Esoteric, I do not expect any extension study results within 4wks, but they perhaps will tell when these are to be published. There could be however some more info about the "placebo" problem because they have had almost 12 months to find out why there was this unexpected problem. Genetics, life style, nutrition, medication were not controlled and could not be controlled in such a small population but as a pilot study they could now know some possible causes. There will be more data after they have the extension study results but it is important to make the right questions to those who will analyse the extension study results and this is why they should have by now some potential answers.
But info about " all programs" will tell us a lot at least a lot more we know now. PBT 1033 is something very new and i think that will be included into "all programs".
"loooobs" informed on "hotcopper" board that:
Emailed the investor relations person about an update and got this reply...
"Thanks for your enquiry, we’re hoping to be in a position to provide an update on all programs in the half year results report due out in late February. We appreciate your patience."
All programs !!!!
There were only 20 cases ! Difficult to find relationships in this kind of material ( compare our "Placebo problem" in the Imagine study), age 51-65, evidently did not have any neurological problem but the abstract did not tell why they had died.
IMO this is a very important study. We do not have a causal relationship between preclinical elevated cognition decline and elevated Abeta levels but a strong relation between these two. 3% decline of abeta with PBT2 during the first year of the Imagine study may be a very valuable effect when amyloid level is above 1.5 in the PET scan ( see the Masters slides last July). It would be nice to know the relationship between Pet scan result and the oligomer content in spinal fluid in this kind of a population.
JAMA Psychiatry. 2015 Jan 28. doi: 10.1001/jamapsychiatry.2014.2476. [Epub ahead of print]
Amyloid-β, Anxiety, and Cognitive Decline in Preclinical Alzheimer Disease: A Multicenter, Prospective Cohort Study.
Pietrzak RH1, Lim YY2, Neumeister A3, Ames D4, Ellis KA5, Harrington K6, Lautenschlager NT4, Restrepo C6, Martins RN7, Masters CL6, Villemagne VL8, Rowe CC9, Maruff P10; for the Australian Imaging, Biomarkers, and Lifestyle Research Group.
Alzheimer disease (AD) is now known to have a long preclinical phase in which pathophysiologic processes develop many years, even decades, before the onset of clinical symptoms. Although the presence of abnormal levels of amyloid-β (Aβ) is associated with higher rates of progression to clinically classified mild cognitive impairment or dementia, little research has evaluated potentially modifiable moderators of Aβ-related cognitive decline, such as anxiety and depressive symptoms.
To evaluate the association between Aβ status and cognitive changes, and the role of anxiety and depressive symptoms in moderating Aβ-related cognitive changes in the preclinical phase of AD.
Design, Setting, and Participants:
In this multicenter, prospective cohort study with baseline and 18-, 36-, and 54-month follow-up assessments, we studied 333 healthy, older adults at hospital-based research clinics.
Main Outcomes and Measures:
Carbon 11-labeled Pittsburgh Compound B (PiB)-, florbetapir F 18-, or flutemetamol F 18-derived measures of Aβ, Hospital Anxiety and Depression Scale scores, and comprehensive neuropsychological evaluation that yielded measures of global cognition, verbal memory, visual memory, attention, language, executive function, and visuospatial ability.
A positive Aβ (Aβ+) status at baseline was associated with a significant decline in global cognition, verbal memory, language, and executive function, and elevated anxiety symptoms m
The treating doctor or surgeon will oder what is used to get the best result for his patient. He does not want any side effects, possibly also partly to avoid any law suits. Surgeons already know the problem but not the solution. So it is a marketing issue, the surgeons need to be informed. And when they are, they also get interested in Osir as a company, perhaps.
ITM posted there her previous figure about PBT2 treated Imagine study patients / AIBL 3 y follow-up cases. It demonstrates beautifully how effective PBT2 is in reducing amyloid in comparison to normal development as found in that 3y follow-up study. Unfortunately placebos did not act as expected according the AIBL study.
