Brain Aβ load can be measured either by CSF Aβ42 or PET amyloid imaging. It is increasingly evident that obtaining estimates of brain Aβ load is necessary for many types of research studies in aging and dementia. Some would argue, for example, that brain Aβ load must be established in all subjects for inclusion in anti-amyloid therapeutic trials. In addition, establishing the presence of Aβ amyloid will likely be an important feature of future revised criteria for AD at all clinical stages. However, in some circumstances it may not be possible to measure Aβ load in all subjects in a study by a single method. This could be due to non-availability of amyloid PET imaging or the expense associated with performing amyloid PET imaging in large numbers of subjects. Some subjects may be unwilling to undergo an LP or may have contraindications to LP, such as use of anticoagulants. In these circumstances we believe that pooling CSF Aβ42 and amyloid PET imaging measures is preferable to the alternative which is to exclude subjects in whom one or the other method of ascertaining brain Aβ load is not available. Transforming CSF Aβ into PIBcalc units enables pooling measures across subjects who have brain Aβ load measured by either CSF or PET imaging.
Justification for our approach is the consistently observed tight inverse correlation between PIB PET and CSF Aβ42 measures [1
]. Although both CSF Aβ42 and PIB PET measures change over time, both do so slowly [7
]. Therefore at a fixed point in time, for any given subject CSF Aβ42 should mirror PIB PET and vice versa.
We chose to transform CSF Aβ42 into unitless PIB PET ratio values (rather than the reverse) for several reasons. PET amyloid imaging provides an image of the pathology in the brain and may therefore have slightly greater “face validity” as a gold standard measure of brain Aβ load compared to CSF Aβ42. PET amyloid imaging can also more easily be standardized across different centers by the common practice of referencing cortical retention to a subject specific standard (cerebellar retention). This is not the case with CSF Aβ42 where absolute units are a function of the specific assay used. Finally, we recognize that PIB PET has regional information and region specific transformations of PIB PET into CSF Aβ42 units might provide useful information. However, most of the PIB PET literature to date has focused on global summary measures of PIB retention [8
]. The value of regional information available from PIB PET seems to be less for example than regional information in other modalities such as MRI or FDG PET. If desired, however, the methods described here can be used to create a region-specific conversion model. With the reverse transform in mind however we did explore the feasibility of converting PIB PET to a CSF-based Aβ load measure informally and this appeared to also work well. A more formal comparison of the advantages and disadvantages of transforming in one direction or the other would require a larger independent sample of subjects with PIB PET and LP.
There are three aspects of generalizability issue that are relevant. First, there is technical generalizability. We recognize that the specific equation for our CSF Aβ42 to PIBcalc conversion model was derived from CSF samples processed on the Luminex platform [21
] and the precise equation might differ for CSF analyzed with different platforms. A different equation might be obtained using a different PIB PET image analysis method and 18
F amyloid PET ligands may not produce the same conversion model that 11
C PIB PET does. Second is patient/subject generalizability. We emphasize that the method applies to ADNI and “ADNI-like” subjects and that for other populations a new conversion model would likely need to be developed. A third aspect is method generalizability. Our objective was not to exhaustively analyze the relationship between PIB PET and CSF Aβ42 under all possible circumstances but to develop an approach for combining these two measurement methods. The apparent success of this method given a relatively small training set suggests that these two measurement methods could be combined in future studies with different imaging or assay parameters with the need to calibrate the two measurements on a relatively small subset of the study population.
Establishing the correct temporal order in which AD biomarkers become abnormal as the disease progresses has been identified as an important research goal [33
]. A key question is which biomarker of brain Aβ load becomes abnormal first, CSF Aβ42 or amyloid PET imaging? Our purpose here was not to detract from this important research question but rather to propose a practical solution to the situation where pooling measures of brain Aβ load ascertained by different methods is beneficial (or perhaps necessary) to accomplish the scientific aims of a study.
A CSF Aβ42 value of 192 pg/ml was initially derived from an autopsy verified sample as an appropriate cut point denoting an abnormal CSF Aβ42 level [21
]. This 192 pg/ml has been used as the normal/abnormal CSF Aβ42 cut point in ADNI [21
]. A commonly used analogous cut point in the PIB PET literature denoting positive from negative PIB PET studies is 1.5. With our transformation method a CSF Aβ42 value of 192 pg/ml corresponds closely to a PIBcalc value of 1.5. The equivalence in cut points using our method to transform CSF Aβ42 into units of PIBcalc supports the validity of this approach. The Bland-Altman plot () illustrates that there are no systematic differences between the two measurement methods. While the limits of agreement of ± 0.48 are probably too wide for the two methods to be considered clinically interchangeable for a given patient, the two methods can be considered statistically equivalent at the study level.
