Tag Archives: Longevity

12-16 Years Younger Than My Chronological Age: What’s My Diet?

My average biological age in 2019 is 12 years younger than my chronological age (46y) based on the Phenotypic Age calculator (https://michaellustgarten.wordpress.com/2019/11/01/biological-age-31-3y-chronological-age-46y/), and 16y younger based on aging.ai (https://michaellustgarten.wordpress.com/2019/11/04/years-of-biological-aging-in-the-past-4-years/). One factor that likely contributes to my relatively youthful biological age is my diet.

Shown below is my average daily dietary intake from January 1 through November 7th, 2019 (n=306 days). I weigh all of my food with a food scale, so these aren’t estimated amounts:

Screen Shot 2019-11-10 at 10.15.49 AM.png

In terms of weight (or volume), green tea is atop the list, as I drink 20 oz/day. Carrots come in second place (for why, see https://michaellustgarten.wordpress.com/2018/07/06/serum-albumin-and-acm/), followed by strawberries, red bell peppers, bananas, watermelon (for the lycopene), cauliflower, blueberries, blackberries, and raspberries. Note that I mix the bananas and berries in my green smoothies, which I drink 3-4x/week, which includes spinach (#11) and parsley (#23).

What does my average daily macro- and micro-nutrient data look like for 2019?

Screen Shot 2019-11-10 at 10.33.03 AM

Note that I purposefully have higher than the RDA values for several nutrients, including Vitamin C (see https://michaellustgarten.wordpress.com/2019/09/19/vitamin-c-dietary-intake-and-plasma-values-whats-optimal-for-health/), Vitamin K (see https://michaellustgarten.wordpress.com/2015/05/08/eat-more-green-leafy-vegetables-reduce-mortality-risk/), selenium (see https://michaellustgarten.wordpress.com/2015/05/28/selenium-dietary-intake-and-plasma-values-whats-optimal-for-health/), and others (see michaellustgarten.com).

In terms of supplements, I use 1000 IU of vitamin D from November – May, and I take a methylfolate-methylB12-B6 supplement, to help keep my homocysteine levels low.

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

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!

Biological Age = 31.3y, Chronological Age= 46y

On June 10, 2019 (for the first time) I measured all of the blood test variables that are included in the biologic age calculator, Phenotypic Age, and ended up with a biological age = 35.39y (https://michaellustgarten.wordpress.com/2019/09/09/quantifying-biological-age/).

While that value is 23% younger than my chronological age (46y), I knew that I could do better! So I tried again on September 17, 2019. Basically, the same biological age, 35.58y:

pheno 8_2019.png

An 23% younger biological age on 2 separate dates, months apart might be good for most, but not for me. So, I tried again on October 29th, 2019, and voila, a biological age of 31.3y, which is 32% younger than my chronological age! How did I do it?

oct pheno.png

From my last blood test until my most recent blood test, I attempted a mild caloric restriction. To maintain my body weight, I require about 2800 calories per day, an amount which is based on daily body weight weighing in conjunction with daily dietary tracking. For the period of time that elapsed between my last 2 blood tests, I averaged 2657 calories/day, which is 3.2% less than the 2745 calories/day that I averaged for the dietary period that corresponded to my September blood test. That I was also in a very mild caloric restriction is confirmed by a reduction in my average body weight, which was (purposefully) down 0.7 lbs from September 17 to October 29th, when compared with the dietary period that corresponded to my September blood test (August 20 – September 17).

This is a superficial analysis of how I further reduced my biological age, but in future posts I’ll report the average dietary intake that corresponded to my relatively youthful biologic age!

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

Sarcopenia, Disease Risk, And The Neutrophil/Lymphocyte Ratio

In an earlier post, based on data from the Baltimore Longitidunal Study on Aging (BLSA), I suggested that total white blood cell (WBCs) counts between 3500 to 6000 cells per microliter of blood may be optimal for reducing disease risk and for maximizing longevity (https://michaellustgarten.wordpress.com/2015/08/13/blood-testing-whats-optimal-for-wbc-levels/).

