Michael Lustgarten - Life-Extension Biohacking

#12

@blsm All I know is above

Perhaps it’s just a one off experiment for the Candida IgG

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#13

Anyone else feel like Michael Lustgarten’s approach might actually work since CR has a net benefit on like 7 biomarkers (except lymphocytes), iirc?

Isn’t this possibly some signal that it, and that CR works as well?

1 Like
#14

@AnUser Yes, I think that aspect is core and helping him (and one of the most valuable things in Bryan Johnson’s protocol too).

And in general would be good even if it does not drives clicks or sell as much as taking people to eat loads of protein and gain big muscle

Have you seen my posts on CR(ON)

(And fasting)

#15

I meant that his approach might both work and that CR itself.
Because CR is expected to work in animal models, and now both method and CR validated in N=1 according to human epidemiological data (ACM). I might be thinking incorrectly on the CR validation part though.

7 or so biomarkers improve on ACM, 1 negative, that means his method in itself might be valid and that’s important.

It was long ago but I will take a look, I think important is to have the correct method of evaluating strategies.

Wonder if the benefits in human CR studies were on biomarkers showing a net benefit on short term ACM risk.

#16

Would love the read about this - can you share more

#17

I don’t have any studies per se, what I meant was:

Mike has a method he believes will improve his longevity.
This method of optimizing biomarkers according to ACM epidemiological or youth data show that CR works really well for him with lots of biomarkers going in the right direction.

Is his method good? I’m thinking that his one actually works and there’s signal in the epidemiological data for ACM and youth because it shows that CR works well. Which validates his approach.

Does that make sense? If so:

Next would be to see if short term studies on calorie restriction improves biomarkers for better epidemiological ACM, including ones he saw improvement in.

I might be missing something here, but I feel like it shows that it works? Unless we believe CR to not work in humans, thus we can’t determine how valid his method is based on it. I might be confusing myself here, though.

#18

I thing the answers to this might be yes - check out the the different Nature journal paper after the Yale Calorie Trial and all the CRONy stuff.

@CronosTempi can probably help with examples from the top of his mind

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#19

For anyone interested in CR studies in humans and the associated research, you should look at the work of professor Luigi Fontana. He’s the primary investigator behind the CALERIE trial. He’s done a ton of CR work in humans over the decades, and if you google around you’ll find his work everywhere.

Here’s a taste - an interview from 2018:

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#20

Thanks @CronosTempi

@A_User and any anyone else interested in this topic (@adssx what is your latest thinking), here is one thread that also references several other recents ones

2 years of mild caloric restriction significantly reduces biological age?

#21

Having an accurate method of evaluating strategies is way more valuable in my view than any intervention. The latter has often undue weight and leads to a lot of waste in supplements and lifestyle changes that don’t work.

#22

@Neo I read through thread on CR but didn’t verify, these things are pretty new to me, Dunedin pace and the other clocks sound interesting. So what I think:

Michael Lustgarten’s approach probably works in so far the markers he optimize have causality and correct curve (e.g I believe he has the wrong curve on apoB and LDL-c which I’m sure you agree with). CR probably works in humans since biomarkers improve in studies, however like any intervention, I’d want to check in regularly how biomarkers move and how I feel.

I’m curious, you mentioned in your thread, outside of how you feel, is the restriction on amount of calories basically dependent on muscle mass, or is it something else? Sudden deaths in e.g anorexia or liquid protein diets (has anyone mentioned this?) I see a lot of case reports and news in the 70’s and 80’s.

What’s causing suddent death etc in anorexia, can this happen on CR at some point? What is measured?

