Cerebrospinal Fluid Aβ43 Is Reduced in Early-Onset Compared to Late-Onset Alzheimer’s Disease, But Has Similar Diagnostic Accuracy to Aβ42

Introduction

Alzheimer’s disease (AD) is often separated according to age, whereby onset prior to age 65 years is considered to be early-onset AD, while onset from an age of 65 years (which is much more common) is termed late-onset AD. Although the pathological burden of amyloid plaques and neurofibrillary tangles has been shown to be greater in early-onset AD than in patients with late-onset AD (Ho et al., 2002; Marshall et al., 2007), imaging studies have indicated the global burden to be similar between the two groups (Rabinovici et al., 2010), though sometimes with variation in regional anatomical distribution of amyloid (Ossenkoppele et al., 2012; Cho et al., 2013). Such putative differences in the distribution of amyloid pathology have not been found to alter levels of the core cerebrospinal fluid (CSF) biomarkers for AD; amyloid beta 1–42 (Aβ42), total tau (t-tau) and phosphorylated tau (p-tau) protein, in early- compared to late-onset AD (Bouwman et al., 2009; Chiaravalloti et al., 2016). Additionally, several studies have shown no correlation between the core biomarkers and age in AD patients (Bouwman et al., 2009; Mattsson et al., 2009; Popp et al., 2010).

However, healthy individuals display increased AD pathology with increasing age (Savva et al., 2009). Older control individuals have been found to have decreased CSF Aβ42 levels compared to younger controls (Bouwman et al., 2009), and the level was negatively correlated with age (Popp et al., 2010). Conversely, CSF t-tau and p-tau correlate positively with age in healthy elderly individuals (Blomberg et al., 2001; Glodzik-Sobanska et al., 2009; Jaworski et al., 2009; Alcolea et al., 2015).

Amyloid beta 1–43 (Aβ43), compared to Aβ42, has an additional threonine at the C-terminal through an alternative γ-secretase cleavage of amyloid precursor protein (APP), and is generally considered likely to be even more aggregation-prone than Aβ42 (Jarrett et al., 1993; Saito et al., 2011; Conicella and Fawzi, 2014), though recent kinetic experiments in vitro dispute this (Chemuru et al., 2016). Aβ43 has been hypothesized to play a role in AD pathogenesis despite its low concentration in human brain tissue, and found to be frequent in both neuritic and diffuse extracellular plaques in both familial and sporadic AD (Welander et al., 2009; Keller et al., 2010; Sandebring et al., 2013). In an APP-expressing transgenic mouse model, Aβ43 has been found to be the first amyloid peptide to deposit in brain (Zou et al., 2013), perhaps seeding subsequent Aβ42 deposition (Conicella and Fawzi, 2014). Moreover, knock-in mice bearing the pathogenic presenilin-1 R278I mutation demonstrated overproduction of Aβ43, impaired short-term memory and acceleration of amyloid-β pathology (Saito et al., 2011). Aβ43 correlates positively with age in patients with AD (Bruggink et al., 2013), and correlates closely with CSF Aβ42 both in patients and control individuals (Bruggink et al., 2013; Lauridsen et al., 2016). As far as we know, no study as yet has compared CSF levels of Aβ43 in early-onset AD with late-onset AD.

We have therefore investigated Aβ43 and Aβ42 in CSF from well-characterized cohorts of patients with early- and late-onset AD. The cut-off between these subtypes of AD has been accepted as 65 years of age, but in the present study we excluded patients with age at onset in the 5-year period 63–67 years to highlight potential age differences. Thus only patients with early-onset AD ≤ 62 years of age, or patients with late-onset AD who were aged ≥68 years were included.

In addition to the Aβ species in CSF from these two subgroups of patients with AD and corresponding control groups, levels of t-tau and p-tau were also determined. For further comparison, levels of several other analytes that have been investigated with respect to AD, and to age, were also assessed. These included two other cytoskeletal intermediate filaments; neurofilament light (NF-L) (Petzold et al., 2007; Vagberg et al., 2015; Olsson et al., 2016) and glial fibrillary acidic protein (GFAP) (Vagberg et al., 2015; Wennstrom et al., 2015), found, respectively, in neurons and glia, YKL-40 (also known as chitinase 3-like protein 1), a protease secreted mainly by astrocytes and considered a marker for gliosis and neuroinflammation (Craig-Schapiro et al., 2010), and progranulin, a growth factor believed to have anti-inflammatory and neuroprotective abilities (Jing et al., 2016).

