Quantifying Biological Age

In an earlier post, I wrote about quantifying my biological age with aging.ai (https://michaellustgarten.com/2018/06/26/maximizing-health-and-lifespan-is-calorie-restriction-essential/). The importance of that post is illustrated by the finding that based on data from 13 blood tests between 2016 – 2019, my average biological age is 29.2y, which is ~33% younger than my chronological age.

On my quest for optimal health, I’m striving to get as accurate as possible when it comes to quantifying biological age. While the aging.ai biomarker set is strongly correlated with biologic age (r = 0.80), in 2018 two papers were published (Liu et al., Levine et al.) that introduced “Phenotypic Age”, which includes a combination of 9 circulating biomarkers + chronological age that is better at predicting biological age (r = 0.94) than aging.ai. It includes analytes that are found on the standard blood chemistry screen, including albumin, creatinine, glucose, lymphocyte %, mean corpuscular volume (MCV), red blood cell distribution width (RDW), alkaline phosphatase, white blood cells, and an analyte that is not found on that panel, C-reactive protein (CRP). In addition, chronological age is included as a covariate.

So what’s my biological age based on the Phenotypic Age calculator? When I input my data from my latest blood test measurement on 6/4/2019, I get a biological age of 35.39y, which is 23% lower than my chronological age of 46. Not bad!

phenoage

To quantify your biological age with the Phenotypic Age calculator, input your data in the Excel file that is embedded within the first paragraph of the following link:

https://forum.age-reversal.net/t/h4b2b5/a-spreadsheet-for-calculating-your-levine-phenotypic-age

 

Also, if you’re interested, please have a look at my book!

 

References

Liu Z, Kuo PL, Horvath S, Crimmins E, Ferrucci L, Levine M. A new aging measure captures morbidity and mortality riskacross diverse subpopulations from NHANES IV: A cohort studyPLoS Med. 2018 Dec 31;15(12):e1002718. doi: 10.1371/journal.pmed.1002718.

Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y, Whitsel EA, Wilson JG, Reiner AP, Aviv A, Lohman K, Liu Y, Ferrucci L, Horvath S. An epigenetic biomarker of aging for lifespan and healthspanAging (Albany NY). 2018 Apr 18;10(4):573-591. doi: 10.18632/aging.101414.

Michael Lustgarten

Ph.D, Physiology, University of Texas Health Science Center at San Antonio, 2009 B.S., Biochemistry, Queens College, 2003 B.A, English Textual Studies, 1994, Syracuse University

9 thoughts on “Quantifying Biological Age

  1. Isn’t this simply a limited view of your “blood age” compared to people eating the Standard American Diet?

    Which doesn’t factor in things like creatine supplementation, whether you worked out the same day as your blood test (potentially increasing CRP) or other confounders.

    Is it saying much beyond than high glucose correlated = bad? Higher albumin = good etc? Where’s LDL particle count for instance, the marker we AFAIK belive is the leading one for development of atherosclerosis?

    Seems like many variables independent of these measurements could point to different biological ages – different tissues could have different biological age profiles. It doesn’t seem useful to generate an overall biological age score just from this, it feels dishonest to me.

    Good to see more research done on blood biomarkers which could help us optimize them, but I do feel the results are being oversold. I remember changing one factor in the aging.ai calc 50% resulting in a ~20 year biological age difference. Not an expert on this topic, so consider these lay observations.

  2. Was hoping you had read it so you could tell me where I’m going wrong, instead of waving me over to the paper 😉 See if I can get around to it this weekend!

  3. I want to point out that the spreadsheet you’re linking to does not calculate your PhenoAge but your Phenotypic Age.

    Both are defined in the article by Morgan Levine, and while Phenotypic Age is a combination of 9 blood biomarkers + chronological age, PhenoAge (or more correctly DNAm PhenoAge) is a combination of the DNA methylation value of 513 specific CpG sites. They both try to estimate your “biological age”, and will yield similar results but are not the same.

    If you want to calculate your DNAm PhenoAge you need to measure the DNA methylation levels of your white blood cells and run it through the DNAm PhenoAge algorithm.

Leave a Reply to Johan Aardal Cancel reply

Next Post

Drink Green Tea, Reduce All-Cause Mortality Risk?

Sun Sep 15 , 2019
Is green tea consumption associated with reduced risk of death risk from all causes? To investigate this question, Tang et al. (2015) performed a meta-analysis of 5 studies, including 200,884 subjects. As shown below, drinking 2-3 cups (16-24 oz.) of green tea per day was associated with maximally decreased all-cause mortality risk, […]
%d bloggers like this: