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.330px-everest_kalapatthar

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.

Single cell histone modifications seem to accumulate randomly during aging

One of the most remarkable findings in aging over the past decade is that it’s possible to track the rate of aging based on stereotyped DNA methylation changes across a diverse set of tissues. These are known as epigenetic clocks.

But as anyone in the gene expression field knows, changes in the levels of epigenetic markers between groups (like young vs older) is confounded by cell type proportion differences between those groups.

This cell type proportion confound makes it harder to tell whether the changes in DNA methylation are truly a marker of aging or whether they are due to cell type proportion variations that may be already known to occur during aging, like naive T cell depletion due to thymus atrophy.

Single cell epigenetics has the potential to address this problem. By measuring DNA methylation patterns within individual cells, you can compare the epigenetic patterns within the same cell type between groups, and don’t have to worry (as much) about overall changes in cell type proportion [1].

I was interested to see whether anyone has used single cell epigenetic profiling, which was just come out within the past couple of years, to measure whether changes in epigenetic marks can be seen within single cells during aging.

First, let’s back up a second and talk about epigenetics. Two of the major factors that defines a cell’s epigenome are its DNA methylation patterns and its histone post-translational modifications.

DNA methylation has been studied a bit in single cells. One study looked at DNA methylation in hepatocytes and didn’t find many differences between old and young cells.

However, as a recent review points out, single cell DNA methylation data are currently limited because of sample quantity within each cell, and can’t easily compare methylation patterns between different cells in the same region of the genome.

On the histone modification front, I found a nice article by Cheung et al 2018, who measured histone post-translational modifications (PTMs) in single cells derived from blood samples. They found that in aging, there was increased variability histone PTMs both between individuals and between cells.

So, in summary, here are some future directions for this research field that it would be prudent to keep an eye one:

  1. How much of the changes in DNA methylation seen in aging are due to changes in relative cell type proportions as opposed to changes within single cells? If we assume that age-related changes in DNA methylation will be similar to age-related changes in histone PTMs, then Cheung et al.’s results suggest that the changes in DNA methylation are probably due to true changes within single cells during aging.
  2. Is there a way to slow or reverse age-related changes in DNA methylation or histone PTMs, perhaps targeted to stem cell populations? It’s not clear that this can be done in a practical way, especially if age-related changes are driven primarily by an increase in variability/entropy.
  3. If it is possible to slow or reverse DNA methylation or histone PTMs, would that help to slow aging and thus “square the curve” of age-related disease? Aging might be too multifactorial for a single intervention like this to make a major difference, though.

[1]: I add “as much” here because differential expression analysis in single cell data is far from straightforward, and e.g. has the potential to be biased by subtle differences in the distribution of sub-cell type spectrum between groups.

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.

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:

Screen Shot 2017-09-17 at 10.48.34 AM
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.

Transplanting dopaminergic neurons into MPTP-treated monkeys improves their symptoms

Over the last few years researchers have figured out how to transform iPS cells into dopamine-producing neurons, raising the possibility of transplanting dopaminergic cells into the brains of patients with Parkinson’s disease (PD).

Kikuchi et al. looked at the effect of dopaminergic cell transplantation into the putamen on PD symptoms in monkeys treated with MPTP, which is a model of PD.

Compared to placebo injections, the stem cell transplantation improved symptoms. Notably, it did so somewhat less well than L-DOPA, but it seems plausible that this therapy could be eventually used once L-DOPA has failed, as L-DOPA tends to do over time in PD.

Screen Shot 2017-09-11 at 5.47.25 PM
Extended Data Fig 2K/I; doi:10.1038/nature23664

Perhaps the best news from this study is that they identified no markers of cancer formation in the transplanted brains after more than a year post-transplant. It’s always good news when your proposed therapy turns out to be less likely to cause brain cancer as a side effect.

Clinical trials will apparently start soon — from which we will have much to learn, and hopefully some good news.