Materials and Methods

Subjects

Study patients were ethnic Norwegians referred to the Department of Neurology, St. Olav’s Hospital (Trondheim University Hospital) by general practitioners, and diagnosed by a neurologist. Some patients were initially diagnosed with amnestic mild cognitive impairment (aMCI, n = 14) according to the International Working Group on Mild Cognitive Impairment criteria (Winblad et al., 2004), but all later developed AD within the next 2 years. Patients with AD were diagnosed according to the NINCDS-ADRDA criteria (McKhann et al., 1984), final total n = 91, whereof 66 were aged ≤62 years at onset (early-onset AD) and 25 were aged ≥68 years at onset of symptoms (late-onset AD).

As controls, CSF samples were obtained either from non-demented elderly volunteers (n = 35) recruited from societies for retired people or caregivers not genetically related to the patient, or from samples stored in the Neurological Research Biobank at the hospital (n = 45). These latter individuals had been referred to the clinic for suspected neurological conditions, but none was subsequently found. Of the total 80 control individuals, 41 were aged ≤62 years, and 39 were aged ≥68 years. For the control groups, CSF cell count, glucose and protein were within standard physiological limits.

The neurological examination performed on most study participants included the Mini Mental State Examination (MMSE) (Folstein et al., 1975). MMSE was performed on all patients, but for many control individuals there had been no reason to carry out an MMSE during the clinical work-up, and where MMSE was available, the minimum score was 28. For the same reason, APOE genotype was not available for most younger controls. The demographic data are shown in Table 1.

TABLE 1

TABLE 1. Demographic and CSF biochemical data.

Sampling of CSF

Cerebrospinal fluid was collected with patients lying on their side, and lumbar puncture carried out at the level L4/L5 or L5/S1. The first 2.5 mL CSF was used for routine clinical investigation. Aliquots of CSF were collected directly into polypropylene cryovials (Corning) immersed in ice-water. No samples used in this study were contaminated by blood, and so were not centrifuged. All samples were frozen within 30 min of lumbar puncture and stored at -80°C until analysis. Ten samples were thawed and then frozen again before core biomarkers were analyzed. One freeze-thaw cycle has previously been shown to not significantly affect core biomarker results (Le Bastard et al., 2015).

ELISA Assays

Cerebrospinal fluid samples were analyzed using ELISA monoplex kits according to the manufacturers’ instructions [Aβ43 (IBL), Aβ42 (Innogenetics), t-tau (Innogenetics), p-tau (Innogenetics), NF-L (UmanDiagnostics), YKL-40 (Bio-Techne, CSF diluted 1:400), GFAP (BioVendor) and progranulin (Adipogen Life Sciences, CSF diluted 1:15)]. Samples were thawed in ice-water prior to analysis, and all samples were analyzed in duplicate. Cross-reactivity for Aβ42 in the Aβ43 ELISA was given as <1%. Although this would contribute slightly to measurements for Aβ43, it would be a constant for both control and patient groups. Aβ43 was reported to have 50× less affinity than Aβ42 for the antibodies in the Aβ42 kit.

Statistical Analysis

Statistical analyses were carried out using SPSS version 24 (IBM) and Stata version 13.1. Due to multiple testing, p-values < 0.01 were considered statistically significant. Distribution of gender between groups and the distribution of the APOE 𝜀4 allele between groups were assessed with Pearson’s χ² (chi-square) test. Differences in age at inclusion between patients with early- or late-onset of AD and respective control groups, as well as for MMSE scores and duration of disease, were assessed with the independent samples Mann–Whitney U-test for pairwise comparisons of groups. CSF analyte levels were log-transformed to approximate a normal distribution. Analyte levels were compared for younger and older participants within control and AD patient groups using t-tests for independent samples. However, when comparing analyte levels between controls and AD patients it was necessary to adjust for age because patients were significantly older than controls in both age groups. Analyte levels for the group of younger controls were therefore compared with those of early-onset AD patients, and older controls with those of late-onset AD patients using linear regression and adjusting for age at inclusion. Correlations between analytes, or between analytes and age at inclusion, were calculated with Pearson’s r. Associations are only tentative as both type 1 and type 2 errors can occur even employing a significance level of p < 0.01 as in the present study. Patterns as a whole have been considered more informative than individual correlations. To investigate potential differences in diagnostic accuracy, receiver operating characteristic (ROC) curves were made for Aβ43 and Aβ42, and the area under each ROC curve (AUC) was calculated. Youden’s index was found to determine where the sum of sensitivity and specificity was maximized. AUC was compared between Aβ43 and Aβ42 for controls and AD patients in corresponding groups [DeLong method (DeLong et al., 1988)]. Levels of analyte ratios were compared between groups, but overall did not separate groups more clearly than single analytes, and are therefore not considered further. Ratio data are given in Supplementary Table S1.

