Tag Archives: aging.ai

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.

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!

Higher Magnesium Intake, Less Arterial Calcification?

Circulating levels of calcium can deposit in the coronary arteries (and in other arterial sites), a process that is known as coronary artery calcification (CAC). Arterial calcification is associated with arterial stiffness, which increases risk for adverse cardiovascular events, including cardiovascular disease-related mortality (Allison et al. 2012).

Can CAC accumulation be slowed/minimized/prevented? One possible factor may involve the dietary intake of magnesium (Mg). As shown below, adults (average age, ~53y) that had a median dietary Mg intake of 425 mg/day had ~50% reduced odds of having any CAC, when compared with lower Mg intakes (Hruby et al. 2014):

cac mg

Getting at least 425 mg of dietary Mg is relatively easy for me. Plotted below is my dietary magnesium intake for the 365 day period from August 31, 2018 until September 2, 2019. All of that comes from food, as I don’t supplement with Mg. In addition, my average daily Mg intake during that period is 786 mg/day (red line):

mg intake

Based on my average Mg intake, my odds for having any CAC should be minimized. However, the best approach would be to actually measure CAC. Stay tuned for that data, sometime later this year!

Which foods contribute to my 786 mg Mg intake/day? ~14% of that comes from spinach, as over that same time period, I averaged 4.82 oz. of spinach/day, which supplies 107 mg of Mg. Other moderate sources of Magnesium (for me) come from carrots and bananas (~59 mg/day each), strawberries (43 mg/day), red bell peppers (37 mg/day), broccoli (26 mg/day), cacao beans (23 mg/day), and others.

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

References

Allison MA, Hsi S, Wassel CL, Morgan C, Ix JH, Wright CM, Criqui MH. Calcified atherosclerosis in different vascular beds and the risk of mortality. Arterioscler Thromb Vasc Biol. 2012 Jan;32(1):140-6.

Hruby A, O’Donnell CJ, Jacques PF, Meigs JB, Hoffmann U, McKeown NM. Magnesium intake is inversely associated with coronary artery calcification: the Framingham Heart Study. JACC Cardiovasc Imaging. 2014 Jan;7(1):59-69.