What can SNP-based heritability tell us about the etiology of AD today?

Here’s a nice article from Baker et al 2022, “What does heritability of Alzheimer’s disease represent?”

Here is a summary of some of their findings:

– The heritability of Alzheimer’s disease is complex and variable, depending on factors like age and population. Regarding age, in samples with older participants, there is a greater likelihood that AD genetic factors will play a role in disease risk.

– SNP-based heritability estimates decrease by 12% when APOE is excluded. When all other genome-wide significant hits were removed, SNP-based heritability only decreased by 1%.

– In APOE e44 carriers, the average age of onset is about 68 years, whereas for APOE e4 non-carriers, it is about 84 years. The authors suggest that for the APOE e4 non-carriers, disease burden is mostly due to the aggregate effect of a large number of common SNPs as well as comorbid disorders.

– When they restricted SNPs to a microglia gene set that was only 3% of SNPs, it still explained between 50% and 93% of the SNP-based heritability. This strongly suggests that microglia are critical for the mechanism of AD. It would have been nice to see this compared to gene sets for other cell types, such as neurons or oligodendrocytes, which could have given a better sense of how variability in the function of these different cell types may contribute to AD risk and also helped to assess the robustness of their methods.

– The authors suggest that for neurodegenerative disorders, heritability estimates should be adjusted for the age-related prevalence of cases. This would help to account for the genetic liability for the disease of individuals who do not yet show symptoms.

– The article provides helpful insights into the complex nature of Alzheimer’s disease heritability. It highlights the need for further research to identify biologically relevant AD gene-sets/pathways that could increase the signal-to-noise ratio by highlighting the most influential SNPs/genes in AD.

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Most of the risk factors associated with Alzheimer’s disease from observational studies are likely wrong

Interesting article from Korologou-Linden et al 2022, “The causes and consequences of Alzheimer’s disease: phenome-wide evidence from Mendelian randomization”.

In this study, the authors used a Mendelian randomization approach to examine the causal relationships between various risk factors and Alzheimer’s disease. They used the UK Biobank, which has a massive sample size of >300,000 participants.

They found that genetic variation at one gene — APOE — is far and away the main mediator of Alzheimer’s disease genetic risk. This replicates why Alzheimer’s has been called a quasi-monogenic disease — APOE has that large of an effect.

They don’t hold anything back in the discussion, basically arguing that their study disagrees with observational studies because their methodology is better and observational studies are wrong, because observational studies can’t identify causality.

Instead, they suggest that associations with Alzheimer’s disease in observational studies are due to reverse causation (i.e. they are symptoms of early/prodromal Alzheimer’s disease, rather than causes) or simply due to selection bias.

This means that the things often associated with Alzheimer’s disease in observational studies — body mass index, blood pressure, and physical activity — might not actually increase the risk of disease. Interestingly, Alzheimer’s genetic risk in this study is actually associated with a lower body mass index and body fat in people 53-72 years old.

They also found that Alzheimer’s disease risk is associated with a lower fluid intelligence score, with no causal effect on educational attainment.

One caveat I have with the study is that I’d like to learn about the associations of these risk factors with other forms of cognitive impairment. Laypeople often use the term “Alzheimer’s” to refer to dementia or age-related cognitive impairment in general.

For example, does a higher genetic risk for elevated blood pressure or body mass index causally affect the risk for vascular-associated cognitive impairment? My guess is that they might, which might cause this study to be a bit misleading if the results were taken in the wrong way.

That said, there seems to be a real effect of APOE on the risk of cognitive impairment that is independent of classical risk factors body mass index, blood pressure, and physical activity. And this study helps to parse out how that might be occurring, which will hopefully help to develop better preventive approaches and treatments.

Prenatal epigenetic age acceleration in Down syndrome

That’s a result of Xu et al 2022, “Accelerated epigenetic aging in newborns with Down syndrome”.

This study furthers our understanding of a syndrome of accelerated aging. The authors show a significant acceleration of an epigenetic aging marker in the blood of people with Down syndrome. Furthermore, they show that this effect is present at birth and is significantly stronger in newborns who have Down syndrome plus GATA1 mutations. This association with GATA1 mutations is intriguing as GATA1 mutations are associated with transient abnormal myelopoiesis. One thing that this study does not do is investigate the mechanism by which this age acceleration occurs.