At least one-third of all deaths in patients with anorexia nervosa are estimated to be due to cardiac causes, mainly sudden death.14,16,20 Cardiovascular complications are common, and they have been reported in up to 80% of the cases; up to 10% of these complications were mainly bradycardia, hypotension, arrhythmias, repolarization abnormalities, and sudden death.14,16,17,21–23 It must be noted that food restriction can lead to increased vagal tone, bradycardia, orthostatic hypotension, syncope, arrhythmias, congestive heart failure, and sudden death.24 Bradycardia presents particularly during the night but neither mean QT nor corrected mean QT length over 24-hour monitoring seem to be different compared with controls.25

With respect to QT abnormalities, QT interval is a measure of myocardial repolarization and its length is associated with life-threatening ventricular tachycardia. Thus, a prolonged QT interval is a biomarker for ventricular tachyarrhythmia and a risk factor for sudden death.17 In EDs, QT interval abnormalities have been studied as a marker of sudden death and also to assess the effect of refeeding. It has been proposed that sudden deaths are a result of cardiac arrhythmias for which a long QT interval on the electrocardiogram would be a marker. The necropsy and clinical findings in three cases of sudden death reported by Isner et al provided evidence that sudden death in anorexia nervosa, like sudden death in liquid-protein dieting, might result from ventricular tachyarrhythmia related to QT interval prolongation.16,26 Nevertheless, the QT interval seems to have a poor predictive value for the recognition of patients who are at particular risk of sudden death. Only QT intervals >600 milliseconds are clearly associated with a significant risk of sudden death, but few ED patients usually have such long QT intervals.27 Considering the QT dispersion, an increase of the QT interval dispersion represents regional differences in myocardial excitability recovery and may lead to an increased arrhythmogenic substrate, with a higher risk for clinically significant ventricular arrhythmia and sudden death. In this case, the predictive value of the increased QT interval dispersion as a marker of sudden acute ventricular arrhythmia or death has been demonstrated.16,22 Both prolonged QT interval and increased QT interval dispersion tend to normalize after refeeding, along with heart rate and heart-rate variability.5,28

Reason I am asking, I’m wondering about the feasibility to be on higher CR might increase (you mentioned hunger feelings) – because we have GLP-1 agonists and other drugs? But of course probably a safer level to not get in anorexia territory for body fat or muscle mass (and as optimal BMI as possible)? Just wondering about the limits and how to track it as well.

But do you think 10-20% long term CR might be more possible with GLP-1 agonists (provided it’s still safe body composition, etc), would that be good?

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#23

@A_User Not an expert but think at too high degree of CR the bodies uses up not just some of your normal (skeletal) muscle, but also some of your heart muscle - which does not seem to be a good thing

You might be right about GLP-a meds as a way to achieve meaningful CR at less hunger hassle. It may or may it be better than not being on CR (@AlexKChen might have thought and experimented with that).

At the same time it may not be as good as achieving the same amount of CR without the GLP-a meds (given eg the potential increase in insulin levels).

Still if the actual choice a person faces is less/no CR without GLPa and more CR (to a reasonable degree) with GLPa, then perhaps that is still better

1 Like
#24

How To Track And Optimize Biomarkers: Blood Test #6 in 2025

AI Summary

Introduction to Biological Age Testing

  • The speaker discusses their experience with biological age testing, noting that they have conducted blood tests 64 times since 2015, with the most recent test being in early 2025.
  • Using Dr. Morgan Lavine’s biological age calculator, known as pheno age, the speaker calculates their biological age to be 35.4 years, which is 17.2 years younger than their chronological age.
  • The speaker indicates that while a 17-year reduction is significant, continuous improvement is necessary to avoid stagnation or regression in biological age.

Importance of Frequent Testing

  • The speaker emphasizes the importance of frequent blood testing, arguing that testing only once or twice a year may not accurately reflect an individual’s average biological markers over time.
  • They suggest that more frequent testing allows for better tracking of year-to-year changes and a more accurate understanding of aging processes.
  • The speaker shares their testing frequency from 2018 to 2025, highlighting a gradual increase in the number of tests conducted each year.

