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After reading Phil Tetlock and Dan Gardner’s book Superforecasters, I’ve decided to try to make prospective, quantitative predictions about AD therapies currently in clinical trials with an endpoint of decreasing cognitive decline.


I have investments in S&P index funds but no individual stocks. I’m funded with an NIH training grant for AD. However, everything in this post is based on public information.

My (in-progress) thesis is on one aspect of the basic biology of AD, but I still don’t know feel that I know all that much about AD, which is such a broad topic. I certainly don’t want to make it seem like I’m calling myself an expert. Still, that seems like a good reason to make predictions: to create an incentive to learn more and hold myself accountable.


Most AD drugs fail, and the hardest barrier to entry is Phase III clinical trials. From 2002-2012, 1/54 drugs that were tested in phase III clinical trials were approved by the FDA (memantine was the only approval; data from here).


data: 10.1186/s13195-016-0207-9

Since 2012, there have been several additional high-profile phase III failures, including the amyloid immunotherapy drug Solanezumab and the BACE inhibitor Verubecestat, and no additional FDA approvals.

This is our reference class: we should expect that ~1.85% of the drugs in Phase III clinical trials are likely to be approved.

Maybe we can raise the probability of a generic AD drug approval a little bit now, since we presumably know more now about science, medicine, and AD in particular now than we did in past decades.

On the other hand, if our current theories driving AD drug development (such as the amyloid hypothesis) happen to be particularly misguided, then the probability of approval might be lower accurate than they were in the past. Plus, it’s plausible that the FDA has more experience and will make the evidence necessary for approval more rigorous now than they might have in the past.

Overall, I think 1-4% is a reasonable prior for an new therapy in a phase III AD clinical trial today.

You might be asking: shouldn’t there be more AD drugs approved by the FDA by chance? If all you need is two-tailed p < 0.05 and this should happen 2.5% of the time, why have only 1.85% of AD drugs been approved? Part of the reason is that FDA approval criteria is more strict than simply p < 0.05, and requires “independent substantiation.” That said, it’s daunting that the probability of phase III AD therapy approval is close to chance levels.

Note about my motivations

I, like most people, urgently hope that all of these drugs work. I’m not being critical about their chances because I want to them to fail, I’m doing it so that I can more build more accurate models of how the AD field operates.

Choosing drugs for evaluation

The amazing AlzForum has a great page where they list 22 therapeutics currently in phase III clinical trials. Of these, here are some that I will not be evaluating:

1. LMTM: has already failed in clinical trials.

2. Vitamin E: has already completed a clinical trial with some mild success.

3. AVP-786: this DXM-containing compound is primarily for treating disinhibition in AD, not cognition.

4. Aripiprazole: for psychosis in AD.

5. Brexpiprazole: for agitation in AD.

6. ITI-007: for agitation in AD.

7. Masitinib: A mast cell inhibitor, but the clinical trial for AD doesn’t seem to be updating anymore and I can’t find information about it online.

8. CPAP: A prevention trial with an unclear path (to me) towards FDA approval.

9. Crenezumab: I’m confused by the possible path to approval for this passive Aβ immunotherapy in the context of late-onset AD, since it has already failed in trials of mild to moderate AD. I’m not saying that it’s impossible, of course, just that I don’t understand well enough how it would work to assign a probability.

10. Gantenerumab: This drug also failed in a Phase III clinical trial already in early stage symptomatic patients, so I am confused by its path to FDA approval.

11. Solanezumab. In late 2016, this Aβ immunotherapy was found to have failed in clinical drugs.

12. Verubecestat. Merck’s BACE inhibitor just had its Phase III clinical trial stopped a few weeks ago.

13. Idalopirdine: Already failed in one Phase III clinical trial.

That leaves 9.

To cover my bases, I also did a search for “alzheimer | Open Studies | Phase 3” at clinicaltrials.gov, where these studies are registered.

Through this search, I found some others I’m not going to consider, because I only want to consider drugs that are going to fail due to lack of efficacy, instead of lack of interest.

1. Coconut oil: specifically the Fuel for Thought version. I will not make a prediction on its FDA approval status since according to the company’s website it is already “generally recognized as safe” by the FDA.

2. tCDS: This modality has one trial that I will not be considering since I don’t know if it has a large enough sample size to achieve FDA approval with only n = 100 in a Phase II/III study.

3. There’s a phase III trial of purified EPA, but I’m not going to consider that because it seems that it seems similar to coconut oil in terms of FDA approval.

