Tracking Deep Sleep-Can It Be Improved?

Deep sleep, the stage of sleep also known as “slow wave sleep” declines during aging. Based on a meta-analysis of 65 studies representing 3,577 subjects (aged 5 years to 102 years; Ohayon et al. 2004), slow wave sleep, expressed as a percentage of total sleep time decreases during aging from 25% in childhood to less than 10% in adults older than 65 years:Screen Shot 2019-02-16 at 5.14.10 PM.png Continue reading “Tracking Deep Sleep-Can It Be Improved?” →

100 Days Of Dietary Data

I’ve posted individual dietary days as an example of what and how much I eat (https://michaellustgarten.com/2015/12/31/130-grams-of-fiber-2400-calories/). However, a few days of examples may not represent the whole dietary picture. To address this, below is my average nutrient intake for the past 100 days (from October 24, 2018-Feb 5 2019):

100 days of nutrition.png

Notice that my average values for many of these variables (i.e. potassium, selenium, Vitamin C, Vitamin K, etc.) are way above the RDA. For more info on that, I have several blog posts that explain the “why” behind that. Where am I getting those nutrients from? Shown below are 100-day averages for my food intake, ranked in order from most consumed (in grams, or ounces, if it’s a drink) to least:

100 days of foods.png

During the past 100 days, my top 5 foods in terms of daily intake include carrots, strawberries, red peppers, watermelon, and cauliflower. Scroll through the list to see how much I average on a daily basis for each food!

 

Have a look at my book, if you’re interested!

Resting heart rate: What’s optimal?

One of the goals of my exercise program is to reduce my resting heart rate (RHR). A stronger heart beats less times per minute, but pumps more blood per beat. In contrast, a weaker heart beats more times per minute, but less blood per beat.

Is there an optimal level for RHR? Based on a meta-analysis of 59 studies that included 1,810,695 subjects, RHR values < 50 beats per minute (bpm) are associated with maximally reduced risk of death from all causes. Conversely, RHR values > 50 bpm are associated with a higher mortality risk (Aune et al. 2017):

Screen Shot 2019-02-02 at 10.48.29 AM

What’s my resting heart rate? Shown below is that data, tracked by WHOOP since August. Note that my RHR wasn’t significantly different from August until October, ranging from 51-53 bpm (average, 51.7). However, because I was tracking my RHR, I noticed that I was overtraining, leading to very high HRs, lower heart rate variability, and less deep sleep (topics for another post!) the day(s) after exercise. So early in November, I changed my exercise routine. As a result, from November until the end of January, my average RHR (49.7 bpm) has been significantly less (p-value =1E-10), and based on January’s average RHR, I’m trending closer to 47 bpm! Also note that * = significantly different when compared with August.

hr

What did I change in my exercise program? Since I’ve been in Boston (~9 years), I’ve walked 15-20 miles per week: it’s 1.1 miles to and from work, plus at least an hour of walking on Saturdays and Sundays. That’s a constant that hasn’t changed. In contrast, I split my 3-day weight training routine, which totaled ~5-6 hours/week into 3-5 days at less than an hour each session, and at a lower intensity with more reps. My strength is still as good as it was before, and as a result, my recovery HRs aren’t as high, thereby leading to a lower average RHR over time,. I’ve been training like that consistently for the past 30 years, but it took wearing a fitness tracker to change it!

 

Reference

Aune D, Sen A, ó’Hartaigh B, Janszky I, Romundstad PR, Tonstad S, Vatten LJ. Resting heart rate and the risk of cardiovascular disease, total cancer, and all-cause mortality – A systematic review and dose-response meta-analysis of prospective studies. Nutr Metab Cardiovasc Dis. 2017 Jun;27(6):504-517.

 

If you’re interested in living longer and healthier, please have a look at my book!

