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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Here's the Excel spreadsheet that I use:
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John Hemming What have you noticed most with your reduced inflammation? Thanks
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Michael The difficulty is that isolating out the cause and effect of any particular change is difficult. People tell me I am less inflamed and the levine spreadsheet has a lower mortality, but that is looking at a number of indicators of health. Physically I am stronger without doing a lot of exercise and my joints are noticably more flexible. The reason I like the spreadsheet biomarkers is that they link to defined health processes.
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You may find this online tool useful. https://agingmetrics.org/CalculatePhenAgeResp.aspx
Enter Levine/Horvath markers directly and calculate - or extract values if lab reports are from Life Extension (run by LabCorp). And most recent ones directly from LabCorp will extract. Lab reports scanned to PDF do not actually contain the data and will not work.
Useful info: https://agingmetrics.org/CalculatePhenotypicAge.html
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JohnnyAdams Thanks for this. However, there is a problem with many of the javascript calculators in that they are prescriptive as to the units of measurement. Sometimes people get molar results, sometimes they get results by weight and there are other results. The spreadsheet is easier to use because you can adjust the formulae to enable putting in source data and then have the correct calculation done.
I was thinking of writing a version which allows different units as options for different measurements, but actually people are best pointed at the spreadsheet.
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After months of research, I finally got my bloods done and used the spreadsheet shared above. However, I have thalassemia minor (no physiological symptoms and my iron panel is good) however, since I have small RBC, my RCDW is large (18) vs. normal range of 10 to 15. This increases my estimated age by nearly 35% (c. 48 vs actual age of 36). Ceteris paribus, if my RCDW was 10 to 15, my phenotypic age would be between 18 and 37 (mid. = 28)
I have also run my number through aging.ai (3.0) which doesn't use RCDW and my phenotypic age is between 20 and 41 depending on selected ethnicity. I get 20 - 22 when ethnicity is Europe, Asia (ex. Middle East) and America (excluding Central and South America). Otherwise, for ME, South/Central America and Africa, I am phenotypically 41. Is there a racial bias or are insufficient data points for Africa, South America, Middle East and Oceania?
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Here's the Levine Phenotypic Age spreadsheet (again)
People have been having trouble downloading the spreadsheet from various locations in the Cloud. Therefore, here it is as an attached file (below).
Note: the extra zero in the denominator (see above) has been removed.
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Bill Faloon is also using Levine's Phenotypic Age to calculate his biological age. See his new post: https://age-reversal.net/wp-content/uploads/2023/08/2023.07.17-Bill-Faloon-Biological-Age-Blood-Test-Panel.pdf
I am also happy I was the one who spot with authors (Zuyun Liu) the mistake in the formula which was corrected in the paper correction of 2019. I was using my own spread sheet before JGC published his super elegant version. Note also the he made (as described above) a calculation to estimate Levine's PhenoAge which requires methylation data.
BTW, I am also ~15 years "younger" than my age at the same Faloon's (chrono) age ... ;-)
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Two interesting studies from the same Swedish team using a similar data set using Levine's PhenotypicAge and PhenoAge and comparison with different BA measurement methodologies.
Mak JKL, McMurran CE, Kuja-Halkola R, et al. Clinical biomarker-based biological aging and risk of cancer in the UK Biobank. Br J Cancer. 2023;129(1):94-103.
Mak JKL, McMurran CE, Hägg S. Clinical biomarker-based biological ageing and future risk of neurological disorders in the UK Biobank. J Neurol Neurosurg Psychiatry. Published online October 26, 2023.
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Comparing clocks. PhenoAgeV2 seem leading with others too ....
"...The most rigorous of the four, AA2 task demonstrates that second-generation aging clocks (PhenoAgeV2
[98], GrimAgeV1 [17], GrimAgeV2 [99], and PhenoAgeV1 [16]) appear on top, particularly
at predicting aging acceleration for the ISD class (Fig. 3E, Supplementary Materials Fig.
A5). Nevertheless, all clocks failed to detect aging acceleration in patients with cardiovascular and
metabolic diseases, at least at the statistically significant level (see Figs. A3 and A4 for results
without FDR correction)..." (red mine)ComputAgeBench: Epigenetic Aging Clocks Benchmark
Dmitrii Kriukov, Evgeniy Efimov, Ekaterina A Kuzmina, Ekaterina E Khrameeva, Dmitry V Dylov
bioRxiv 2024.06.06.597715; doi: https://doi.org/10.1...24.06.06.597715
https://www.biorxiv.....06.06.597715v1
"The success of clinical trials of longevity drugs relies heavily on identifying integrative health and aging biomarkers, such as biological age. Epigenetic aging clocks predict the biological age of an individual using their DNA methylation profiles, commonly retrieved from blood samples. However, there is no standardized methodology to validate and compare epigenetic clock models as yet. We propose ComputAgeBench, a unifying framework that comprises such a methodology and a dataset for comprehensive benchmarking of different clinically relevant aging clocks. Our methodology exploits the core idea that reliable aging clocks must be able to distinguish between healthy individuals and those with aging-accelerating conditions. Specifically, we collected and harmonized 66 public datasets of blood DNA methylation, covering 19 such conditions across different ages and tested 13 published clock models. We believe our work will bring the fields of aging biology and machine learning closer together for the research on reliable biomarkers of health and aging."
