This is the first study to use CSF biomarkers to assess the relevance and classification of coincident neurodegenerative disease pathologies, as determined by autopsy-confirmed diagnoses in a large neuropathological cohort. We show that patients with overlapping diagnoses represent an important subgroup in clinical cohorts, but are often not identified correctly with the current biomarkers. It is also the first study to demonstrate that using samples classified by clinical diagnosis leads to an underestimation of biomarker sensitivity and specificity values and shifts the cutoffs. Our results confirm that Aβ42, p-tau and t-tau as measured by ELISA and ‘Luminex assays enable the differentiation of AD from FTLD in a sample of demented patients and that AD can be distinguished from FTLD and controls in a cohort sample of MCI and early dementia patients, i.e., at early disease stages. In summary, these biomarkers are helpful to distinguish subjects with an underlying neurodegenerative disease (FTLD, AD or both) from cognitively normal controls.
The cases studied here included patients with typical AD pathology overlapping with FTLD-TDP or α-synuclein proteinopathies. In our sample we found that 28.9 % of the cases presented with multiple neurodegenerative disease pathologies, in agreement with previously described samples [13
Our study demonstrates that the patients studied here were mainly classified as AD by a diagnostic biomarker panel, which includes p-tau, t-tau and Aβ, regardless of the presence of other co-morbid neurodegenerative pathologies. While this may not be surprising since the biomarkers studied here are among the most informative AD biomarkers, it is an important observation that is highly relevant for neurodegenerative disease clinical trials because disease modifying therapies specifically directed against amyloid or tau might reduce the burden of tangles or plaques, but may not affect deposits of other disease proteins. In the absence of cognitive improvement, this could be mistakenly interpreted as a failure of the treatment to ameliorate plaque and tangle pathology rather than a failure to target the appropriate protein aggregate. Therefore, it is critically important to discover and validate biomarkers for non-AD pathologies.
As shown here, the bias introduced by the use of clinical categories when establishing biomarker cutoffs can have an important effect on the calculated sensitivity and specificity of CSF tau and Aβ biomarker-based disease classification. Indeed, this may help explain the variability of published results validating these biomarkers based on clinical diagnosis and the variation in these biomarker cutoffs and estimated group probabilities. Recently, a large clinical sample with a small subset of neuropathological cases found that 34 %
of the patients diagnosed as dementia of the non-AD type were found to have an AD CSF biomarker profile, and were thought to be incorrectly classified by these biomarkers and, therefore, the specificity of these biomarker was not deemed to be good [54
]. The non-AD dementia clinical diagnoses in this study that showed a higher percentage of AD CSF profile were FTD and CBS patients that in our cohort have a 65.2 and 33 %
clinical-pathological agreement, similar to other studies [34
], Thus, these clinical diagnoses may not be accurate for establishing CSF biomarker cutoffs or predicting the nature of the underlying neurpathology. Here, we demonstrate that the use of clinical diagnoses instead of neuropathological diagnoses underestimates at about 10–20 %
sensitivity and specificity values for CSF tau and Aβ biomarkers and that cohorts with larger numbers of subjects with neuropathological diagnoses are needed to establish the real accuracy of biomarkers as well as to establish cutoffs for classification.
These analyses point to the urgent need for reliable and informative CSF biomarkers that can be used to identify more homogeneous samples of AD or FTD patients with respect to underlying neuropathology for clinical trials, as well as for imaging, genetic and other studies of neurodegenerative dementias. Indeed, this study emphasizes the importance of recognizing that a large subset of patients who are classified as AD by currently available CSF biomarker measurements also may have other concomitant neurodegenerative disorders that may contribute to their cognitive impairments.
We have developed two classification algorithms. The first was designed for a dementia stage that would consist of patients with an underlying AD or FTLD neuropathological diagnosis (and a small subset of DLB cases without AD). The second was designed for earlier stages and offers three diagnostic categories: AD neuropathology, FTLD neuropathology, and a group that potentially has no neurodegenerative disease. This would allow classification of patients in an MCI stage as having or not having an underlying AD neuropathology, which would be useful for clinical trials, or as having or not having any neurodegenerative disease. In our cohort, we observed that 25.4 % of the subjects had an MCI diagnosis and another 28.2 %
had a dementia diagnosis with a CDR of 1.0. The diagnostic accuracy did not differ in the group of more impaired patients when compared with the less impaired patients. This is in agreement with other studies that show that CSF Aβ and tau levels are stable in MCI and AD patients [29
] and indicates that established CSF tau and Aβ cutoffs are useful not only at the dementia stage but also in the MCI stage of disease. However, recently a study that included MCI patients with a median follow-up of 9.2 years, found that early converters (less than 5 years) had higher t-tau and p-tau levels than late converters (more than 5 years), while Aβ showed no changes [8
]. Most of our MCI patients converted to dementia in less than 5 years (data not shown); therefore, our model cannot be inferred to apply to the early stages of MCI without further studies.