Dietary Cholesterol Vs. Plasma Cholesterol: My n=1 Data

With use of a food scale,  I’ve been tracking my daily macro- and micronutrient intake every day since April 2015. In addition, I have 15 blood test measurements during that period, and accordingly, I’m able to examine correlations between my dietary intake with my circulating biomarkers. In this post, I’ll address the question, is my dietary cholesterol intake significantly correlated with plasma levels of cholesterol?

1. Plasma levels of total cholesterol vs. dietary cholesterol:

tc.png

In the plot we see a borderline significant (p = 0.06), moderate correlation (r = 0.5) between my plasma total cholesterol with my dietary cholesterol intake. However, note that total cholesterol is comprised of “good” and “bad” parts, with HDL as the “good”, and with non-HDL cholesterol, including LDL and VLDL, as the “bad”. What does that data look like?

2. Plasma levels of non-HDL (LDL+VLDL) cholesterol vs. dietary cholesterol:

nonhdl.png

In the plot we see a highly significant (p = 0.006), strong correlation (r = 0.67) between my non-HDL cholesterol levels with my dietary cholesterol intake. It’s not possible to show causation via correlation, but this data suggests that my dietary cholesterol intake may be driving increased levels of non-HDL cholesterol.

3. Plasma levels of HDL cholesterol vs. dietary cholesterol:

hdl.png

In the plot, first note that in contrast with the positive correlations between total and non-HDL cholesterol with my dietary cholesterol intake, the correlation between HDL with my dietary cholesterol intake is negative (i.e., going in the opposite direction; r = 0.51), and borderline significant (p = 0.054).

Cumulatively, it looks like my dietary cholesterol intake may be related to increased “bad” cholesterol and decreased “good” cholesterol. As a limitation of this approach, although I’ve shown blood test data for 15 measurements (which is a decent sample size), I only have 4 measurements with an average daily cholesterol intake around 200 mg or greater. In the near future, I expect to average 200 mg of daily cholesterol (or more) per day, so let’s see if these correlations hold up!

 

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

 

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 populations. J 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.

Maximizing Health And Lifespan: Is Calorie Restriction Essential?

My goal is to break the world record for lifespan, 122 years, which is currently held by Jean Calment. How do I plan to do that? A good start would be calorie restriction (CR), a diet where you eat 10-30%+ less calories than your normal intake. CR is the gold standard for increasing lifespan in a variety of organisms, including yeast, flies, worms, and rodents (McDonald et al. 2010).

With the goal of maximizing my health and lifespan, in April 2015, I started a CR diet. Inherent in that was weighing all my food and recording it in an online website that tracks macro-and micro-nutrients. From then until March 2016, I was pretty good at keeping my calories relatively low, as I averaged 2302 calories. However, since 3/2016, it’s been exceedingly difficult to keep my calories that low, as I’ve averaged 2557 calories. So is having a higher calorie intake worse for my lifespan goal than a lower calorie intake?

Maybe not. In addition to tracking my daily nutrition since 2015, I’ve also had regular blood testing performed. I’ve measured the typical things that you get at a yearly checkup, including the metabolic panel (LDL, HDL, VLDL, creatinine, ALT, etc.) and the complete blood count (red and white blood cells, etc.). By tracking my daily nutrition and circulating biomarkers, I’m able to quickly intervene on any potential aging and disease-related mechanisms by using my diet to optimize my circulating biomarkers.

On my quest for optimal health and lifespan, biological age is more important than my chronological age (I’m 45y). So what’s my biological age? Between 2016-2018, the group at Insilico Medicine published 2 papers that included  circulating biomarker data from more than 200,000 people (Putin et al. 2015, Mamoshina et al. 2018) to derive a biological age predictor (aging.ai). So what’s my biological age?

Shown below is my predicted biological age over 11 blood tests from 3/2016 to 6/2018:

agingai

Although I wasn’t on a CR diet during that time, my average biological age was 29.2 years, which is ~35% younger than my chronological age. Would my biological age be even younger with a lower calorie intake? I’m working on reducing my calorie intake again (it’s not easy for me), so stay tuned for that!