Yes pierreluke, PBT2 improves cognition in 3 months( Euro study), takes down amyloid plaques some 3 %/ y in the Imagine study and slows down hippocampus atrophy 35%/ y (trend).
Beta-2 microglobulin is related to iron metabolism and known also to be involved in aggregation of amyloid fibers in dialyses related amyloidosis.
J Alzheimers Dis. 2015 Jan 22. [Epub ahead of print]
Bayesian Graphical Network Analyses Reveal Complex Biological Interactions Specific to Alzheimer's Disease.
Rembach A1, Stingo FC2, Peterson C3, Vannucci M4, Do KA2, Wilson WJ5, Macaulay SL6, Ryan TM1, Martins RN7, Ames D8, Masters CL1, Doecke JD5.
With different approaches to finding prognostic or diagnostic biomarkers for Alzheimer's disease (AD), many studies pursue only brief lists of biomarkers or disease specific pathways, potentially dismissing information from groups of correlated biomarkers. Using a novel Bayesian graphical network method, with data from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, the aim of this study was to assess the biological connectivity between AD associated blood-based proteins. Briefly, three groups of protein markers (18, 37, and 48 proteins, respectively) were assessed for the posterior probability of biological connection both within and between clinical classifications. Clinical classification was defined in four groups: high performance healthy controls (hpHC), healthy controls (HC), participants with mild cognitive impairment (MCI), and participants with AD. Using the smaller group of proteins, posterior probabilities of network similarity between clinical classifications were very high, indicating no difference in biological connections between groups. Increasing the number of proteins increased the capacity to separate both hpHC and HC apart from the AD group (0 for complete separation, 1 for complete similarity), with posterior probabilities shifting from 0.89 for the 18 protein group, through to 0.54 for the 37 protein group, and finally 0.28 for the 48 protein group. Using this approach, we identified beta-2 microglobulin (β2M) as a potential master regulator of multiple proteins across all classifications, demonstrating that this approach can be used across many data sets to identify novel
Hippocampal volumes (mm3) were as follows: controls: left = 3060 (SD 502), right = 3120 (897); mild cognitive impairment (MCI): left = 2596 (447), right = 2686 (473); and Alzheimer's disease (AD): left = 2301 (492), right = 2445 (525). Volumes significantly correlated with atrophy severity at Scheltens' scale (Spearman's ρ = -0.468, P = .0005). Cerebrospinal fluid spaces (mm3) were as follows: controls: left = 23 (32), right = 25 (25); MCI: left = 15 (13), right = 22 (16); and AD: left = 11 (13), right = 20 (25). Five subjects (3.7%) presented with unusual anatomy.
This work provides reference hippocampal labels for the training and certification of automated segmentation algorithms. The publicly released labels will allow the widespread implementation of the standard segmentation protocol.
Alzheimers Dement. 2015 Jan 20.
Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol.
Boccardi M1, Bocchetta M2, Morency FC3, Collins DL4, Nishikawa M5, Ganzola R6, Grothe MJ7, Wolf D8, Redolfi A9, Pievani M9, Antelmi L10, Fellgiebel A8, Matsuda H5, Teipel S11, Duchesne S6, Jack CR Jr12, Frisoni GB10; European Alzheimer's Disease Consortium (EADC) and Alzheimer's Disease Neuroimaging initiative (ADNI) Working Group on The Harmonized Protocol for Manual Hippocampal Segmentation; Alzheimer's Disease Neuroimaging Initiative
The European Alzheimer's Disease Consortium and Alzheimer's Disease Neuroimaging Initiative (ADNI) Harmonized Protocol (HarP) is a Delphi definition of manual hippocampal segmentation from magnetic resonance imaging (MRI) that can be used as the standard of truth to train new tracers, and to validate automated segmentation algorithms. Training requires large and representative data sets of segmented hippocampi. This work aims to produce a set of HarP labels for the proper training and certification of tracers and algorithms.
