Tag Archives: all-cause mortality

DHEA-S Is A Weakness In My Data: Blood Test #5 in 2022

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Papers referenced in the video:
Total cholesterol and all-cause mortality by sex and age: a prospective cohort study among 12.8 million adults
https://pubmed.ncbi.nlm.nih.gov/30733566/

The Value of Serum Albumin and High-Density Lipoprotein Cholesterol in Defining Mortality Risk in Older Persons with Low Serum Cholesterol
https://pubmed.ncbi.nlm.nih.gov/11559371/

Reference intervals of nine steroid hormones over the life-span analyzed by LC-MS/MS: Effect of age, gender, puberty, and oral contraceptives
https://pubmed.ncbi.nlm.nih.gov/31201927/

Incomplete Pattern of Steroidogenic Protein Expression in Functioning Adrenocortical Carcinomas
https://pubmed.ncbi.nlm.nih.gov/32751564/

Serum dehydroepiandrosterone sulfate levels predict longevity in men: 27-year follow up study in a community based cohort (Tanushimaru study)
https://pubmed.ncbi.nlm.nih.gov/18422949/

AGE Products Impact Lifespan: Impact Of Hyperglycemia, Kidney Function, And The Microbiome

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Papers referenced in the video:

Oral glycotoxins determine the effects of calorie restriction on oxidant stress, age-related diseases, and lifespan https://pubmed.ncbi.nlm.nih.gov/18599606/

Reduced oxidant stress and extended lifespan in mice exposed to a low glycotoxin diet: association with increased AGER1 expression https://pubmed.ncbi.nlm.nih.gov/17525257/

Gut microbiota drives age-related oxidative stress and mitochondrial damage in microglia via the metabolite N 6-carboxymethyllysine https://pubmed.ncbi.nlm.nih.gov/35241804/

Plasma Carboxymethyl-Lysine, an Advanced Glycation End Product, and All-Cause and Cardiovascular Disease Mortality in Older Community-Dwelling Adults https://pubmed.ncbi.nlm.nih.gov/19682127/

Advanced glycation end products and their circulating receptors predict cardiovascular disease mortality in older community dwelling women https://pubmed.ncbi.nlm.nih.gov/19448391/

Acute Hyperglycemia Causes Intracellular Formation of CML and Activation of ras, p42/44 MAPK, and Nuclear Factor KappaB in PBMCs https://pubmed.ncbi.nlm.nih.gov/12606501/

Experimental Hyperglycemia Alters Circulating Concentrations and Renal Clearance of Oxidative and Advanced Glycation End Products in Healthy Obese Humans https://pubmed.ncbi.nlm.nih.gov/30823632/

Novel associations between blood metabolites and kidney function among Bogalusa Heart Study and Multi-Ethnic Study of Atherosclerosis participants https://pubmed.ncbi.nlm.nih.gov/31720858/

Serum Carboxymethyl-lysine, a Dominant Advanced Glycation End Product, is Associated with Chronic Kidney Disease: the Baltimore Longitudinal Study of Aging https://pubmed.ncbi.nlm.nih.gov/19853477/

How Many Steps Per Day Is Associated With Reduced Risk Of Death For All Causes?

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Paper referenced in the video:

How many steps a day to reduce the risk of all-cause mortality? A dose–response meta-analysis https://pubmed.ncbi.nlm.nih.gov/34808011/

Epidemiological Studies vs n=1: What’s Optimal For Dietary Cholesterol?

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Papers referenced in the video:

Dietary Intakes of Eggs and Cholesterol in Relation to All-Cause and Heart Disease Mortality: A Prospective Cohort Study https://pubmed.ncbi.nlm.nih.gov/32400247/

Associations of Dietary Cholesterol or Egg Consumption With Incident Cardiovascular Disease and Mortality https://pubmed.ncbi.nlm.nih.gov/30874756/

HDL Update: Age-Related Changes, All-Cause Mortality Risk, And Progress Towards The Optimal Range

In November 2020, I made a HDL video based on a meta-analysis in ~3.4 million subjects that was published in July 2020. In Dec 2020, a larger study (n=15.8 million subjects) was published-those data are presented in the video, and compared against the meta-analysis.

In addition, I’ve tested my HDL 2 more times since November 2020, so how’s my progress for getting it into the optimal range? Also, I attempt to derive clinical significance by identifying correlations for higher HDL with lower Lp(a) and hs-CRP.

Video link: https://www.youtube.com/watch?v=MUuKlpyvZaU

Carotenoids Are Associated With A Younger Epigenetic Age And Reduced All-Cause Mortality Risk

Papers referenced in the video: DNA methylation

GrimAge strongly predicts lifespan and healthspan:

https://pubmed.ncbi.nlm.nih.gov/30669…

GrimAge outperforms other epigenetic clocks in the prediction of age-related clinical phenotypes and all-cause mortality:

https://pubmed.ncbi.nlm.nih.gov/33211…

Dietary intake and blood concentrations of antioxidants and the risk of cardiovascular disease, total cancer, and all-cause mortality: a systematic review and dose-response meta-analysis of prospective studies: https://pubmed.ncbi.nlm.nih.gov/30475…

Albumin is included as a biological age predictor:

https://www.ncbi.nlm.nih.gov/pmc/arti…

https://www.ncbi.nlm.nih.gov/pmc/arti…

https://www.ncbi.nlm.nih.gov/pmc/arti…

https://pubmed.ncbi.nlm.nih.gov/30993…

Age-related change data for albumin:

https://pubmed.ncbi.nlm.nih.gov/26071…

Associations of cardiovascular biomarkers and plasma albumin with exceptional survival to the highest ages: https://www.nature.com/articles/s4146…

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.