4. Albumin/IVIg: There’s a study of albumin and IVIG plasmapharesis in AD, but it’s unclear if this trial is still ongoing, as it hasn’t been updated in almost two years. IVIg has previously been unsuccessful in AD.

5. Nasal insulin: I’m also confused about the path to monetization and FDA approval of this drug, so I’m not going to evaluate it.

However, I did find sodium oligomannurarate and JNJ-54861911 using this search, and I’m adding them to the list. Another 2 makes 11 total therapies for predictions.

Efficacy predictions 

1. Levetiracetam (a widely used, FDA-approved anti-epileptic drug). AFAIK this is not yet in phase III: there’s one trial in phase II, although there is a phase III trial planned. Let’s assume for the purposes of prediction that the MCI phase III trial happens, with a primary endpoint of decreasing the rate of cognitive decline.

The idea that AD might be related to circuit/neuronal network dysfunction is very much in the air right now, eg following the report last month that flashing light on the retinas at particular frequencies to induce gamma rhythms leads to dramatic cognitive improvements in the 5xFAD mouse model of AD.

Levetiracetam is already widely used in AD patients with seizures, making it likely to be safe.

It could be true that Levetiracetam really does affect Aβ processing on the cellular level and decrease Aβ levels. I don’t really buy this, even before one of the papers on which this was based was retracted.

There has been one study evaluating the effect of Levetiracetam in humans in a within-study design, but the effect is pretty weak and non-existent at the highest dose, for reasons that are unclear to me.

One could imagine that Levetiracetam were successful in reducing cognitive decline in individuals without overt seizures, it might make sense to reconceptualize one aspect of AD around something like microseizures. We know that seizures are much more likely following strokes and other brain injuries, making this a plausible hypothesis.

Probability of FDA approval by the end of 2023: 2%

2. ALZT-OP1 (combination of inhaled cromolyn and oral ibuprofen). Currently in a 600-early AD patient clinical trial. Trials of NSAIDs and ibuprofen have failed in phase III trials multiple times, despite epidemiologic evidence suggesting that they should be beneficial.

By adding inhaled cromolyn, another anti-inflammatory drug that is approved as an asthma prophylactic, the funders are hoping that their trial will be different. A nice 2015 study showed that IP cromolyn administration decreases soluble monomeric Aβ-42 by about half in the APPswe/PS1dE9 mouse model of AD. Otherwise, there’s not much else published.

If this works, it would really emphasize the importance of systemic (as opposed to brain-specific) inflammation in AD and maybe mast cells in particular. But given the previous failures of systemic anti-inflammatory treatments, it seems pretty unlikely.

Probability of FDA approval by the end of 2023: 1.5%

3. AZD3293 (Lilly’s BACE inhibitor). BACE is a critical part of the amyloid processing pathway, and this small molecule inhibits it. AZD3293 has been shown in human studies to robustly decrease plasma and CSF Aβ42 and soluble AβPP β.

AZD3293 is in two large clinical trials, NCT02245737 with estimated n = 2202 and NCT02783573 with estimated n = 1899. The trials are for relatively early AD, with requirements of MMSE > 21 and > 20, respectively.

What is great about Eli Lilly’s large investment in AZD3293 is that we will have a very good sense of whether and how effective this drug is, as well as the extent to which decreasing CSF/brain amyloid leads to improved cognition. Assuming they release the data that they generate for analysis (which they probably will), they deserve a lot of credit for this undertaking.

Unfortunately, the negative Verubecestat (Merck’s BACE inhibitor) trial results that came out a few weeks ago are disheartening for the prospect of BACE inhibition in AD. It’s also possible that off-target effects of BACE inhibition, such as myelination, may decrease cognitive decline to an equivalent or greater degree as the benefits from decreased Aβ production.

However, it’s still totally plausible that this drug will work where the Merck BACE inhibitor did not, since there will clearly be differences in their effects, including pharmacokinetics and off-target effects.

Overall this feels like another clear of the amyloid hypothesis: if you lower Aβ levels, will you reduce the rate of cognitive decline? If this drug also doesn’t work while reducing Aβ, it seems that it will really be time for the field to go back to the basics.

Probability of FDA approval by the end of 2023: 5%

4. Aducanumab (Aβ passive immunotherapy). This is a monoclonal antibody that preferentially binds parenchymal, aggregated forms of Aβ.

It was derived from healthy, older donors who were cognitively healthy, with the assumption that it may have helped prevent them from developing AD.