However, within WBCs, neutrophils increase, whereas lymphocytes decrease during aging (Ruggiero et al. 2007, Starr and Dreary 2011). As a result, the ratio between neutrophils with lymphocytes (NLR) increases during aging from ~1.5 in 20 year olds to ~1.8 in adults older than 75y (Li et al. 2015):

Screen Shot 2019-10-06 at 3.40.06 PM

An increased neutrophil/lymphocyte ratio during aging may be bad for health and disease risk. First, a higher neutrophil/lymphocyte ratio is associated with sarcopenia (defined as the age-related loss of muscle mass and physical function) in older adults (average age, 72y; Öztürk et al. 2018):

Screen Shot 2019-09-13 at 7.46.06 AM

Second, risk of death for all causes is significantly increased for older adults (average age, 66y) that had higher NLR values (60-80%, >80%, equivalent to NLR = 1.92-2.41, > 2.41), when compared with lower NLR values (< 20%, 20-40%, 40-60%, equivalent to NLR < 1.90; Fest et al. 2019):

nlr

Similarly, all-cause mortality risk was 30% increased in older adults (average age, 54y) that had NLR values > 1.77, when compared with < 1.77, and 40% increased for NLR values > 2.15, when compared with < 2.15 (Kime et al. 2018).

What are my NLR values? Over 17 blood test measurements from 2015 – 2019, my average NLR is 1.11. So far so good!

nlr

 

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

 

References

Fest J, Ruiter TR, Groot Koerkamp B, Rizopoulos D, Ikram MA, van Eijck CHJ, Stricker BH. The neutrophil-to-lymphocyte ratio is associated with mortality in the general population: The Rotterdam Study. Eur J Epidemiol. 2019 May;34(5):463-470.

Kim S, Eliot M, Koestler DC, Wu WC, Kelsey KT. Association of Neutrophil-to-Lymphocyte Ratio With Mortality and Cardiovascular Disease in the Jackson Heart Study and Modification by the Duffy Antigen Variant. JAMA Cardiol. 2018 Jun 1;3(6):455-462. doi: 10.1001/jamacardio.2018.1042.

Li J, Chen Q, Luo X, Hong J, Pan K, Lin X, Liu X, Zhou L, Wang H, Xu Y, Li H, Duan C. Neutrophil-to-Lymphocyte Ratio Positively Correlates to Age in Healthy PopulationJ Clin Lab Anal. 2015 Nov;29(6):437-43. doi: 10.1002/jcla.21791.

Öztürk ZA, Kul S, Türkbeyler İH, Sayıner ZA, Abiyev A. Is increased neutrophil lymphocyte ratio remarking the inflammation in sarcopenia? Exp Gerontol. 2018 Sep;110:223-229.

Ruggiero C, Metter EJ, Cherubini A, Maggio M, Sen R, Najjar SS, Windham GB, Ble A, Senin U, Ferrucci L. White blood cell count and mortality in the Baltimore Longitudinal Study of AgingJ Am Coll Cardiol. 2007 May 8;49(18):1841-50.

Starr JM, Deary IJ. Sex differences in blood cell counts in the Lothian Birth Cohort 1921 between 79 and 87 years. Maturitas. 2011 Aug;69(4):373-6.

Resting Heart Rate: Year-Over-Year Update

A few months ago, I presented data that a resting heart rate (RHR) ~40 beats per min is associated with maximally reduced risk of death from all causes (https://michaellustgarten.wordpress.com/2019/02/02/resting-heart-rate-whats-optimal/). I started tracking my RHR data in August 2018, and I now have more than a full year of data. RHR increases during aging, so how does my RHR look over that past year+?

rhr

As you can see, the trend line (red) is down, not up, which suggests that my fitness program is on the right track. My improvements for RHR can be better illustrated by comparing year-over-year changes for August 2018 with August 2019, and similarly, for September:

rhr data

The challenge will be continuous improvement for RHR. Eventually it will plateau, and I’ll respond by modifying my fitness program to make further gains.

 

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

LP(a), cardiovascular disease, and all-cause mortality: What’s optimal?

