algorithm is as effective at identifying AD as highly trained and skilled clinicians.

Cited by 28 publications

References 28 publications

Association of Plasma Clusterin Concentration With Severity, Pathology, and Progression in Alzheimer Disease

Association of Plasma Clusterin Concentration With Severity, Pathology, and Progression in Alzheimer Disease

A proposed metabolic strategy for monitoring disease progression in Alzheimer's disease

A Blood Gene Expression Marker of Early Alzheimer's Disease

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