Optimizing Biologic Age: Lymphocyte %

The percentage of lymphocytes is one of the 9 blood test variables included in the biological age calculator, Phenotypic Age (https://michaellustgarten.wordpress.com/2019/09/09/quantifying-biological-age/). The reference range for lymphocyte % is 20 – 40% of the total amount of white blood cells (WBCs), but are higher or lower values optimal for health and longevity?

To answer that question, it’s important to know how levels of lymphocytes change during aging, and its association with risk of death for all causes. In one of the earliest studies to examine how the percentage of lymphocytes changes with age, Levine (2013) reported that lymphocyte % significantly decreased during aging in 9,389 adults (age range, 30 – 75y). However, the absolute values for these changes, i.e. from 40% to 30%, for ex., was not reported.

Similarly, lymphocyte % decreased during aging in a much larger study (377,686 subjects; age range, 18 – 85y; Wang et al. 2017):

Screen Shot 2019-11-16 at 9.38.37 AM

Interestingly, for women, lymphocyte % decreased from 27% to 21% from 20 – 35y, increased from 21% to 26% from 35 – 55y, then again decreased from 26% to 20% from 55y to 85y. In contrast, lymphocyte % more steadily decreased for men, from 28% to 17% from 20 – 85y.

Based on the aging data, higher values for lymphocyte % are are associated with biologic youth, whereas lower values are found in older adults. Although there are few studies that have investigated associations between lymphocyte % with aging or disease risk, in contrast, more studies have been published for absolute levels of lymphocytes.

In a small study of 106 older adults (> 85y) that were healthy (i.e. free of disease) at baseline, lymphocytes  less than 1.14*10^9 cells/L (equivalent to 1140*10^6 cells/L) was associated with an increased risk of death for all causes, when compared with 1850*10^6 cells/L (Izaks et al. 2003):

lympho mort

In a larger study (624 subjects), lymphocytes less than 1540*10^6 cells/L was associated with a significantly shorter average lifespan (~5y; 0.5 proportion remaining below), when compared with 1540 – 2040*10^6 cells/L . Also note that survival for the group that had 1540 – 2040*10^9 lymphocytes/L was not significantly different from the group that had more than 2040*10^9 lymphocytes/L (Leng et al. 2005):Screen Shot 2019-11-16 at 8.36.34 AM.png

In agreement with the smaller studies, lymphocytes < 1300 and < 1200*10^6 cells/L in women and men (red and blue, far left), respectively was associated with an increased all-cause mortality risk, when compared with average lymphocyte values ~1900*10^6 cells/L (decile 5) in a larger study that included 262,394 non-smokers (age range, 37 – 73y; Welsh et al. 2018):

Screen Shot 2019-10-08 at 7.26.51 AM

Collectively, these data suggest that higher values for lymphocyte % and for the absolute amount of lymphocytes may be optimal for minimizing disease risk and for maximizing longevity. If both are low, can they be raised? Circulating levels of lymphocytes are reduced during zinc deficiency (Fraker and King, 2001), so monitoring zinc intake, then increasing it to at least the RDA may be a first step towards increasing lymphocyte levels and %.

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

References

Fraker PJ, King LE. A distinct role for apoptosis in the changes in lymphopoiesis and myelopoiesis created by deficiencies in zincFASEB J. 2001 Dec;15(14):2572-8.

Izaks GJ, Remarque EJ, Becker SV, Westendorp RG. Lymphocyte count and mortality risk in older persons. The Leiden 85-Plus Study. J Am Geriatr Soc. 2003 Oct;51(10):1461-5.

Leng SX, Xue QL, Huang Y, Ferrucci L, Fried LP, Walston JD. Baseline total and specific differential white blood cell counts and 5-year all-cause mortality in community-dwelling older womenExp Gerontol. 2005 Dec;40(12):982-7.

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.

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.

Welsh C, Welsh P, Mark PB, Celis-Morales CA, Lewsey J, Gray SR, Lyall DM, Iliodromiti S, Gill JMR, Pell J, Jhund PS, Sattar N. Association of Total and Differential Leukocyte Counts With Cardiovascular Disease and Mortality in the UK Biobank. Arterioscler Thromb Vasc Biol. 2018 Jun;38(6):1415-1423. doi: 10.1161/ATVBAHA.118.310945.