It is probably the most promising drug in AD right now and its dose-dependent amyloid and cognitive effects in humans were described in a Nature paper in September 2016.

This seems to be the consensus “most likely to work” of the current drugs in clinical trials. There is still some reason for skepticism, though.

First, it’s not entirely clear to me why this amyloid reduction technique works when so many other Aβ therapies have failed.

And while Fig 3 of the Nature shows some nice dose-dependent effects, the error bars are still pretty high.

There are currently two phase III clinical trials for the drug, each with n = 1350 participants, requiring a positive amyloid PET scan and CDR = 0.5 and MMSE 24-30, which is early in the disease process. Results in 2019 and 2020.

Probability of FDA approval by the end of 2023: 20%

5. Azeliragon (small molecule RAGE inhibitor). This drug failed clinical trials in the mid-2000s, but the lower dose may have shown an effect, and now it has been taken back to clinical trials at the lower dose for a phase III trials in participants with MMSE 21-26 and an MRI scan showing a diagnosis of probable AD.

If this drug works at the lower dose, it suggests that astrocyte and microglia inflammation are a particularly strong targets in AD.

Probability of FDA approval by the end of 2023: 2%

6. E2609 (Biogen’s BACE inhibitor). This small molecule has been shown to reduce Aβ in the CSF and serum of non-human primates. This is being tested in a large (n = 1330) phase III clinical trial. Results expected by 2020.

As with the other BACE inhibitors, it’s plausible but not clear to me why this drug should succeed where Verubecestat failed, so I will give it the same probability as AZD3293. However, it also has only one trial as opposed to AZD3293’s two, so it seems slightly less well powered to detect a small effect on improving cognition.

Probability of FDA approval by the end of 2023: 4%

7. Nilvadipine (L-type Ca channel blocker). This anti-hypertensive drug was previously considered a possible therapy for AD in the context of reducing blood pressure, which can decrease the rate of cognitive decline. It made a splash in 2011 when it was found to decrease Aβ in vitro, and it is currently in a Phase III trial that should report results by the end of this year.

Given the spate of Aβ therapy failures, the Aβ reduction is not as promising as it was 6 years ago, although the drug may have other effects and may also reduce cognitive decline through its effects on blood pressure.

Probability of FDA approval by the end of 2023: 2.5%

8. Sodium oligomannurarate. There is not much info about this drug online, besides the clinical trial notice and this phase II trial report from Medscape which notes that it did not meet the primary cognitive endpoint (ADAS-cog/12) in its phase II trial. Not much info to go on here.

Probability of FDA approval by the end of 2023: 0.75%

9. JNJ-54861911 (Janssen BACE inhibitor). Another BACE inhibitor that is currently in a phase II/III trial.

Probability of FDA approval by the end of 2023: 4%

10. CAD106 (Active Aβ immunotherapy) + CNP520 (BACE inhibitor). This is an active vaccination strategy for Aβ, which would be fantastic for the field if it worked, since it is likely to be much cheaper than passive Aβ immunotherapy.

These drugs are currently being tested in a large Phase 2 trial (n = 1340).

Overall, the probability here feels similar to the probability of the other Aβ therapies. The combination of the active immunotherapy alongside BACE inhibition makes this trial intriguing.

Probability of FDA approval by the end of 2023: 4.5% (that at least one of the two or the combination will be approved)

11. Pioglitazone (PPARγ agonist, insulin sensitizing, small molecule). This drug is approved to treat type 2 diabetes. PPARγ agonism has been shown to play a role in inflammatory processes in the brain.

It is being studied in an extremely large study (n = 3494) that is coupled with a genetic risk model that includes APOE e4 and TOMM40.

I think that this trial has some potential, based in part on mouse model data as well as a variety of data suggesting an interplay between hyperglycemia-associated toxicity and risk of AD.

However, most of the early phase human data has been negative, including a study by the NIA (n = 25) and a study by the University of Colorado (n = 68).

Another problem with this trial is that — to the best of my knowledge — TOMM40 variants are no longer thought to be strongly associated with the risk of AD.

That said, there are some really interesting possible mechanistic angles here, including a possible role for pioglitazone in regulating myelin phagocytosis by immune cells, which may interact with AD.

Probability of FDA approval by the end of 2023: 4% (prediction)

Screen Shot 2017-06-07 at 3.04.53 PM

Overall probability that at least one drug will be approved for cognition in late-onset AD within the next 6 years (not necessarily one of the above): 35%

Obviously, these predictions are highly correlated.

