1. Combination of blood-based biomarkers and neuropsychological assessment enables reliable classification of tested subjects by controls, mild cognitive impairment and Alzheimer's disease
Martin Kleinschmidt

2. Alzheimer’s Disease: The classical and the more detailed Amyloid hypothesis

3. Alzheimer’s Disease: Reducing plaques by lowering total Aβ
Reducing total Aβ
Approaches designed to reduce total Aβ
Intercalation: Scyllo-inositol (ELND005) failed to show efficacy, Ph2, Aug 2010
Passive Immunization: Bapinuzumab & Solanezumab reduce Aβ, but do not improve
cognition (18mts treatment 25% reduction)
Aducanumab showed promising results in Ph1b
- β-secretase inhibition: Specific β-secretase inhibitors block 15% Aβ deposition in tg2576, mts treatment, 25% in 7mts treatment, only minimal cognition improvement, no patient trial yet
- γ-secretase inhibition: Semagacestat worsened cognition in patients, studies terminated

4. Alzheimer’s Disease: Reducing aggregation by lowering pGlu-AβQC
pGlu-Aβ drives aggregation
pGlu-Aβ pathogenic hypothesis
Pathogenic species of AD may not be the monomeric Aβ40 or Aβ42
N-terminally truncated Aβ species develop EARLY in AD
N-terminally truncated Aβ species make up >20% of deposits
Most prevalent (>60%) N-terminally truncated Aβ are pyroglutamated Aβ (pGlu-Aβ)
pGlu-Aβ species seed to full-length Aβ, have fast aggregating kinetics, are long-living, accumulate aggressively in AD brain
pGlu-Aβ accumulation correlates well with AD progression and pathology
Early intervention is decisive for treatment success – Early diagnosis is required

5. Alzheimer’s Disease: Strategy for suitable biomarkers
MRI and PET
pros: established methods, state of the art
cons: resource intensive, not suitable for phase I, good in phase II / III
CSF biomarkers (e.g. Aβ42/Aβ40, Tau, P-Tau)
pros: reliable biomarker, state of the art, commercially available diagnostic tools
cons: invasive, most volunteers decline LP, not suitable for area-wide screening
Plasma (e.g. Aβ42/Aβ40, Tau, P-Tau)
pros: blood withdrawal is generally accepted, suitable for initial screening
cons: currently no reliable biomarker, 10-times lower Aβ conc. than in CSF,
but 100-times higher overall protein content
Development of plasma based biomarker assay systems

6. Aβ quantification: ELISA vs. extraction assay
Spiking of human plasma and ELISA buffer with 20 ng/ml Aβ(1-42)
Quantification of Aβ(1-42) in spiked plasma / ELISA buffer by specific ELISA
Recovery rate was analyzed in two independent experiments
spiked ELISA buffer
spiked plasma
Aβ peptides can be difficult to detect in conventional immunoassays
Observing no signal or too much signal – both can be caused by plasma proteins
Many diagnostic studies analyzing plasma Aβ have failed or gave inconsistent data
Conclusion: Quantitative extraction of Aβ peptides and removing of interfering
proteins should improve reliability of plasma Aβ determination

7. Separation of A peptides
Aβ quantification: ELISA vs. extraction assay
Plasma
separation
Capture Aβ
from plasma
Blood withdrawal
Separation of A peptides
Quantification of the
different Aβ species
Aβ(pE3-40/42)?
100 50
Specific pattern of Aβ peptides might
characterize stage of Alzheimer
Using pan-specific anti-Aβ antibody recovery (~13%) rate comparable with ELISA

8. Aβ quantification: ELISA vs. extraction assay
SPR interaction analysis – monovalent vs. bivalent binding of Aβ
Monovalent binding – KD= 7.43 nM
Bivalent binding – KD= nM
Elevation of binding strength due to avidity effect
Every anti-Aβ antibody is immobilized by many anti-mouse antibodies to the magnetic beads
pan: epitope aa 17-24; C40: epitope aa 35-40; C42: epitope aa 37-42
Recovery rate from spiked plasma: 13 %
Recovery rate from spiked plasma: 98 %

9. Aβ quantification: ELISA vs. extraction assay
Quantification of pE3-Aβ in plasma and CSF
pE3-Aβ is not quantifiable neither in human plasma nor in human CSF,
although these species are the most abundant in brain deposits

10. Healthy controls or possible MCI (MMSE ≥ 21)
Diagnostic study: Parameters
Design: Cross-sectional
Multicentric (three centers in Halle, one in Magdeburg, one in Kiel)
Blinded Biomarker analysis: experimenter does not know classification
age groups
survey
target
Healthy controls or possible MCI (MMSE ≥ 21)
18-30 yrs 22 20
40-65 yrs 25
66-85 yrs 78
AD (MMSE < 21) 18 40
total
143
140

