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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The relative important weight of RDW in Levine's Phenotypic Age might be possibly explained:
"...Variability in red blood cell (RBC) volumes (RBC distribution width: RDW) increases with age and is a strong predictor of mortality, incident CAD and cancer. In a study of 116,666 UK Biobank volunteers, genetic variants explained 29% of RDW individuals aged over 60 years and 33.8% of RDW in those aged < 50 years [222]. RDW was associated with 194 independent genetic signals (119 intronic), 71 implicated in autoimmune disease, body mass index, Alzheimer’s disease, longevity, age at menopause, bone density, myostasis, Parkinson’s disease and age-related macular degeneration. Pathway analysis showed enrichment for telomere maintenance, ribosomal RNA and apoptosis..."
Morris BJ, Willcox BJ, Donlon TA. Genetic and epigenetic regulation of human aging and longevity. Biochim Biophys Acta Mol Basis Dis. 2019;1865(7):1718-1744.
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link gives a 404 error - page not found
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Paul Beauchemin I put a an alternate download link in my post here to help out: http://www.nickengerer.org/longevity-and-wellness/three-biological-age-tests
hopefully the authors here can fix it soon!
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What a great resource! I am very glad I found this discussion. I shared my results, along with a comparison to 2x other free online tests here:
http://www.nickengerer.org/longevity-and-wellness/three-biological-age-tests
For those of you having issues downloading the excel sheet above, I provided another link for getting it in my post.
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Dr Nick Engerer
Happy you liked this resource. Please also refer to the other thread and my comment to your interesting post: https://forum.age-reversal.net/t/3654ra?r=y7v4mz
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used the spreed sheet to order importance of variable without age for me(I think it is a correlated factor plus it is uncontrollable). Used a 10% improvement to look at the age improvement generated. Had a slightly different order than JGC. RDW and MCV still most important. Third/fourth factors related to kidney function, which based on my blood work is not surprising.
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Is that link supposed to still be live? I can't seem to open it.
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Charles Richardson
I just tried the originsl link:
https://www.dropbox.com/s/8wj94be28lt9k7q/DNAmPhenoAge_gen.xls?dl=0
and it works for me.
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JGC Thanks, I got a 404 error on that link, but I had downloaded it from somewhere else...can't remember exactly where, but I have it.
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Charles Richardson FYI to you and to others who can't get the above link to work, I have posted the spreadsheet file on my website as well:
http://www.nickengerer.org/longevity-and-wellness/three-biological-age-tests
That should help you & others!
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JGC I get the 404 error as well when clicking on the link posted 2 days ago. I tried with Safari and Chrome.
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Jenna Taylor Jenna Taylor Hey guys, I also had this issue, but had a copy of the spreadsheet so I posted it in my blog post about this topic here:
http://www.nickengerer.org/longevity-and-wellness/three-biological-age-tests
That should solve your problem :-) -
Jenna Taylor
I put a copy of the Levine Spreadsheet on MS Cloud. Here's the LINK to it. Hope that works better than DropBox.
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JGC God Bless, Thank you
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THANK YOU, JGC . I was able to open it. Looking forward to seeing the results. Also, thank you for posting this here it is a real service for those of us who are "light" on the math. Jenna
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I have seen the exel sheet that uses Levine's paramteres & coefficents to calculate
MortScore
What confuses me is a parameter "t" in years
I donot tunderstand the meaning of it.
For example, you can put 10 years or 20 years.
Mortality Score changes. And that is expected. The longer the time frame, the higher the probability of mortality.
However, the resulting phenoage also changes. That does not make sense to me.
Can someone please explain?
Thank You
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Zisos Katsiapis
Based on the input parameters, among other things the spreadsheet calculates the "Mortality Score", which is the probability that you will die of age-related causes in the next "t" years. In their paper, Levine, et al, use t=10 years, but you can change this if you wish.
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JGC
Thank you for your responce.
Yes, I have noticed it. And it is reasonable that as I change "t" from 10 years to 20 years, the probability that I will die will increase. The excel sheet reflects that.
But this is not my question.
There is also another value in the sheet: phenoage
I assume that it is my "Biological Age", as computed by the formula.
This I did not expect to change, when I change the value of "t".
For example, if I use t=20, my phenoage is increased substantialy !