For example, if one of the remaining BACE inhibitors works, then that makes it more likely that others will too.

As another example, if any of the amyloid therapies finally work, then that makes it more likely that the others will.

If any of the drugs work, that makes it more likely that all of the others will, because maybe clinical trial strategies (eg, enrolling patients earlier in the disease process) are generally more apt than they were previously.

There’s also some uncertainty around how the FDA will work over the next 6 years. I’m talking about cognitive efficacy approvals, not biomarker approvals.

To be explicit: if a drug is approved preliminarily based on biomarkers but not cognitive efficacy, I’m not going to count it as an approval for the purposes of these predictions. I

‘ll note that I’m a bit nervous in making these predictions public. What if they are all horribly wrong?

But I hope that we will move towards a world where people make more quantitative public predictions and are incentivized to do so. Of course, I plan to evaluate these predictions in 6 years and hold myself accountable.

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Classic Paper: Elkes J, Elkes C. Effect of chlorpromazine on the behavior of chronically overactive psychotic patients. Br Med J. 1954;2(4887):560-5

In 1950, a group of anesthesiologists in France were trying to find new drugs for anesthesia. They tested the newly synthesized drug chlorpromazine on animals (dogs, rodents, and mice) and found that it led to drowsiness and indifference to aversive stimuli.

Since this was the 1950’s, they were able to quickly try it on people as a booster for anesthesia. They found that people who took chlorpromazine did not lose consciousness, but it did have a profound calming effect. Quickly people thought of trying it on patients with psychosis, for which the available treatments were very limited.

This study by Joel Elkes and Charmian Elkes, who were married, was the first to report a placebo-controlled trial on the effect chlorpromazine in psychosis. It appears that the majority of the data collecting and work was done by Charmian, rather than Joel. Screen Shot 2016-04-11 at 8.19.22 PM

They used a classic crossover study design, testing each patient on both chlorpromazine and an inert placebo (although they do not use the word “random”). They used notes written by the doctors and nurses that were blind to the treatment type to decide whether or not the patient had improved.

Of the 23 patients with a type of psychosis in their study, 7 (30%) showed “definite improvement” when they were taking the drug compared to when they were not, 11 (48%) showed “slight improvement,” and 5 (22%) showed “no improvement.”

Other interesting notes from the paper:

  • They describe the effect of chlorpromazine as symptomatic, since the psychosis itself did not abate: “the essentially symptomatic nature of the response has already been stressed, and cannot be overemphasized. Although affect became more subdued, and attitude and behaviour reflected this improvement, the ingrained psychotic thought disorder seemed to be unchanged.”
  • Because of their detailed records, they noted significant weight gain in 9/23 of the patients (in all of whom the drug led to at least a slight improvement), which has been borne out in both chlorpromazine and in the drug class in general: almost all antipsychotics result in weight gain. Of this effect, they say: “For the present we are inclined to attribute this to improved eating habit as the patients became less tense, less preoccupied, or less assaultive; though more direct metabolic effects of the drug cannot be excluded.”
  • They also tried it on 3 patients with senile dementia, all of whom had “no improvement.” This is yet another example of how Alzheimer’s is where drug discovery goes to die.

Notably, the mechanism remained pretty unknown until the mid-1960s, when it was shown that dopamine metabolites correlated with the chlorpromazine dose given to animals. In 1976, Seeman et al. found a nearly perfect correlation (on the log-log scale) between the ability of antipsychotic drugs to displace haloperidol from binding to the dopamine receptor and the clinical dose required for its effect.

Screen Shot 2016-04-24 at 2.47.22 PM

Seeman et al., 1976

Interestingly, you can see in this figure that chloroprazamine actually has one of the less strong dopaminergic affinities and higher doses required for controlling schizophrenia. Despite this, it and its derivatives have on to become some of the most game-changing psychiatric drugs of all time.


Shen WW. A history of antipsychotic drug development. Compr Psychiatry. 1999;40(6):407-14.
Elkes J, Elkes C. Effect of chlorpromazine on the behavior of chronically overactive psychotic patients. Br Med J. 1954;2(4887):560-5.
Bak M, Fransen A, Janssen J, Van os J, Drukker M. Almost all antipsychotics result in weight gain: a meta-analysis. PLoS ONE. 2014;9(4):e94112.

Seeman P, Lee T, Chau-wong M, Wong K. Antipsychotic drug doses and neuroleptic/dopamine receptors. Nature. 1976;261(5562):717-9.