Ethics Statement

The study was conducted according to the Helsinki Declaration. Written, informed consent was obtained from all patients or suitable proxies, and from all control individuals. The Neurological Research Biobank has been licensed by the Norwegian Directorate for Health Affairs, and the research was approved by the Regional Committee for Medical Research Ethics (approval 2010/226 REK Midt, 2013/467 REK Midt, 2013/150 REK Sør-Øst).

Results

When comparing participant groups, there were no significant differences in the distribution of gender. The median age at inclusion in both younger and older control groups was significantly lower than for corresponding patient age groups. There was no significant difference in the duration of disease between the two patient groups. No significant differences were found in MMSE scores between individuals in the respective control or patient groups. Both patient groups had significantly lower median MMSE scores than their respective control group. There was increased frequency of the APOE 𝜀4 allele in combined patient compared to combined control groups (p = 0.001) (Table 1).

Cerebrospinal fluid levels of the various analytes are shown in Table 1, and scatter plots for amyloid peptides are shown in Figure 1 and in Supplementary Figures S1A–F for the other analytes. Additionally, correlations between CSF levels of Aβ peptides and other analytes, and between Aβ peptide levels and age were calculated. CSF Aβ43 was significantly decreased in patients with early-onset AD compared to late-onset AD, but no significant difference was found between the two patient groups for Aβ42. There were highly significant reductions in the levels of both Aβ43 and Aβ42 in CSF of patients with AD compared to controls. No significant differences in levels of Aβ43 or Aβ42 were found between the two control groups. Both CSF Aβ43 and Aβ42 were excellent at separating corresponding controls from patients in the AD groups, with AUCs of 0.93 or better and no significant difference in AUCs between Aβ43 and Aβ42 (Table 2). Aβ43 and Aβ42 correlated significantly with each other in all four participant groups (r = 0.58–0.85, p ≤ 0.006).

FIGURE 1

FIGURE 1. (A,B) Amyloid levels in cerebrospinal fluid. Scatter plots for all four participant groups with median lines added for each group. Values for the mean ± 1 SD are given in Table 1. Statistical analysis was performed with pairwise group comparisons of log-transformed analyte levels between controls and AD patients aged ≤62 years (age-adjusted), and between controls and AD patients aged ≥68 years (age-adjusted), as well as between younger and older groups of controls, and younger and older groups of patients with AD. (A) Aβ43, (B) Aβ42. ^∗∗Significantly different at the p < 0.001 level. AD, Alzheimer’s disease; Aβ, amyloid beta; EOAD, early-onset AD; LOAD, late-onset AD.

TABLE 2

TABLE 2. Diagnostic accuracy of β-amyloids for the separation of controls and patients.

A significant positive association between Aβ42 and age at inclusion was found in younger controls (r = 0.55, p = 0.001) and in early-onset AD (r = 0.38, p = 0.002). For older controls a trend was found for a negative correlation (r = -0.42, p = 0.012), but this was lost in late-onset AD. For Aβ43, a positive correlation with age at inclusion was found in the early-onset AD group (r = 0.43, p = 0.002), but the association was not significant in the other three participant groups.

Results for t-tau and p-tau were similar in nature, and both correlated with each other in all groups (r = 0.76–0.93, all p < 0.001). Their levels were significantly increased in patients compared to the corresponding control group, but there was no difference between patients with early- or late-onset AD. However, the older control group had significantly higher levels of the tau species compared to younger controls (Table 1). Associations between tau proteins and Aβ peptides were found only in younger controls (r = 0.43, p = 0.016 to r = 0.52, p = 0.003), not older controls or either AD group.

