Usability of cerebrospinal fluid biomarkers in a tertiary memory clinic

Plasma and cerebrospinal fluid ABeta42 for the differential diagnosis of Alzheimer's disease dementia in participants diagnosed with any dementia subtype in a specialist care setting

BACKGROUND

Dementia is a syndrome that comprises many differing pathologies, including Alzheimer's disease dementia (ADD), vascular dementia (VaD) and frontotemporal dementia (FTD). People may benefit from knowing the type of dementia they live with, as this could inform prognosis and may allow for tailored treatment. Beta-amyloid (1-42) (ABeta42) is a protein which decreases in both the plasma and cerebrospinal fluid (CSF) of people living with ADD, when compared to people with no dementia. However, it is not clear if changes in ABeta42 are specific to ADD or if they are also seen in other types of dementia. It is possible that ABeta42 could help differentiate ADD from other dementia subtypes.

OBJECTIVES

To determine the accuracy of plasma and CSF ABeta42 for distinguishing ADD from other dementia subtypes in people who meet the criteria for a dementia syndrome.

SEARCH METHODS

We searched MEDLINE, and nine other databases up to 18 February 2020. We checked reference lists of any relevant systematic reviews to identify additional studies.

SELECTION CRITERIA

We considered cross-sectional studies that differentiated people with ADD from other dementia subtypes. Eligible studies required measurement of participant plasma or CSF ABeta42 levels and clinical assessment for dementia subtype.

DATA COLLECTION AND ANALYSIS

Seven review authors working independently screened the titles and abstracts generated by the searches. We collected data on study characteristics and test accuracy. We used the second version of the 'Quality Assessment of Diagnostic Accuracy Studies' (QUADAS-2) tool to assess internal and external validity of results. We extracted data into 2 x 2 tables, cross-tabulating index test results (ABeta42) with the reference standard (diagnostic criteria for each dementia subtype). We performed meta-analyses using bivariate, random-effects models. We calculated pooled estimates of sensitivity, specificity, positive predictive values, positive and negative likelihood ratios, and corresponding 95% confidence intervals (CIs). In the primary analysis, we assessed accuracy of plasma or CSF ABeta42 for distinguishing ADD from other mixed dementia types (non-ADD). We then assessed accuracy of ABeta42 for differentiating ADD from specific dementia types: VaD, FTD, dementia with Lewy bodies (DLB), alcohol-related cognitive disorder (ARCD), Creutzfeldt-Jakob disease (CJD) and normal pressure hydrocephalus (NPH). To determine test-positive cases, we used the ABeta42 thresholds employed in the respective primary studies. We then performed sensitivity analyses restricted to those studies that used common thresholds for ABeta42.

MAIN RESULTS

We identified 39 studies (5000 participants) that used CSF ABeta42 levels to differentiate ADD from other subtypes of dementia. No studies of plasma ABeta42 met the inclusion criteria. No studies were rated as low risk of bias across all QUADAS-2 domains. High risk of bias was found predominantly in the domains of patient selection (28 studies) and index test (25 studies). The pooled estimates for differentiating ADD from other dementia subtypes were as follows: ADD from non-ADD: sensitivity 79% (95% CI 0.73 to 0.85), specificity 60% (95% CI 0.52 to 0.67), 13 studies, 1704 participants, 880 participants with ADD; ADD from VaD: sensitivity 79% (95% CI 0.75 to 0.83), specificity 69% (95% CI 0.55 to 0.81), 11 studies, 1151 participants, 941 participants with ADD; ADD from FTD: sensitivity 85% (95% CI 0.79 to 0.89), specificity 72% (95% CI 0.55 to 0.84), 17 studies, 1948 participants, 1371 participants with ADD; ADD from DLB: sensitivity 76% (95% CI 0.69 to 0.82), specificity 67% (95% CI 0.52 to 0.79), nine studies, 1929 participants, 1521 participants with ADD. Across all dementia subtypes, sensitivity was greater than specificity, and the balance of sensitivity and specificity was dependent on the threshold used to define test positivity.