Tracking Year-to-Year Changes

  • The speaker presents data from 43 blood tests conducted since 2018, which shows a trend in biological age changes over the years.
  • In 2018-2019, the average biological age recorded was 34.3 years, while the average for 2020-2021 was 33.9 years, indicating a slight improvement.
  • Despite fluctuations, the speaker achieved their best biological age of 32.1 years in 2022, with subsequent years showing slight increases in biological age.

Understanding Biological Age Variability

  • The speaker addresses the variability in biological age data, noting that while some tests may show improved results, it is essential to present a comprehensive view rather than cherry-picking favorable data.
  • They stress the importance of tracking all data points to gain a true understanding of aging trends rather than focusing solely on the best results.
  • The speaker notes that the biological age clock typically increases by 0.9 years for every year of chronological age, making their current biological age of 33.1 years a positive outcome against expected rates of aging.

Focus on Mean Corpuscular Volume (MCV)

  • The speaker shifts focus to the mean corpuscular volume (MCV), a crucial biomarker that reflects the average volume of red blood cells, emphasizing its significance in health assessments.
  • They explain that MCV is often overlooked but is critical as red blood cells constitute about 80 percent of all human cells.
  • The speaker suggests that tracking MCV alongside other metrics like hemoglobin and red blood cell variability can provide valuable insights into overall health.

Optimal MCV Levels and Trends

  • The speaker indicates that an optimal MCV level is around 90 femtoliters, based on literature regarding aging and health risks.
  • They present their MCV data from 2015 to 2025, showing a troubling upward trend in MCV that needs addressing.
  • Despite the average MCV being close to the optimal target, the speaker highlights the importance of continuous monitoring and improvement of this biomarker.

Dietary Influence on MCV

  • The speaker explores dietary correlations with MCV, noting their meticulous tracking of food intake since 2015, which allows for detailed analysis of how diet impacts biomarkers.
  • They mention utilizing a diet tracking app to correlate dietary intake with blood test results, thereby establishing a connection between specific foods and MCV levels.
  • The speaker identifies Brazil nuts as having a positive correlation with higher MCV, suggesting that their intake may need to be reduced to achieve optimal MCV levels.

Iterative Process for Improvement

  • The speaker emphasizes an iterative approach to dietary adjustments, suggesting that changes in food intake should be monitored for their effects on MCV and other biomarkers.
  • They indicate plans to reduce Brazil nut intake in hopes of lowering MCV levels and improving overall biological age metrics.
  • The speaker concludes by inviting viewers to stay tuned for further updates on their blood test results and additional insights into their aging research and biohacking efforts.
#25

What’s The Biochemistry Of Fitness In 80yr Olds?

AI Summary:

Muscle Mass and Aging

  • Muscle mass and function decline during aging, which is a significant concern as individuals grow older.
  • Research indicates that muscle mass and strength peak around the age of 30, after which a decline occurs.
  • The rate of decline can vary, with a faster decline indicating a risk of sarcopenia, while a slower decline is considered part of normal aging.
  • Higher levels of muscle mass and strength are correlated with increased longevity and improved quality of life.
  • The discussion raises the question of whether it is possible to mitigate the decline in muscle mass and strength as one ages.
  • Aging is described as a biochemical process that unfolds over decades, prompting an exploration of the biochemistry behind fitness levels in older adults.

Study Overview

  • A recently published study focused on serum proteomic and metabolomic signatures to differentiate high versus low physical function in octogenarians.
  • The study included 80-year-olds, with a balanced representation of men and women across three groups: high functioning, normal functioning, and low functioning.
  • Despite small sample sizes, significant differences in physical function were observed among the groups.
  • Physical function was assessed using three measures: walking speed, grip strength, and V2 peak, a metric related to aerobic capacity.
  • The high functioning group demonstrated a V2 peak of 32 milliliters of oxygen consumed per minute per kilogram of body weight, significantly higher than the normal and low functioning groups.
  • Walking speeds also reflected these differences, with the high functioning group showing the best results.

