This study is timely in comparing two widely used platforms (ELISA and bead-based multiplex assays [X-MAP]) for measuring cerebrospinal fluid (CSF) analytes related to Alzheimer’s disease. The results support assays for CSF Aβ42 and total tau as being reliable across platforms, although at present, precise cutoff levels need to be established by users. Measurement of CSF levels of phospho-tau181 showed greater variability between the platforms, for unclear reasons. A particular strength of the study was amyloid imaging on all participants, allowing comparison of cutoffs of CSF biomarkers with amyloid positive or negative scans. The study does not discuss why each platform reads out different concentrations of Aβ42, tau and p-tau181 in CSF, although levels of each of these analytes were correlated using different assays; the strength of correlation does not allow for a facile conversion of levels between platforms.

A different standardization problem—how to obtain consistent readouts (and low variation) within a laboratory when assays are run intermittently on different days...  Read more

This study is timely in comparing two widely used platforms (ELISA and bead-based multiplex assays [X-MAP]) for measuring cerebrospinal fluid (CSF) analytes related to Alzheimer’s disease. The results support assays for CSF Aβ42 and total tau as being reliable across platforms, although at present, precise cutoff levels need to be established by users. Measurement of CSF levels of phospho-tau181 showed greater variability between the platforms, for unclear reasons. A particular strength of the study was amyloid imaging on all participants, allowing comparison of cutoffs of CSF biomarkers with amyloid positive or negative scans. The study does not discuss why each platform reads out different concentrations of Aβ42, tau and p-tau181 in CSF, although levels of each of these analytes were correlated using different assays; the strength of correlation does not allow for a facile conversion of levels between platforms.

A different standardization problem—how to obtain consistent readouts (and low variation) within a laboratory when assays are run intermittently on different days (rather than in large batches)—is the subject of ongoing research and harmonization efforts spearheaded by Kaj Blennow and others.

View all comments by Douglas Galasko

This paper by Irwin at al. raises some important points.

For a study on CSF frontotemporal dementia (FTD) biomarkers, they managed to collect a relatively fair number of patients. Also, the postmortem confirmation is a strong point, as FTD is heterogeneous and clinical diagnosis is not simple. However, for the kind of conclusions (very high sensitivity and specificity), the study is not powered enough (although we realize it is not easy to obtain high numbers of characterized, postmortem confirmed FTD patients). In the end, the sensitivity and specificity numbers are based on just 10 FTD patients versus 10 AD patients, and thus every single patient may influence these numbers strongly.

Their approach to use the natural log to transform ELISA data to Luminex data leads to a good fit. But, on the other hand, there is an overfitting of the data: In the majority of figures of the Ln-transformed data of the test sets in Figure 2, the correlation coefficient is high due to a single outlier, and the trend lines are thus too steep. Since the correlation coefficients of the test...  Read more

This paper by Irwin at al. raises some important points.

For a study on CSF frontotemporal dementia (FTD) biomarkers, they managed to collect a relatively fair number of patients. Also, the postmortem confirmation is a strong point, as FTD is heterogeneous and clinical diagnosis is not simple. However, for the kind of conclusions (very high sensitivity and specificity), the study is not powered enough (although we realize it is not easy to obtain high numbers of characterized, postmortem confirmed FTD patients). In the end, the sensitivity and specificity numbers are based on just 10 FTD patients versus 10 AD patients, and thus every single patient may influence these numbers strongly.

Their approach to use the natural log to transform ELISA data to Luminex data leads to a good fit. But, on the other hand, there is an overfitting of the data: In the majority of figures of the Ln-transformed data of the test sets in Figure 2, the correlation coefficient is high due to a single outlier, and the trend lines are thus too steep. Since the correlation coefficients of the test sets are therefore much lower than the training set, it is clear that the current transformation model cannot be extrapolated to other cohorts or even single patients. This is clearly also due to the fact that the training set of 40 data points does not provide sufficient power to obtain a generalizable formula.

Finally, we find that the aim not very clearly stated: Is it to compare two platforms? Or is it to define cutoff points for discriminating AD from FTD? The title may be taken as the aim is a comparison of the two platforms. Instead, what they did is a merging of data obtained from two different platforms, which are not easily interchangeable (Jongbloed et al., in press).

References:
Jongbloed W et al. Discriminatory and Predictive Capabilities of ELISA and Multiplex Platforms in a Longitudinal Alzheimer Study. Alzheim & Dement. 2012, in press.

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