AUTHORS' CONCLUSIONS

Our review indicates that measuring ABeta42 levels in CSF may help differentiate ADD from other dementia subtypes, but the test is imperfect and tends to misdiagnose those with non-ADD as having ADD. We would caution against the use of CSF ABeta42 alone for dementia classification. However, ABeta42 may have value as an adjunct to a full clinical assessment, to aid dementia diagnosis.

The cerebrospinal fluid "Alzheimer profile": easily said, but what does it mean?

BACKGROUND

We aimed to identify the most useful definition of the "cerebrospinal fluid Alzheimer profile," based on amyloid-ß1-42 (Aβ42), total tau, and phosphorylated tau (p-tau), for diagnosis and prognosis of Alzheimer's disease (AD).

METHODS

We constructed eight Alzheimer profiles with previously published combinations, including regression formulas and simple ratios. We compared their diagnostic accuracy and ability to predict dementia due to AD in 1385 patients from the Amsterdam Dementia Cohort. Results were validated in an independent cohort (n = 1442).

RESULTS

Combinations outperformed individual biomarkers. Based on the sensitivity of the best performing regression formulas, cutoffs were chosen at 0.52 for the tau/Aβ42 ratio and 0.08 for the p-tau/Aβ42 ratio. Ratios performed similar to formulas (sensitivity, 91%-93%; specificity, 81%-84%). The same combinations best predicted cognitive decline in mild cognitive impairment patients. Validation confirmed these results, especially regarding the tau/Aβ42 ratio.

CONCLUSIONS

A tau/Aβ42 ratio of >0.52 constitutes a robust cerebrospinal fluid Alzheimer profile. We recommend using this ratio to combine biomarkers.

Application of the PredictAD software tool to predict progression in patients with mild cognitive impairment

BACKGROUND

The PredictAD tool integrates heterogeneous data such as imaging, cerebrospinal fluid biomarkers and results from neuropsychological tests for compact visualization in an interactive user interface. This study investigated whether the software tool could assist physicians in the early diagnosis of Alzheimer's disease.

METHODS

Baseline data from 140 patients with mild cognitive impairment were selected from the Alzheimer's Disease Neuroimaging Study. Three clinical raters classified patients into 6 categories of confidence in the prediction of early Alzheimer's disease, in 4 phases of incremental data presentation using the software tool. A 5th phase was done with all available patient data presented on paper charts. Classifications by the clinical raters were compared to the clinical diagnoses made by the Alzheimer's Disease Neuroimaging Initiative investigators.

RESULTS

A statistical significant trend (p < 0.05) towards better classification accuracy (from 62.6 to 70.0%) was found when using the PredictAD tool during the stepwise procedure. When the same data were presented on paper, classification accuracy of the raters dropped significantly from 70.0 to 63.2%.

CONCLUSION

Best classification accuracy was achieved by the clinical raters when using the tool for decision support, suggesting that the tool can add value in diagnostic classification when large amounts of heterogeneous data are presented.

Added Diagnostic Value of (11)C-PiB-PET in Memory Clinic Patients with Uncertain Diagnosis

Introduction

The added diagnostic value of ¹¹C-PiB-PET for the assessment of the accumulation of cortical beta-amyloid in memory clinic patients with uncertain diagnosis remains undetermined.

Methods

All patients who underwent PiB-PET at the Copenhagen Memory Clinic between March 2008 and November 2011 were included in this uncontrolled, retrospective study. The standard diagnostic evaluation program included physical and neurological examination, cognitive and functional assessment, a cranial CT or MRI, functional imaging and cerebrospinal fluid sampling. Based on anonymized case reports, three experienced clinicians reached a consensus diagnosis and rated their confidence in the diagnosis before and after disclosure of PiB-PET ratings. PiB-PET scans were rated as either positive or negative.