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Classic Paper: Cade JF. Lithium salts in the treatment of psychotic excitement. Med J Aust 1949; 2:349-352

Screen Shot 2016-04-11 at 5.33.09 PM

some wells in the British Isles were known for their salubrious effects on mental illness; this may have been due to their lithium content

Prior to 1949, treatments for mania were limited. That year, John Cade published a paper showing the usefulness of lithium in treating patients with mania (“psychotic excitement”).

Interestingly enough, the finding was apparently a surprise to Cade. He was studying guinea pigs in order to see whether uric acid added to the convulsive toxicity of urea, but he needed to find a way to make uric acid soluble in water to be able to inject it into the guinea pigs. (Confusingly enough, urea and uric acid have almost nothing to do with one another chemically.)

For this, he used the lithium salt of urate, and was surprised to find that it was protective against the urea-induced convulsions. He then injected lithium carbonate alone into guinea pigs, and noted that after a couple of hours, they became lethargic and unresponsive to stimuli.

Skipping straight from this effect in guinea pigs (not even a disease model!! — this would never be allowed today) to humans, Cade then reports on 10 cases of patients with mania who were successfully treated with lithium, including longitudinal cases of chronic mania where the mania subsided during lithium treatment and recrudesced when lithium was discontinued.

Other interesting aspects of this paper:

  • Cade notes that historically, water from certain wells was associated with improvements in mental illness, and speculates that “it is very likely that their supposed efficacy was a real efficacy and directly proportional to the lithium content of the waters.”
  • Cade notes that lithium treatment “would be much preferred” to what is usually now considered the cruel treatment of prefrontal leucotomy, even though this (1949) was the year that the Nobel prize was awarded for it, and its use continued into the mid-1950s.
  • All of the cases reported on were men between ages 40 and 65 years old, indicating a total lack of evidence for generalization of the effect across more diverse patient populations.

Recent meta-analysis (2013) has shown that antipsychotics are more effective than lithium in the treatment of acute mania (e.g., the standardized mean difference in manic symptoms for haloperidol is -0.56, while for lithium it is -0.37), but lithium is still often used in combination with antipsychotics in the treatment of mania.

Overall, this short paper is among the best I’ve read in terms of scientific puzzle solving, although you could argue that Cade got lucky.


Cade JF. Lithium salts in the treatment of psychotic excitement. 1949. Bull World Health Organ. 2000;78(4):518-20.

Cipriani A, Barbui C, Salanti G, et al. Comparative efficacy and acceptability of antimanic drugs in acute mania: a multiple-treatments meta-analysis. Lancet. 2011;378(9799):1306-15.

Doig MT, Heyl MG, Martin DF. Lithium and mental health. J Chem Educ. 1973;50(5):343-5.

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A nice behind-the-scenes look in Noah Gray’s post discussing this article studying connections between the striatum and basal ganglia mice. Here’s what the reviewers were wondering:

[They] also raised general novelty issues, since it is well-known from many brain areas that any manipulation of circuits on a gross level can lead to innervation changes. A somewhat broad damnation, but worth considering nonetheless. This criticism also related to the next, namely that to really make a valuable contribution to the competitive field of circuit development, the authors would need to expand this study further, supplying additional data allowing a better understanding of other components within the circuit. For example, exploring how exactly the corticostriatal inputs influence basal ganglia synaptogenesis. It begged the question: do the authors understand the physiology and timing well enough to predict how their manipulations would affect the striatum, not by disrupting the striatum itself, but through control of the descending cortical circuits?

Here is an earlier BL post on MSNs in the striatum, which the authors of this article selectively silence as a part of their circuit manipulation.

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Leegaard et al’s article is a worthy summary of recent efforts to map the brain’s connections at various levels of detail and the benefits that we would realize from cross-validating them. Here is one example:

Using the same approach, Axer et al. (2011b) show how 3-D [polarized light imaging] derived fiber orientation vectors can subsequently be used as a basis for high-resolution tractography of fiber tracts, potentially suitable for bridging microscopic and macroscopic connectome representations. The importance of correlating various non-invasive MRI derived measurements to cellular-level morphological data is also emphasized by Annese (2012), presenting the perspective that whole-brain histological maps (Figures 1E,F) created using large-scale digital microscopy spanning several histological modalities will support the analysis and interpretation of MRI-based connectivity studies.

They also have an awe-inspiring figure showing off the results of many different new techniques.

