In the first 45 minutes, discuss each of the biomarkers contained within Levine’s Biological Age calculator, Phenotypic Age.
After that, I answer questions from the audience and we discuss all things related to aging.
Measurement of biological age with Levine’s Phenotypic Age calculator is strongly correlated with chronological age (r=0.94; see https://michaellustgarten.wordpress.com/2019/09/09/quantifying-biological-age/). In 2019, I measured all 9 of its analytes 3 times, with biological age readings of 35.39y, 35.58y, and 31.3y, for an average 2019 biological age of 34.09y (see https://michaellustgarten.wordpress.com/2019/11/01/biological-age-31-3y-chronological-age-46y/). That’s 12 years younger than my chronological age in 2019, 46y!
On Feb 12, I had my first blood test measurement of 2020. I expected to see a worse biological age, as over the past week, I’d been hit with the flu, and since my last measurement in 2019, I made a few changes to my diet that I didn’t expect to favorably affect it. In contrast, I’ve been purposefully in a mild caloric restriction in an attempt to reduce my body fat from a relatively lean 10-12% to lower values. Since my last blood test 3+ months ago, my average calorie intake was 2553, which is 5-10% less than my maintenance intake, 2700-2800 calories/day. So how did these variables affect my biologic age? Let’s have a look at the data!
My biological age was 32.75y, which is less than my 2019 average value, and better than I expected considering the factors mentioned above! Note that there is room for improvement, including my creatinine and WBC levels, which both increased when compared with my average 2015-2019 values (which included 23 blood tests). My average daily fiber intake has been ~100g/day for a few years, and over the past 3 months, I purposefully reduced that to ~70g/day. Conversely, I increased my intake of meat, eggs, and cheese intake during that period, to see if eating less fiber and more animal products would negatively impact my blood test results. For me, eating more animal protein and less total fiber may not be optimal, as my creatinine levels also rose in 2019 when I performed a similar dietary experiment. Note that creatinine levels increase with age (see https://michaellustgarten.wordpress.com/2019/11/18/optimizing-biologic-age-creatinine/), so if I can avoid that by altering my diet, I will. For the next blood test, I’ll reduce, but not eliminate my intake of meat, eggs, and cheese, and I expect that my creatinine levels will decrease back towards my average 2015-2019 value of 0.94 mg/dL.
Also note my WBCs-although they’re not higher than the 3.5-6 optimal range (see https://michaellustgarten.wordpress.com/2019/10/11/blood-testing-whats-optimal-for-wbc-levels/), they’re increased when compared with my average 2015-2019 value of 4.5. This increase is more than likely a result of the flu/infection that I’m battling. Once it passes, I expect it to return to close to my average WBC value, ~4.5.
Going forward, I expect my creatinine and WBCs to come down to their average values, which would result in a biological age that is closer to 30y on my next blood test. Stay tuned for that data!
To quantify your biological age using Levine’s Phenotypic Age calculator, here’s the Excel link! DNAmPhenoAge_gen (1)
If you’re interested, please have a look at my book!
To make it easier to review the aging and all-cause mortality data for the circulating biomarkers that are contained within the biological age calculator, Phenotypic Age (see https://michaellustgarten.wordpress.com/2019/09/09/quantifying-biological-age/), here’s a checklist!
4. C-reactive protein: https://michaellustgarten.wordpress.com/2019/10/19/optimizing-biological-age-crp/
5. Lymphocyte %: https://michaellustgarten.wordpress.com/2019/11/16/lympho-mortal/
6. Mean corpuscular volume (MCV): https://michaellustgarten.wordpress.com/2019/10/14/optimizing-biological-age-mcv/
7. Red cell distribution width (RDW%): https://michaellustgarten.wordpress.com/2019/09/25/optimizing-biological-age-rdw/
8. Alkaline phosphatase: https://michaellustgarten.wordpress.com/2019/10/07/alkaline-phosphatase/
Creatinine is one of the 9 blood test variables included on the biological age calculator, Phenotypic Age (https://michaellustgarten.wordpress.com/2019/09/09/quantifying-biological-age/). The reference range for women and men is 0.5 – 1.1, and 0.6 – 1.2 mg/dL respectively, but within that range, what’s optimal for health and longevity?
To answer that question, it’s important to know how circulating levels of creatinine change during aging, and its association with risk of death for all causes. Creatinine increases during aging, as reported in studies of 9,389 adults (age range, 30 – 75y; Levine 2013), and in 377,686 subjects (age range, 18 – 85y; Wang et al. 2017). However, the absolute values for these changes, i.e. from 0.8 to 1.0 mg/dL, for ex., was not reported in either study.
