New blood tests can reveal your life expectancy

New blood tests can reveal your life expectancy

Voltor Longo mentioned this new work as being very promising so it likely is a better way to measure biological age. Personally I haven't taken any such test because I haven't found one good and affordable enough - if this gets commercialized it might be the ticket. 

 

OTOH while my health is better than when I was 20, do I really want to know what the biology says? All tests are subject to error, and I don't do measurements unless I know how to mitigate a negative result. I can't do anything beyond what I'm already doing for health, so there's not much point, unless the number comes out fantastic (which I guess it probably would honestly) which would have positive psychological benefits. 

42replies Oldest first
  • Oldest first
  • Newest first
  • Active threads
  • Popular
  • That's an interesting article, Dan. 

    Assessments of biological age are very controversial. From my extensive study of the topic, it is very difficult to assess biological age in a way that is scientifically beyond reproach. Several groups are working on making a test panel that is useful for this assessment, but I haven't heard of any remarkable successes yet. Epigentic age (called "Horvath's clock") seems like a promising one. 

    Like
  • Related to Maximus's reply and the article cited by Dan Mc, one of the best article recently read on the subject is the paper by Morgan Levine et al.

    Levine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018;10(4):573-591.

    Like 1
  • While I fully agree with Maximus about how much controversial is the assessment of biological age (BA) I feel both various epigenetic clocks such as DNAm PhenoAge (see Morgan Levine’s paper I refer to in my previous post) and machine learning/AI driven tools are approaching a good estimation.

    However, the nature of aging is such only a real system biology approach will be truly useful (meaning a convergence of all methodologies at different system levels: cell/tissue/organ/organism)

    Also keep in mind relative values, meaning changes in time vs. absolute values, are likely more important as they should reflect the effectiveness of our interventions.

    The good of DNAm PhenoAge is, IMHO, the good methodology it was used to determine it:

    -        Training on well-known and well-characterized cohorts,

    -        Focusing on both morbidity and mortality,

    -        Robust statistic (penalized Cox regression) to reduce the number of (clinically relevant) biomarkers to a small number then used as weights in the Gomptertz’s mortality curve resulting in a first BA assessment (phenoage),

    -        Regression of phenoage on DNA methylation sites to sort out 513 CpGs sites. Here a fascinating topic is correlation vs. causation which I do not think is resolved yet,

    -        Assessment of estimations on well characterized cohorts at each state of the process.

    On the machine learning/Ai front, you might also try using very simple AI tool as aging.ai where you can input you blood markers and have a guess of BA.

    Have you ever used aging.ai or young.ai ?

    Like 1
      • Max Petomoderator
      • Researcher, website & forum admin
      • Maximus
      • 1 yr ago
      • Reported - view

      albedo I, too, like the epigenetic clocks and PhenoAge. That they correlate with chronological age so well, and also relate to mortality risk, suggests to me that aging may be programmed, and that the epigenetic age is tracking that program. 

      If this is true, then these epigenetic clocks may also be useful for testing whether age-reversal interventions are effective. Which is useful for our goals! 

      Like
      • albedo
      • albedo
      • 1 yr ago
      • 1
      • Reported - view

      Maximus 

      Oh dear … here we enter really in troubled waters with the various theories of aging or rather should I say “hypothesis”.

      I really did not make my mind yet and sometime I say: well, whatever the right hypothesis will turn to be, this does not allow me sitting and do nothing.

      The “programmed” hypothesis goes back to August Weisman in the 19th century. In the meantime, yes, genetics did huge progresses and we do have feature of aging which might be considered programmed and genes influencing the process but we are really far, I guess, for having determined a network of genes which you can mutate to slow/stop the aging process. Yes, you can epigenetically reprogram and that is one of the most recent exciting developments I know of where you can rejuvenate (e.g. in special “progeric” mice for the time being) by in vivo resetting the epigenetic landscape. But that is not all. E.g. I still have trouble in reconciling this with the fact the mortality increases from very early times of our adulthood and not later in life. And there are also other arguments against the fascinating programmed hypothesis.