Cerebral blood flow regulation systematically decreases after a stroke

In everyday life, your muscles, metabolism, and nervous system work together to ensure that your cerebral blood flow meets the metabolic needs of your various brain regions. So if you are trying to scrutinize an impressionist painting, your body will likely relocate more blood flow to your visual cortex.

Following a stroke, this cerebral blood flow regulation is impaired. But, the degree and spread of the impairment is unknown. To investigate this, Hu et al. measured systemic blood pressure (BP) and used a transcranial doppler to measure cerebral blood flow velocity (BFV) at the same time.

In their model, better regulation of cerebral blood flow corresponds to a sharper phase shift between blood pressure (BP) and cerebral blood flow velocity (BFV). Individuals with the highest score of a 9 on their autoregulation index (ARI) have more regulation than those with the lowest score of 0, which corresponds to no phase shift.

When they compared patients who had experienced MCA infarcts (a common type of stroke) and healthy controls, they found that stroke patients had significantly less phase coupling between blood pressure and cerebral blood flow. This effect was pronounced over a wide range of blood pressure oscillation frequencies.

Given enough time and the right conditions, can the body repair its ability to regulate cerebral blood flow following a stroke? When the researchers examined this, they found no statistically significant difference between the BFV-BP phase difference and time since stroke.

But, that doesn’t mean that there’s a statistically significant lack of difference. So, further longitudinal studies will be needed to help clarify whether, in certain people in certain environments, the brain improves its cerebral regulation following stroke.


Hu K, Lo M-T, Peng C-K, Liu Y, Novak V (2012) A Nonlinear Dynamic Approach Reveals a Long-Term Stroke Effect on Cerebral Blood Flow Regulation at Multiple Time Scales. PLoS Comput Biol 8(7): e1002601. doi:10.1371/journal.pcbi.1002601

Denervation of neuromuscular junctions in the extensor digitorum longus of aging mice

How does the connection morphology of motor neuron axons and muscle fiber endplates change with age? Chai et al recently published some results addressing, in part, this question.

Their study compared young 3 month and geriatric 29 month old mice, which, as the authors note, correspond to roughly 20 and 80 years in humans, respectively. However, it’s always important to keep in mind that mice differ from humans in many important ways.

The researchers cut out muscle tissue, sectioned it in 20 um segments, and double stained with antibodies for both synaptophysin (to detect pre-synaptic nerve terminals) and α-bungarotoxin (to detect postsynaptic muscle endplates).

They then classified neuromuscular junctions that stained positive for both synaptophysin and α-bungarotoxin as innervated, and classified junctions positive for α-bungarotoxin only as denervated. Below is an example of a confocal image of a double stained tissue slice.

EDL = extensor digitorum longus; synaptophysin = red; α-bungarotoxin = green; overlay = yellow; white circle = example of endplate positive for only α-bungarotoxin; scale bars = 75 um; doi:10.1371/journal.pone.0028090.g002 part d-f

Across all samples analyzed, ~7 +/- 2% of neuromuscular junctions were fully denervated in 3 month old mice and ~20 +/- 3% of neuromuscular junctions were fully denervated in 29 month old mice. Such denervation could help account for any age-related decrease in muscle function.

Interestingly and importantly, the researchers did not find a similar trend in the soleus. The lack of concordance underscores some of the variability across tissues of the same type in aging.


Chai RJ, Vukovic J, Dunlop S, Grounds MD, Shavlakadze T (2011) Striking Denervation of Neuromuscular Junctions without Lumbar Motoneuron Loss in Geriatric Mouse Muscle. PLoS ONE 6(12): e28090. doi:10.1371/journal.pone.0028090

Proteins differentially expressed in the aging hippocampus

In their review of the “neuroproteome” associated with aging and cognitive decline, VanGuilder and Freeman discuss some of the technical approaches and findings in the field.