Very low, low, and high-density lipoproteins (VLDL, LDL, HDL, respectively) are commonly measured on the standard blood chemistry panel as measures of cardiovascular disease risk. Not included on that panel is another lipoprotein, Lp(a), which is a modified form of LDL. What’s the relationship between Lp(a) with disease risk?

A meta-analysis of 36 studies that included 126,634 subjects reported that Lp(a) > 30 mg/dL (65 nmol/L) was significantly associated with an increased risk for heart attacks, coronary heart disease-related deaths, and ischemic strokes (Erqou et al.  2009):

Screen Shot 2019-08-31 at 8.32.04 PM

Investigating further, of 2,100 candidate genes that were evaluated for predicting heart disease risk, genetic variation in the LPA gene was the strongest genetic risk factor (Clarke et al. 2009). Of the Lp(a)-related genes, SNPs for rs3798220 (increased risk allele = C) and rs10455872 (increased risk allele = G) were associated with a 92% and a 70% increased risk for coronary heart disease, respectively.

Based on these data, Lp(a) values less than 50 mg/dL (108 nmol/L) have been recommended, with 1-3 grams/day of niacin, which reduces Lp(a) levels, as the primary treatment for minimizing cardiovascular disease risk (Nordestgaard et al. 2010).

However, cardiovascular disease is only 1 outcome. What’s the data for Lp(a) and risk of death from all causes, not just cardiovascular disease-related deaths? In a study of 10,413 adults (average age, 55y), the lowest risk of death from all causes was reported for Lp(a) values of 270 mg/L (equivalent to 27 mg/dL, and 58 nmol/L). The log of 270 is 2.43, which corresponds to the lowest mortality risk on the chart below (Sawabe et al. 2012):

Screen Shot 2019-09-01 at 11.54.55 AM

Interestingly, all-cause mortality risk was significantly increased only for Lp(a) values < 80 mg/L (log 80 = 1.90; equivalent to 17 nmol/L), when compared with intermediate (80 – 550 mg/L; log values from 1.9 – 2.7 on the chart; equivalent to 17 – 118 nmol/L) and high Lp(a) (> 550 mg/L; log values > 2.7 on the chart; equivalent to > 118 nmol/L).

In addition to low Lp(a) values, an increased risk of death from all causes (and a shorter lifespan) have also been reported for high Lp(a). When compared with Lp(a) < 21 nmol/L, Lp(a) > 199 nmol/L was associated with a 20% increased all-cause mortality risk (Langsted et al. 2019). In addition, median lifespan was 1.4 years shorter for subjects that had  Lp(a) values > 199 nmol/L, when compared with < 21 nmol/L.

Based on the studies of Sawabe and Langsted, both low and high Lp(a) values may be bad for disease risk. What are my Lp(a) values?

I’ve been tracking Lp(a) for the past 14 years, first, approximately 1x/year until I was 40, and second, 9 times since 2015, when I started daily nutrition tracking. In addition, I’ve measured it 4x in 2019, with the goal of getting it closer to the 58 nmol/L value of the Sawabe study. When I first started measuring Lp(a) in 2005, it was ~150 nmol/L, which is way higher than the < 65 nmol/L that was reported for reduced cardiovascular disease risk in the Erqou meta-analysis, and the 58 nmol/L value that was reported for maximally reduced all-cause mortality risk in the Sawabe study:

Picture1

Fortunately, I was able to reduce my Lp(a) levels from those first values to levels closer to ~100 nmol/L, which is still too high. For the first 8 Lp(a) measurements, I didn’t track my nutrition, so I can’t say which factors helped me to reduce it. Also, note that I didn’t include the blood test measurement where I tried high dose niacin (3 g/day), which reduced my Lp(a) to 84 nmol/L, but also worsened my liver function,. My liver enzymes, AST and ALT doubled on high-dose niacin! What good is a reduced risk for cardiovascular disease if my risk for liver disease simultaneously goes up? Obviously, I quickly discontinued use of niacin to reduce Lp(a).