One hypothesis based on this finding is that it might help explain why people with Down syndrome have an increased susceptibility to Alzheimer’s disease. Lore has long been that this is due to the triplication of amyloid precursor protein, however, this study suggests that age acceleration may also play at least a part in the increased susceptibility of people with Down syndrome to aging-associated cognitive impairment and Alzheimer-type neuropathology.

from https://onlinelibrary.wiley.com/doi/10.1111/acel.13652

Everest regression and the effect of age in Alzheimer’s disease

A new-to-me concept is the idea of an Everest regression — “controlling for altitude, Everest is room temperature” — wherein you use a regression model to remove a critical property of an entity, and then go on to make inappropriate/confusing/misleading inferences about that entity.

My immediate thought is that this is an excellent analogy for one of my concerns regarding regressing out the effect of age in studies of Alzheimer’s disease (AD). It’s such a tricky topic.

On the one hand, not everyone who reaches advanced age develops the amyloid beta plaques and other features that defines the cluster of AD pathology. Whereas there are potentially other changes in brain biology that you will see in advanced aging but not AD, such loss of dendritic spines, epigenetic changes, and accumulation of senescent cells.

On the other hand, advanced age is the most important risk factor for AD and explains most of the variance in disease status on a population basis. Arguably, a key part of why some “oldest old” folks do not have AD are protective factors. There have also been suggestions that accelerating aging is part of AD pathophysiology; although, as far as I can tell, the evidence for this remains preliminary. From this perspective, advanced age in AD is like the high altitude of Everest — it’s one of the key associated features.

So if you are trying to find the effects of AD pathophysiology, for example in a study of postmortem human brain samples, should you adjust for the effect of age or not? This is a practical and tricky question without a clear answer. It probably depends on your underlying model of how AD develops in the first place.

So I think it’s worthwhile to be cognizant of the potential hazards of adjusting for age — namely, that you risk inadvertently performing an Everest regression and removing an important chunk of the pathophysiology that you actually want to understand.

Deep brain stimulation for Alzheimer’s disease

In the past 20 years, deep brain stimulation (DBS) has been used for over 100,000 patients with Parkinson’s disease. The success of this procedure has led investigators to try DBS for other neurologic conditions, such as Alzheimer’s disease (AD).

In 2016, Lozano et al reported on one of the largest trials for DBS in AD, the “ADvance” trial, in which they targeted the fornix, a bundle of nerve fibers in the center of the brain that is the major output tract of the hippocampus.

This was a well-run, double-blind, randomized study. One of the nice aspects about brain stimulation trials is the ease of performing a sham stimulation arm. That is, treatment can be randomly turned either “on” and “off” for a period of time, allowing a subset of participants to serve as controls (stimulation turned “off”) for a period of time before they actually do get the stimulation (stimulation turned “on”) in case it is actually helpful.

In terms of the trial results, one of the patients (out of 42) had an implant infection. Overall, the trial did not show a significant benefit mitigating the decline in ADAS-13 or CDR-SB scores (measures of cognitive function):

Lozano et al 2016; doi: 10.3233/JAD-160017

While this trial did not show efficacy at their sample sizes, personally I expect that DBS for early AD could work to at least alleviate symptoms, if the right circuits were targeted at the right time.

My reasoning here is that we know that a few other cognitive strategies can help slow the course of AD, including processing speed training and acetylcholinesterase inhibitors.

There are at least 4 active DBS trials for AD on clinicaltrials.gov:

• One trial at Hospital San Carlos, Madrid, studying the effect of DBS in the fornix compared to the nucleus basalis.
• One trial at Beijing Tiantan Hospital, studying the PINS stimulation device (unclear brain region).
• One trial at Chinese PLA Hospital, Beijing, studying DBS of the fornix.
• One trial at UCLA Longevity Center, studying a Low Intensity Focused Ultrasound Pulsation procedure to either activate or inhibit hippocampal neurons as a method to treat AD.

It will be interesting to monitor this growing field in the coming years.

