Here’s my latest academic paper!
The Kidney–Gut–Muscle Axis in End-Stage Renal Disease is Similarly Represented in Older Adults
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Monthly Archives: December 2019
Epigenetic Aging: Inflammation, Exercise, Smoking
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):
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):
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!
Epigenetic Aging: Can It Be Slowed With Diet?
Having a faster rate of epigenetic aging, as measured by the epigenetic age metric, AgeAccelGrim, is associated with a significantly increased risk of death for all causes in a variety of cohorts, including the Framingham Heart Study (FHS), the Women’s Health Initiative (WHI) study, the InChianti study, the Jackson Heart Study (JHS), and collectively, when evaluated as a meta-analysis (Lu et al. 2019):
With the goal of minimizing disease risk and maximizing longevity, can epigenetic aging be slowed? Shown below is the correlation between dietary components with AgeAccelGrim. Dietary factors that were significantly associated (the column labelled, “p”) with a younger epigenetic age were carbohydrate intake, dairy, whole grains, fruit, and vegetables. In contrast, dietary fat intake and red meat were associated with older epigenetic ages (Lu et al. 2019):
Note that dietary recall data as a means for identifying nutrient intake can be unreliable-a better measure of dietary intake is circulating biomarkers. Are there associations between circulating biomarkers of nutrient intake with epigenetic aging?
Higher blood levels of carotenoids, including lycopene, alpha- and beta-carotene, lutein+zeaxanthin, and beta-cryptoxanthin were associated with a younger epigenetic age (Lu et al. 2019):
If your goal is optimal health and longevity, eating foods that are rich in these nutrients may be an important strategy for slowing epigenetic aging. Which foods contain these nutrients? Carotenoids are found almost exclusively in vegetables and fruits. For example, lycopene is enriched in watermelon and tomatoes, alpha- and beta-carotene is high in carrots, orange vegetables (sweet potato, squash, pumpkin) and greens, lutein+zeaxanthin is prevalent in greens, and beta-cryptoxanthin’s highest levels are found in butternut squash and red bell peppers.
If you’re interested, please have a look at my book!
References
Nutrient composition data: https://reedir.arsnet.usda.gov/codesearchwebapp/(S(ujsr52ygvp0tw13m1luk0rny))/CodeSearch.aspx
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.
Resting Heart Rate, Heart Rate Variability: Still Making Progress?
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:
From 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:
Note 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!
Which Blood Test Analyte Is Most Important For Predicting Biologic Age?
Three studies have investigated the ability of blood test analytes to predict biological age. First, when considering the top 20 variables that were associated with biological age in aging.ai, albumin contributed most to this prediction, almost 2x more than circulating levels of glucose (Mamoshina et al. 2018):
Second, albumin was one of the 9 blood test variables that were best able to predict biological age when using the Phenotypic Age calculator. However, as shown below, it didn’t come in first place, but fifth. Interestingly, the analyte that contributed most to biological age prediction was the red cell distribution width (RDW%), with glucose again in second place (Levine et al. 2018):
Third, Earls et al. (2019) used the Klemera-Doubal algorithm (Klemera and Doubal, 2006) in conjunction with blood test data to predict biological age. Regardless if the blood was analyzed by Labcorp or Quest, higher levels of albumin (the left side of both images below) were associated with the greatest reduction in biological age, up to 5 years! In contrast, HbA1c was associated with a higher biological age when measured by Labcorp (top image, right side), and second to lead in the Quest analysis (bottom image, right side). Interestingly, glucose came in third and fifth in the Labcorb and Quest data sets, respectively, in terms of its positive association with biological age.
Glucose would’ve been an obvious choice, but would you have guessed that albumin may be just as important, and potentially more important for predicting biological age?
If you’re interested, please have a look at my book!
References
Earls JC, Rappaport N, Heath L, Wilmanski T, Magis AT, Schork NJ, Omenn GS, Lovejoy J, Hood L, Price ND. Multi-Omic Biological Age Estimation and Its Correlation With Wellness and Disease Phenotypes: A Longitudinal Study of 3,558 Individuals. J Gerontol A Biol Sci Med Sci. 2019 Nov 13;74(Supplement_1):S52-S60. doi: 10.1093/gerona/glz220.
Klemera P, Doubal S. A new approach to the concept and computation of biological age. Mech Ageing Dev. 2006;127:240–248. doi:10.1016/j. mad.2005.10.004
Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y, Whitsel EA, Wilson JG, Reiner AP, Aviv A, Lohman K, Liu Y, Ferrucci L, Horvath S. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018 Apr 18;10(4):573-591. doi: 10.18632/aging.101414.
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.
Michael Lustgarten 