      Then you have the quite intuitive “wear & tear” or “damage” based, sort of physics driven, hypothesis of aging based on stochastic damage driven by interactions with the environment and metabolism. But also this has issues. I think SENS is mostly adoptive of this approach calling for “repair” and often I feel to endorse this vision. Somewhat it looks like freeing me from entering a very controversial area, which counts I guess in the 10-20 different hypothesis, and rather be proactive against aging trying a “repair and maintain" approach. But on a more scientific basis I do realize many scientists want to understand what aging really is even if, as one friend once told me, dying in doing it ......! Many call for a system biology and multilevel approach to such a hugely complex phenomenon to maximize chances of deep understating.

      Possibly the damage and programmed hypothesis will one day converge, maybe based on a better understanding of why and how evolution evolved aging.

      Like 1
      • Max Petomoderator
      • Researcher, website & forum admin
      • Maximus
      • 1 yr ago
      • 1
      • Reported - view

      albedo I appreciate your thoughtful response, Albedo. Back in 2007, I felt similarly to what you said: 

      albedo said:
      well, whatever the right hypothesis will turn to be, this does not allow me sitting and do nothing.

      So I began studying aging and what we can do about it. 

      I, too, have not yet made up my mind whether the age-associated increase in the human mortality rate is caused by damage accumulation, programmed aging, or both. 

      Regarding programmed aging and the increase in mortality very early in adulthood, I can actually see that as an argument for programmed aging. This could be somehow related to the cessation/completion of adolescence, when many factors change significantly (e.g. life-long decline in IGF-1, growth hormone secretion, melatonin production, etc.).

      One theory I wonder about is whether the age-related increase in mortality may be caused by the program of development (adrenarche and gonadarche) continuing, even though physiological maturity has reached its end. This is very similar to what Mikhail Blagosklonny has said about "hyperfunction". Maybe that's true, but I'm not yet sure how it can cause so much of the atrophy of glands and tissues associated with aging. And why all of the atrophy in the context of reduced IGF-1 and other growth factors? 

      But just because programmed aging is an intriguing theory (to me), doesn't mean I necessarily believe it to the exclusion of others. As you said in your post, I can not sit and do nothing, and will work on bringing degenerative aging under complete medical control, regardless of the mechanism(s). 

      Like 1
      • albedo
      • albedo
      • 1 yr ago
      • Reported - view

      Maximus

      Regarding programmed aging and the increase in mortality very early in adulthood, I can actually see that as an argument for programmed aging. This could be somehow related to the cessation/completion of adolescence, when many factors change significantly (e.g. life-long decline in IGF-1, growth hormone secretion, melatonin production, etc.).

      Thank you Maximus, I understand your point but I am still confused (not your fault of course but only my lack of understanding!) and would need to go back to studying in more depth, but in short: shouldn't evolution favor more offsprings, which a longer living organism would generate, so disfavoring a "program" starting in early adulthood? In other worlds, if the hypothesis is right, evolution would have "programmed" aging later in life (I think this is what some would call the "decreasing of evolutionary pressure") not in earlier adulthood as on the contrary data seem to show (I think this based on the experimental mortality Gompertz's "law"). Some (Josh Middeldorf??) might associate this to a non-neodarwinian hypothesis of aging as a "protection" of the specie but I might be completely off track! That is what I meant earlier by "troubled waters": for every single hypothesis, there are pro- and counter-arguments and I feel the necessity of the experimental approach to avoid constantly swinging between them!

      Like
      • Max Petomoderator
      • Researcher, website & forum admin
      • Maximus
      • 1 yr ago
      • 1
      • Reported - view

      albedo 
      You're welcome Albedo. I'm enjoying discussing with you. 

      You said: 

      albedo said:
      shouldn't evolution favor more offsprings, which a longer living organism would generate, so disfavoring a "program" starting in early adulthood?

      That's a good idea, but I'm not sure that's the case. I have been wondering: perhaps longevity needed to be balanced with the amount of survival time required to reproduce. 

      For example, humans take a relatively long time to be reproductively mature and active. I have a suspicion that the developmental program to maturity dictates the lifespan, because that developmental program is the cause of degenerative aging. 