Microglia can last a lifetime

An important paper from Füger et al last month, in which they labelled individual microglia in mouse brains and tracked their locations over 1.5 years. Here were some of their major findings:

• The median lifespan of microglia was estimated to be approximately 2.5 years, which is close to the mean lifespan of the mice that they were studying. So, it is fair to think of microglia as long-lived tissue macrophages. It is also clear how changes in microglia epigenetics in earlier life could affect late-life cognitive outcomes.
• Microglia died at a higher rate in older mice, suggesting that aging may lead to alterations in microglia function that could affect neurodegenerative disease.
• In APPPS1 mice, microglia proliferate 3x more than usual in areas of the cortex without amyloid plaque, but only proliferate a normal amount in areas of the cortex with amyloid plaque. This suggests that any increase in microglia near plaque is likely due to migration, not local proliferation.

Subtypes of Alzheimer’s atrophy on MRI associate with different rates of subsequent cognitive decline

An interesting study from Risacher et al splits ADNI participants into three subtypes of Alzheimer’s, based on whether their baseline atrophy was more severe in the hippocampus and/or cortex. These groups were previously defined based on where in the brain pathologic tau deposits are predominant on postmortem exam, but the authors adapted them to the MRI level. Here were their definitions:

• Hippocampal sparing (HpSpMRI) = Hippocampal volume:Cortical volume ratio > 75th percentile, Hippocampal volume > median, Cortical volume < median. (n = 33)
• Limbic predominant (LPMRI) = Hippocampal volume:Cortical volume ratio < 25th percentile, Hippocampal volume < median, Cortical volume > median.  (n = 38)
• Typical AD (tADMRI) = all other participants (n = 158)

For participants who had 24 month longitudinal data, they found that the hippocampal sparing subtype had the worst progression of cognitive decline, despite a similar baseline cognition profile:

Figure 2C from PMID: 29070667

 

Phosphorylated tau accumulation is seen decades after prefrontal leucotomy

We know that many traumatic brain injuries (TBIs) can be devastating. An important research topic is predicting what the effect a TBI of a particular type and severity will have on neuropathology and behavior.

Neuropathology is relatively easier to measure, but it is still hard to tell causality because a lot of the “markers” of TBI seen on neuropathologic exam are also sometimes seen in individuals who never had a TBI. Although their degree or distribution might be different.

Behavioral effects of TBI are especially hard to measure because you need standardized measures across time in both TBI-affected and TBI-unaffected individuals, controlling for all of the other factors that are known to affect behavior. A tough nut to crack.

Shively et al. recently described their clever study to address the causality of neuropathologic changes in TBI.

They compared the postmortem brains from donors with schizophrenia treated with prefrontal leucotomy (n = 5; more than 40 years prior to death) to age-matched donors with schizophrenia who hadn’t undergone leuctomy (n = 5).

Leucotomy, an obsolete treatment for schizophrenia, involved traumatic interruptions of white matter axons in the prefrontal cortex via burr holes. Here is what the lesions look like on MRI:

From Uchino et al., an MRI of a person with a history of prefrontal leucotomy shows bilateral frontal white matter lesions; PMID:11156773

These authors looked at cortical tissue slices cut in the coronal plane at the leucotomy site, as well as slices rostral and caudal to the site.

Shively et al.; PMC5325841

Here were some of their findings:

• They found phosphorylated tau in neurons and astrocytes in cortex adjacent to the leucotomy site in 5/5 of the donors treated with leucotomy, but not in the rostral/caudal sites or in the donors who did not have leucotomy.
• The p-tau tended to be at sulcal depths or surrounding small blood vessels. This is similar to what is seen in CTE.
• They also found amyloid beta depositions in the cortex near the leucotomy sites, but only in the 3/5 donors who had at least one APOE ε4 allele.

Overall, this is really nice study that allows us to see the effect of TBI-associated axon injury in humans in a precisely controlled manner. What we see is that it causes phosphorylated tau accumulations in a similar distribution to that of CTE.

Anti-tau antibodies for the treatment of Alzheimer’s disease

One of the exciting alternatives to the amyloid immunotherapies in clinical trials for Alzheimer’s disease (AD) are anti-tau antibodies.