This study has several strengths. First it is the largest cohort of subjects with a neuropathologically confirmed diagnosis from whom ante-mortem CSF tau and Aβ measures were obtained. Only two other studies have reported similar data from >100 subjects [9
]. Second, we present results for the two most commonly used biomarker platforms, i.e. Luminex and ELISA and give the classification algorithm for both assays. Third, the control subjects were closely age matched to the other groups and since the patients had postmortem neuropathological confirmation of their diagnoses, we were able to study a patient cohort that had well established coincident neuropathological diagnoses. Finally, the large sample size here allowed us to divide our cohort into a training–validation and a test set.
All the subjects with a main diagnosis of DLB in our sample, except one, had a secondary diagnosis of AD and were classified as cases with coincident neuropathological diseases. Other studies report DLB cases without an AD diagnosis; however, in our series of 1,240 neuropathological cases in the CNDR INDD, there are 81 cases with a coincident AD diagnosis but only 11 cases (12 % of all DLB cases) with a main diagnosis of pure DLB with no coincident AD diagnosis. The fact that only 3 % of DLB cases did not have a coincident AD neuropathological diagnosis could be due to a selection bias of cases based on a dementia clinic recruitment of the sample. This skewed representation might be due to a referral bias of our patients to a dementia clinic, which may result in a significant overlap between DLB and AD pathology in many patients drawn from such clinics. Finally, based on the small numbers of DLB cases studied here, we cannot make any claims on the overall CSF signature of DLB patients. We confirm previous results of lower CSF t-tau levels in cases with DLB (data not shown) [63
], but the overlap of CSF biomarker measurements together with a lower prevalence of DLB present a significant challenge for improving the recognition of DLB based on CSF biomarkers [58
]. However, it is possible that measuring other species of Aβ, including Aβ1–40ox
, could improve the classification of DLB using CSF biomarkers [40
One of the weaknesses of this study and previous studies is the lack of neuropathologically diagnosed controls. Previous studies using unsupervised analyses have shown that up to 36 %
of cognitively normal subjects have a pathological AD CSF signature [10
] and this is in agreement with a long preclinical or prodromal phase of AD during which time AD neuropathology is accumulating [1
] and the observation that CSF Aβ measures are most dynamic in the MCI stage of AD but are stable when AD dementia becomes clinically manifest [27
]. Thus, it is possible that some of the cognitively normal subjects, classified as AD by the CSF measurements, are asymptomatic subjects at risk for AD [12
] and these subjects may have shifted our cutoffs. While we also indicated that biomarkers could improve clinical diagnoses, we acknowledge that for the comparison shown in supplementary table 5 we used some subjects who had been used to train the logistic regression models. Finally, cases with vascular dementia were not included in our study and we therefore have mainly focused on neurodegenerative diseases in this study.
In summary, our study demonstrates the importance of using neuropathological diagnoses for establishing biomarker cutoffs and provides important new insights into the interpretation of CSF biomarkers for the classification of neurodegenerative dementias. We also emphasize the important clinical need to develop specific biomarkers for PD, PDD and DLB as well as other forms of FTLD in order to be able to accurately classify subjects with other coincident neurodegenerative disease pathologies as the basis for their neurodegenerative dementia. In addition, studies analyzing biomarkers in early stages of the disease are needed because of the dynamic characteristics of some biomarkers. Finally, this study underlines the critical importance of CSF biomarker assay standardization to increase accuracy for the early diagnosis of neurodegenerative diseases, especially since many patients with a neurodegenerative dementia will show evidence of coincident neurodegenerative disease pathologies and more than one neuropathologically confirmed neurodegenerative disorder as the underlying basis for their cognitive impairment.