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 PhenoAge, 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 PhenoAge 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!


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



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



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

3 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!

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