CSF biomarkers cutoffs: The importance of coincident neuropathological diseases

Department of Pathology and Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, CNDR, University of Pennsylvania School of Medicine, 3rd Floor Maloney Building, 3600 Spruce Street, Philadelphia, PA 19104, USA.
Acta Neuropathologica (Impact Factor: 10.76). 04/2012; 124(1):23-35. DOI: 10.1007/s00401-012-0983-7
Source: PubMed


The effects of applying clinical versus neuropathological diagnosis and the inclusion of cases with coincident neuropathological diagnoses have not been assessed specifically when studying cerebrospinal fluid (CSF) biomarker classification cutoffs for patients with neurodegenerative diseases that cause dementia. Thus, 142 neuropathologically diagnosed neurodegenerative dementia patients [71 Alzheimer's disease (AD), 29 frontotemporal lobar degeneration (FTLD), 3 amyotrophic lateral sclerosis, 7 dementia with Lewy bodies, 32 of which cases also had coincident diagnoses] were studied. 96 % had enzyme-linked immunosorbant assay (ELISA) CSF data and 77 % had Luminex CSF data, with 43 and 46 controls for comparison, respectively. Aβ(42), total, and phosphorylated tau(181) were measured. Clinical and neuropathological diagnoses showed an 81.4 % overall agreement. Both assays showed high sensitivity and specificity to classify AD subjects against FTLD subjects and controls, and moderate sensitivity and specificity for classifying FTLD subjects against controls. However, among the cases with neuropathological diagnoses of AD plus another pathology (26.8 % of the sample), 69.4 % (ELISA) and 96.4 % (Luminex) were classified as AD according to their biomarker profiles. Use of clinical diagnosis instead of neuropathological diagnosis led to a 14-17 % underestimation of the biomarker accuracy. These results show that while CSF Aβ and tau assays are useful for diagnosis of AD and neurodegenerative diseases even at MCI stages, CSF diagnostic analyte panels that establish a positive diagnosis of Lewy body disease and FTLD are also needed, and must be established based on neuropathological rather than clinical diagnoses.