YKL-40 was not significantly increased in patients compared to the respective control group. However, a significant increase was found between early- and late-onset AD, as well as between younger and older controls. There was a pattern for a relationship between the Aβ peptides and YKL-40 in younger controls and early-onset AD, but correlation coefficients were low (all r = 0.33–0.44, p < 0.05 except for Aβ43 and YKL-40 in early-onset AD, p = 0.003).

A highly significant increase in the level of NF-L was found in early-onset AD compared to younger controls, but this difference was lost between late-onset AD and older controls. There was no difference between the two groups of patients, but older controls had significantly higher levels of NF-L compared to younger controls. Levels of GFAP in patients with early-onset AD were significantly higher than in younger controls. Older controls also had significantly increased levels compared to the younger controls, but no significant differences between the patient groups were found. No significant group differences in progranulin levels were found in this material.

Discussion

The most interesting result in this study is that the reduction in CSF levels of Aβ43 was more marked in early-onset compared to late-onset AD, and therefore seems to be age-related. This difference was not found for Aβ42. As expected, there was a clear and highly significant reduction in the concentration of both Aβ43 and Aβ42 in the CSF of the patient groups compared to corresponding controls. However, the data do not suggest that Aβ43 has better diagnostic accuracy for AD than Aβ42.

The increased deposition of parenchymal Aβ species in the AD brain has been suggested as the reason for the reduced amounts of Aβ peptides measured in CSF (usually Aβ42), based on the idea that less may be available for passage over the brain-CSF barrier (Fagan et al., 2006). Recent results from imaging studies showed that although CSF Aβ43 is strongly associated with cerebral amyloid deposits, even at early stages of clinical cognitive impairment (subjective cognitive decline and MCI), there were no relative differences in deposition between Aβ42 and Aβ43. Aβ43 therefore provided no diagnostic improvement over the established marker Aβ42 (Almdahl et al., 2017). Also in the present study comparing early- and late-onset AD versus the corresponding control group, no improvement to diagnostic accuracy was found for Aβ43 compared to Aβ42.

It is not immediately obvious why Aβ43 would be reduced more in early-onset than late-onset AD, other than that there is an age difference between the patient groups. However, two studies comparing amyloid imaging in early- and late-onset AD report regional (though not identical) differences in fibrillar amyloid deposition (Ossenkoppele et al., 2012; Cho et al., 2013). It is therefore possible there are age-related differences in the topographical deposition of Aβ peptides, but whether this would produce differences in CSF concentrations of the peptides in early-onset compared to late-onset AD remains unclear. We did not find a similar reduction for CSF Aβ42 in early-onset AD, and this result is very similar to previously published data (Gronning et al., 2012).

In healthy individuals, several studies have found little or no correlation between age and Aβ42 levels in CSF (Hansson et al., 2006; Bouwman et al., 2009; Mattsson et al., 2009; Popp et al., 2010). Similarly in patients with AD, a number of articles report no correlation between age and Aβ42 levels (Bouwman et al., 2009; Mattsson et al., 2009; Popp et al., 2010). Our data were similar in this respect. Even though significant correlations between age and Aβ42 in controls and patients were found, none were strong, and there was no pattern. The significance of weak correlations is dependent on the number of samples included and the significance level applied. Given that even strong correlations do not guarantee biological relevance, it can be questioned whether these fairly weak correlations are sufficiently reliable to warrant speculation of underlying physiological changes.

