Category Archives: quantified self

Blood Testing: What’s Optimal For Triglycerides?

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In terms of all-cause mortality risk, is the reference range for circulating triglycerides (TG, <150 mg/dL) optimal?

A meta-analysis of 38 studies in 360,556 subjects with a median age of 48y and a 12-year follow-up reported lowest all-cause mortality risk for subjects with TG values less than 90 mg/dL (equivalent to ~1 mmol; Liu et al. 2013). As shown below, each successive 90 mg/dL increase was associated with a 12% higher all-cause mortality risk. A person with a value closer to the high end of the reference range, ~150 would have a ~7% increased mortality risk compared someone with a value ~90. In other words, there would be 7 more deaths per 100 total people at a TG value of 150, compared with the death rate for people with values less than 90.

tg mortal

Added importance for the association between TG values less than 90 with all-cause mortality risk come from studies of people who have lived longer than 100 years, centenarians. As shown below, triglyceride values less than 101 mg/dL have been reported in 9 of 11 centenarian studies:

tg mort

What’s my TG value? As shown below, I’ve measured triglycerides 23 times since 2015, with an average value of 52 mg/dL:tg 2020

With the goal of keeping triglyceride levels low, are there dietary factors that influence it? When compared with my dietary data, the strongest correlation (r = 0.73, R2=0.5339) is present for triglycerides with my calorie intake. In other words, a higher daily calorie intake is associated with higher levels of triglycerides:

tg vs cals 2020

Based on this correlation, should my triglycerides start to rise in the future, a first step would be reducing my average daily calorie intake, which since October 2019 has been ~2550 calories/day.

 

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

References

Liu J, Zeng FF, Liu ZM, Zhang CX, Ling WH, Chen YM. Effects of blood triglycerides on cardiovascular and all-cause mortality: a systematic review and meta-analysis of 61 prospective studies. Lipids Health Dis. 2013 Oct 29;12:159.

Biological Age = 32.75y, Chronological Age = 47y: First 2020 measurement

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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!

2020 BA

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!

Resting Heart Rate, Heart Rate Variability: January 2020 Update

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How’s my progress on the road to achieving a resting heart rate (RHR) as close to 40 beats per minute (bpm) as possible? Shown below is my RHR data for August 2018-Jan 2019, which corresponds to the 6-month period after I started tracking RHR. When compared with that period, am I still making year-over-year progress?

jan hr

First, note that my Jan 2019 RHR value of 47.4 bpm seems dramatically reduced when compared with Aug-Dec 2018. My computer crashed in Jan 2019, and I lost 4 days of January 2019 RHR data, with remaining data for 27 days. Accordingly, I didn’t expect to be better than that, year-over-year. Nonetheless,  my average RHR for Jan 2020 is 46.9 bpm, which is superficially better, but it isn’t statistically different from Jan 2019 (p = 0.13). However, my RHR is still going in the right direction!

What about my heart rate variability (HRV)? Relative to Jan 2019 (56.6), my HRV in Jan 2020 was significantly higher (76; p=0.003), but note that I didn’t additionally improve my HRV relative to December 2019 (86.3).

hrvjan2

I’ve been consistent with my exercise program, including weekly workouts (3-4x, ~1 hr each session) and walking (15-20 miles), so are there other variables that may explain the sudden increase in HRV from Nov 2019-Jan 2020? During that time, I’ve been cutting my calorie intake by a small amount (~100-200 cals/day) below my body weight maintenance intake, with the goal of getting leaner. As a result, I’ve slowly decreased my body weight from 157 to 154 during that time. Although there is a weak negative correlation between my body weight with HRV (R2=0.0553), this association is statistically significant (p=0.024). So reducing body weight may have played a role in the sudden HRV increase:

hrv bw

For those who may have missed my other post updates for RHR and/or HRV:
Dec 2019 update: https://michaellustgarten.wordpress.com/2020/01/01/resting-heart-rate-heart-rate-variability-december-2019-update/

Oct, Nov 2019 update: https://michaellustgarten.wordpress.com/2019/12/05/resting-heart-rate-heart-rate-variability-still-making-progress/

Sept 2019 update: https://michaellustgarten.wordpress.com/2019/10/08/resting-heart-rate-year-over-year-update/

Also, why a RHR as close to 40 bpm may be optimal: https://michaellustgarten.wordpress.com/2019/02/02/resting-heart-rate-whats-optimal/

 

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

 

Uric acid: What’s optimal?