      For example, in mice, the time required after birth to become reproductively mature is very short compared to humans: weeks, and not over a decade like humans. And mice have a very short lifespan compared to humans. 

      In brief: one theory I'm considering is that degenerative aging is merely an extension of the program of physiological development. Thus, lifespans of various animals will correlate with their time to sexual maturity, because it is that program of physiological development that causes degenerative aging. 

      In short: the faster an animal reaches sexual maturity, the faster they die. 

      I did a little research on this, and it's pretty consistent. For example, cows reach sexual maturity  at only 1 year old, even though they are so big! But they only live around 20 years. In other words, like mice, the "developmental program" of cows is more aggressive than humans, but they also age faster, and die more quickly, than humans. 

      Humans, perhaps because they had much less predation than other animals (maybe because we're more clever, use tools, work in groups, etc.), it was OK to take 10+ years to become reproductively active. According to my theory, our "maturity program" is slower, and that gives us our longer lifespans. We develop (and age!) more slowly than most other animals. 

      And this is still consistent with your idea, in a way: we may not live long enough to procreate for hundreds of years, but the duration of our reproductive activity is far longer than a mouse (or even a cow) During this time, humans have much more time than many other organisms do, which is available to produce more offspring. Imagine if mice were reproductively active for 40+ years like humans are...

      ___
      Maximus 

      Like 1
      • albedo
      • albedo
      • 1 yr ago
      • 1
      • Reported - view

      Maximus 

      Thank you for your educated and well researched reply !

      Just for my own record here I think this must be the paper Dan Mc mentions in his original post and I need to read it but from first approach it looks much like the Morgan Levine et al. paper I already have mentioned in my previous post.

      Like 1
      • Max Petomoderator
      • Researcher, website & forum admin
      • Maximus
      • 1 yr ago
      • Reported - view

      albedo Thanks Albedo. 

      Yes, that will be an interesting paper from Levine about Phenotypic age. Thank you for linking it for us. 

      Like
      • JGC
      • JGC
      • 1 yr ago
      • Reported - view

      albedo 

      I have found that all of the blood variables needed to calculate the DNAm PhenoAge  given in Morgan Levine’s paper can be obtained from blood work provided by LifeExtension with their C-Reactive Protein (CRP) ($42) blood test and their Chemistry Panel & Complete Blood Count (CBC) ($35) blood test.  I have just ordered these for my wife and me.   When the results are available, for the creatine and glucose entries one must convert mg/mL to umole/L and mmol/L, respectively, but otherwise there should be no problems.  We plan to determine our DNAm PhenoAge values before and after senolytic treatments with fisetin and with D+Q.

      Like
      • albedo
      • albedo
      • 1 yr ago
      • Reported - view

      JGC 

      That is great JGC. It would be nice if you can share some results here. If you arrive also to gather additional data point before and after treatment that would possibly also reveal trends.

      Like
      • Iðunn
      • Iunn
      • 1 yr ago
      • Reported - view

      JGC How are you going to run the results? I don't see any obvious formula given in the paper to let you plug in your numbers and get your PhenoAge.

      Like
      • Iðunn
      • Iunn
      • 1 yr ago
      • Reported - view

      JGC Oh, sorry: I just now read your later reply. Is your Mathematica file a fixed document that just contains your results and your PhenoAge worked out, or can it be used to input one's own values and have it crunch out one's own PhenoAge?

      Thanks!

      Like
      • JGC
      • JGC
      • 1 yr ago
      • Reported - view

      Iðunn 

      The formulas needed are given in Supplement 1 of the Levine paper.  My Mathematica notebook has two "vectors", one containing the blood-work values for me and the other for my wife, with two of the values multiplied by conversion factors to put them into umol/L and mmol/L.  Another user, if they had Mathematica, could just put in his own values.  Also, the calculation gives the mortality probability in 10 years, but the 10 can be changed to get mortality over another period.

      I may later do an Excel spreadsheet that makes the same calculations, but it was initially easier for me to use Mathematica.