Generally speaking, our results for the core biomarkers in controls and in AD, as well as the association with age, agree broadly with previous studies (Blomberg et al., 2001; Bouwman et al., 2009; Glodzik-Sobanska et al., 2009; Popp et al., 2010; Alcolea et al., 2015; Chiaravalloti et al., 2016; Olsson et al., 2016). Age is also important for other substances analyzed in the present study. In recent years several reports have shown that YKL-40 increases throughout middle-age in cognitively healthy individuals, suggesting that a certain level of neuroinflammation is physiological in normal aging (Alcolea et al., 2015; Sutphen et al., 2015), as well as being an aspect of AD (Wennstrom et al., 2015). The present study agrees with the finding of increased YKL-40 levels with increased age. There was a pattern of positive correlations between Aβ species and YKL-40 in the younger groups. Most of the correlations were rather weak so it is uncertain whether this represents a physiological relationship, but tentatively agrees with previous data indicating that markers of inflammation, including YKL-40, and Aβ42 in normal aging and the early AD pathological process, are related (Alcolea et al., 2015). NF-L is well known to be increased in the CSF of patients with AD (Petzold et al., 2007; Olsson et al., 2016), and the present results are in accordance with this, but only in connection with early-onset AD. No significant difference was found for CSF NF-L between late-onset AD and older controls, which probably reflects the increase of CSF NF-L in normal aging (Vagberg et al., 2015). Similarly, we found an increase in CSF GFAP in patients with early-onset AD compared to younger controls, but again perhaps due to the increase in CSF levels of GFAP with age (Vagberg et al., 2015), this difference was lost between late-onset AD and older controls. The results for the older groups agree with one study (Wennstrom et al., 2015), but not with another study that found increased GFAP levels in AD patients compared to controls (Jesse et al., 2009). No changes in the concentration of progranulin in CSF from patients with AD were found compared to controls, as previously demonstrated (Morenas-Rodriguez et al., 2016).

Taken together, few differences were detected between early- and late-onset AD when analyzing CSF for potential markers of disease, even though the two groups had been clearly defined with respect to a difference in age. When comparing patients and controls, more differences were associated with early-onset rather than late-onset AD, perhaps because both patients and controls tend to suffer more comorbidities with increasing age which can cloud differences between patients with AD and controls. The main strength of the present study was to employ clinically well-defined patient and control cohorts that were large enough to distinguish differences and similarities in Aβ43 and Aβ42. In light of an earlier report (Lauridsen et al., 2016), future studies should probably concentrate on examining CSF Aβ43 and Aβ42 in relation to early stages of the AD process, including amnestic MCI, subjective cognitive decline, and cognitively intact individuals who have a pathological pattern of core biomarkers in CSF, or increased amyloid deposition in brain.

Author Contributions

CL planned and performed the laboratory work together with IM, PP, and GBe. SS was responsible for clinical aspects together with GG and GBr. CL analyzed the data and ØS advised on statistical analysis. LW was responsible for study design and data collation together with CL and SS. LW, SS and GBr supervised the project. CL and LW wrote the manuscript. All authors contributed to critical revision and finalization of the manuscript.

Funding

CL holds a Ph.D. scholarship from the Dementia Disease Initiation (DDI) Consortium, through the Research Council of Norway (NASATS-NevroNor grant 217780/H10).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgment

The authors are sincerely grateful to all study participants.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/article/10.3389/fnagi.2017.00210/full#supplementary-material

References

Alcolea, D., Martinez-Lage, P., Sanchez-Juan, P., Olazaran, J., Antunez, C., Izagirre, A., et al. (2015). Amyloid precursor protein metabolism and inflammation markers in preclinical Alzheimer disease. Neurology 85, 626–633. doi: 10.1212/wnl.0000000000001859

PubMed Abstract | CrossRef Full Text | Google Scholar

Almdahl, I. S., Lauridsen, C., Selnes, P., Kalheim, L. F., Coello, C., Gajdzik, B., et al. (2017). Cerebrospinal fluid levels of amyloid beta 1-43 mirror 1-42 in relation to imaging biomarkers of Alzheimer’s disease. Front. Aging Neurosci. 9:9. doi: 10.3389/fnagi.2017.00009

CrossRef Full Text | Google Scholar

Blomberg, M., Jensen, M., Basun, H., Lannfelt, L., and Wahlund, L. O. (2001). Cerebrospinal fluid tau levels increase with age in healthy individuals. Dement. Geriatr. Cogn. Disord. 12, 127–132. doi: 10.1159/000051246

CrossRef Full Text | Google Scholar

Bouwman, F. H., Schoonenboom, N. S., Verwey, N. A., van Elk, E. J., Kok, A., Blankenstein, M. A., et al. (2009). CSF biomarker levels in early and late onset Alzheimer’s disease. Neurobiol. Aging 30, 1895–1901. doi: 10.1016/j.neurobiolaging.2008.02.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Bruggink, K. A., Kuiperij, H. B., Claassen, J. A., and Verbeek, M. M. (2013). The diagnostic value of CSF amyloid-beta(43) in differentiation of dementia syndromes. Curr. Alzheimer Res. 10, 1034–1040. doi: 10.2174/15672050113106660168