This illustrative figure shows some of the major cellular players involved and lists some example proteins involved in four important pathways:

"numerous cell types (microglia (green), astrocytes (orange), oligodendrocytes (blue), and neurons (violet)) and subcellular components (mitochondria (brown), endoplasmic reticulum (green), cytoskeleton (orange/red), and synaptic machinery) are affected by brain aging"; doi: 10.3389/fnagi.2011.00008

As you can see, many proteins have been implicated, although the degree of up-/down-regulation of these proteins is not fully elucidated.

The authors mention the value of standardizing efforts to profile the proteome in important brain regions across the lifespan of rodent models. This step would make these results more robustly quantitative and help iterate towards a consensus.


VanGuilder H. D. and Freeman W. M (2011) The hippocampal neuroproteome with aging and cognitive decline: past progress and future directions. Front. Ag. Neurosci. 3:8. doi: 10.3389/fnagi.2011.00008

Synaptic pruning in the mediodorsal thalamus

More neurons are born than necessary, and synaptic pruning is the process by which neurons that have not made as many functional synaptic connections with other neurons are preferentially degraded.

Abitz et al counted cells in the medial thalamus of newborn and adult brains using a optical fractionator and Giemsa staining which binds to phosphate groups of DNA. They distinguished small neurons from glial cells on the basis of chromatin pattern, the size / shape of the nucleus, and the visibility of the nucleolus. Here’s an example of the Giemsa stained  cells via micrographs:

scale bar = 10 micrometers, doi:10.1093/cercor/bhl163

They found an average of 11.2 million neurons in the newborn MD thalamus, which decreased to an average of 6.43 million neurons in adults, probably as a result of synaptic pruning. On the other hand, they found 36.3 million glial cells in adults, much higher than the 10.6 million they found in newborns, suggesting that glial progenitor cells still have a few proliferation cycles to undergo in development.

Elsewhere, Elston et al measured the number of spines in the average pyramidal cell of macaque brains in the primary visual cortex (V1), the inferior temporal gyrus (TE), and the prefrontal cortex (PFC) at different stages of development. They found an inverted U shaped curve of spine number with log age:


The authors conclude that “synaptic activity thresholds that reinforce synapses and stabilize dendritic spines may vary across cortex.” It is interesting that the regions follow the same general trend in each region, peaking at 3.5 months.


Maja Abitz , Rune Damgaard Nielsen , Edward G. Jones , Henning Laursen , Niels Graem , and Bente Pakkenberg. Excess of Neurons in the Human Newborn Mediodorsal Thalamus Compared with That of the Adult. Cerebral Cortex Advance Access published on January 11, 2007, DOI 10.1093/cercor/bhl163.

Guy N. Elston, Tomofumi Oga, and Ichiro Fujita. Spinogenesis and Pruning Scales across Functional Hierarchies.  J. Neurosci. 29: 3271-3275; doi:10.1523/JNEUROSCI.5216-08.2009

Changes in myelinated axons following juggling in diffusion tensor imaging

Scholz et al performed diffusion tensor imaging on 48 adults randomly placed in either a juggling or control group. By the end of the 6-week training each of the adults in the juggling group could perform 2 cycles of the 3 ball cascade, which is somewhat but not overly impressive. As compared to their pre-scanning fractional anisotropy, a somewhat loose measure of myelination, fiber density, and axon diameter, the juggling group had a percent increase of ~ 5.5 +/- 1.5 % immediately following the training, and a percent increase of ~ 4 +/- 1 % four weeks later. The control group had no real increase following training, which makes sense because they didn’t do anything!

The increase four weeks post-training indicates that although the effects of the training diminish somewhat over time, they should last for at least a little while. Perhaps further studies could continue to perform diffusion tensor imaging on the adults to see when the percent increases due to training are extinguished, if ever. A back of the envelope calculation based on a linear trend would suggest that after ~ 15 weeks following training the increased would be gone. But the reality may be wildly different.


Scholz J et al. 2009 Training induces changes in white-matter architecture. Nature Neuroscience 12 1370-1371. doi:10.1038/nn.2412