Also note the data on the chart since 2015, when I started daily nutritional tracking. Over that period, my average value over 9 Lp(a) measurements is 95.3 nmol/L. Although my average Lp(a) is still higher than it should be, it’s better than my pre-tracking Lp(a) average value of 115.6 nmol/L (p-value = 0.03 for the between-group comparison). In addition, on my last 3 measurements, my Lp(a) values were 75, 82, and 79 nmol/L. How have I been reducing it?

As I’ve mentioned in many blog posts, I’ve been weighing, logging, and tracking my nutrient intake since 2015. When I blood test, I can use the average dietary intake that corresponds to the blood test result, and with enough blood test results, I can look at correlations between my diet with blood test variables. Based on this approach, one possibility is my daily sodium intake. Shown below is a moderately strong correlation (r = 0.61, R^2 = 0.366) between my daily sodium intake with Lp(a). The higher my sodium intake, the lower my Lp(a) values.

lpa vs na.png

Can the strength of this approach be improved? Interestingly, I identified another moderately strong correlation (r = 0.69) between my lycopene intake with Lp(a): the higher my lycopene intake, the higher my Lp(a)! I then decided to include both sodium and lycopene in a linear regression model, and the correlation for both of these nutrients with Lp(a) is 0.90! So what will I do with this info?

The highest that my average dietary sodium intake has been in any blood testing period is ~2500 mg. Sodium levels higher than that seem to negatively affect my sleep, so I’m not interested in going higher than 2500 mg/day. Also, there may be a plateau effect for sodium, as values ~2500 mg/day didn’t associate with significantly lower Lp(a) values when compared with 2300 mg/day. I can, in contrast, reduce my lycopene intake, which comes almost exclusively from my daily watermelon intake. I usually eat ~7 oz/day, and for my next blood test I’ll reduce this to 5 oz/day. Based on the regression equation that includes sodium and lycopene, with a 2300 mg sodium intake and the amount of lycopene that corresponds to 5 oz. of daily watermelon (~6700 micrograms, down from ~9000 micrograms), I should expect to see a Lp(a) value ~67 nmol/L on my next blood test. If not, I’ll repeat this approach, looking for strong correlations between my diet with Lp(a), followed by tweaking my diet to obtain biomarker results that are close to optimal. Stay tuned my my next blood test data, coming in about 2 weeks!

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

 

References

Clarke, R., J. F. Peden, J. C. Hopewell, T. Kyriakou, A. Goel, S. C. Heath, S. Parish, S. Barlera, M. G. Franzosi, S. Rust, et al. 2009. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. N. Engl. J. Med. 361: 2518–2528.

Erqou, S., S. Kaptoge, P. L. Perry, A. E. Di, A. Thompson, I. R. White, S. M. Marcovina, R. Collins, S. G. Thompson, and J. Danesh. 2009. Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality. JAMA. 302: 412–423.

Langsted A, Kamstrup PR, Nordestgaard BG. High lipoprotein(a) and high risk of mortalityEur Heart J. 2019 Jan 4. [Epub ahead of print].

Sawabe M, Tanaka N, Mieno MN, Ishikawa S, Kayaba K, Nakahara K, Matsushita S; JMS Cohort Study Group. Low Lipoprotein(a) Concentration Is Associated with Cancer and All-Cause Deaths: A Population-Based Cohort Study (The JMS Cohort Study). PLoS One. 2012; 7(4): e31954. PLoS One. 2012;7(4):e31954.

Nordestgaard BG, Chapman MJ, Ray K, Borén J, Andreotti F, Watts GF, Ginsberg H, Amarenco P, Catapano A, Descamps OS, Fisher E, Kovanen PT, Kuivenhoven JA, Lesnik P, Masana L, Reiner Z, Taskinen MR, Tokgözoglu L, Tybjærg-Hansen A; European Atherosclerosis Society Consensus Panel. Lipoprotein(a) as a cardiovascular risk factor: current status. Eur Heart J. 2010 Dec;31(23):2844-53.

Serum Albumin Decreases During Aging: Can Diet Help?