PubMed Abstract | CrossRef Full Text | Google Scholar

Chemuru, S., Kodali, R., and Wetzel, R. (2016). C-terminal threonine reduces Abeta₄₃ amyloidogenicity compared with Abeta₄₂. J. Mol. Biol. 428(2 Pt A), 274–291. doi: 10.1016/j.jmb.2015.06.008

PubMed Abstract | CrossRef Full Text

Chiaravalloti, A., Koch, G., Toniolo, S., Belli, L., Lorenzo, F. D., Gaudenzi, S., et al. (2016). Comparison between early-onset and late-onset Alzheimer’s disease patients with amnestic presentation: CSF and (18)F-FDG PET study. Dement. Geriatr. Cogn. Dis. Extra 6, 108–119. doi: 10.1159/000441776

PubMed Abstract | CrossRef Full Text | Google Scholar

Cho, H., Seo, S. W., Kim, J. H., Suh, M. K., Lee, J. H., Choe, Y. S., et al. (2013). Amyloid deposition in early onset versus late onset Alzheimer’s disease. J. Alzheimers Dis. 35, 813–821. doi: 10.3233/jad-121927

PubMed Abstract | CrossRef Full Text | Google Scholar

Conicella, A. E., and Fawzi, N. L. (2014). The C-terminal threonine of Abeta43 nucleates toxic aggregation via structural and dynamical changes in monomers and protofibrils. Biochemistry 53, 3095–3105. doi: 10.1021/bi500131a

PubMed Abstract | CrossRef Full Text | Google Scholar

Craig-Schapiro, R., Perrin, R. J., Roe, C. M., Xiong, C., Carter, D., Cairns, N. J., et al. (2010). YKL-40: a novel prognostic fluid biomarker for preclinical Alzheimer’s disease. Biol. Psychiatry 68, 903–912. doi: 10.1016/j.biopsych.2010.08.025

PubMed Abstract | CrossRef Full Text | Google Scholar

DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837–845. doi: 10.2307/2531595

CrossRef Full Text | Google Scholar

Fagan, A. M., Mintun, M. A., Mach, R. H., Lee, S. Y., Dence, C. S., Shah, A. R., et al. (2006). Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans. Ann. Neurol. 59, 512–519. doi: 10.1002/ana.20730

PubMed Abstract | CrossRef Full Text | Google Scholar

Folstein, M. F., Folstein, S. E., and McHugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–198. doi: 10.1016/0022-3956(75)90026-6

CrossRef Full Text | Google Scholar

Glodzik-Sobanska, L., Pirraglia, E., Brys, M., de Santi, S., Mosconi, L., Rich, K. E., et al. (2009). The effects of normal aging and ApoE genotype on the levels of CSF biomarkers for Alzheimer’s disease. Neurobiol. Aging 30, 672–681. doi: 10.1016/j.neurobiolaging.2007.08.019

PubMed Abstract | CrossRef Full Text | Google Scholar

Gronning, H., Rahmani, A., Gyllenborg, J., Dessau, R. B., and Hogh, P. (2012). Does Alzheimer’s disease with early onset progress faster than with late onset? A case-control study of clinical progression and cerebrospinal fluid biomarkers. Dement. Geriatr. Cogn. Disord. 33, 111–117. doi: 10.1159/000337386

PubMed Abstract | CrossRef Full Text | Google Scholar

Hansson, O., Zetterberg, H., Buchhave, P., Londos, E., Blennow, K., and Minthon, L. (2006). Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol. 5, 228–234. doi: 10.1016/s1474-4422(06)70355-6

CrossRef Full Text | Google Scholar

Ho, G. J., Hansen, L. A., Alford, M. F., Foster, K., Salmon, D. P., Galasko, D., et al. (2002). Age at onset is associated with disease severity in Lewy body variant and Alzheimer’s disease. Neuroreport 13, 1825–1828. doi: 10.1097/00001756-200210070-00028

CrossRef Full Text | Google Scholar

Jarrett, J. T., Berger, E. P., and Lansbury, P. T. Jr. (1993). The carboxy terminus of the beta amyloid protein is critical for the seeding of amyloid formation: implications for the pathogenesis of Alzheimer’s disease. Biochemistry 32, 4693–4697. doi: 10.1021/bi00069a001

PubMed Abstract | CrossRef Full Text | Google Scholar

Jaworski, J., Psujek, M., and Bartosik-Psujek, H. (2009). Total-tau and phospho-tau(181Thr) in cerebrospinal fluid of neurologically intact population increase with age. Folia Biol. 55, 126–131.