      Like
  • I am curious to know if anyone here has ever used tools such as aging.ai or young.ai ? I did but have doubts.

    Like
      • JGC
      • JGC
      • 1 yr ago
      • 1
      • Reported - view

      albedo 

      I'll be glad to share the results of our DNAm PhenoAge  values before and after fisetin and D+Q, but it may be several months before the results are available.

      My wife and I have used Aging AI recently with blood-work values from general physical exams taken in early July.  It got out sexes right and said that my predicted age is 35.0 and hers is 37.0.  Actually, I'm about to turn 84 and she is about to turn 79.  I attribute the huge differences to the fact that we've both been taking 85 mg/day of metformin, plus other supplements from LifeCodeRX, but it could be just that their algorithm is screwed up.  It will be interesting to see what our DNAm PhenoAges are.

      Like 1
      • albedo
      • albedo
      • 1 yr ago
      • Reported - view

      JGC 

      I am quite skeptic of results and probably trends are more informative than absolute values. Have you tried which version (they have 3)? I got lower guessed age with version 3.0 than 1.0 the latter being more accurate I guess because based on more biomarkers.

      Like
  • Also, even if more research oriented than strictly in tracking BA, I wonder if someone here knows something along the same lines as the following paper (more systemic) but recent (the paper is from a UK workshop of experts in 2012 !) and with longitudinal tracking:

    Lara J, Cooper R, Nissan J, et al. A proposed panel of biomarkers of healthy ageing. BMC Med. 2015;13:222.

    Like
      • JGC
      • JGC
      • 1 yr ago
      • Reported - view

      albedo 

      FYI, we used Aging AI 3.0, which they claim is just as accurate as the earlier versions but needs fewer input variables. In my opinion, what is critically missing from all three Aging AI algorithms is any measure of the degree of inflammation present.

      Yesterday my wife and I had our blood drawn in preparation for obtaining the blood values needed for the DNAm PhenoAge calculation.  When available (in perhaps a week) I'll report how the "ages" obtained compare to our Aging AI "ages".  (Note: we also took our first (of 4) 800 mg doses of Fisetin.)

      The Cooper-Nissan paper to which you refer is certainly  comprehensive, but it is not at all clear how an individual like me (as opposed to a research group) can use it for anything useful.

      Like
      • albedo
      • albedo
      • 1 yr ago
      • Reported - view

      JGC 

      On the first point I tend to disagree with you JGC. They provide for v1.0 a MAE (error) of 5.5 years while for v3.0 the give 5.9 year. It looks to me more logical as v1.0 might be more accurate in prediction as containing more parameters.

      For the reference I provided I agree it might not be of practical use for all of us but I wished to know if something similarly comprehensive exist but more recent and if studies have been done longitudinally on cohorts to see changes. However, as the list comes out from an expert group, it is still useful to me. E.g. as I understand from your posts, you particularly care (rightly) about inflammation they mention IL-6 which for example I never really measured. I just discovered LP(a) which many of us got tested in a lipid panel can be driven by IL-6 so a low level of LP(a) might be a proxy of IL-6 and in addition you can combine that information with other common inflammation markers such as hr-CRP, Ferritin and others which might even be more organ specific.

      Like
      • JGC
      • JGC
      • 1 yr ago
      • Reported - view

      albedo 

      Our recent blood tests, LifeExtension's CBC Chemistry Profile and C Reactive Protein measurement, provide all the values needed for the DNAm PhenoAge calculation and for Aging AI 3.0, but they do not include several of the values that Aging AI 1.0 wants, so I can't do that one.  I suppose I could order more blood-work, but the difference in an estimated 5.5 year error and a 5.9 year error does not seem that significant to me.

      Like
      • albedo
      • albedo
      • 1 yr ago
      • Reported - view

      JGC 

      Yes I think at this stage the most important if the DNAm PhenoAge input and would not bother too much with aging.ai . Do not bother to reply and focus on the more important.