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    • "This is often not the case in a chronic disease as AD. Nonetheless, it has been shown that cut-off values derived by using the clinical diagnosis as the reference test lead to a shift in the cut-off value as compared to using the autopsy-confirmed diagnosis [17], with possibly suboptimal sensitivity and specificity as a result. However, well-characterized samples like those obtained from clinical trials or worldwide consortia are not widely available in quantities sufficient for repeated testing. "
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    ABSTRACT: Current technologies quantifying cerebrospinal fluid biomarkers to identify subjects with Alzheimer's disease pathology report different concentrations in function of technology and suffer from between-laboratory variability. Hence, lab- and technology-specific cut-off values are required. It is common practice to establish cut-off values on small datasets and, in the absence of well-characterized samples, to transfer the cut-offs to another assay format using 'side-by-side' testing of samples with both assays. We evaluated the uncertainty in cut-off estimation and the performance of two methods of cut-off transfer by using two clinical datasets and simulated data. The cut-off for the new assay was transferred by applying the commonly-used linear regression approach and a new Bayesian method, which consists of using prior information about the current assay for estimation of the biomarker's distributions for the new assay. Simulations show that cut-offs established with current sample sizes are insufficiently precise and also show the effect of increasing sample sizes on the cut-offs' precision. The Bayesian method results in unbiased and less variable cut-offs with substantially narrower 95% confidence intervals compared to the linear-regression transfer. For the BIODEM datasets, the transferred cut-offs for Aβ 1-42 are 167.5 pg/mL (95% credible interval [156.1, 178.0] and 172.8 pg/mL (95% CI [147.6, 179.6]) with Bayesian and linear regression methods, respectively. For the EUROIMMUN assay, the estimated cut-offs are 402.8 pg/mL (95% credible interval [348.0, 473.9]) and 364.4 pg/mL (95% CI [269.7, 426.8]). Sample sizes and statistical methods used to establish and transfer cut-off values have to be carefully considered to guarantee optimal diagnostic performance of biomarkers.
    No preview · Article · Oct 2015 · Journal of Alzheimer's disease: JAD
    • "When the biomarker and the reference test are dependent, the diagnostic accuracy of the biomarker can be either underestimated or overestimated, depending on the strength of the association [8] [9]. Recently, Toledo et al. [10] demonstrated that using the clinical diagnosis as a perfect reference leads to an underestimation of cerebrospinal fluid (CSF) AD biomarker sensitivity and specificity values and shifts the cut-offs compared to using the autopsy confirmed diagnosis as reference test. "
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    ABSTRACT: Studies investigating the diagnostic accuracy of biomarkers for Alzheimer's disease (AD) are typically performed using the clinical diagnosis or amyloid-β positron emission tomography as the reference test. However, neither can be considered a gold standard or a perfect reference test for AD. Not accounting for errors in the reference test is known to cause bias in the diagnostic accuracy of biomarkers. To determine the diagnostic accuracy of AD biomarkers while taking the imperfectness of the reference test into account. To determine the diagnostic accuracy of AD biomarkers and taking the imperfectness of the reference test into account, we have developed a Bayesian method. This method establishes the biomarkers' true value in predicting the AD-pathology status by combining the reference test and the biomarker data with available information on the reliability of the reference test. The new methodology was applied to two clinical datasets to establish the joint accuracy of three cerebrospinal fluid biomarkers (amyloid-β1-42, Total tau, and P-tau181) by including the clinical diagnosis as imperfect reference test into the analysis. The area under the receiver-operating-characteristics curve to discriminate between AD and controls, increases from 0.949 (with 95% credible interval [0.935,0.960]) to 0.990 ([0.985,0.995]) and from 0.870 ([0.817,0.912]) to 0.975 ([0.943,0.990]) for the cohorts, respectively. Use of the Bayesian methodology enables an improved estimate of the exact diagnostic value of AD biomarkers and overcomes the lack of a gold standard for AD. Using the new method will increase the diagnostic confidence for early stages of AD.
    No preview · Article · Apr 2015 · Journal of Alzheimer's disease: JAD
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    • "Aβ deposition can be measured using PET tracers (Clark et al., 2012a; Ikonomovic et al., 2008) which correlate with decrease in Aβ 1–42 in CSF (Fagan et al., 2009; Toledo et al., 2011). Both measures show a high accuracy for predicting AD neuropathology (Clark et al., 2012a; Shaw et al., 2009; Silverman et al., 2001; Toledo et al., 2012). CSF concentrations have shown promise in predicting conversion from MCI to AD (Hampel et al., 2010a, 2010b; Schuff et al., 2009; Shaw et al., 2009). "
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    ABSTRACT: This study evaluates the individual, as well as relative and joint value of indices obtained from magnetic resonance imaging (MRI) patterns of brain atrophy (quantified by the SPARE-AD index), cerebrospinal fluid (CSF) biomarkers, APOE genotype, and cognitive performance (ADAS-Cog) in progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) within a variable follow-up period up to 6 years, using data from the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1). SPARE-AD was first established as a highly sensitive and specific MRI-marker of AD vs. cognitively normal (CN) subjects (AUC = 0.98). Baseline predictive values of all aforementioned indices were then compared using survival analysis on 381 MCI subjects. SPARE-AD and ADAS-Cog were found to have similar predictive value, and their combination was significantly better than their individual performance. APOE genotype did not significantly improve prediction, although the combination of SPARE-AD, ADAS-Cog and APOE ε4 provided the highest hazard ratio estimates of 17.8 (last vs. first quartile). In a subset of 192 MCI patients who also had CSF biomarkers, the addition of Aβ1–42, t-tau, and p-tau181p to the previous model did not improve predictive value significantly over SPARE-AD and ADAS-Cog combined. Importantly, in amyloid-negative patients with MCI, SPARE-AD had high predictive power of clinical progression. Our findings suggest that SPARE-AD and ADAS-Cog in combination offer the highest predictive power of conversion from MCI to AD, which is improved, albeit not significantly, by APOE genotype. The finding that SPARE-AD in amyloid-negative MCI patients was predictive of clinical progression is not expected under the amyloid hypothesis and merits further investigation.
    Full-text · Article · Dec 2014 · Clinical neuroimaging
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