A Spreadsheet for Calculating Your Levine Phenotypic Age

  An excellent paper by M. E. Levine, et al, entitled "An epigenetic biomarker of aging for lifespan and healthspan" describes a technique for combining nine blood-work values with calendar age to calculate your Mortality Score (probability of death in the next ten years) and your Phenotypic Age, i.e., your apparent biological age as implied by your blood variables.  The calculation procedure is rather arcane, involving non-obvious unit conversions, exponentials, and logarithms, so I have produced an Excel spreadsheet (LINK) for performing these calculations.

  Levine, et al., also used an elaborate DNA analysis of many blood samples to find what they call the DNAm PhenoAge, a measure of the degree of DNA methylation present, a phenomenon associated with aging.  They correlate this measure with the Phenotypic Age, showing that they track very well.  My spreadsheet uses a fit to their plots to estimate your DNAm PhenoAge and the modified Mortality Score that it implies.

  You may already have blood-work giving the nine blood variables needed to use this spreadsheet, but if not they can be obtained by purchasing the blood-work of LifeExtension's Chemistry Panel & Complete Blood Count (CBC) ($35) and their C-Reactive Protein (CRP), Cardiac ($42).  On the spreadsheet at the upper line of blue numbers, you simply enter your values in place of the ones presently there and enter your decimal calendar age in the last column.  The calculated results then appear in red on the last line.

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  • @ JGC

    It is good to have started a fresh new thread on the Levine's paper and providing your calculator. Thank you again. However, I feel worth also to refer everyone to the thread started by Dan Mc on the same topic/paper where there is an important follow on discussion in particular about integration of several categories of possible biomarkers of aging (e.g. molecular, clinical, anthropometric, machine learning driven, ...), the role of inflammation as characterized by biomarkers as CRP and IL-6 and the intriguing role "geometric" factors as MCV/RDW have.

    A nice follow up here could be to gather information on trends along the years rather that only measurement at one point in time as better tacking the interventions we are trying.

    I have written to Dr Levine along these line (no reply yet) and wonder if you got a reply on the CRP vs. MCV/RDW role.

    Reply Like
      • JGC
      • JGC
      • 1 mth ago
      • Reported - view

      albedo 

      Dr. Steve Horvath answered, saying that they did not have any explanation of the relative weight strengths because it was a numerical optimization that didn't involve human judgement.  My own theory is that the two blood geometry factors are so important in determining the Phenotypic Ager because senescent blood cells have a different geometry from normal blood cells and their increased presence trends to increase both MCV and RDW.

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      • albedo
      • albedo
      • 1 mth ago
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      JGC 

       

      I believe It is a reasonable hypothesis and it wold be worth some research for which I lack time. AFAIK it is quite well established both MCV and RDW increase with age and are associated to mortality and morbidity as cardio vascular diseases (see also the other thread). As much of this is mediated by “inflammaging” it would be interesting to test whether the Senescence-Associated Secretory Phenotype of the cells (SASP) which characterizes senescence in particular in response to inflammatory signalling, likely in all tissues, incl. blood, is also associated to morphological changes which would mechanistically explain the high weights in the Levine’s regression in step 1. Note also that in the regression weights it is rather the variance of MCV (i.e. RDW) which is impactful rather than the MCV itself which makes me wonder, once again, about the higher heterogeneity of the aging population sample as I was mentioning in the previous thread. Worth to follow!

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      • JGC
      • JGC
      • 1 mth ago
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      albedo 

        To modify my hypothesis slightly, the blood geometry factors may not reflect senescent blood cells per se, since the body replaces blood cells fairly regularly.  Rather, enlarged out-of-geometry blood cells reflect the presence of toxins and bad chemical signals generated by senescent body cells with which the blood cells come into close contact.

        I tried to refine our estimate of the influence of the factors used in computing the  Levine PhenoAge by making a 5% increase in each single variable and observing the change (in years) it made in the PhenoAge.  The results, ranking the influences in order, are shown in the tablebelow.  What doesn't show up here is the size of the expected variation of the variable.  For example, the C-reactive protein value can change by orders of magnitude in the presence of a wound or infection.