PubMed Abstract | Google Scholar

Jesse, S., Steinacker, P., Cepek, L., von Arnim, C. A., Tumani, H., Lehnert, S., et al. (2009). Glial fibrillary acidic protein and protein S-100B: different concentration pattern of glial proteins in cerebrospinal fluid of patients with Alzheimer’s disease and Creutzfeldt-Jakob disease. J. Alzheimers Dis. 17, 541–551. doi: 10.3233/jad-2009-1075

PubMed Abstract | CrossRef Full Text | Google Scholar

Jing, H., Tan, M. S., Yu, J. T., and Tan, L. (2016). The role of PGRN in Alzheimer’s disease. Mol. Neurobiol. 53, 4189–4196. doi: 10.1007/s12035-015-9358-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Keller, L., Welander, H., Chiang, H. H., Tjernberg, L. O., Nennesmo, I., Wallin, A. K., et al. (2010). The PSEN1 I143T mutation in a Swedish family with Alzheimer’s disease: clinical report and quantification of Abeta in different brain regions. Eur. J. Hum. Genet. 18, 1202–1208. doi: 10.1038/ejhg.2010.107

PubMed Abstract | CrossRef Full Text | Google Scholar

Lauridsen, C., Sando, S. B., Shabnam, A., Moller, I., Berge, G., Grontvedt, G. R., et al. (2016). Cerebrospinal fluid levels of amyloid beta 1-43 in patients with amnestic mild cognitive impairment or early Alzheimer’s disease: a 2-year follow-up study. Front. Aging Neurosci. 8:30. doi: 10.3389/fnagi.2016.00030

CrossRef Full Text | Google Scholar

Le Bastard, N., De Deyn, P. P., and Engelborghs, S. (2015). Importance and impact of preanalytical variables on Alzheimer disease biomarker concentrations in cerebrospinal fluid. Clin. Chem. 61, 734–743. doi: 10.1373/clinchem.2014.236679

PubMed Abstract | CrossRef Full Text | Google Scholar

Marshall, G. A., Fairbanks, L. A., Tekin, S., Vinters, H. V., and Cummings, J. L. (2007). Early-onset Alzheimer’s disease is associated with greater pathologic burden. J. Geriatr. Psychiatry Neurol. 20, 29–33. doi: 10.1177/0891988706297086

PubMed Abstract | CrossRef Full Text | Google Scholar

Mattsson, N., Zetterberg, H., Hansson, O., Andreasen, N., Parnetti, L., Jonsson, M., et al. (2009). CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302, 385–393. doi: 10.1001/jama.2009.1064

PubMed Abstract | CrossRef Full Text | Google Scholar

McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., and Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34, 939–944. doi: 10.1212/WNL.34.7.939

CrossRef Full Text | Google Scholar

Morenas-Rodriguez, E., Cervera-Carles, L., Vilaplana, E., Alcolea, D., Carmona-Iragui, M., Dols-Icardo, O., et al. (2016). Progranulin protein levels in cerebrospinal fluid in primary neurodegenerative dementias. J. Alzheimers Dis. 50, 539–546. doi: 10.3233/jad-150746

PubMed Abstract | CrossRef Full Text | Google Scholar

Olsson, B., Lautner, R., Andreasson, U., Ohrfelt, A., Portelius, E., Bjerke, M., et al. (2016). CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis. Lancet Neurol. 15, 673–684. doi: 10.1016/s1474-4422(16)00070-3

CrossRef Full Text | Google Scholar

Ossenkoppele, R., Zwan, M. D., Tolboom, N., van Assema, D. M., Adriaanse, S. F., Kloet, R. W., et al. (2012). Amyloid burden and metabolic function in early-onset Alzheimer’s disease: parietal lobe involvement. Brain 135(Pt 7), 2115–2125. doi: 10.1093/brain/aws113