A, B = diffusion MRI in vivo; C, D = detailed fiber architecture via 3-d polarized light imaging (ex vivo); E, F = digital histology of tract stained for myelin; G = combined optogenetics and fMRI; H = knife edge scanning microscopy; I = GUI of the Human Connectome Project; J = data mining to reconstruct hippocampal connections; K = connectivity-based cortical parcelation; L, M = GUI of the Connectome Viewer; N = structural network motifs; O = connectome matrix of the rat brain; P = connectome matrix of the rat hippocampus; Q = a model of connectivity in the developing tapdole spinal cord; doi: 10.3389/fninf.2012.00014 


Leergaard TB, Hilgetag CC and Sporns O (2012) Mapping the connectome: multi-level analysis of brain connectivity. Front. Neuroinform. 6:14. doi: 10.3389/fninf.2012.00014

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Interesting paper by Knafo et al from a couple of months ago that I’ve been meaning to talk about. This group previously made a synthetic peptide (called FGL) that mimics the active site of a cell adhesion molecule involved in neurite outgrowth (NCAM).

When they subcutaneously inject some amount of this molecule into rats, they learn to navigate a water maze faster. So, cognitive enhancement. Not bad.

Down to mechanism. They fix rat hippocampal sections and investigate a variety of neural morphology measures. No significant differences, although the synapse density looks pretty close to significance to me.

left = representative em images showing synapses, right = differences on various types of synapses

Instead, it seems like this peptide is exerting its effects by recruiting more AMPA receptors to the postsynaptic densities of excitatory CA1 cells. Probably their strongest evidence for this is that, in hippocampal slice cultures, more GFP-tagged AMPA receptors are delivered to the synapse 24-36 hrs after the addition of the peptide.

rectification index = ratio of whole-cell clamped responses at -60 and +40 mV; Inf. = cells infected with GFP (green); FGL = memory-enhancing synthetic peptide

So this is an example where neural connectivity, by itself, seems quite unable to explain all the physiology and behavior in their experiments. Of course, it’s possible that concomitant with the functional (but not structural) changes in the hippocampus, there are correlated structural (but not functional) changes elsewhere in the rat’s brain. But that seems less parsimonious.


Knafo S, Venero C, Sánchez-Puelles C, Pereda-Peréz I, Franco A, et al. (2012) Facilitation of AMPA Receptor Synaptic Delivery as a Molecular Mechanism for Cognitive Enhancement. PLoS Biol 10(2): e1001262. doi:10.1371/journal.pbio.1001262

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The ADNI has collected longitudinal MRI scans and genotypes from ~ 100 individuals with AD, ~200 with MCI, and ~150 healthy eldery controls, making it quite the boon for Alzheimer’s research. In a paper that caught my eye, Silver et al took this data set and did some cool things with it.

First, they did some genotype filtering, such as removing SNPs with a frequency of less than 10% for the less common allele. This means that both alleles for each SNP they include will be relatively common, which ensured that their subsequent regression would have adequate sample sizes for each group. Next, they map the SNPs to genes, and then they map the genes to genetic pathways.

schematic illustration; unfilled squares/circles = genes/SNPs that do not map to any pathways/genes; orange = SNP that maps to more than one pathway; http://arxiv.org/abs/1204.1937

To integrate the brain data, the researchers calculated the change from baseline in each voxel in scans separated by 6 and 18 months. They then did a voxel-wise ANOVA on the rate of change to determine which voxels show differences between AD and healthy controls.

What I find amazing is that even using a conservative correction for multiple comparisons, they still found that 148,023 out of 2,153,231 (7%) of the voxels showed a difference between AD cases and healthy controls. That shows you the extent of AD damage; see below for the specific regions most affected.

top = log p-value distribution for differences in rate of change between AD patients and healthy controls; bottom = final set of voxels that passed the p-value threshold; http://arxiv.org/abs/1204.1937

Finally, they estimate which SNPs and pathways have the strongest associations with changes in AD-affected brain regions. The top three pathways they blame are the chemokine pathway, the jak-stat pathway, and the tight junction pathway.

The first two of these are related to cytokine signaling and thus continue to emphasize the role of inflammation in AD progression. Tight junction proteins have also been associated with AD to explain the loss in BBB integrity. So, although the AD picture remains messy, studies and data sets like this should help.


Silver M, et al. 2012 Identification of gene pathways implicated in Alzheimer’s disease using longitudinal imaging phenotypes with sparse regression. arXiv:1204.1937v1

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