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      • albedo
      • albedo
      • 4 wk ago
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      JGC 

      The following paper adds to what mentioned previously and confirms the important role of RDW.  WRT mortality and morbidity the paper is no surprise as the meta-analysis is based on similar cohorts as the Levine et al paper. However an interesting comment on the mechanisms seems confirming what discussed: the role of inflammation (and oxidative stress), the higher heterogeneity point I made in my previous posts and the independent prediction capability:

      The mechanisms through which RDW increases with age and is associated with mortality have not been defined; however, it is possible that oxidative stress and inflammation play a role given that both can reduce RBC survival (16,17), leading to a more mixed population of RBC volumes in the circulation. In patients with conditions characterized by increased levels of oxidative stress, such as Down syndrome (18), poor pulmonary function (19), and dialysis (20), RDW values are elevated. Analyses of the NHANES III data showed that decreased serum antioxidant levels, including carotenoids, selenium, and vitamin E, were also associated with increased RDW, although adjusting for these antioxidants did not meaningfully attenuate the RDW-to-mortality association (5). In addition to reduced RBC survival, inflammation might further influence RDW levels by disrupting erythropoiesis. Indeed, it is believed that the increased prevalence of anemia with advancing age is due, in part, to the effects of proinflammatory cytokines on inhibiting the proliferation of erythroid progenitor cells and downregulating erythropoietin receptor expression (17,21,22). Perturbations in erythropoiesis can lead to more variation in cell sizes exiting the bone marrow and might increase RDW. Importantly, however, RDW was more strongly associated with mortality in nonanemic than in anemic older adults (Figure 4). In the NHANES III, adjustment for C-reactive protein, fibrinogen level, and white blood cell count did not substantially alter the effect of RDW on mortality, even though each of these factors was strongly related to RDW and mortality (5).

      Patel KV, Semba RD, Ferrucci L, et al. Red cell distribution width and mortality in older adults: a meta-analysis. J Gerontol A Biol Sci Med Sci. 2010;65(3):258-65.

      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2822283/

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      • JGC
      • JGC
      • 3 wk ago
      • Reported - view

      albedo 

      That's interesting.  I note that the "blood age" version 3.0 calculation on the Aging AI website doesn't even include RDW as one of its input variables.

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      • albedo
      • albedo
      • 3 wk ago
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      JGC 

      Yes, thank you. I did not realize that. MCV is included though. ML/AI are black boxes though which lack the explanatory power of system biology approaches but can generate hypotheses to test. On the other side Horvath’s reply to your note seems also to indicate we do not have a biological understanding underlying the step 1 algorithm neither. I also understand the role of DNA methylation in step 2 is also not completely clear (they try to understand in step 3) in particular its associative or causative role in aging.

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      • albedo
      • albedo
      • 3 days ago
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      JGC 

      Along the lines of the answer you got from Dr. Horvath (thank you), Dr. Levine answered my note on the relative roles played by the clinical biomarkers used for the phenotypic age calculation. While recognizing the RDW's big impact on the estimate, she warns the weights listed in the table were not standardized to allow for a direct comparison.

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      • DanMcL
      • skipping my funeral
      • danmc
      • 3 days ago
      • Reported - view

      JGC Thanks for the Aging AI page. I'm 52, the spreadsheet puts me at 40 and Aging.ai 3.0 puts me at 28, FWIW.

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      • albedo
      • albedo
      • 2 days ago
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      DanMcL 

      DanMcL said:
      JGC Thanks for the Aging AI page. I'm 52, the spreadsheet puts me at 40 and Aging.ai 3.0 puts me at 28, FWIW.

      Interesting. Have you tried also the V 1.0, just curious? It includes 41 parameters (vs. 19 of V 3.0) and I think it should be more accurate. In my case V 3.0 has been giving systematically way too low values.

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      • albedo
      • albedo
      • 2 days ago
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      JGC 

       

      JGC said:
      albedo

      Dr. Steve Horvath answered, saying that they did not have any explanation of the relative weight strengths because it was a numerical optimization that didn't involve human judgement. My own theory is that the two blood geometry factors are so important in determining the Phenotypic Ager because senescent blood cells have a different geometry from normal blood cells and their increased presence trends to increase both MCV and RDW.

      Along the lines of the answer you got from Dr. Horvath (thank you), Dr. Levine answered my note on the relative roles played by the clinical biomarkers used for the phenotypic age calculation. While recognizing the RDW's big impact on the estimate, she warns the weights listed in the table were not standardized to allow for a direct comparison.

      “...In statistics, standardized [regression] coefficients, also called beta coefficients or beta weights, are the estimates resulting from a regression analysis that have been standardized so that the variances of dependent and independent variables are 1.[1] Therefore, standardized coefficients refer to how many standard deviations a dependent variable will change, per standard deviation increase in the predictor variable…. Standardization of the coefficient is usually done to answer the question of which of the independent variables have a greater effect on the dependent variable in a multiple regression analysis, when the variables are measured in different units of measurement (for example, income measured in dollars and family size measured in number of individuals).”

      https://en.wikipedia.org/wiki/Standardized_coefficient

      This is something I would expect to be considered on future follow on works. Would you agree?