This is what I do not understand.
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Zisos Katsiapis
There are two angles from where we can look at your question to hopefully clarify:
Construction
You need to realize how the Phenotypic Age (*) is calculated. You need to spend a bit of time to understand the construct and refer to the original paper (DOI: 10.18632/aging.101414) and the Supplementary Methods (mainly the “Overview of the phenotypic age estimate” and the Step 4 in the “Statistical details on the Gompertz proportional hazards model for phenotypic age estimation” sections): in essence once you have the mortality score, you convert in units of years, which gives the Phenotypic Age, by solving the CDF (Cumulative Distribution Function) equation CDF(120,xj)=CDF.univariate(120,agej) for the variable agej. Hence, if the mortality score changes, it is normal phenotypic age (so constructed) changes too.
Chronological Age
This and other methods to assess the biological age (BA) vs chronological age (CA) use CA as one of the biomarkers (in Levine you have 9 clinical biomarker + age). Some do not agree on the procedure and propose methods not using CA. This is a discussion in progress. In any case what also seems intuitive is that, as we age, the heterogeneity of health status of individuals, basically the spread around the CA, increases which is what a BA (like the Levine’s Phenotypic Age) tries to precisely capture, hopefully predicting healthspan and lifespan. Therefore, also here, BA increases as CA increases. I recollect some talking generally about BA as a metaphor of the individual health heterogeneity as we age.
I hope this helps.
(*) To avoid confusion, as I often emphasized also in this thread, this is how Levine refers to it in her two papers (DOI: 10.18632/aging.101414, DOI: 10.1371/journal.pmed.1002718): I think she reserves what you call phenoage to “DNAm PhenoAge” which involves DNA methylation.
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albedo
Going into the details of the calculation does not really help me. I can see the calculations. But intuitively it does not make sense to me.
As I understand it, both "Levine’s Phenotypic Age" and “DNAm PhenoAge” attempt to measure someone's "Biological Age". It depends only on the health condition of the person, and should depend ONLY in facts (I.e. Blood measurements). Not in parameters that I choose.
Is my understanding correct?
Now in regard to calculations:
When I choose 10 years for "t", in the excel I get a value for "MortScore"
By Entering 20 years, for "t" "MortScore" will increase. That makes sense. There is more chance that I will die in 20 years, than in 10 years.
But my health condition does not change just because I chose an arbitrary parameter of "t"=20 instead of 't"=10 years. Since my health condition does not change when I change the parameter , it seems resonable to me that "Ptypic Age" and "DNAm Phenoage" (which are measures of my Biological Age) should not change. But in the excel sheet, they do ! This does not make sense to me. -
Zisos Katsiapis
The spreadsheet actually calculates the mortality score first and then converts that to the phenoage. The relation for the conversion, supplied by the Levine group, is based on the assumption that t=10 years. It's probably to generalize the conversion relation to include a variable t, but that's not what was in the paper.
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JGC
Thank you for clarification.
Maybe a note should be made in the excel sheet that calculations of "Ptypic Age" and "DNAm Phenoage" are valid only for t=10 years
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Zisos Katsiapis
You have risen an interesting point on the assumption of the 10 years parameter. Maybe we should focus on the mortality rate rather that phenotypic age the latter being a convenient conversion to units of years. This is what Levine's team write in the second paper (DOI: 10.1371/journal.pmed.1002718): "...In general, a person’s
Phenotypic Age signifies the age within the general population that corresponds with
that person’s mortality risk. For example, 2 individuals may be 50 years old chronologically, but one may have a Phenotypic Age of 55 years, indicating that he/she has the average mortality risk of someone who is 55 years old chronologically, whereas the other may have a Phenotypic Age of 45 years, indicating that he/she has the average mortality risk of someone who is 45 years old chronologically..." -
albedo I think you are right. Isn't mortality risk and frailty all we really care about anyway? (other than looking old)
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albedo
This makes a lot of sense.
Your interpretation of "Ptypic Age" and "DNAm Phenoage" clears my confusion.
I had assumed that "Ptypic Age" and "DNAm Phenoage" should be interpreted as a proxy for my current Biological Age. It cannot be interpreted as such, because it would mean that I can change my current Biological Age by changing the parameter "t".
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