Using an unbiased proteomics approach (2D-DIGE LC-MS/MS), this study identified 47 novel candidate CSF protein biomarkers for early AD. Subsequently, by evaluating a subset of these candidate biomarkers by ELISA, this study validated the utility of four candidate biomarkers for distinguishing groups with mild, very mild, or no dementia (CDR 1, 0.5, 0, respectively). Further statistical analyses demonstrated that these biomarkers could improve the accuracy of ‘established’ biomarkers Aβ42 and tau for the diagnosis of early AD.
The results from the 2D-DIGE LC-MS/MS portion of this study suggest that many of the recognized neuropathological changes of AD are represented by changes in the CSF proteome. Most of the 47 candidate biomarker proteins identified in this study can be placed into structural and/or functional categories (e.g. synaptic adhesion, synaptic function, dense core synaptic vesicle proteins, inflammation/complement, protease activity/inhibition, apolipoproteins, etc.) associated with accepted neuropathophysiological changes in AD (). Unsupervised clustering analyses of these 2D-DIGE data, performed without the influence of CSF Aβ42, tau, p-tau181 and APOE genotype, additionally suggest that these biomarker candidates collectively show utility for discriminating groups with and without mild DAT ().
Candidate CSF biomarkers reflect AD-related pathophysiologic changes.
In the second phase of this study, designed to measure a subset of candidate biomarker proteins in two independent sample sets by ELISA, four of the eleven candidate biomarkers that were tested showed capacity to distinguish clinical groups. However, seven candidate biomarkers did not show statistically significant differences between clinical groups in either the smaller ‘discovery’ cohort or the larger ‘validation’ cohort. Superficially, this ‘failure rate’ might cast doubt on the list of candidate biomarkers identified through 2D-DIGE. However, it is important to note that 2D-DIGE is sensitive to changes in concentrations of minor protein isoforms and post-translational modifications that may not significantly alter the global concentrations of a ‘parent’ protein, which would be measured by ELISA. Therefore, it is not surprising that some of the candidate biomarker ELISAs did not replicate the findings from 2D-DIGE. Transthyretin provides a prime example: all of the significant gel-features ascribed to transthyretin (gel features # 20, 52, 57, 58, 60, 77, 78, 79, 84, 87, 110, 115; ) showed unusual electrophoretic patterns and were dwarfed by the canonical transthyretin gel features that did not individually show statistical differences (). In fact, whereas most of the significant transthyretin 2D-DIGE gel features were decreased in AD, the global transthyretin levels measured by ELISA in the ‘discovery’ and ‘validation’ cohorts were actually mildly increased in groups with cognitive impairment (CDR>0) relative to those without (CDR 0) ( and ). To measure the sub-species of transthyretin that were identified by 2D-DIGE as decreasing in AD will require assays that specifically target relevant post-translational modifications and exclude other forms of transthyretin. Similarly, other 2D-DIGE biomarker candidates may also require specifically tailored assays for accurate, high-throughput measurement.
Nevertheless, four candidate biomarkers were successfully validated in both cohorts, and two others showed non-significant trends by ELISA in the larger ‘validation’ cohort (). This larger cohort represented three different cognitive stages: normalcy, very mild dementia, and mild dementia (CDR 0, CDR 0.5, CDR 1, respectively), and revealed different patterns of CSF biomarker levels, vis-a-vis
cognitive status. The CSF concentration of YKL-40, an astrocytic marker of plaque-associated neuroinflammation 
, is increased by the very earliest stage of clinical disease (CDR 0.5). Transthyretin 
and cystatin C 
, two proteins with neuroprotective qualities that are implicated in preventing amyloidogenesis of Aβ peptide, show a similar pattern. In contrast, the concentrations of NrCAM, a synaptic adhesion molecule 
, chromogranin A, a dense core synaptic vesicle protein 
, and carnosinase I, a neuronal dipeptidase responsible for degradation of the anti-oxidant and metal-chelating dipeptide carnosine 
do not decline until mild dementia ensues (CDR 1).
Like the current leading CSF biomarkers for AD (Aβ42, tau and p-tau181), all of these biomarker candidates show ranges with substantial overlap between clinically defined groups. This issue of overlapping values, common among candidate AD CSF biomarkers reported to date, suggests that any one biomarker will be insufficient to accurately identify early AD, and that an ensemble of complementary biomarkers will be required to provide adequate sensitivity and specificity. Therefore, to identify an optimal combination of these biomarkers that can distinguish the early clinical stages of AD from cognitive normalcy, we applied stepwise logistic regression analyses to the ELISA data from our ‘validation’ cohort (, and ). These analyses suggest that four candidate AD biomarkers (YKL-40, NrCAM, chromogranin A, carnosinase I) can improve the ability of tau to classify individuals into CDR 0, CDR 0.5 and CDR 1 groups with appreciable accuracy.