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The reference range for uric acid is 4.0 – 8 mg/dL for men, and 2.5 – 7 mg/dL for women. Are these values optimal for health? To answer that question, let’s have a look at how circulating levels of uric acid change during aging, and their association with risk of death for all causes.

Uric acid increases during aging in both men and women. Kuzuya et al. (2002) studied how uric acid changes during a 10-year intervals for various birth cohorts, including 32yr olds, 39yr olds, 47yr olds, 56yr olds, and 65 yr olds (1960-1969, 1950-1959, 1940-1949, 1930-1939, 1920-1929 birth cohorts, respectively). For each birth cohort, uric acid levels increased during aging for men (left image below), whereas they increased for women starting at 40 years old:

Screen Shot 2020-01-06 at 7.18.04 AM

In terms of mortality risk, lowest risk of death for all causes was associated with uric acid levels of 5 – 7 mg/dL for men and 4 – 6 mg/dL for women in the 9,118 adults (average age, 43y) of Hu et al. (2019). Also note the U-shaped curve for both genders, whereas mortality risk increases at both low and high levels of uric acid:

Screen Shot 2020-01-05 at 2.55.00 PM.png

Similarly, the lowest risk of death for all causes was associated with uric acid levels of 7 mg/dL for men, and 4 mg/dL for women in the 375,163 adults (average age, 40y) of Cho et al. (2018), with mortality risk significantly increasing at uric levels < 3.5 and > 9.5 mg/dL for men, < 2.5 and > 7.5 mg/dL for women. Collectively, these 2 studies in middle-aged adults suggest that uric acid levels ~ 4 mg/dL for women and ~7 for men may be optimal for reducing risk of disease for all causes. It’s also important to note that both low and higher values are associated with an increased mortality risk.

The data for the Hu and Chu studies are in younger adults, so how does the data look in older adults? Lowest all-cause mortality risk was associated with uric acid levels between 4 – 5 mg/dL in the 121, 771 older adults (average age, 73y) of Tseng et al. (2018), with mortality risk significantly increasing below 4 and > 8:

Screen Shot 2020-01-05 at 2.43.46 PM.png

What are my uric acid levels? From 2016 to 2018, I measured it 15x, and although my average value of 5.2 mg/dL is not too low or too high in terms of an increased all-cause mortality risk, it increased during that 3-year period (R2 = 0.2886). When considering that uric acid increases during aging, can I reduce it with diet?

ua ml

Because I track my daily nutritional intake, I can look for correlations between my dietary intake with circulating biomarkers. Interestingly, a moderately strong correlation between my lycopene intake with uric acid (R2 = 0.3343, p=0.024) was present from 2016 to 2018:

ua vs lyco.png

Lycopene is found almost exclusively in tomatoes and watermelon. If these foods are related to my increasing levels of uric acid, if I ate less of them, I’d expect to see a corresponding decrease in uric acid. So, in 2019, I ate less of these foods, thereby reducing my average lycopene intake from 11,585 to 9,132 micrograms per day. How did that affect circulating levels of uric acid?

In 6 measurements for 2019, my average uric acid level was 4.6 mg/dL, a value that was significantly different (p=0.02) from the 2016-2018 average of 5.2 mg/dL. Whether eating less watermelon and tomatoes caused the decrease is unknown, but it’s good to know that uric acid can be potentially modified with dietary change!

my ua

 

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

References

Cho SK, Chang Y, Kim I, Ryu S. U-Shaped Association Between Serum Uric Acid Level and Risk of Mortality: A Cohort Study. Arthritis Rheumatol. 2018 Jul;70(7):1122-1132. doi: 10.1002/art.40472.

Hu L, Hu G, Xu BP, Zhu L, Zhou W, Wang T, Bao H, Cheng X. U-Shaped Association of Serum Uric Acid with All-cause and Cause-Specific Mortality in US Adults: A Cohort Study. J Clin Endocrinol Metab. 2019 Oct 25. pii: dgz068. doi: 10.1210/clinem/dgz068.

Kuzuya M, Ando F, Iguchi A, Shimokata H. Effect of aging on serum uric acid levels: longitudinal changes in a large Japanese population group. J Gerontol A Biol Sci Med Sci. 2002 Oct;57(10):M660-4.

Tseng WC, Chen YT, Ou SM, Shih CJ, Tarng DC; Taiwan Geriatric Kidney Disease (TGKD) Research Group. U-Shaped Association Between Serum Uric Acid Levels With Cardiovascular and All-Cause Mortality in the Elderly: The Role of Malnourishment. J Am Heart Assoc. 2018 Feb 10;7(4). pii: e007523. doi: 10.1161/JAHA.117.007523.