There are several of these drugs in earlier stages of development, although none that I know of in phase 3. To take two concrete examples, let’s focus in on BioGen’s two anti-tau immunotherapies:

• BMS-986168/BIIB092 = an humanized IgG4 monoclonal antibody targeting extracellular tau
• BIIB076 = a monoclonal antibody against both monomeric and fibrillar tau

Both of these drugs are also being tested in PSP, which is a relatively rare, classical familial tauopathy in a way that AD isn’t — because in PSP, the 1-5% of familial cases are known to be caused by certain MAPT mutations. Whereas I don’t know of well-validated genetic mutations in MAPT that are associated with increased risk of Alzheimer’s, except for some preliminary reports of small statistical associations, such as this one.

To try to force myself to be accountable and quantitative, what is my prediction for the probability that each of these two drugs will be approved by the FDA by the end of 2025? Same rules and disclosures as my previous post about this, but two years extended because these drugs are in earlier stages of development.

I’m going with 2.5% for BIIB092 (in phase II) and 1.5% for BIIB076 (still in phase I). Clearly abnormalities in tau proteins are highly associated with pathogenesis in AD, indeed more strongly associated than Aβ, and there have been a number of suggestions that the tau abnormalities are causal.

But in my opinion, we don’t know for sure yet that these tau abnormalities are truly causal, and that stopping tau aggregation will be helpful.

On one hand, if an anti-tau antibody works, why shouldn’t an anti-NFL antibody, or any of the other proteins that are markers of axonal damage in AD and are inversely associated with cognitive status? Maybe they all would, but this thought experiment is a bit troubling to me.

On the other hand, anti-tau antibodies have already been shown to be helpful in an APP-overexpressing AD mouse model, improving both cognitive function and the proportion of mushroom dendritic spines.

Castillo-Carranza et al 2015 Fig 1D; TOMA = anti-tau oligomer-specific monoclonal antibody, Tg2576 = APP-overexpressing AD mutant mouse; http://www.jneurosci.org/content/35/12/4857.long

It is asking a lot, but I would be more confident about the clinical relevance of this type of mouse study if it were shown that immunotherapies against other protein markers of axon damage, such as anti-NFL antibodies, were not successful in ameliorating cognitive decline, as a negative control.

Certainly I will be rooting for these anti-tau drugs to be successful in clinical trials and I think they make a lot of sense, but like most AD drugs in development, my prediction is that they are a long shot.

Problems with the diagnosis of idiopathic normal pressure hydrocephalus

Idiopathic normal pressure hydrocephaus (NPH) is a diagnosis of occult hydrocephalus with normal CSF pressure on LP that was first described in 1965 and is often considered one of the treatable causes of dementia.

The original paper used the now uncommon brain imaging technique of pneumoencephalography, which involved draining the CSF, injecting air as a contrast medium, and performing a brain xray:

Figure 2 from Adams et al 1965 showing uniformly enlarged ventricles; doi: 10.1056/NEJM196507152730301

At my med school we learned NPH by the triad of “wet, wobbly, and wacky”, referring to its classic triad of symptoms: urinary incontinence, gait disturbance, and cognitive impairment.

Like many symptom triads, these symptoms are non-sensitive, with the full triad seen in <60% of patients. It is also non-specific, as urinary incontinence is seen in ~20-40% of those over 60, gait impairment is seen in ~20% over those over 75, and mild cognitive impairment is seen in ~35% of those over 70.

Espay et al explain all of this in the introduction of their critical literature review of idiopathic NPH. One of their major points is that ventricle enlargement is also non-specific, as it is common in other neurodegenerative diseases such as AD, DLB, and PSP.

Here are some of their other points:

• There are no specific clinical, imaging, or neuropathologic findings in NPH.
• The determination of ventricle enlargement on MRI is subjective and not standardized.
• A “true” diagnosis is dependent upon a treatment response to CSF diversion via a ventriculoperitoneal shunt (VPS), which is circular and problematic.
•  There has never been a well-defined RCT to evaluate the use of VPS in NPH.
• Because many patients diagnosed with NPH may in fact have NPH that is secondary rather than a precursor to other neurodegenerative diseases, the fact that VPS may lead to short-term cognitive amelioration even in these patients suggests that VPS should still be considered as a way to improve cognition even in patients that are diagnosed with these neurodegenerative diseases.

Overall, this paper is well worth a read for people interested in treatments for dementia.