Tag Archives: Creatinine

Quantifying Biological Age: Blood Test #6 in 2022

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The Excel file to calculate Levine’s Biological Age is embedded in this link from my website:
https://michaellustgarten.com/2019/09/09/quantifying-biological-age/

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 Analysis: Italian Centenarians

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Papers referenced in the video: Laboratory parameters in centenarians of Italian ancestry https://pubmed.ncbi.nlm.nih.gov/17681733/

Risk Factors For Hyperuricemia In Chinese Centenarians And Near-Centenarians https://pubmed.ncbi.nlm.nih.gov/31908434/

Fasting glucose level and all-cause or cause-specific mortality in Korean adults: a nationwide cohort study https://pubmed.ncbi.nlm.nih.gov/32623847/

High-density lipoprotein cholesterol and all-cause mortality by sex and age: a prospective cohort study among 15.8 million adults https://pubmed.ncbi.nlm.nih.gov/33313654/

Predicting Age by Mining Electronic Medical Records with Deep Learning Characterizes Differences between Chronological and Physiological Age https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716867/

11 Centenarian TG studies: https://michaellustgarten.com/2020/02/19/blood-testing-whats-an-optimal-value-for-triglycerides-2/

Association of Hemoglobin Concentration and Its Change With Cardiovascular and All-Cause Mortality https://pubmed.ncbi.nlm.nih.gov/29378732/

U-shaped mortality curve associated with platelet count among older people: a community-based cohort study https://pubmed.ncbi.nlm.nih.gov/26265696/

Blood counts in adult and elderly individuals: defining the norms over eight decades of life https://pubmed.ncbi.nlm.nih.gov/32030733/

Effect of aging on serum uric acid levels: longitudinal changes in a large Japanese population group https://pubmed.ncbi.nlm.nih.gov/12242321/

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

Implication of liver enzymes on incident cardiovascular diseases and mortality: A nationwide population-based cohort study https://pubmed.ncbi.nlm.nih.gov/29491346/

Association of the Aspartate Aminotransferase to Alanine Aminotransferase Ratio with BNP Level and Cardiovascular Mortality in the General Population: The Yamagata Study 10-Year Follow-Up https://pubmed.ncbi.nlm.nih.gov/27872510/

White blood cell count and mortality in the Baltimore Longitudinal Study of Aging https://pubmed.ncbi.nlm.nih.gov/17481443/

Age and sex variation in serum albumin concentration: an observational study https://pubmed.ncbi.nlm.nih.gov/26071488/

The gamma gap predicts 4-year all-cause mortality among nonagenarians and centenarians https://pubmed.ncbi.nlm.nih.gov/29348636/

Age-related changes in clinical parameters and their associations with common complex diseases https://pubmed.ncbi.nlm.nih.gov/26623014/

Methionine Restriction Extends Lifespan-What’s Optimal For Protein Intake? n=1 Analysis

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Papers referenced in the video:

Life-Span Extension in Mice by Preweaning Food Restriction and by Methionine Restriction in Middle Age https://pubmed.ncbi.nlm.nih.gov/19414512/

Low methionine ingestion by rats extends life span https://pubmed.ncbi.nlm.nih.gov/8429371/

Fasting glucose level and all-cause or cause-specific mortality in Korean adults: a nationwide cohort study https://pubmed.ncbi.nlm.nih.gov/32623847/

Total plasma homocysteine and cardiovascular risk profile. The Hordaland Homocysteine Study https://pubmed.ncbi.nlm.nih.gov/7474221/

Predicting Age by Mining Electronic Medical Records with Deep Learning Characterizes Differences between Chronological and Physiological Age https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716867/

Association between low-density lipoprotein cholesterol and cardiovascular mortality in statin non-users: a prospective cohort study in 14.9 million Korean adults https://pubmed.ncbi.nlm.nih.gov/35218344/

Blood counts in adult and elderly individuals: defining the norms over eight decades of life https://pubmed.ncbi.nlm.nih.gov/32030733/

Optimizing Blood Cholesterol Levels: What’s My Data?

In an earlier video, I presented data for total cholesterol (TC) levels in blood in terms of changes during aging and all-cause mortality risk. I’ve measured TC 25 times in the past 5 years, and in this video, I present that data, and my approach to optimize it.

https://www.youtube.com/watch?v=PBv_hXwUqHM&feature=emb_logo

Serum Creatinine: What’s Optimal?

In this relatively short clip, I talk about how serum levels of creatinine change during aging, what levels are associated with risk of death for all causes, and I show my own data for 15+ years!
 

Quantifying Biological Age: Checklist

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!

1. Albumin: https://michaellustgarten.wordpress.com/2019/09/22/optimizing-serum-levels-of-albumin-data-from-20-blood-tests/

2. Creatinine: https://michaellustgarten.wordpress.com/2019/11/18/optimizing-biologic-age-creatinine/

3. Glucose: https://michaellustgarten.wordpress.com/2019/10/04/blood-glucose-whats-optimal/

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/

9. White blood cells: https://michaellustgarten.wordpress.com/2019/10/11/blood-testing-whats-optimal-for-wbc-levels/

 

Optimizing Biologic Age: Creatinine (and eGFR)

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):

Screen Shot 2019-11-04 at 6.14.02 AM.png

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):

Screen Shot 2019-11-18 at 7.28.27 AM

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):
Screen Shot 2019-08-17 at 1.54.30 PM.png

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):

Screen Shot 2019-08-17 at 1.59.26 PM.png

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):

egfr mort

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!

 

References

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 rateAnn 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 ageJ 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 StudyKidney Int. 2007 Sep;72(5):632-7.