Levels of serum albumin peak at about 20 years old (~4.6 g/dL for males, ~4.4 g/dL for females), then decrease during aging, as shown for the 1,079,193 adults of Weaving et al. (2016):

Screen Shot 2018-07-04 at 1.19.29 PM.png

Similar age-related decreases for serum albumin albumin have also been reported in smaller studies: Gom et al. 2007 (62,854 subjects); Dong et al. 2010 (2,364 subjects); Le Couteur et al. 2010 (1,673 subjects); Dong et al. 2012 (1,489 subjects).

Why is it important that serum levels of albumin decrease during aging? Reduced levels of albumin are associated with an increased risk of death from all causes. For example, in the 1,704,566 adults of Fulks et al. 2010, serum albumin levels > 4.4 g/dL and 4.5 g/dL for females and males, respectively, were associated with maximally reduced risk of death from all causes, regardless of age (younger than 50y, 50-69y, or 70y+):

albumin mort.png

The association between reduced levels of serum albumin with an increased risk of death from all causes have also been found in smaller studies. In a ~9 year study of 7,735 men (age range, 40-59y), when serum albumin was less than 4 g/dL, the mortality rate was 23/1000/per year, compared with 4/1000/per year for subjects with values greater than 4.8 g/dL (Phillips et al. 1989):

albumin 3 mort

Similarly, in older adults (average age, ~80y, 672 subjects), serum albumin levels  greater than 4.5 g/dL (equivalent to 45 g/L) were associated with significantly reduced all-cause mortality risk, when compared with compared with < 4.1 g/dL (equivalent to 41 g/L, Takata et al. 2010):

albumin 2 mort

Decreased levels of serum albumin (less than 4 g/dL) being associated with an increased all-cause mortality risk was also identified in a 12-year study of 287 older adults (average age, ~75y, Sahyoun et al. 1996).

Can the age-related decrease in serum albumin be minimized, or prevented? Shown below is my data for serum albumin since 2005, when I was 32y:

alb

First, note the period from when I was 32y until 40y. No age-related decrease! My average albumin value over 7 measurements was 4.74 g/dL. Unfortunately, I didn’t track my dietary info during that time.

Also note the period from 43y to 45y. First, my albumin levels are significantly higher than the first period, 4.92 g/dL (p=0.027)! Second, again note the absence of an age-related decrease. Based on the data of Weaving et al. (2016), my albumin levels should be around 4.4 g/dL, but I’ve got them going in the opposite direction! How have I been able to do that?

Since April 2015, with use of a food scale, I’ve been tracking my daily dietary intake, including macro and micronutrients (54 variables). For each orange data point in the second period, I have an average dietary intake for each of the 54 variables that I can use to correlate with serum albumin. Based on that data, I can make an educated guess at what could potentially increase, or decrease it.

Of the 54 dietary variables that I track, only 3 were significantly correlated with albumin: positive associations for alpha-carotene (r = 0.66, p = 0.027), beta-carotene (r = 0.75, p =0.007), and a negative association for Vitamin K (r = -0.64, p = 0.03). Shown below is the strongest correlation of the three, beta-carotene, vs. serum albumin.

bcarot alb.png

The majority of my alpha and beta-carotene intake comes from carrots, with a smaller amount coming from butternut squash. Interestingly, beta-cryptoxanthin, a Vitamin A metabolite that is abundant in butternut squash, was not significantly associated with serum albumin. Butternut squash is also a good source of alpha- and beta-carotene, so if  butternut squash was driving the correlation between the carotenes with albumin, I’d expect beta-crypoxanthin to also be significantly associated with it. However, since it’s not, carrots are the most likely source driving the association. Also note that the my average intake of Vitamin K is dramatically higher (1410 mcg; range, 1080-2203 mcg) than the RDA or AI, which are ~100-120 mcg/day. The negative association between my Vitamin K intake with albumin suggests that I should keep it closer to 1100 mcg/day to potentially keep my albumin levels high.

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

 

References

Dong MH, Bettencourt R, Barrett-Connor E, Loomba R. Alanine aminotransferase decreases with age: the Rancho Bernardo Study. PLoS One. 2010 Dec 8;5(12):e14254.

Dong MH, Bettencourt R, Brenner DA, Barrett-Connor E, Loomba R. Serum levels of alanine aminotransferase decrease with age in longitudinal analysis. Clin Gastroenterol Hepatol. 2012 Mar;10(3):285-90.e1.