It may appear counter-intuitive that Aβ42 and p-tau181, which individually discriminate very mild AD and mild AD from cognitively normal groups quite well, were not incorporated into either ‘optimal’ biomarker panel by the stepwise logistic regression analyses. Likewise, NrCAM was included in the optimal CDR 0 vs CDR>0 biomarker panel (AUC 0.896) even though its mean levels did not independently show a statistical difference between CDR 0 and CDR>0 groups. In considering this outcome, it may be worth noting that if NrCAM, transthyretin, chromogranin and cystatin C are removed from consideration, the stepwise logistic regression model for the CDR 0 vs CDR>0 comparison yields an ‘optimal’ biomarker panel that includes only tau, Aβ42 and carnosinase I, with an AUC of 0.849 (not shown). In this restricted analysis, the paired contribution of Aβ42 and carnosinase I to tau is apparently greater than that of YKL-40. These analyses illustrate how ‘unpredictable’ and context-dependent optimal biomarker combinations can be, and suggest that biomarker complementarity may be more important to consider than each biomarker's independent performance, when choosing a biomarker panel. Of course, it will be necessary to replicate these findings in additional independent cohorts. It will also be essential to evaluate a greater number of candidate biomarkers in similar fashion, in order to construct a biomarker panel with even greater accuracy.
Another worthwhile feature to consider when evaluating and selecting CSF biomarkers is relative concentration in the blood (plasma, serum), because biomarker measurements in CSF can be artifactually influenced by subtle blood contamination at the time of lumbar puncture; from this perspective, ideal CSF biomarkers show CSF concentrations that are equal to or greater than those in blood. An additional reason to assess plasma/serum concentrations of candidate CSF biomarkers is to determine if venipuncture, which is more easily performed than lumbar puncture, might yield equivalent information. Among the six CSF biomarkers identified by stepwise logistic regression analysis in the current study, Aβ42 and tau 
, YKL-40 
, and chromogranin A 
show higher levels in CSF than in plasma; carnosinase I levels appear similar in CSF and serum 
; NrCAM levels appear higher in serum than in CSF, although the forms of NrCAM present in these fluids may differ 
. Concerning independent utility as biomarkers for AD, only plasma YKL-40 and serum NrCAM have shown promise 
, albeit inferior to that of CSF YKL-40 and NrCAM demonstrated here. Plasma tau concentrations in AD and controls are below the level of detection of the most commonly used tau assays, and plasma Aβ42 
and plasma chromogranin A (R.Perrin et al., unpublished data) concentrations show no significant differences among CDR groups. Serum carnosinase activity
likewise has not shown significant differences between AD and controls in one small study 
, though a difference between AD and mixed dementia (including vascular dementia) has been reported 
. To our knowledge, an evaluation of plasma or serum carnosinase I concentrations
in the context of AD has not yet been performed or reported. Further assessment of the potential of these and other proteins as candidate AD biomarkers in plasma or serum, complete with evaluation of their performance as ensembles, remains an important task for future studies. Currently, however, this panel of six biomarkers appears likely to show much greater promise in its application to CSF.
Indeed, by providing proof of concept, this study outlines a scheme to categorize the early stages of AD using CSF protein biomarkers that reflect established features of the pathophysiological evolution of the disease (). Building upon previous findings that low CSF Aβ42 can identify cognitively normal individuals with plaques (preclinical AD) 
, and that tau/Aβ42 and YKL-40/Aβ42 ratios can predict risk of developing cognitive impairment 
, this minimal panel of six CSF biomarkers (YKL-40, NrCAM, chromogranin A, carnosinase I, tau and Aβ42) begins to segregate individuals into six clinicopathological categories: normal cognition without amyloid plaques, normal cognition with amyloid plaques (preclinical AD), normal cognition at increased risk to develop dementia (converters), very mild dementia (CDR 0.5), very mild dementia at increased risk for progression, and mild dementia (CDR 1) ().
Hypothetical model defines early stages of AD by temporal pattern of CSF protein biomarker levels.
We acknowledge that this minimal panel of biomarkers currently has insufficient sensitivity and specificity for clinical application, particularly because it has not been fully evaluated for its ability to discriminate AD from non-AD causes of dementia (although Aβ42, p-tau181, tau, and specific fragments of chromogranin A and cystatin C have shown some ability to distinguish AD from frontotemporal lobar degeneration [FTLD]) 
. The incorporation of additional biomarkers that are likely to discriminate early AD from cognitive normalcy, such as those identified in the first phase of this study, or other biomarkers that have already shown promise for distinguishing AD from other leading causes of dementia (e.g. agouti related peptide, eotaxin-3, and hepatocyte growth factor 
, complement C3a des-arg and integral membrane protein 2B CT 
, for FTLDs; and alpha-synuclein 
, apoH and vitamin D binding protein 
for Lewy body disorders), would likely improve the panel's diagnostic utility. However, even in its current form, this initial panel might show value if applied in the context of clinical trial design, wherein simple enrichment of study populations for characteristics of interest would increase efficiency and power and reduce duration and cost. A biomarker panel like this one might also allow clinical trials to evaluate stage-specific responses to treatment, which may differ. Finally, because most of these biomarkers reflect underlying pathological changes in real time, it is appealing to speculate that these biomarkers may have additional utility for evaluating clinically imperceptible treatment responses (as in 
) and for monitoring neuropathological – rather than cognitive – decline.