Category Archives: aging.ai

Quantifying Biological Age: Blood Test #3 in 2022

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Levine’s Biological age calculator is embedded as an Excel file in this link from my website: https://michaellustgarten.com/2019/09/09/quantifying-biological-age/

An epigenetic biomarker of aging for lifespan and healthspan https://pubmed.ncbi.nlm.nih.gov/29676998/

Underlying features of epigenetic aging clocks in vivo and in vitro https://pubmed.ncbi.nlm.nih.gov/32930491/

Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations https://pubmed.ncbi.nlm.nih.gov/29340580/

Blood Test #2 in 2022: Diet

Join us on Patreon! https://www.patreon.com/MichaelLustgartenPhD

Levine’s Biological age calculator is embedded as an Excel file in this link from my website: https://michaellustgarten.com/2019/09/09/quantifying-biological-age/

Attempting To Further Reduce Biological Age: hs-CRP

Join us on Patreon! https://www.patreon.com/MichaelLustgartenPhD

Levine’s Biological age calculator is embedded as an Excel file in this link: https://michaellustgarten.wordpress.com/2019/09/09/quantifying-biological-age/

Papers referenced in the video:

The baseline levels and risk factors for high-sensitive C-reactive protein in Chinese healthy population https://immunityageing.biomedcentral.com/articles/10.1186/s12979-018-0126-7

Commonly used clinical chemistry tests as mortality predictors: Results from two large cohort studies https://pubmed.ncbi.nlm.nih.gov/33152050/

Quantifying Biological Age With Aging.ai: 24 Blood Tests Since 2009

The maximal reduction for biological age when using the biological age calculator, Phenotypic Age, is ~20 years. In other words, if I’m 80 years old and my biomarkers are all reflective of youth, the lowest possible biological age will be ~60 years old. One reason for that is the inclusion of chronological age in the prediction of biological age, which adds strength to the correlation while simultaneously limiting the maximal biological age reduction.

To account for the possibility that youthful biomarkers at an older chronological age can yield a biological age that is more than 20 years younger, it’s important to quantify biological age using a tool that doesn’t include chronological age in its calculation. Aging.ai fits that criterion, and in the video I present biological age data with use of aging.ai for 24 blood tests since 2009.

Optimizing Biological Age With Aging.ai: Blood Urea Nitrogen

Blood urea nitrogen (BUN) is one of the 19 variables found on the biological age calculator, aging.ai. It measures the amount of nitrogen, as contained in urea (i.e., blood urea nitrogen, BUN) in your blood. The reference range for BUN is 5 – 20 mg/dL, but within that range, what’s optimal?

First, BUN increases during aging, from 11 – 13 mg/dL in 20 yr olds to 20 – 22 mg/dL in 90 yr olds (Wang et al. 2017):

Screen Shot 2019-11-21 at 5.55.45 AM

The importance of the age-related increase in BUN is illustrated by the finding that risk of death for all causes increases above 15 mg/dL:

BUN

Also note that maximally decreased risk for all cause mortality was associated with BUN values between 5 – 15 mg/dL. In addition, even though a BUN value = 20 mg/dL is technically within the reference range, risk of death for all causes would be 50% higher when compared with someone that had BUN levels between 5 – 15 mg/dL. Collectively, based on the aging and all-cause mortality data, I’d argue that 5 – 13 mg/dL may be the optimal range for BUN.

Assuming normal kidney function (see https://michaellustgarten.wordpress.com/2019/11/18/optimizing-biologic-age-creatinine/), if your BUN is higher than 15 mg/dL, can it be reduced? Note that urea production is almost perfectly correlated (r = 0.98) with dietary protein intake (Young et al. 2000):
urea nitrog

In other words, the main source of dietary nitrogen is protein, so if you eat a lot of protein, you’ll make a lot of urea. Circulating levels of urea can be easily calculated by measuring BUN, via: Urea [mg/dL]= BUN [mg/dL] * 2.14). Therefore, measuring BUN can then be used to determine if your protein intake is too high or too low.

What’s my BUN? As shown below, I’ve measured BUN 22 times since 2015. In line with the Young et al. (2000) data that showed an almost perfectly linear correlation between dietary nitrogen intake with urea production, similarly, as my dietary protein intake has increased, so have my BUN levels. The correlation between my dietary protein intake with BUN is strong (= 0.76, R^2 = 0.575, p-value = 4.3E-05):

upd bun

Note that my BUN is (purposefully) below 15 mg/dL, the upper limit for reduced all-cause mortality risk in Solinger and Rothman (2013), and within the 11 – 13 mg/dL range reported for the 20 yr olds of Wang et al. (2017).

For more recent tracked data, see the video! 