Biochemical Analysis

  • The study aimed to identify metabolites associated with physical function among the 80-year-olds, utilizing a complex plot to represent the data.
  • The y-axis of the plot indicated negative log10 p-values, representing relative metabolite levels, while the x-axis indicated a metric of function.
  • Metabolites positioned to the right of zero were associated with higher physical function, while those to the left correlated with poorer function.
  • Phosphotylcholine and its variants were highlighted as biomarkers indicative of better physical function and potentially lower cardiovascular disease risk.
  • The amino acid tryptophan was also identified as a metabolite associated with better physical function, showing higher plasma levels in the high functioning group.
  • Conversely, indoxyl sulfate, a metabolite derived from gut bacteria, was associated with worse physical function, with higher levels found in the lower functioning group.

Personal Biochemical Tracking

  • The speaker discusses their personal journey in optimizing biochemistry and maintaining a youthful profile through at-home metabolomics testing.
  • Plasma levels of tryptophan were tracked over multiple tests, showing a stable average across the years with no significant differences detected.
  • The speaker emphasizes the importance of maintaining higher plasma tryptophan levels for better physical function, particularly as levels tend to decline with aging.
  • Indoxyl sulfate levels, however, increased over the testing period, which is concerning given its association with poorer physical function.
  • The speaker expresses a desire to optimize their biochemistry further, particularly focusing on metabolites that influence physical function positively.

Metabolite Analysis and Trends

  • The analysis includes a comprehensive overview of metabolites associated with higher physical function, revealing 44 metabolites that showed positive correlations.
  • Notable groups of metabolites include lysophospholines and sphingomyelins, with the former linked to mitochondrial function and the latter potentially associated with neurodegenerative diseases.
  • The speaker highlights the importance of resisting age-related changes in these metabolites to maintain physical function.
  • The analysis also considers metabolites associated with worse physical function, including cysteine and its oxidation product, paracryol sulfate, both of which showed negative correlations.
  • Overall, the findings suggest that maintaining a stable biochemistry over time could correlate with stable physical function.
  • The speaker concludes by emphasizing the need to monitor and optimize multiple metabolites to support overall health and longevity.
#26

Is Nicotinic Acid Bad For Longevity?

AI Summary:

Longevity Interventions

  • Various interventions that aim to extend longevity include rapamycin, acarbos, 17 alpha-estradiol, canagliflozin, and calorie restriction, which have been shown to extend lifespan in mice by 12 to 32 percent.
  • The discussion raises the question of whether there is a common metabolomic signature across these five longevity interventions.
  • Understanding this signature could provide insights into improving human health and longevity.
  • Specific triglycerides were identified as being downregulated across all five longevity-promoting interventions, suggesting a potential link between triglyceride levels and lifespan extension.

Metabolomic Data and Triglycerides

  • A screenshot from a preprint study illustrates the levels of triglycerides TG483 and TG53, which contain different fatty acids, including moristic acid, palmitate, and linoleic acid.
  • The y-axis of the graph indicates the log two-fold change in triglyceride levels compared to controls, while the x-axis categorizes the five interventions by color.
  • Results show that levels of triglycerides TG483 and TG53 were significantly reduced across all five longevity interventions, although this is an association rather than causation.
  • Future studies are required to determine if specifically reducing these triglycerides can impact longevity.

Human Data on Triglycerides and Longevity

  • A study on various metabolite groups examined their association with all-cause mortality risk and longevity, highlighting triglycerides with fewer than or equal to 56 carbons and three double bonds.
  • Both TG483 and TG53 fit this category, which was associated with increased all-cause mortality risk and a decreased likelihood of reaching 85 years of age.
  • Supplementary data from the study provided specific associations between individual triglycerides and mortality risk, confirming that higher levels of TG483 and TG53 correlate with an increased risk of death.
  • The hazard ratios for TG483 and TG53 indicate a 5 percent and 8 percent higher risk of all-cause mortality, respectively.