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      • DanMcL
      • skipping my funeral
      • danmc
      • 2 days ago
      • Reported - view

      albedo I'll try the earlier version, but assuming they used machine learning (a deep NN) for this, while larger (more input parameters) is generally better even more it's the population data. For example, if I was doing this (I'm a machine learning engineer) I would just take the blood results and age from a large population and make a conv net from that. Easy, but the population has a hidden skew which is it is made up of people who eat the SAD (Standard American Diet) and probably few if nobody like us who practice extreme health habits. So we're probably outliers, my numbers (cholesterol, WBC, etc) are so low they flag in the reports. 

      So your typical 28 year old they measured is still eating SAD, so perhaps we don't fit in the sample set. 

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      • albedo
      • albedo
      • 2 days ago
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      DanMcL 

      DanMcL said:
      albedo I'll try the earlier version, but assuming they used machine learning (a deep NN) for this, while larger (more input parameters) is generally better even more it's the population data. For example, if I was doing this (I'm a machine learning engineer) I would just take the blood results and age from a large population and make a conv net from that. Easy, but the population has a hidden skew which is it is made up of people who eat the SAD (Standard American Diet) and probably few if nobody like us who practice extreme health habits. So we're probably outliers, my numbers (cholesterol, WBC, etc) are so low they flag in the reports.

      So your typical 28 year old they measured is still eating SAD, so perhaps we don't fit in the sample set.

      Hi Dan. It is great to have you insight on these ML estimators as you work in the field and you must understand exactly what they are doing. I suggest you read both papers they refer to in their web site resp. for V 1.0 and V 3.0. But maybe in the meantime you might refer to the same group overview, see section "4.1.3. Multi-modal biomarkers" in the review paper:

      Zhavoronkov A, Mamoshina P, Vanhaelen Q, Scheibye-knudsen M, Moskalev A, Aliper A. Artificial intelligence for aging and longevity research: Recent advances and perspectives. Ageing Res Rev. 2018;49:49-66.

      https://www.ncbi.nlm.nih.gov/pubmed/30472217

      Reply Like
      • DanMcL
      • skipping my funeral
      • danmc
      • 2 days ago
      • Reported - view

      albedo Thanks for the pointers, I'll try to find some time. I am focused on another project you folks might find interesting though, which is a DNN for doing regression analysis of biomarkers. For example, in another thread (under self testing) I talk about a bunch of biomarkers I measure every morning, based originally on Steve Perry's set and augmented. I've since added ECG, galvanic skin response (autonomous emotional state), EEG and a few others. Anyhow once I've got a collection I'll see what can be done with it. Regression analysis (e.g. how the variables change relative to one another) is one approach. The problem is of course is having a population of 1, and even if I put a web page up there are few people measuring this much data. I might ping Steve and get access to his data. Anyhow, WIP ...

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  • JGC  thanks for the spreadsheet. FWIW I have Ptypic Age of 40.35 and est DNAm Age of 39.97 (I'm 53) - so by this measure I'm biologically 40, which is approximately what I would guess to be my biological age, so pretty good on that count. 

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  • Here's an useful small hack - put your numbers in and vary them to see how you can make your bio age go backwards and by what degree. For instance driving your glucose down by 10 points gives you an extra year (at least for my numbers). Higher albumin is better (liver function I believe), as is lower creatinine (indicating healthy kidneys). This can be useful as a tool for what to work on with your blood panel. For example weighing the value of driving glucose down compared to the difficulty (e.g. lifestyle) of doing so.

    I think for me the takeaway is the this looks like a good way to estimate the biological age of your blood only. This should be combined with other measures (e.g. reaction time, cardiovascular health, VO2max, etc) to create an overall estimate of your biological age. For weighings, in the absence of better estimates they could all simply be averaged together. 

    For example, a typical 10 year old has excellent blood work, excellent cardio, balance, reaction time, etc and would come out as being 10 years old. But compare to a poor kid growing up in a nutrition starved environment, presumably that would reflect in their results (slow reaction time, blood work, etc) and they could come out older. 

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  • JGC Is the current link to the spreadsheet based directly on the Levine algorithm, or your modified version where you tried to "correct" it to align more closely to the derived methylation profile? As I've posted before, the former is IMO more valuable as it is more directly related to actual health outocmes than the methylation profile; if this is the "modified" version, may I suggest/request that you post both versions and explain the difference?

    (Thank you again for doing this at all!).

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      • JGC
      • JGC
      • 4 days ago
      • Reported - view

      Iðunn 

      The last two boxes on the last line of the spreadsheet are based on my fit to the methylation profile and are labeled with "est." indicating that it is an estimate.  Everything else is an implementation of the Levine algorithm.  The spreadsheet calculates the Levine values and then uses them in the s=estimate.  I don't see the need to post both versions.  Just ignore those two boxes if you don't trust them.

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