      In any case, the aging.ai prediction should not change much when you lack some values. Sometime I left blank or used extrapolations from other measurement I did or used a mid point in my lab ref ranges. In v1.0 they note that the markers with a ** are "Required parameter for minimal prediction accuracy of 70% within 10 year frame". In v1.0 reference paper they write: "...Many users expressed no desire to specify all 41 parameters of the blood test, so we added an option to enter only the 10 most important markers. The average number of missing values provided by the volunteer testers was 18.5 markers per person. There are several strategies for filling skipped values, including zero, mean, mode and median over all values of each marker. Evaluation of these 4 strategies on the aging.ai data showed that median filling strategy has the best performance in terms of both R2 and epsilon-prediction accuracy (Figure 4C & D)."

      In my case and for the anecdote, over the 10 years range 50-60 years old of chronological age, I find:

      - systematically higher level of predicted age with v1.0 vs. v3.0. The midpoint value at 55 goes from 32.4 years (v3.0) to 50.2 years (v1.0) or +55 %. I was expecting this a bit as, intuitively, more parameters should get a predicted age closer to the real one.

      - while the v3.0 results (plot: predicted vs. chronological) does not show a trend and remains basically flat over the 10 years range, the v1.0 seems to show a small trend downward, mostly in later years, possibly hinting to some impact of lifestyle changes.

      I must say I am quite skeptic about what all this means and will take a DNAm test.

      Like
  • OK, I now have the results of our LifeExtension blood work done four days ago in preparation for calculating our DNAm PhenoAge from the Levine, et al paper referenced above.  The actual calculation is rather formidable, involving lots of exponentials and natural logarithms, so instead of using Excel I wrote a Mathematica notebook to do the evaluation  (which is available HERE).  The paper provides a procedure for calculating the probability of mortality in the next 10 years and  what they call the "DNAm PhenoAge", which is based on this 10-year mortality probability.  My wife and I had these blood tests done in preparation for several monthly rounds of senolytic treatments with first Fisetin and then D+Q, which we started a few days ago. 

    The results of the calculations (assuming that I did them correctly) are rather shocking.  I just had my 84-year birthday and my wife will turn 79 in a couple of weeks.  The calculations say that I have a 97.8% chance of mortality in the next 10 years and that my wife's mortality probability is 72.5%.  Further my DNAm PhenoAge is 98.7 years and hers in 86.6 years.

    Looking at what goes into producing the above values, the three most important factors are: (1) actual age, (2) red cell distribution width, and (3) mean cell volume.  The least important contribution to the calculation is the C-reactive protein, a measure of inflammation.  I find this rather surprising and counter-intuitive.  I would expect inflammation to play a large role in mortality and blood cell volume and distribution width to be rather minor factors.

    Like 2
      • albedo
      • albedo
      • 1 yr ago
      • 1
      • Reported - view

      JGC 

      Wonderful information, thank you. I am traveling but will get back asap on this. I recollect CRP was given as mg/dL and log in Levine's paper. I have it in mg/L so how do I need to input it in the calculator? log10, log2 or?

      Like 1
      • JGC
      • JGC
      • 1 yr ago
      • 1
      • Reported - view

      albedo 

      Thanks for asking this question, which caused me to realize I had made an error.  The entry in Table 1 and Table S1 of the paper for C-reactive protein wants the value on mg/dL and has a parenthetic log in the description line.   However, I had just put in the value in the value in mg/L as given on my blood analysis.   When I convert to mg/dL and take the natural logarithm, my 10 year mortality probability drops to 0.891 and my wife's to 0.527.  My DNAm PhenoAge goes to 92.7 and hers to 80.6.  Those values are are still higher than I would like, but more in the ballpark.  (I think "log" means natural log, as used elsewhere in the paper, but that's a guess.)  I'll replace the Mathematica notebook in DropBox with the corrected one.

      Like 1
      • JGC
      • JGC
      • 1 yr ago
      • 2
      • Reported - view

      albedo 

      OK I made an Excel spreadsheet that does the DNAm PhenoAge calculation from blood test inputs.  The link to the spreadsheet is HERE.  On the blue INPUT line, you simply replace my data from blood tests with your own, and on the red output line the spreadsheet will calculate your probability of mortality in the next 10 years and your DNAm PhenoAge.