The PIBcalc data (derived from CSF) in our supporting (n = 362) sample are consistent with those reported for PIB PET imaging studies [8
]. The median CN retention ratio was < 1.5 while the median AD ratio was > 2. The MCI ratio lies between CN and AD. Taking a PIBcalc value of 1.5 as a cutoff denoting an abnormal PIB value, 87/93 (94%) AD subjects, 132/164 (80%) MCI subjects, and 47/105 (45%) CN subjects fall into the abnormal range. Perhaps the most compelling independent evidence of the data transformation method validation however comes from comparing group-wise values of PIBcalc in the supporting sample (n = 362) with PIB PET values in the ADNI PIB PET cohort (n = 102). These 464 (102 + 362) subjects were all drawn from the same ADNI sample. As illustrated in both the group-wise Aβ load distributions and also the density plots were essentially identical when measured by PIB PET vs PIBcalc (CSF Aβ42). In addition, the AUROC curves for distinguishing CN from AD subjects were nearly identical for PIB PET in the ADNI PIB PET sample and for PIBcalc in the supporting sample. This equivalent diagnostic performance across two different sets of subjects drawn from the same sample lends validity to the measurement transformation method.
We acknowledge that our supporting sample ideally would have consisted of subjects who underwent both PIB PET and LP, and which was independent of the sample used to train the conversion algorithm. This would have permitted evaluating PIBcalc values (calculated from CSF) directly against actual PIB PET imaging values acquired at the same time in the supporting sample. Unfortunately, the number of subjects in ADNI who underwent LP and PIB PET at the same time was too small (n = 41) to permit splitting the group into independent training and test samples. While acknowledging that the training sample was small we would like to emphasize that the multiple imputation measurement error (MIME) method which we describe fully in the Appendix
deals with the sample size in a principled manner by accounting for model-estimation uncertainty. With greater recruitment of subjects undergoing both LP and amyloid PET imaging into ADNI this will be possible in the future, and may be possible now in some non-ADNI samples [38
When Aβ load in PIBcalc units is derived from CSF, a proper statistical analysis must take into account the uncertainty underlying the conversion process. This uncertainty can be considered as coming from two sources. First, there is uncertainty in the conversion model regression equation and residual standard error estimate since a different training sample would provide different, albeit similar, parameter estimates. Second, because of subject-level factors, intrinsic measurement error, and other sources of unexplained variation, there is uncertainty about how far from the regression line a subject's true PIB PET value would be. This uncertainty illustrated by the prediction interval error bars in . We explain in the Appendix
how to propagate these sources of uncertainty through the analysis stage using the approach of measurement error multiple imputation (MIME) [23
]. In the MIME framework, the unavailability of PIB PET in some subjects (i.e., those with CSF Aβ42 and no PIB PET scan) is treated as a missing data problem and the statistical technique of multiple imputation is used to first impute a set of plausible PIB PET values given the subject's CSF and APOE genotype and second to analyze the data in a manner that accounts for the imputation process. The essential idea is that standard errors of model estimates from an analysis that pools PIB PET and CSF Aβ42 need to be adjusted to reflect the uncertainty in the conversion model.
In summary, our data supports the conclusion that CSF Aβ42 can be successfully transformed into calculated PIB (PIBcalc) measures of Aβ load. We emphasize that the exact parameters of the conversion model are specific to the ADNI study sample and the CSF platform and amyloid PET imaging methods employed. The method however is generalzable in that the approach to calibrating CSF to PIB PET can be applied under a variety of study conditions and study populations provided a validation subsample of moderate size (i.e., a training sample) is available. Therapeutic or observational studies can be performed with brain Aβ load measured by either CSF Aβ42 or amyloid PET imaging at baseline and the data can be pooled across subjects using well-established multiple imputation techniques that account for the uncertainty in a CSF-based calculated PIB value. We advocate this approach in clinical trials only
for baseline inclusion/exclusion and subject stratification purposes. Anti-amyloid treatment may affect the relationship between CSF Aβ42 and PIB PET in unknown ways and until this is established, we would not recommend pooling CSF Aβ42 and PIB PET data for purposes of measuring therapeutic Aβ load reduction [40