11. Diagnostic study – Neuropsychology and Biomarker
neuropsychological analysis (independent variable)
learning & memory: CERAD (word lists), WMS-R (narration), ROCF (recall)
attention & executive func.: ROCF (copy), RWT (semantic/phonetic, alternating),
TMT (psycho-motor activity/cog. Flexibility)
cognitive decline: premorbid IQ (MWT-B, social formula), “non-hold” function
(HAWIE-R Similarity)
functioning: performance activities of daily living (FAST)
Analysis of major depression episode (HADS-D & BDI) – subject exclusion
Genetic risk factor – Apo E allele analysis
plasma and serum biomarker (dependent variables)
plasma Aβ(1-40)
plasma Aβ(1-42)
total hCCL2 / pE1-hCCL2 in serum
auto antibodies against Aβ(1/pE3-x): IgG2, IgG3, IgM, IgA, IgG, Ig’s
pro-inflammatory cytokines: IL-10, IL-1β, IFN-γ, IL-6 and TNF-α

12. Diagnostic study: Neuropsychological Tests - overview
multivariate classification scheme to create the independent variable
Kleinschmidt et al., 2015

13. Diagnostic study: Neuropsychological classification
sample size of further analyses N = 94
Controls (18-30 yrs)
Controls (40-65 yrs)
Controls (66-85 yrs)
naMCI
aMCI
MCI AD n 19 13 3 14 15
age (years)
24 (3.5)
54 (9.0)
74 (6.5) -
77 (5.7)
74 (5.7)
74 (4.8)
female
53%
39%
58%
43%
50%
47%
Kleinschmidt et al., 2015
excluded cases:
classification impossible n = 19
possible Major depression episode n = 14
exclusion by CRF or premorbid IQ < 85 n = 12
naMCI n = 3
blood samples missed n = 1

14. Diagnostic study: ApoE allele status
Kleinschmidt et al., 2015
ApoE allele frequencies is similar to distribution in German population
ApoE ε4 allele is the major genetic risk factor for Alzheimer’s disease
76% of AD cases are carrying the ApoE ε4 allele

15. Diagnostic study: Plasma Aβ level
Absolut level were normalized to an internal standard in every measurement
One-way ANOVA, contrasts: t-statistics (α=0.05)
Kleinschmidt et al., 2015
Aβ(1-42) is significant lower in MCI & AD compared to controls
Age-dependent increase of Aβ(1-40) (maybe prevent Aβ deposition)
Stepwise decrease of Aβ(1-42)/Aβ(1-40) ratio, similar to data of CSF analysis

16. Diagnostic study: Plasma Aβ level vs. cognitive test
Plot CERAD delayed recall (z-score) vs. Aβ(1-42) / Aβ(1-40) ratio
Pearson, r = 0.50, p<0.001
Kleinschmidt et al., 2015
Aβ(1-42)/Aβ(1-40) ratio of the entire cohort correlate with memory impairments in verbal delayed recall

17. Diagnostic study: Plasma Aβ level vs. Apo E
***
Kleinschmidt et al., 2015
One-way ANOVA, contrasts: t-statistics (α=0.05)
Reduced Aβ(1-42) is strongly associated with ApoE ε4, similar to CSF data
Aβ(1-42) is more closely linked to ApoE allele than to clinical status
Aβ(1-40) does not correlate with ApoE allele status

18. Diagnostic study: Inflammatory marker in serum
Kleinschmidt et al., 2015
No differences in biomarker levels between controls, aMCI, MCI and AD
Large inter-individual variations of CCL2 in controls, due to peripheral processes
Levels of IL-1β, IL-6, IFN-γ were mainly below LLOQ

19. Diagnostic study: Autoantibodies against pE3-Aβ
pE3-Aβ is not quantifiable in blood or CSF, but maybe leave an “imprint”
Quantification of different (sub)classes of anti-pE3-Aβ autoantibodies
Kleinschmidt et al., 2015
H test, contrasts: Mann-Whitney (α=0.05)
One-way ANOVA, contrasts: t-statistics (α=0.05)
Strong increase in oldest controls and decrease in AD exclusively of IgG2
Stepwise decrease of IgG2 / total IG ratio (comparable to plasma Aβ)
Autoantibodies against Aβ(1-x) do not differ (data not shown)

20. Diagnostic study: Autoantibodies vs. plasma Aβ and ApoE
Plot Aβ(1-42) / Aβ(1-40) ratio vs. anti-pE3-Aβ IgG2 / total Ig ratio
Kleinschmidt et al., 2015
Spearman rank corr. r=-0.33, p<0.001
One-way ANOVA, p=0.529
anti-pE3-Aβ IgG2 / total IG ratio correlate inversely with plasma Aβ
Autoantibodies do not correlate with Apo E allele status
Indicating an independent influence of autoantibodies and ApoE isoforms on cognitive performance, most likely by modifying Aβ metabolism

21. Diagnostic study: Multivariate visualization of data
Biomarker readouts were standardized to z-scores
Spider plots contain group medians of seven different dimensions
Total sum of all seven profile measures (area score) was calculated
Stepwise decrease of area score
In 93% aMCI, 100% MCI and 100% AD
area score is below 1172
At a glance evaluating existence and
progression of Alzheimer’s disease
Kleinschmidt et al., 2015

22. Acknowledgements Prof. Hans-Ulrich Demuth Kathrin Gnoth Nadine Taudte
Nadine Jänckel
Mercedes Scharfe
Claudia Göttlich
Anja Weber
Prof. Bernd Leplow
Robby Schönfeld
Medical facilities in Halle:
Gallmed GmbH (Monitoring)
Clinics for geriatrics of Diakonie
Neurology Eger & Jakob
Daniel Bittner
Prof. Josef Aldenhoff