Results

A total of 57 patients (17 females, 30 males; age 65.7 years, range 44.2–82.6) were included in the study. Twenty-seven had a positive PiB-PET scan. At the first diagnostic evaluation, 16 patients were given a clinical Alheimer's disease diagnosis (14 PiB positive). Of the 57 patients, 13 (23%) were diagnostically reclassified after PiB-PET ratings were disclosed. The clinicians’ overall confidence in their diagnosis increased in 28 (49%) patients.

Conclusion

PiB-PET adds to the specialist clinical evaluation and other supplemental diagnostic investigations in the diagnostic classification of patients with uncertain diagnosis in a specialized memory clinic.

Does Alzheimer's disease with early onset progress faster than with late onset? A case-control study of clinical progression and cerebrospinal fluid biomarkers

BACKGROUND

Early-onset Alzheimer's disease (EOAD) is generally thought to have a more rapid course compared to late-onset Alzheimer's disease (LOAD). The faster progression of EOAD observed in some studies has also been thought to correlate with cerebrospinal fluid (CSF) biomarkers. Our clinical experience has not been suggestive of any difference in disease progression; therefore, we decided to investigate whether differences in clinical progression and CSF biomarkers between EOAD and LOAD could be demonstrated.

METHODS

Case-control study with 42 patients, 21 EOAD and 21 matched LOAD patients. Rates of progression were calculated and these, as well as CSF biomarker levels, were statistically compared.

RESULTS

There were no statistically significant differences in clinical progression between the EOAD group and the LOAD group. There was no significant difference in the absolute values of CSF biomarkers, but a tendency towards lower levels of β-amyloid in patients with EOAD was observed.

CONCLUSIONS

Our findings did not converge with results from the majority of previous studies, which have been suggestive of a faster clinical progression in EOAD. Possibly, the very strict algorithm by which our patients were matched explains our findings. However, the findings should be repeated in a larger study population.

cNEUPRO: Novel Biomarkers for Neurodegenerative Diseases

“clinical NEUroPROteomics of neurodegenerative diseases” (cNEUPRO) is a Specific Targeted Research Project (STREP) within the sixth framework program of the European Commission dedicated to the search for novel biomarker candidates for Alzheimer's disease and other neurodegenerative diseases. The ultimate goal of cNEUPRO is to identify one or more valid biomarker(s) in blood and CSF applicable to support the early and differential diagnosis of dementia disorders. The consortium covers all steps required for the discovery of novel biomarker candidates such as acquisition of high quality CSF and blood samples from relevant patient groups and controls, analysis of body fluids by various methods, and finally assay development and assay validation. Here we report the standardized procedures for diagnosis and preanalytical sample-handling within the project, as well as the status of the ongoing research activities and some first results.

Markers of Alzheimer's disease in a population attending a memory clinic

BACKGROUND

New marker-based criteria for the diagnosis of Alzheimer's disease (AD) were recently proposed. We describe their operational translation in 144 consecutive patients referred to our Memory Clinic.

METHODS

Visual ratings of hippocampal atrophy and of cortical glucose hypometabolism in magnetic resonance imaging and positron emission tomography, and concentrations of total tau and Abeta1-42 in cerebrospinal fluid were assessed in 12 patients with subjective memory complaints (SMCs) (Mini-Mental State Examination [MMSE] score, 28.0 +/- 1.1 [mean +/- SD]), 37 with mild cognitive impairment (MCI) (MMSE, 25.1 +/- 3.6), 55 with AD (MMSE, 21.1 +/- 3.5), and 40 with non-AD dementia (MMSE, 21.6 +/- 5.5).

RESULTS

The sensitivity for AD of each individual biomarker was higher (65% to 87%) than for MCI (18% to 50%). Each biomarker's specificity for SMC and non-AD dementias was good to moderate (83% and 53%). Positivity for at least one marker increased the probability 38 times of belonging to the AD group (P < 0.0001).

CONCLUSION

The new diagnostic criteria can be operationalized in clinical routines, but longitudinal studies of MCI patients will need to assess the criteria's prognostic value.