Sixty-eight 1.5 T and 67 3 T volumetric structural ADNI scans from different subjects, balanced by age, medial temporal atrophy, and scanner manufacturer, were segmented by five qualified HarP tracers whose absolute interrater intraclass correlation coefficients were 0.953 and 0.975 (left and right). Labels were validated as HarP compliant through centralized quality check and correction.
Hippocampal volumes (mm3) were as follows: controls: left = 3060 (SD 502), right = 3120 (897); mild cognitive impairment (MCI): left = 2596 (447), right = 2686 (473); and Alzheimer's disease (AD): left = 2301 (492), right = 2445 (525). Volumes significantly correlated with atrophy severity at Scheltens' scale (Spearman's ρ
Alzheimers Dement. 2015 Jan 22.
Relationship between hippocampal atrophy and neuropathology markers: A 7T MRI validation study of the EADC-ADNI Harmonized Hippocampal Segmentation Protocol.
Apostolova LG1, Zarow C2, Biado K3, Hurtz S4, Boccardi M5, Somme J6, Honarpisheh H7, Blanken AE8, Brook J9, Tung S3, Lo D3, Ng D3, Alger JR8, Vinters HV10, Bocchetta M11, Duvernoy H12, Jack CR Jr13, Frisoni G14; EADC-ADNI Working Group on the Harmonized Protocol for Manual Hippocampal Segmentation; EADC-ADNI Working Group on the Harmonized Protocol for Manual Hippocampal Segmentation.
The pathologic validation of European Alzheimer's Disease Consortium Alzheimer's Disease Neuroimaging Center Harmonized Hippocampal Segmentation Protocol (HarP).
Temporal lobes of nine Alzheimer's disease (AD) and seven cognitively normal subjects were scanned post-mortem at 7 Tesla. Hippocampal volumes were obtained with HarP. Six-micrometer-thick hippocampal slices were stained for amyloid beta (Aβ), tau, and cresyl violet. Hippocampal subfields were manually traced. Neuronal counts, Aβ, and tau burden for each hippocampal subfield were obtained.
We found significant correlations between hippocampal volume and Braak and Braak staging (ρ = -0.75, P = .001), tau (ρ = -0.53, P = .034), Aβ burden (ρ = -0.61, P = .012), and neuronal count (ρ = 0.77, P smaller than .001). Exploratory subfield-wise significant associations were found for Aβ in CA1 (ρ = -0.58, P = .019) and subiculum (ρ = -0.75, P = .001), tau in CA2 (ρ = -0.59, P = .016), and CA3 (ρ = -0.5, P = .047), and neuronal count in CA1 (ρ = 0.55, P = .028), CA3 (ρ = 0.65, P = .006), and CA4 (ρ = 0.76, P = .001).
The observed associations provide the pathological confirmation of hippocampal morphometry as a valid biomarker for AD and the pathologic va
Arch Biochem Biophys. 2015 Jan 20. pii: S0003-9861(15)00023-5. doi: 10.1016/j.abb.2015.01.007. [Epub ahead of print]
How our bodies fight amyloidosis: effects of physiological factors on pathogenic aggregation of amyloidogenic proteins.
Huang L1, Liu X1, Cheng B2, Huang K3.
The process of protein aggregation from soluble amyloidogenic proteins to insoluble amyloid fibrils plays significant roles in the onset of over 30 human amyloidogenic diseases, such as Prion disease, Alzheimer's disease and type 2 Diabetes Mellitus. Amyloid deposits are commonly found in patients suffered from amyloidosis; however, such deposits are rarely seen in healthy individuals, which may be largely attributed to the self-regulation in vivo. A vast number of physiological factors have been demonstrated to directly affect the process of amyloid formation in vivo. In this review, physiological factors that influence amyloidosis, including biological factors (chaperones, natural antibodies, enzymes, lipids and saccharides) and physicochemical factors (metal ions, pH environment, crowding and pressure, etc), together with the mechanisms underlying these proteostasis effects, are summarized.