In terms of all-cause mortality risk, creatinine levels of 0.8 mg/dL (blue line; 95% confidence interval (CI), red dotted line) were associated with the lowest risk of death for all causes, with risk being significantly reduced for creatinine values between 0.6 – 1.1 mg/dL in the 30,760 older adults (median age, 69y) of Solinger and Rothman (2013):
Note the U-shaped mortality curve for creatinine: all-cause mortality increased when it was both less than or greater than 0.8 mg/dL. More specifically, risk of death for all causes was significantly increased when serum levels of creatinine were less than ~0.55 and greater than 1.5 mg/dL.
Few studies have investigated the association between serum (or plasma) levels of creatinine with all-cause mortality risk, as most studies use creatinine in conjunction with chronological age, gender, and ethnicity to estimate kidney function (eGFR). For example, the MDRD equation (Levey et al. 2006) is commonly used to calculate eGFR, and if you’re interested in converting your creatinine levels into eGFR, here’s a link to calculate it (https://www.mdcalc.com/mdrd-gfr-equation). As creatinine goes up, eGFR goes down, and is indicative of worse kidney function. Based on that, we should expect to see an age-related decrease in kidney function, as measured by eGFR. Is this true?
eGFR decreases during aging, from values ~125 in 20 year old women and men to ~50 mL/min/1.73m^2 in adults older than 90y in the 385,918 subjects (age range, 18 – 100y) of Wang et al. (2017):
Similarly, eGFR decreased from ~90 (thick black line; 95% CI, dashed lines: 75 – 130 mL/min/1.73m^2) in young men (18-24y) to less than 70 (thick black line; 95% CI, dashed lines: 45 – 90 mL/min/1.73m^2) in men older than 75y (Baba et al. 2015):
In women, eGFR decreased from values ~100 (thick black line; 95% CI, dashed lines: 70 – 135 mL/min/1.73m^2) in youth to ~70 (thick black line; 95% CI, dashed lines: 50 – 95 mL/min/1.73m^2) in women older than 75y (Baba et al. 2015):
Similar findings have been reported for the age-related decline in eGFR in other studies, including Wetzels et al. (2007). When comparing young adults (18-24 year olds) with older adults (> 85y), median eGFR values declined from ~95 to ~65 mL/min/1.73m^2 in men, and from ~90 to ~60 mL/min/1.73m^2 in women.
What’s the effect of reduced kidney function (i.e. increased creatinine, decreased eGFR) on risk of death for all causes? In a meta-analysis of 46 studies that included 2,051,158 subjects, risk of death for all causes was significantly increased when eGFR was less than 52 in women (red, below), and less than 44 in men (blue), when compared with eGFR values between 90 – 104 mL/min/1.73m^2 (95 was used as the reference; Nitsch et al. 2013):
In sum, creatinine increases during aging, which is associated with an increased all-cause mortality risk. Similarly, eGFR, which includes circulating values for creatinine, decreases during aging, and is also associated with an increased all-cause mortality risk. Therefore, if you’re tracking your creatinine levels with the goal of optimizing your biological age, it’s important to try to keep creatinine levels relatively low (i.e. around 0.8 mg/dL), and to avoid its age-related increase!
If you’re interested, please have a look at my book!
Baba M, Shimbo T, Horio M, Ando M, Yasuda Y, Komatsu Y, Masuda K, Matsuo S, Maruyama S. Longitudinal Study of the Decline in Renal Function in Healthy Subjects. PLoS One. 2015 Jun 10;10(6):e0129036.
Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, Kusek JW, Van Lente F; Chronic Kidney Disease Epidemiology Collaboration. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006 Aug 15;145(4):247-54.
Levine ME. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? J Gerontol A Biol Sci Med Sci. 2013 Jun;68(6):667-74. doi: 10.1093/gerona/gls233.
Nitsch D, Grams M, Sang Y, Black C, Cirillo M, Djurdjev O, Iseki K, Jassal SK, Kimm H, Kronenberg F, Oien CM, Levey AS, Levin A, Woodward M, Hemmelgarn BR; Chronic Kidney Disease Prognosis Consortium. Associations of estimated glomerular filtration rate and albuminuria with mortality and renal failure by sex: a meta-analysis. BMJ. 2013 Jan 29;346:f324. doi: 10.1136/bmj.f324.
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. doi: 10.1515/cclm-2013-0167.
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 age. J Biomed Inform. 2017 Dec;76:59-68. doi: 10.1016/j.jbi.2017.11.003.
Wetzels JF, Kiemeney LA, Swinkels DW, Willems HL, den Heijer M. Age– and gender-specific reference values of estimated GFR in Caucasians: the Nijmegen Biomedical Study. Kidney Int. 2007 Sep;72(5):632-7.