      In my previous posts, I had the Albumin variable in the wrong units (gm/dL instead of gm/L) and the calculation was giving suspiciously large numbers.  Now is says that my Mortatlity Score is 0.497 and my wife's is 0.207, both reasonable.  My DNAm PhenoAge is 79.7 years and my wife's is 67.7 years, both lower than our calendar ages.

      Like 2
      • albedo
      • albedo
      • 1 yr ago
      • 1
      • Reported - view

      JGC 

      Thank you ! Wonderful piece of work and so telling about you that you have openly shared it! Independently and a bit in the hurry, I went through the same formidable exp's and ln's effort directly in Excel (but in a rather awful way when comparing to you elegant spreadsheet!) and can confirm all your calculations. It was good also my lab, in EU, gave the biomarkers values directly in SI units ;-) which made the task of comparing with your work easier.

      If I may, I suggest a coupe of minor improvements, maybe the last subject to further discussion:

      spelling: albumin

      spelling: creatinine (vs. creatine which is a different thing)

      IMHO, what we calculated here is what they call "phenotypic age" in their paper (Step 1). I would rather reserve the term of PhenoAge to what they perform in Step 2 when regressing the cohort DNA methylation data on the phenotypic age. I need to better understand this step though and wonder if you have additional insight. OTOS, you use PhenoAge vs DNAn PhenoAge so it looks right. You might also refer to this presentation by Dr Morgan Levine:

      http://gero.usc.edu/CBPH/files/4_25_2018_PAA/1.Levine.pdf

      Like 1
      • Iðunn
      • Iunn
      • 1 yr ago
      • Reported - view

      JGC This is fantastic: thank you so much for doing this and sharing!

      Like
      • JGC
      • JGC
      • 1 yr ago
      • Reported - view

      albedo 

      Thanks for the corrections and suggestions, which I implemented.  I replaced the old spreadsheet with the updated one, which is HERE.

      In the "Terms" row of the spreadsheet, in which one can compare relative contributions to the linear combination variable, I'm still bothered by the fact that the dominant terms in the calculation are Age, Red Cell Distribution Width, Mean Cell Volume, and Glucose, while the C-reactive Protein (indicating inflammation) is in next-to-last place.  It seems counter-intuitive to me that inflammation would have such a small (and logarithmic) effect on predicted mortality and that two essentially "geometric" blood factors would be so important.  I sent the corresponding author of the Levine paper an e-mail asking about this, but he has not answered.

      Like
      • JGC
      • JGC
      • 1 yr ago
      • Reported - view

      albedo 

      On the Phenotypic Age graph from the Levin talk that you show, it is interesting that in the 80+ region (where I am)  the data leaves the red line and flattens as the Phenotype Age begins to over-predict the DNAm PhenoAge.  It appears that  there needs to be a fit with a polynomial instead of a straight line,  leading to a correction.  That would give a better prediction of  DNAm PhenoAge in the 80+ region.

      Like
      • albedo
      • albedo
      • 1 yr ago
      • Reported - view

      JGC 

      I did also notice this and intrigued by the weight of CRP inflammation, e.g. when compared to RDW. I do not have an easy answer lacking the required expertise. However I try to give some food for thought.

      First, to me this is another proof point you need to integrate a larger fraction of biomarkers of aging. One critic a bit along these lines was done to a previous paper by Levine's by Mitnitski and Rockwood on frailty indexes in particular.

      I feel more progress will happen when a system biology approach will be taken to the biological age determination, integrating several approaches such as the epigenetic (as DNA methylation), clinically biomarkers (as for the Phenotypic Age), anthropometric (as in Mitnitski/Rockwood) and AI/Machine Learning (as in Aging.Ai). IMHO this is also to be expected as, similarly, a system biology and multi-level approach to aging is important for a better understanding of what aging really is (which BTW does not mean to seat and wait for doing something against it as I wrote in a previous post in this Forum ;)). Levine replies in this sense to the Mitnitski and Rockwood critic to a previous paper of her:

      "... I agree with Mitnitski and Rockwood that a systems biology approach is important, and in moving forward, algorithms need to incorporate interactions between various systems/levels, which may rely on more advanced computational techniques—such as machine learning...."