Gom I, Fukushima H, Shiraki M, Miwa Y, Ando T, Takai K, Moriwaki H. Relationship between serum albumin level and aging in community-dwelling self-supported elderly population. J Nutr Sci Vitaminol (Tokyo). 2007 Feb;53(1):37-42.

Dong MH, Bettencourt R, Barrett-Connor E, Loomba R. Alanine aminotransferase decreases with age: the Rancho Bernardo Study. PLoS One. 2010 Dec 8;5(12):e14254.

Fulks M, Stout RL, Dolan VF. Albumin and all-cause mortality risk in insurance applicants. J Insur Med. 2010;42(1):11-7.

Le Couteur DG, Blyth FM, Creasey HM, Handelsman DJ, Naganathan V, Sambrook PN, Seibel MJ, Waite LM, Cumming RG. The association of alanine transaminase with aging, frailty, and mortality. J Gerontol A Biol Sci Med Sci. 2010 Jul;65(7):712-7.

Phillips A, Shaper AG, Whincup PH. Association between serum albumin and mortality from cardiovascular disease, cancer, and other causes. Lancet. 1989 Dec 16;2(8677):1434-6.

Sahyoun NR, Jacques PF, Dallal G, Russell RM. Use of albumin as a predictor of mortality in community dwelling and institutionalized elderly populationsJ Clin Epidemiol. 1996 Sep;49(9):981-8.

Takata Y, Ansai T, Soh I, Awano S, Sonoki K, Akifusa S, Kagiyama S, Hamasaki T, Torisu T, Yoshida A, Nakamichi I, Takehara T. Serum albumin levels as an independent predictor of 4-year mortality in a community-dwelling 80-year-old population. Aging Clin Exp Res. 2010 Feb;22(1):31-5.

Weaving G, Batstone GF, Jones RG. Age and sex variation in serum albumin concentration: an observational study. Ann Clin Biochem. 2016 Jan;53(Pt 1):106-11.

Circulating Liver Enzymes: AST and ALT, What’s Optimal For Health?

Two blood markers of liver health are aspartate aminotransaminase (AST) and alanine aminotransaminase (ALT). AST and ALT are proteins that are usually found inside liver cells, but when there is liver cell damage, they’re released into the blood. It’s important to note that these proteins can also be elevated in the blood because of muscle damage. The reference range for AST is 10-40 U/L, and 7-56 U/Lfor ALT, but are these values optimal for health and longevity?

In a meta-analysis that included ~9 million adults (average age, 51y) that were followed for up to 20 years, Kunutsor et al. (2014) reported the association between AST and ALT with all-cause mortality risk. For AST (4 studies, 9,046,609 subjects), 10-15 U/L was associated with maximally reduced all-cause mortality risk:

ast acm.png

For ALT (8 studies, 9,087,436 subjects), 12-15 U/L was associated with maximally reduced all-cause mortality risk:

alt acm

While these studies are relevant for middle-aged adults between ~50-70y, what about at older ages? Shown below are the AST and ALT values for adults older than 100 years (centenarians):

ast alt cent.png

Interestingly, the centenarians’ AST and ALT values are not far from the meta-analysis data for middle-aged adults. For example, the centenarians’ AST values range from 17-23, and their ALT values from 9-14.

What are my my AST and ALT values? As shown below, I’ve measured them 9 times in the past 10 years. Based on the all-cause mortality and centenarian data my AST and ALT values are too high!

my ast alt

What am I doing to reduce my AST and ALT? Fructose is metabolized by the liver, where high amounts can increase liver cell damage, resulting in increased circulating AST and ALT (Le et al. 2009, Perez-Pozo et al. 2010). Therefore, I’ve reduced my total dietary fructose intake from ~16-18% during the 3 months prior to my last blood test (August, 2015), to ~11-14%. I plan on retesting within the next 2 months, to see if this approach works!

