This 2-year longitudinal study examined five commonly used CSF biomarkers for AD in a study of healthy controls, stable MCI and MCI patients who progressed to AD. There were three main findings. First, at baseline and at follow-up, all the CSF biomarkers separated declining MCI patients from stable MCI and normal controls. Second, all biomarkers were statistically significant predictors of the decline from MCI to AD with P-tau231 and T-tau the strongest univariate predictors. Third, only IP demonstrated longitudinal effects.
At baseline, all CSF measures accurately separated MCI subjects who later declined to AD from stable MCI subjects and from NL-NL. MCI-AD patients had higher CSF concentrations of P-tau231
, T-tau, IP, P-tau231
ratios as well as lower Aβ42
measures as compared to either stable MCI or to normal elderly controls. This finding is in agreement with previous findings [2
]. Our results confirm that before the onset of clinically overt AD, there are changes in the CSF biochemical composition that reflect AD pathology: neurofibrillary tangles, amyloid plaques [4
], and oxidative damage to neuronal cell membranes [30
Interestingly, IP was the only biomarker that showed longitudinal effects, such that IP levels increased over 2 years in association with MCI-AD conversion as compared to the same time interval for stable MCI and NL subjects. This finding is consistent with the results from smaller samples of MCI patients previously published by our group showing that longitudinal IP level provides diagnostic separation of MCI from healthy controls, and adds new evidence that longitudinal IP changes can be used to track the progression from MCI to AD [12
]. Since IP is a marker of membrane lipid peroxidation and inflammation, these data suggest that the increase of CSF IP levels in cognitively deteriorating patients reflects progressive neuronal oxidative stress and progression of neurodegenerative changes [35
]. Although the cross-sectional baseline IP levels showed high correlation with P-tau231
and T-tau (r’s ~ .70), there was no correlation between longitudinal changes in these analytes. This finding suggests the unlikely summary that the processes of inflammation and neurodegeneration are not parallel. However, it remains of extreme interest to answer the question whether there is an order or staging effect, i.e. if oxidative stress precedes neurodegeneration or is merely a consequence of an already existing neurodegenerative process. The present 2 time point study of MCI patients is insufficient to answer this question. Possibly a study including normal subjects that experience longitudinal changes related to AD would reveal this sequence. It is also possible that other factors such as clearance of tau which is poorly understood and the dilution of a brain derived protein such as the tau molecule in the CSF has affected the sensitivity to measure brain progression effects [14
]. As such, both additional groups and improved characterization of the physiology of tau are needed to understand the relationship to inflammation.
As recommended by the consensus report of the NIA Working Group on Biological Measures [11
], an ideal diagnostic AD biomarker should have both sensitivity and specificity of at least 80% in separating AD from normal aging. We report that the prediction of decline with P-tau231
exceeds the recommended 80% threshold of sensitivity and specificity and, shows the highest specificity (80% recognition of non-declining MCI patients) among all biomarkers analyzed in the study. However, although our results show that only P-tau231
meets the criteria stated by the consensus group and provides highest sensitivity and specificity figures, its prediction accuracy was not statistically different from that of T-tau, T-tau/Aβ42/40
ratios. However, larger samples may provide the statistical power to detect differences between these biomarkers.
The changes in P-tau231
are known to reflect neurofibrillary pathology [24
] and clinical studies show that elevated levels confer diagnostic specificity for AD [10
]. Although CSF P-tau231
and T-tau changes were better predictors of future cognitive decline than Aβ42/40
and IP, it remains to be established which biomarker is the first useful predictor of AD to be detected. A recent predictor CSF study by Fagan et al. suggested that CSF Aβ reductions occur in normal subjects prior to clinical decline and may precede tau elevations [16
], but evidence for biomarker staging requires longitudinal data for a large number of clinical starting points and this is not yet available.
Although several studies show that the use of CSF T-tau/Aβ42
ratios yield good AD diagnostic accuracy [2
] and MCI-AD prediction effects [16
], it is usually not reported if the ratio statistically increments the prediction accuracy over the univariate measures. We observed a significant additive effect in our study only with the addition of Aβ42
to IP. Statistically, combinations of CSF measures will increase AD-prediction accuracy provided that the two markers are not highly correlated. Accordingly, our analysis shows a high correlation between most CSF biomarkers. Aβ42
and IP were weakly correlated and therefore have a greater potential to show incremental effects.
In the present study, only subjects with baseline diagnoses of NL or MCI were examined. The homogeneity of our study population was achieved by applying clearly defined baseline diagnostic criteria and excluding patients with cerebrovascular disease and other identifiable causes of poor cognitive performance. Therefore, we excluded subjects that in clinical settings have to be considered as part of the differential diagnosis of AD. It remains to be established to what extent our results apply to more heterogeneous patient samples. Future CSF biomarker studies are warranted to examine this issue.
Because the APOE4 genotype is a well known risk factor for AD [27
], we examined its effects on the CSF biomarkers. We observed across the diagnostic groups that the Aβ42/40
ratio was lower in carriers as compared with non-carriers. Interestingly, we did not find any APOE genotype differences for the other CSF analytes. These data suggest a limited role for APOE genotype in the interpretation of CSF biomarkers. Our results are in agreement with Engelborghs et al. [15
], but not with the CSF results reported by Prince et al. [37
] who reported a link between APOE4 genotype and Aβ metabolism. Further study of these relationships is warranted.
A limitation of the current longitudinal dataset was the reliance on only one follow-up observation period. Based on other work, one would expect that about 12-15 % of amnestic MCI patients (and less for the non-amnestic) will progress within 1 year to AD [33
]. While our data are consistent with this expectation, with a 2-years follow-up interval our results may underestimate the differences between stable and progressing MCI as our MCI-MCI subject group are likely to include MCI patients who will later develop dementia. In other words, the inclusion of future declining MCI subjects within the stable MCI group would have the conservative effect of reducing the statistical differences between groups. Consequently, expanding the observation period may increase the baseline prediction accuracies for the biomarkers. As an example, in a similarly designed study published by Hansson et al., using T-tau and Aβ42
, an observation period greater than 4 years provided sensitivity and specificity values predicting MCI to AD decline as high as 95% and 83%, respectively,[20
]. The fact that our sensitivity and specificity estimates for P-tau231
are about 80% suggests that reliable predictions can be made over a 2 year interval.
Although several AD-prediction studies are published, these reports do not include longitudinal CSF measurements [8
]. Our paper presents longitudinal CSF data for five most common biomarkers, and tested the incremental potential of combining them in the prediction models. Our results identify P-tau231
as the better predictor of AD at the MCI stage and that IP may be useful for monitoring the course of decline to AD. We conclude that CSF biomarkers may facilitate the design of secondary prevention trials studies by enabling subject enrichment and monitoring clinical course. Future studies with larger, more naturalistic study populations with longer and more frequent follow-up intervals will further contribute to determining the most useful sets of biomarkers and relationships among the CSF biomarkers for AD.