      Levine ME. Response to Dr. Mitnitski's and Dr. Rockwood's letter to the editor: Biological age revisited. J Gerontol A Biol Sci Med Sci. 2014;69(3):297-8.

      https://academic.oup.com/biomedgerontology/article/69A/3/297/707010

      Second, it is not a matter of one biomarker – one disease but it is rather a matter of, again, integration and network of biomarkers.

      For example CRP is a marker of chronic inflammation which might turn to increase mortality risk maybe later in the course of life while CVD or other might take their toll earlier. And sure enough, RDW, which turned out to have the highest weight in the phenotypic age determination, has an higher impact for determining heart coronary disease and mortality. E.g. see:

      Pilling LC, Atkins JL, Kuchel GA, Ferrucci L, Melzer D. Red cell distribution width and common disease onsets in 240,477 healthy volunteers followed for up to 9 years. PLoS One. 2018;13(9):e0203504. Published 2018 Sep 13. doi:10.1371/journal.pone.0203504

       https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6136726/

      Perlstein TS, Weuve J, Pfeffer MA, Beckman JA. Red blood cell distribution width and mortality risk in a community-based prospective cohort. Arch Intern Med. 2009;169(6):588-94.

      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3387573/

      Third, one biomarkers might measure different things and I think that is always the challenge of determining its clinical relevance.  E.g. I recently discovered, for different reasons, that CA 19-9, a marker typically used in pancreatic cancer, is very useful to assess the therapeutic effectiveness of lung infection to NTM (non-tuberculosis mycobacterium) possibly mediated by the bronchial inflammation level. So maybe, to ease your concern, it is quite possible RDW is very much related to inflammation and is maybe “telling” similar things and while you pointed (righty) to CRP for inflammation, you might be better off with RDW for the same reason, e.g. see:

      Lippi G, Targher G, Montagnana M, Salvagno GL, Zoppini G, Guidi GC. Relation between red blood cell distribution width and inflammatory biomarkers in a large cohort of unselected outpatients. Arch Pathol Lab Med. 2009;133(4):628-32.

      https://www.ncbi.nlm.nih.gov/pubmed/19391664

      Like
      • albedo
      • albedo
      • 1 yr ago
      • Reported - view

      JGC 

      I also noticed that flattening, but it is real?

      Yes indeed another fit might be better but I do not know. Did Levine tried it out? I do not think she explains this in detail in her paper.

      I think there two problems, one which is statistical in nature and the other one related to the concept of biological age itself.

      Statistically, the few data in that region of the model makes me wonder about the value of any extrapolation. Would you agree?

      Second, it is quite known that the predictive value of a biomarker of aging, maybe independently from its nature, decreases due to the increased heterogeneity of the sample population at old age.

      Like
      • JGC
      • JGC
      • 1 yr ago
      • Reported - view

      albedo 

      The Lervine data for very old individuals is sparse, but it's consistently below the linear dependence line, so I take it seriously.  I read some points off the Levine graph and then used the fitting capabilities of Mathematica 11.3  to get a rough correction procedure from those points.  Here, to be taken with a grain of salt, are the results.

      If the Phenotypic Age, as calculated from my Excel spreadsheet, is P, then the corresponding DNAm PhenoAge estimate D is:
      D(P) = P/(1 + 1.28047*Exp(0.0344329 (P-182.344))).
      The Mortality Score MS corresponding to this value of D is:
      MS(D) = 1 - Exp[-0.0005203635*Exp[0.090165*x]]
      Below is a plot showing my fit to the points that I extracted.

       

      For me, my calendar age is 84, and the spreadsheet says that my Phenotypic Age is 79.73 and my Mortality Score is 0.498.  These fits give my corrected Phenotypic Age, i.e., my estimated DNAm PhenoAge, as 76.86 and my corrected Mortality Score as 0.413. 