Using Diet to Optimize Circulating Biomarkers: Serum Bicarbonate

In an earlier post, I wrote about the association between biomarkers of systemic acid-base balance (serum bicarbonate, the anion gap, urinary pH) with all-cause mortality risk (https://atomic-temporary-71218033.wpcomstaging.com/2015/08/28/serum-bicarbonate-and-anion-gap-whats-optimal/). Based on these data, systemic acidity may not be optimal for health and longevity, when compared with more alkaline values. Can circulating acid-base biomarkers be optimized through diet?

One way to optimize serum bicarbonate is with a low dietary PRAL (potential renal acid load). For a given food, PRAL is a measure of how much acid or base that the kidney will see. In subjects with normal kidney function (or with chronic kidney disease, CKD), a low dietary PRAL (alkaline-forming) is associated with high serum bicarbonate, whereas a high dietary PRAL (acid-forming) is associated with reduced serum bicarbonate (Ikizler et al. 2015):

bicarb pral

So how can we achieve a low dietary acid intake (low PRAL), with the goal of increasing serum bicarbonate? The answer is to abundantly consume foods with a low PRAL (vegetables), while minimizing foods with a high PRAL (animal products, grains). Let’s have a look at the PRAL values for several food groups (Remer and Manz, 1995):

All of the meat and meat products shown below have acid-forming, positive PRAL values:

PRAL meat

Similarly, fish have acid-forming, positive PRAL values:

fish pral

While PRAL values for milk, dairy, and eggs are generally acid-forming, there is a wider range, compared with meat and fish. For example, parmesan and cheddar cheese have high PRAL values (34.2, 26.5, respectively), whereas milk and yogurt have PRAL values ~1:

dairy pral

Grains are similar to animal products in terms of their PRAL values:

grain pral

In contrast, all of the vegetables on the list below have very low, alkaline-forming PRAL values. The All-Star for a low PRAL is spinach (-14):
veg pral

Similarly, most fruits have alkaline forming, low PRAL values. Although raisins seem to be the PRAL All-Star, their data (and all of the other foods on the list) are based on 100g (299 calories for raisins). For an equivalent amount of calories for strawberries, their PRAL equates to -20.6, which is similar to the raisin PRAL. Also included on the list are nuts, which contain a range of PRAL values from negative (hazelnuts) to positive (walnuts, peanuts):

fruit pral

What’s my dietary PRAL? To determine that, it’s first important to define the PRAL equation: PRAL = (0.49 * protein intake in g/day) + (0.037 * phosphorus intake in mg/day) – (0.02 * potassium intake in mg/day) – (0.013 * calcium intake in mg/day) – (0.027 * magnesium intake in mg/day; Remer and Manz, 1994). Using my latest 7-day average dietary data yields a very low, alkaline-forming PRAL, -121.9: (protein, 88g; phosphorus, 2038 mg; potassium, 9868 mg; calcium, 1421 mg; magnesium, 901 mg)! It’s important to note that the major contributor to my very low PRAL value comes from the potassium term. Because of my abundant vegetable intake, my potassium intake is very high, resulting in a highly alkaline PRAL. Considering that PRAL values of -40 were associated with serum bicarbonate values of ~28, my serum bicarbonate value of 31 on my last blood test (8/2015) may in part be explained by my very low dietary PRAL value, -121.9.

Another measure of dietary acid load is NEAP (net endogenous acid production). In subjects with normal (and reduced, CKD) kidney function, a high NEAP diet (acid-forming) is associated with reduced serum bicarbonate, whereas a low NEAP diet (alkaline-forming) is associated with higher serum bicarbonate values (Ikizler et al. 2015):

neap bicarb

NEAP is more easily calculated than PRAL-all you need to know are your dietary protein and potassium intakes: NEAP = (54.5 * protein intake in grams/day)/(potassium intake in mEq/day) -10.2 (Frassetto et al. 1999). To convert your daily potassium intake from mg to mEq, divide by 39.1. Using my 7-day average protein and potassium intake data yields a NEAP = (54.5 * 88)/(9868/39.1) – 10.2 = 8.8. Based on the plot above for NEAP vs. serum bicarbonate, that again puts me on the far left, which is associated with serum bicarbonate values greater than 28.

Collectively, eating more potassium-rich vegetables will reduce PRAL and NEAP, which is associated with systemic alkalinity, as measured by an elevated serum bicarbonate. Because high serum bicarbonate levels are associated with reduced all-cause mortality risk, this may be an important strategy for improving health and longevity!

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

References

Frassetto LA, Todd KM, Morris RJC, Sebastian A. Estimation of net endogenous noncarbonic acid production in humans from diet potassium and protein contents. Am J Clin Nutr. 1998;68:576-583.

Ikizler HO, Zelnick L, Ruzinski J, Curtin L, Utzschneider KM, Kestenbaum B, Himmelfarb J, de Boer IH. Dietary Acid Load Is Associated With Serum Bicarbonate but not Insulin Sensitivity in Chronic Kidney Disease. J Ren Nutr. 2016 Mar;26(2):93-102.

Remer T, Manz F. Estimation of the renal net acid excretion by adults consuming diets containing variable amounts of protein. Am J Clin Nutr. 1994;59:1356-1361.

Remer, T. and Manz, F. Potential renal acid load of foods and its influence on urine pH. Journal of the American Dietetic Association 1995 ;95(7), 791-797.