Resting Heart Rate, Heart Rate Variability: December 2019 Update

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In earlier posts, I reported year-over-year improvements for my resting heart rate (RHR), from 51.5 (bpm) when I first started tracking in August 2018 to 48 bpm in November 2019 (https://michaellustgarten.wordpress.com/2019/12/05/resting-heart-rate-heart-rate-variability-still-making-progress/). Did my year-over-year RHR improvement continue in December 2019?

As shown below, in December 2018, my average RHR was 49.5 bpm. In December 2019, it was 47.5! These data are significantly different (p=6.5E-05):

rhr dec 2019

While RHR is one metric of cardiovascular health, heart rate variability (HRV) is another. With a stronger heart, the expectation would be a lower RHR, but a higher HRV. December 2019 was my best month ever for HRV, with an average HRV value of 86.3!

hrv 12 2019

Also note that December 2019’s HRV value is significantly different when compared with December 2018 (p=1.6E-11).

How am I able to continuously improve my RHR, and recently, my HRV? I average 15-20 miles of walking per week, and 3-4 days/week of structured exercise (1 hr/session), including a combination of weights, core, and stretching. My average HR during my structured workouts had been ~105 bpm prior to the past few months, but in November and December 2019 I made more of an effort to minimize rest periods, and included higher reps to keep my exercise HR as high as possible. My goal is to get my RHR to 40 bpm, which is associated with maximally reduced risk of death for all causes (https://michaellustgarten.wordpress.com/2019/02/02/resting-heart-rate-whats-optimal/). Stay tuned for more RHR and HRV data next month!

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

Epigenetic Aging: Inflammation, Exercise, Smoking

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Besides diet (https://michaellustgarten.wordpress.com/2019/12/07/slowing-epigenetic-aging-with-diet/), are there other factors that may impact epigenetic aging? First, let’s have a look at clinically relevant variables, including inflammation, the lipid profile, kidney function, blood pressure, and body size/dimensions (Liu et al. 2019):

EA crp.png

One of the strongest correlations for the clinical variables with epigenetic aging (AgeAccelGrim) is found for C-reactive protein (CRP), with higher CRP being associated with an older epigenetic age. This data supports the hypothesis that CRP levels as low as possible may be representative of biological youth, which I’ve previously written about (https://michaellustgarten.wordpress.com/2019/10/19/optimizing-biological-age-crp/). Similarly, higher values for insulin, glucose, triglycerides, systolic blood pressure, BMI, and the waist/hip ratio were correlated with an older epigenetic age, whereas higher HDL was correlated with a younger epigenetic age. Significant correlations were not identified for total or LDL cholesterol, creatinine, or diastolic blood pressure.

Investigating further, the strongest correlation for epigenetic aging was found for smoking, as current smokers had an older epigenetic age. In contrast, those who exercised, drank alcohol, and that had higher levels of education and income had younger epigenetic ages (Liu et al. 2019):

exerc ea

 

References

Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, Hou L, Baccarelli AA, Li Y, Stewart JD, Whitsel EA, Assimes TL, Ferrucci L, Horvath S. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY). 2019 Jan 21;11(2):303-327. doi: 10.18632/aging.101684.

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

Resting Heart Rate, Heart Rate Variability: Still Making Progress?

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Although many of my posts aimed at improving health and longevity are focused on diet, in this post I’ll show data that demonstrates that I’ve been able to steadily improve my cardiovascualr fitness. In earlier posts I reported that a resting heart rate (RHR) of 40 beats per minute (bpm) was associated with lowest risk for all-cause mortality (https://michaellustgarten.wordpress.com/2019/02/02/resting-heart-rate-whats-optimal/), and I noted my own RHR progress in a year-over-year update, from values of 51.5 – 52.7 bpm in August – Sept 2018 to 49.3 – 48.7 bpm during the same months in 2019 (see https://michaellustgarten.wordpress.com/2019/10/08/resting-heart-rate-year-over-year-update/). Have I continued to make progress?

Shown below are 2 more months of RHR data, from August – November 2018, and the data for those months in 2019:
rhr 4 moFrom August – November 2018, I reduced my RHR from ~52 to 50 bpm, whereas in 2019, I made smaller progress, but the trend is still downward, from 49 to 48 bpm. The 2018 data is significantly different from the 2019 data, as assessed by single-factor ANOVA (p = 8E-14).

Adding strength to these findings is that my heart rate variability (HRV), as a second index of cardiovascular health, has increased during the same period:

hrv2Note that from August – November 2018, my average daily HRV value never topped 48, whereas during the same 4 months in 2019, it was never lower than 52.1, with my best ever HRV values found in November. The 2018 is significantly different when compared with 2019, again based on single-factor ANOVA (p = 5.2E-13).