      Like
      • Iðunn
      • Iunn
      • 1 yr ago
      • Reported - view

      JGC But, hang on: the DNAm PhenoAge is built up from the Phenotypic age, not the other way around — and "the phenotypic age measure used to select CpGs is a better predictor of morbidity and mortality outcomes than DNAm PhenoAge." "Correcting" the Phenotypic Age to match up more closely with the DNAm PhenoAge would be putting the horse before the cart.

      Like
      • JGC
      • JGC
      • 1 yr ago
      • Reported - view

      Iðunn 

      In the Levine paper they report two approaches, which they connect.  Their Step 1 is based on blood work and Step 2 on methylation of DNA.  In the latter, they focus on evidence that a subject's DNA has become methylated as a marker of aging by analyzing some 500+ regions of a DNA analysis for evidence of methylation.  They correlate this with Phenotypic Age from Step 1 to get weighting factors for producing what they call the DNAm PhenoAge.  Since the latter is based on a direct analysis of DNA damage, I take it to be a better indication of aging effects than the blood work.  However, I suppose that is a matter of choice and opinion.

      Of course, there are large uncertainties in either of these aging measures, but at least my fitting allows one to work both sides of the street.  Assuming that the main effects of aging are from the buildup of senescent cells, such buildup will show up both in average DNA damage and in the increasing presence of senescent cells in the bloodstream, which the Levine Step 1 weighting suggests shows up in average blood-cell geometry.

      My present goal is to self-experiment with the plausible and currently accessible senolytic treatments (e.g., Fisetin and D+Q) on my wife and me, and to look at both of these markers of aging after each treatment sequence to see if either or both of the senolytic treatments have observable effects. 

      Like
      • albedo
      • albedo
      • 1 yr ago
      • Reported - view

      JGC 

      Thank you for sharing. I also applied the correction to my data and got similar results (proportionally less than yours due to my younger calendar age). Yet, I still wonder about the effect of heterogeneity of population at old ages and predictability (the second point in my post above) but I also start to believe the effect. Flattening of DNAm age clock has been shown, barring confounders and issue of repeatability with other cohorts, in centenarians by the team of Claudio Franceschi in Bologna (btw, he is the father of "inflammaging" which I am sure resonates with your thinking).

      Like
      • JGC
      • JGC
      • 1 yr ago
      • Reported - view

      albedo 

      I updated the Excl spreadsheet HERE to include the correction for the DNAm age and the corresponding mortality score.

      Like
  • Just curious when the Levine et al paper on Phenotypic age which Dan refers to in his original post will be published. For the moment it is still at preprint level.

    Like 1
  • Thanks for sharing this! I have it a try, and compared it with AgingAI and the RealAge test and write up my results here:

    http://www.nickengerer.org/longevity-and-wellness/three-biological-age-tests

    Like
      • albedo
      • albedo
      • 7 days ago
      • Reported - view

      Dr Nick Engerer

      It is great you are monitoring yourself. Just a couple of comments if you feel to purse this path:

      1. have you ever used the V1.0 of aging.ai? It includes more biomarkers? I find typically higher values that V3.0, i.e. closer to chronological age (CA).

      2. have you made a comparison between aging.ai and Levine's Phenotypic Age not only on one point but across several years. I found differences in trends and values which yet become similar in recent years.

      3. an important point you make is the bias toward CA of aging.ai. I do not understand if that is true and if so why but in general I wonder about the statistical bias which all these methodologies might carry. Be aware that also Levine's Phenotypic Age includes CA in the formulas giving the biological age (BA). There are advantages to that but also disadvantages. If you have enough longitudinal data needed for points 1 and 2, you may try also one of the methods known in the literature NOT to make use of CA, focusing in particular directly on the difference between BA and CA, e.g. see the work by Mitnitski et al. who wrote extensively on these aspects, e.g. in:

      Mitnitski A, Howlett SE, Rockwood K. Heterogeneity of Human Aging and Its Assessment. J Gerontol A Biol Sci Med Sci. 2017;72(7):877-884, doi:10.1093/gerona/glw089

      Like
Like1 Follow
  • 1 Likes
  • 7 days agoLast active
  • 42Replies
  • 704Views
  • 7 Following