3/23/2016 Update: My average daily fructose intake, expressed as a percentage of total calories, for the 3-month period before my August 2015 blood test was 15.9%. During the 3-month period before my latest blood test (3/2016), my average daily fructose intake was 12.9%. Although a 3% decrease doesn’t seem like much, the difference between these 2 values is highly statistically significant (p value = 7.5E-12). Nonetheless, my liver enzymes didn’t change, with AST and ALT values of 28 and 30, respectively.

My next attempt to reduce my liver enzymes involves reducing my daily green tea intake.  High doses of green tea have been shown to negatively affect the liver (Mazzanti et al. 2009). I currently drink ~6 cups of green tea per day, which may be too much. To test that hypothesis, I’ll reduce my daily green tea to 4 cups/day, and retest my liver enzymes in a few months.

12/8/2017 Update: Since 3/2016, I’ve tested my blood 7 times, and on each measurement, my ALT and AST were both still in the mid 30’s (or higher!). The green tea reduction experiment didn’t work, nor did ~30g of milk thistle seeds/day for 30+ days, nor did reducing my fructose intake to ~9% of total calories. Because I’ve tracked my nutrition in concordance with blood testing, I can look at which nutrients correlate with my liver enzymes, and reduce/increase certain foods that may impact them. Interestingly, my dietary niacin levels (x-axis), which average 41 mg/day (including all data since 2015) were strongly correlated with ALT (y-axis; r = 0.7, R^2=0.50):

Screen Shot 2018-01-06 at 1.21.59 PM

Note that the RDA for niacin is ~15 mg/day for males, and my average niacin intake in more than 2-fold higher than that! This may be a case where higher than the RDA is not optimal for health. Niacin in high doses, albeit in grams, not milligrams, is well known to induce liver damage, so isn’t it possible that my 2-fold higher than the RDA niacin intake is inducing liver damage? Sometime in January, I’ll retest my liver enzymes (and everything else, of course) while reducing my dietary niacin intake from the low 40’s to the low 30’s. As I’ve mentioned in previous posts, I eat lots of mushrooms, around 300 grams at a time, which supply around 11 mg of niacin. That’s atop the list to reduce my niacin intake. Stay tuned for the data!

1/6/2018 Update: Finally, progress! On my 1/3/2018 blood test, I was able to reduce my ALT from my average 37 U/L (over 9 different tests) value to 29! To reduce it, I tried two main things: reducing my dietary niacin intake, and increasing my selenium intake.

First, as noted above, the moderately strong correlation between my dietary niacin intake with ALT suggested that reducing it may also reduce my ALT. From 12/6/2017 to 1/2/2018, I reduced my average daily niacin intake from 41 mg/day to 33.1 mg. Interestingly, in adding that data to my 9 other blood test measurements over the last 27 months, the correlation between my niacin intake with my ALT remained strong (r = 0.75, R^2 = 0.58).

Second, I also increased my dietary selenium intake, which may be involved in affecting my ALT levels. Superficially, when examining the correlation between my average selenium intake (186 mcg/day; x-axis) with ALT (y-axis), we see a very weak negative correlation (r = -0.24, R^2 = 0.06):
Screen Shot 2018-01-06 at 1.43.33 PM.png

Then why did I increase my daily selenium intake from an average value of 186 mg/day to 207 mg/day for the 1-month period that preceded my latest blood test? I discovered that the correlation between dietary selenium density (selenium intake/100 calories) with ALT was strong (= -0.69, R^2 = 0.47):

Screen Shot 2018-01-06 at 1.37.32 PM

Why did I look at dietary selenium density instead of its absolute value? If I eat more calories, one would expect higher levels of selenium (or other nutrients), assuming I’m not eating junk. By accounting for my calorie intake, I may be better able to see how dietary nutrients affect my circulating biochemistry, rather that only looking at the absolute values for each nutrient. Also note that the correlation between niacin density (mg niacin/100 calories) was not as strong (= 0.53, R^2 = 0.28) as the correlation between selenium density with ALT.

Is my ALT sensitive to changes in niacin, selenium, or both? Alternatively, maybe it wasn’t niacin or selenium, but an aberrant reading? I’ll keep my niacin relatively low, and my selenium relatively high, so let’s see on my next test at the end of the month.

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

 

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