Here are the my biomarker values corresponding to each blood test, for anyone who wants to double check the results:
data

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

References

Mamoshina P, Kochetov K, Putin E, Cortese F, Aliper A, Lee WS, Ahn SM, Uhn L, Skjodt N, Kovalchuk O, Scheibye-Knudsen M, Zhavoronkov A. Population specific biomarkers of human aging: a big data study using South Korean, Canadian and Eastern European patient populations. J Gerontol A Biol Sci Med Sci. 2018 Jan 11.

McDonald RB, Ramsey JJ. Honoring Clive McCay and 75 years of calorie restriction research. J Nutr. 2010 Jul;140(7):1205-10.

Putin E, Mamoshina P, Aliper A, Korzinkin M, Moskalev A, Kolosov A, Ostrovskiy A, Cantor C, Vijg J, Zhavoronkov A. Deep biomarkers of human aging: Application of deep neural networks to biomarker development. Aging (Albany NY). 2016 May;8(5):1021-33.

Sean Manaea’s No-Hitter: Will He Ascend to Become a Dominant #1?

Sean Manaea threw a no-hitter last night, against arguably one of the best lineups in baseball. Post-no-hitter, the question is whether Manaea will ascend into becoming an elite starting pitcher, or will be go the way of pitchers like Philip Humber and Homer Bailey, who also threw a no-hitters, but had a hard time getting outs after that?

To answer this question, we’ll look at where Manaea’s been, and make a prediction about where he’s going. First, in Manaea’s 3-year career at Indiana State, he was solid, but not spectacular: 15-12, 3.13 ERA, 1.24 WHIP. Also note that he had a relatively high BB-rate: 3.9 BB/9 IP. Why is the BB-rate important? When ranking starting pitchers (SP) by IP in 2017, the average BB-rate for these pitchers was 2.5 BB/9IP, and only 4 of the top 20 had BB-rates higher than 3.1: Martinez (3.12), Verlander (3.15), Dickey (3.17), Gonzalez (3.54). I’ve chosen to focus on IP because true #1’s throw a lot of IP. For examples, see Sale, Kluber, Sherzer, Verlander. Can you be a #1 starter if you don’t throw around 200 IP? I don’t think so, and I’m even more old school than that: I think true #1’s should throw closer to 250 IP than 200 IP. Note that Manaea has never thrown more than 160IP in a single year, so whether he can get to 200 IP is currently unknown.

What about Manaea’s minor league career? When compared with his college career, it’s virtually identical. In the minors, he had a cumulative 16-9 record, 2.84 ERA, 1.25 WHIP, and 3.5 BB/9IP. Again, the BB-rate is too high, and also note that he didn’t throw more than 121 IP in any of his 3 minor league seasons. Stretching a SP from 120 IP/yr in the minors to 200 IP+ in the majors may be too much without increasing injury risk. For more on that, see (https://www.si.com/mlb/2018/02/19/young-pitcher-year-after-effect-report-card).

Prior to 2018, based on Manea’s performance in college and the minors, I’d would’ve projected that he’d become an average-to-slightly above average SP, but not a dominant #1. Pitchers who throw ~160 IP are closer to being #4 or #5 starters than #1s. The no-hitter argues against that, right? Also, note that Manaea’s BB-rate in 2018 is currently 1.5 BB/9IP. Granted, it’s a small sample size, 36IP, and whether Manaea can continue to keep the BB-rate that low in 2018 in unknown. But consider this: when average or slightly-above average pitchers ascend to becoming dominant #1s, the BB-rate is almost always dramatically reduced. For great examples of that, see Randy Johnson, Corey Kluber, and Clayton Kershaw.

So can Manaea keep the BB-rate low and throw 200 IP+? If he can, he will have ascended into becoming a dominant #1 in 2018.

Stats via http://thebaseballcube.com/players/profile.asp?ID=163027