PubMed Abstract | CrossRef Full Text | Google Scholar

Petzold, A., Keir, G., Warren, J., Fox, N., and Rossor, M. N. (2007). A systematic review and meta-analysis of CSF neurofilament protein levels as biomarkers in dementia. Neurodegener. Dis. 4, 185–194. doi: 10.1159/000101843

PubMed Abstract | CrossRef Full Text | Google Scholar

Popp, J., Lewczuk, P., Frommann, I., Kolsch, H., Kornhuber, J., Maier, W., et al. (2010). Cerebrospinal fluid markers for Alzheimer’s disease over the lifespan: effects of age and the APOEepsilon4 genotype. J. Alzheimers Dis. 22, 459–468. doi: 10.3233/jad-2010-100561

PubMed Abstract | CrossRef Full Text | Google Scholar

Rabinovici, G. D., Furst, A. J., Alkalay, A., Racine, C. A., O’Neil, J. P., Janabi, M., et al. (2010). Increased metabolic vulnerability in early-onset Alzheimer’s disease is not related to amyloid burden. Brain 133(Pt 2), 512–528. doi: 10.1093/brain/awp326

PubMed Abstract | CrossRef Full Text | Google Scholar

Saito, T., Suemoto, T., Brouwers, N., Sleegers, K., Funamoto, S., Mihira, N., et al. (2011). Potent amyloidogenicity and pathogenicity of Abeta43. Nat. Neurosci. 14, 1023–1032. doi: 10.1038/nn.2858

PubMed Abstract | CrossRef Full Text | Google Scholar

Sandebring, A., Welander, H., Winblad, B., Graff, C., and Tjernberg, L. O. (2013). The pathogenic abeta43 is enriched in familial and sporadic Alzheimer disease. PLoS ONE 8:e55847. doi: 10.1371/journal.pone.0055847

PubMed Abstract | CrossRef Full Text | Google Scholar

Savva, G. M., Wharton, S. B., Ince, P. G., Forster, G., Matthews, F. E., and Brayne, C. (2009). Age, neuropathology, and dementia. N. Engl. J. Med. 360, 2302–2309. doi: 10.1056/NEJMoa0806142

PubMed Abstract | CrossRef Full Text | Google Scholar

Sutphen, C. L., Jasielec, M. S., Shah, A. R., Macy, E. M., Xiong, C., Vlassenko, A. G., et al. (2015). Longitudinal cerebrospinal fluid biomarker changes in preclinical Alzheimer disease during middle age. JAMA Neurol. 72, 1029–1042. doi: 10.1001/jamaneurol.2015.1285

PubMed Abstract | CrossRef Full Text | Google Scholar

Vagberg, M., Norgren, N., Dring, A., Lindqvist, T., Birgander, R., Zetterberg, H., et al. (2015). Levels and age dependency of neurofilament light and glial fibrillary acidic protein in healthy individuals and their relation to the brain parenchymal fraction. PLoS ONE 10:e0135886. doi: 10.1371/journal.pone.0135886

PubMed Abstract | CrossRef Full Text | Google Scholar

Welander, H., Franberg, J., Graff, C., Sundstrom, E., Winblad, B., and Tjernberg, L. O. (2009). Abeta43 is more frequent than Abeta40 in amyloid plaque cores from Alzheimer disease brains. J. Neurochem. 110, 697–706. doi: 10.1111/j.1471-4159.2009.06170.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Wennstrom, M., Surova, Y., Hall, S., Nilsson, C., Minthon, L., Hansson, O., et al. (2015). The inflammatory marker YKL-40 is elevated in cerebrospinal fluid from patients with Alzheimer’s but not Parkinson’s disease or dementia with Lewy bodies. PLoS ONE 10:e0135458. doi: 10.1371/journal.pone.0135458

PubMed Abstract | CrossRef Full Text | Google Scholar

Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L. O., et al. (2004). Mild cognitive impairment–beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J. Intern. Med. 256, 240–246. doi: 10.1111/j.1365-2796.2004.01380.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Zou, K., Liu, J., Watanabe, A., Hiraga, S., Liu, S., Tanabe, C., et al. (2013). Abeta43 is the earliest-depositing Abeta species in APP transgenic mouse brain and is converted to Abeta41 by two active domains of ACE. Am. J. Pathol. 182, 2322–2331. doi: 10.1016/j.ajpath.2013.01.053

PubMed Abstract | CrossRef Full Text | Google Scholar