How am I improving my cardiovascular fitness? That’s a topic for another post, but note that my strength is still pretty good, as evidenced by my 12 pull-ups in the video below!

https://www.youtube.com/watch?v=dLktQvFz70Q

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

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

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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!

Biological Age = 31.3y, Chronological Age= 46y

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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!

Optimizing Biological Age With Aging.ai: Platelets

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Platelets are one of the 19 variables that are included in the biological age calculator, aging.ai.  The reference range is 150-400 platelets per nanoliter (*10^9/L), but within that range, what’s optimal?

In a study of 21,635 adults older than 35y (average age wasn’t reported), platelets between 230-270 were associated with a maximally reduced risk of death from all causes (Bonaccio et al. 2016):

platets acm

Similarly, in a study of 21,252 adults (average age 53y), values ~250 were associated with maximally reduced risk of death from all causes Vinholt et al. (2017) :

plat2 acm

What about in older adults? In a study of 159,746 postmenopausal women (average age, 63y), maximally reduced risk of death from all causes was associated with platelet values between 200-256 (Kabat et al. 2017).

In a smaller study (36,262 older adults, average age, 71y), platelet values ~250 were associated with maximally reduced risk for all-cause mortality. Interestingly, even at platelet values ~250, mortality risk was highest for non-Hispanic whites, when compared with lower mortality risk for non-Hispanic blacks and Hispanics (Msaouel et al. 2014):

plat ethnicity

In 5,766 older adults (average age, 73y), platelets higher than 200-300 was associated with an increased risk of death from all causes (van der Bom et al 2009). Risk for values between 100-199 was not different when compared against 200-299, but there was a non-significant trend towards increased risk (1.05, 95% CI: 0.97, 1.14).

In 131,308 older adults (~73y), maximally reduced risk of death from all causes was associated with platelet values between 200-300, whereas risk significantly increased below and above that range, respectively Tsai et al. (2015):

plat eld

In sum, the data suggests that platelet values ~250 may be optimal for heath, with 200-300 as the “optimal range” within the 150-400 reference range. What are my values? Over the past 16 years, I’ve measured my platelets 25 times, and 6x, my platelets were below this 200-300 range. I’m not too worried about it, though, as most of my measurements are within that range!

plt.png

Are there any variables that are correlated with platelets? For me, the strongest correlation over 18 tracked blood tests from 2015 – 2019 is my body weight. As my weight increases, my platelets are higher (r = 0.64, p-value = 0.006). Platelets have been reported to increase in association with elevated inflammation (CRP; Izzi et al. 2018), but I only have 3 co-measurements for CRP with platelets. I have a blood test scheduled for next week, more data coming soon!

 

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

 

References

Bonaccio M, Di Castelnuovo A, Costanzo S, De Curtis A, Donati MB, Cerletti C, de Gaetano G, Iacoviello L; MOLI-SANI Investigators. Age-sex-specific ranges of platelet count and all-cause mortality: prospective findings from the MOLI-SANI study. Blood. 2016 Mar 24;127(12):1614-6.

Izzi B, Bonaccio M, de Gaetano G, Cerletti C. Learning by counting blood platelets in population studies: survey and perspective a long way after Bizzozero. J Thromb Haemost. 2018 Sep;16(9):1711-1721. doi: 10.1111/jth.14202.

Kabat GC, Kim MY, Verma AK, Manson JE, Lin J, Lessin L, Wassertheil-Smoller S, Rohan TE. Platelet count and total and cause-specific mortality in the Women’s Health Initiative. Ann Epidemiol. 2017 Apr;27(4):274-280.

Msaouel P, Lam AP, Gundabolu K, Chrysofakis G, Yu Y, Mantzaris I, Friedman E, Verma A. Abnormal platelet count is an independent predictor of mortality in the elderly and is influenced by ethnicity. Haematologica. 2014 May;99(5):930-6.

Tsai MT, Chen YT, Lin CH, Huang TP, Tarng DC; Taiwan Geriatric Kidney Disease Research Group. U-shaped mortality curve associated with platelet count among older people: a community-based cohort study. Blood. 2015 Sep 24;126(13):1633-5.

Vinholt PJ, Hvas AM, Frederiksen H, Bathum L, Jørgensen MK, Nybo M. Thromb Res.Platelet count is associated with cardiovascular disease, cancer and mortality: A population-based cohort study. 2016 Dec;148:136-142.