References

Solinger AB, Rothman SI. Risks of mortality associated with common laboratory tests: a novel, simple and meaningful way to set decision limits from data available in the Electronic Medical Record. Clin Chem Lab Med. 2013 Sep;51(9):1803-13.

Wang Z, Li L, Glicksberg BS, Israel A, Dudley JT, Ma’ayan A. Predicting age by mining electronic medical records with deep learning characterizes differences between chronological and physiological ageJ Biomed Inform. 2017 Dec;76:59-68. doi: 10.1016/j.jbi.2017.11.003.

Young VR, El-Khoury AE, Raguso CA, Forslund AH, Hambraeus L. Rates of urea production and hydrolysis and leucine oxidation change linearly over widely varying protein intakes in healthy adults. J Nutr. 2000 Apr;130(4):761-6.

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

Which Blood Test Analyte Is Most Important For Predicting Biologic Age?

Three studies have investigated the ability of blood test analytes to predict biological age. First, when considering the top 20 variables that were associated with biological age in aging.ai, albumin contributed most to this prediction, almost 2x more than circulating levels of glucose (Mamoshina et al. 2018):

Screen Shot 2019-12-01 at 1.04.59 PM.png

Second, albumin was one of the 9 blood test variables that were best able to predict biological age when using the Phenotypic Age calculator.  However, as shown below, it didn’t come in first place, but fifth. Interestingly, the analyte that contributed most to biological age prediction was the red cell distribution width (RDW%), with glucose again in second place (Levine et al. 2018):

Screen Shot 2019-12-01 at 1.21.58 PM

Third, Earls et al. (2019) used the Klemera-Doubal algorithm (Klemera and Doubal, 2006) in conjunction with blood test data to predict biological age. Regardless if the blood was analyzed by Labcorp or Quest, higher levels of albumin (the left side of both images below) were associated with the greatest reduction in biological age, up to 5 years! In contrast, HbA1c was associated with a higher biological age when measured by Labcorp (top image, right side), and second to lead in the Quest analysis (bottom image, right side). Interestingly, glucose came in third and fifth in the Labcorb and Quest data sets, respectively, in terms of its positive association with biological age.

Screen Shot 2019-12-01 at 12.59.22 PM

Glucose would’ve been an obvious choice, but would you have guessed that albumin may be just as important, and potentially more important for predicting biological age?

 

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

References

Earls JC, Rappaport N, Heath L, Wilmanski T, Magis AT, Schork NJ, Omenn GS, Lovejoy J, Hood L, Price ND. Multi-Omic Biological Age Estimation and Its Correlation With Wellness and Disease Phenotypes: A Longitudinal Study of 3,558 Individuals. J Gerontol A Biol Sci Med Sci. 2019 Nov 13;74(Supplement_1):S52-S60. doi: 10.1093/gerona/glz220.

Klemera P, Doubal S. A new approach to the concept and computation of biological age. Mech Ageing Dev. 2006;127:240–248. doi:10.1016/j. mad.2005.10.004

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.

Mamoshina P, Kochetov K, Putin E, Cortese F, Aliper A, Lee WS, Ahn SM, Uhn L, Skjodt N, Kovalchuk O, Scheibye-Knudsen M, Zhavoronkov A. Population specific biomarkers of human aging: a big data study using South Korean, Canadian and Eastern European patient populations. J Gerontol A Biol Sci Med Sci. 2018 Jan 11.

1.7 Years of Biological Aging In The Past 3.6 Years

In an earlier post (https://michaellustgarten.wordpress.com/2018/06/26/maximizing-health-and-lifespan-is-calorie-restriction-essential/), I documented my aging.ai biologic age for 13 blood test measurements from 2016 – 2019. If you missed that post, here are those data:
agingai2Note that note my average biologic age has slowly increased from 2016 to 2019, from 28y in 2016 (2 measurements), to 29.25y in 2017 (6 measurements), to 29.5y in 2018 (6 measurements), to 30y in my June 2019 measurement.

To gain more insight into my 2019 prediction for biologic age, I kept measuring. On September 17, 2019, I had my worst biological age to date, 33y, based on the blood test data below:
Screen Shot 2019-11-03 at 3.51.05 PM.png

Seeing a biological age that high (for me) was the motivation that I needed to finally stick to a mild caloric restriction, which I hypothesized would positively affect my biological age. I wrote about this in my recent Phenotypic Age post (https://michaellustgarten.wordpress.com/2019/11/01/biological-age-31-3y-chronological-age-46y/). Did it work? Shown below is my blood test data for October 29th.

Screen Shot 2019-11-03 at 4.07.28 PM

Based on that data, my biological age was 28y, and when averaging the 3 measurements in 2019 (so far!), my average biological age is 29.67y. When considering that my average biological age in 2016 was 28y, it looks like I’ve only aged ~1.7 years in 3.58 years of elapsed time!

 

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