Michael Fossel Michael is President of Telocyte

October 10, 2017

Should everyone respond the same to telomerase?

A physician friend asked if a patient’s APOE status (which alleles they carry, for example APOE4, APOE3, or APOE2) would effect how well they should respond to telomerase therapy. Ideally, it may not make much difference, except that the genes you carry (including the APOE genes and the alleles for each type of APOE gene, as well as other genes linked to Alzheimer’s risk) determine how your risk goes up with age. For example, those with APOE4 alleles (especially if both are APOE4) have a modestly higher risk of Alzheimer’s disease (and at a lower age) than those with APOE2 alleles (expecially if both are APOE2).

Since telomerase doesn’t change your genes or the alleles, then while it should reset your risk of dementia to that of a younger person, your risk (partly determined by your genes) would then operate “all over again”, just as it did before. Think of it this way. If it took you 40 years to get dementia and we reset your risk using telomerase, then it might take you 40 years to get dementia again. If it took you 60 years to get dementia and we reset your risk using telomerase, then it might take you 60 years to get dementia again. It wouldn’t remove your risk of dementia, but it should reset your risk to what it was when you were younger. While the exact outcomes are still unknown, it is clear is that telomerase shouldn’t get rid of your risk, but it might be expected to reset that risk to what it was several years (or decades) before you were treated with telomerase. Your cells might act younger, but your genes are still your genes, and your risk is still (again) your risk.

The same could be said for the rate of response to telomerase therapy. How well (and how quickly) a patient should respond to telomerasse therapy should depend on how much damage has already occurred, which (again) is partially determined by your genes (including APOE genes and dozens of others). Compared to a patient with APOE2 alleles (the “good” APOE alleles), we might expect the clinical response for a patient with APOE4 alleles (the “bad” APOE alleles) to have a slightly slower respone to telomerase, a peak clinical effect that was about the same, and the time-to-retreatment to be just a big shorter. The reality should depend on how fast amyloid plaques accumulates (varying from person to person) and how fast we might be able to remove the plaque (again, probably varying from person to person). The vector (slope of the line from normal to onset of dementia) should be slightly steeper for those with two APOE4 alleles than for two APOE3 alleles, which would be slightly steeper than for two APOE2 alleles. Those with unmatched alleles (APOE4/APOE2) should vary depending upon which two alleles they carried.

To give a visual idea of what we might expect, I’ve added an image that shows the theoretical response of three different patients (a, b, and c), each of whom might respond equally well to telomerase therapy, but might then need a second treatment at different times, depending on their genes (APOE and other genes) and their environment (for example, head injuries, infections, diet, etc.). Patient c might need retreatment in a few years, while patient a might not need retreatment for twice as long.

 

October 4, 2017

The End Hangs on the Beginning

A major stumbling block in our understanding of age-related disease, such as Alzheimer’s, is a propensity to focus on large numbers of genes, proteins, etc., without asking what lies “upstream” that results in the associations between such genes (etc.) and the disease. While some would tout the advantages of using Artificial Intelligence to attack the problem, the problem with AI is that (like most scientists) it focuses on finding solutions only once the problem has been defined ahead of time. If, for example, we define Alzheimer’s as a genetic disease, then we will find genes, but will never reassess our unexamined assumption that AD is genetic. If we assume that it’s genetic, then we only look at genes. If we assume it’s due to proteins, then we only look at proteins. If we assume that it’s environmental, then we only look at the environment. Data analysis and AI, no matter how powerful, is limited by our assumptions. We tend to use large data analysis (and AI) in the same mode: without ever realizing we have narrowed our search, we assume that a disease is genetic and then accumulate and analyze huge amounts of data on gene associations. While AI can do this more efficiently than human scientists, the answers will always remain futile if we have the wrong question. Once we make assumptions as to the cause, we only look where our assumptions direct us. If we look in the wrong place, then money and effort won’t correct our unexamined assumptions and certainly won’t result in cures.

It’s like asking “which demons caused plague in Europe in the middle ages”? If we assume that the plague was caused by demons, then we will never (no matter how hard-working the researcher, how large the data set we crunch, or how powerful the AI we use) discover that the plague was caused by a bacteria (Yersinia pestis). If you look for demons, you don’t find bacteria. If you look for genes, you don’t find senescent changes in gene expression. The ability to find answers is not merely limited by how hard we work or how large the data sample, but severely and unavoidably limited by how we phrase our questions. We will never get anywhere if we start off in the wrong direction.

To quote the Latin phrase, “Finis origine pendet“. The end hangs on the beginning.

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