Category Archives: aging.ai

Blood Test #4 in 2022: Supplements, Diet

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

Bristle Discount Link (Oral microbiome quantification):
ConquerAging15

https://www.bmq30trk.com/4FL3LK/GTSC3/

Cronometer Discount Link (Daily diet tracking):
https://shareasale.com/r.cfm?b=1390137&u=3266601&m=61121&urllink=&afftrack=

You can support the channel by buying me a coffee!
https://www.buymeacoffee.com/mlhnrca


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/

Quantifying Biological Age: Blood Test #4 in 2022

Here’s my latest video!

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

Bristle Discount Link (Oral microbiome quantification):
ConquerAging15

https://www.bmq30trk.com/4FL3LK/GTSC3/

Cronometer Discount Link (Daily diet tracking):
https://shareasale.com/r.cfm?b=1390137&u=3266601&m=61121&urllink=&afftrack=

You can support the channel by buying me a coffee!
https://www.buymeacoffee.com/mlhnrca


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/

Quantifying Biological Age: Blood Test #3 in 2022

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/

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!