Tracking Triglycerides

  • The video discusses the importance of tracking triglycerides to optimize health and longevity, posing the question of whether total triglycerides can serve as a proxy for TG483 and TG53 levels.
  • Plasma levels of TG483 and TG53 were measured using at-home metabolomics, which includes data on over 600 metabolites.
  • The correlation between total triglycerides measured by venipuncture and TG483 and TG53 was analyzed, revealing no significant correlation.
  • The lack of correlation suggests that maintaining low total triglycerides may not directly correlate with lower levels of TG483 and TG53 for the individual in question.

Dietary Influences on Triglycerides

  • The presenter calculated correlations between dietary intake and levels of TG483 and TG53, utilizing a food scale for accurate measurement of food intake since 2015.
  • Data was entered into a diet tracking app, allowing for analysis of correlations between dietary components and triglyceride levels across multiple blood tests.
  • Results indicated that higher nasin intake was significantly correlated with increased levels of TG483 and TG53, with strong correlation coefficients and low p-values indicating statistical significance.
  • The implications of this correlation suggest that increased nasin intake could potentially be detrimental to longevity, based on its association with triglyceride levels linked to mortality risk.

Outlier Analysis and Future Testing

  • The presenter identified potential outliers in the data, particularly related to an intentional increase in nasin intake for testing purposes, raising questions about the validity of those data points.
  • After removing outliers, the analysis still indicated a positive correlation between nasin intake and triglyceride levels, though it approached but did not reach statistical significance.
  • The presenter plans to further investigate the effects of nicotinamide supplementation on triglyceride levels, as previous nicotinic acid supplementation raised concerns about its impact on longevity-related biomarkers.
  • Future tests will explore the relationship between nicotinamide and triglyceride levels, along with its potential effects on NAD levels.
1 Like
#27

Higher HRV, Lower RHR: 2,577 Days Of Tracking

AI Summary:

Introduction to Biomarkers

  • The focus on optimizing biomarkers aims to promote longevity by assessing various organ systems.
  • Two key metrics discussed are resting heart rate and heart rate variability, which provide insights into heart health, the nervous system, and adrenal function.
  • These metrics are influenced by the balance between the sympathetic and parasympathetic nervous systems, especially through the vagus nerve.
  • Sympathetic activation of the adrenal gland also releases norepinephrine, affecting both resting heart rate and heart rate variability.

Optimal Ranges for Heart Rate Metrics

  • The optimal range for heart rate variability is identified as high variability with a low resting heart rate.
  • For women, an optimal heart rate variability is at least 70 milliseconds, while for men, it is 75 milliseconds.
  • A resting heart rate of less than 45 beats per minute is considered ideal.
  • A new Patreon tier offers detailed insights into optimal biomarkers, including heart rate metrics and additional data on 33 other biomarkers.

Personal Data Overview

  • The presenter has tracked nearly 2600 days of heart rate variability and resting heart rate data since August 2018.
  • Data collection was conducted using a Whoop device, which has been worn continuously over the seven-year period.
  • The average heart rate variability has shown a consistent increase from an initial value of 47 milliseconds in 2018 to a recent average of 59 milliseconds.
  • Despite fluctuations, including a regression in 2021, the overall trend has been an increase in heart rate variability.

Trends in Resting Heart Rate

  • The average resting heart rate has decreased from 50.9 beats per minute in 2018 to as low as 42.4 beats per minute in 2024.
  • This decline in resting heart rate is notable as it follows an inverse U-shaped trend typically observed during aging.
  • The presenter emphasizes the importance of viewing heart rate variability and resting heart rate together for a comprehensive understanding of cardiovascular health.
  • Current resting heart rate data for 2025 indicate a slight increase to 43.3 beats per minute, which is still lower than the initial tracking values.

Factors Influencing Heart Rate Metrics

  • Three primary factors are identified that may significantly influence heart rate variability and resting heart rate: skin temperature, body weight, and average daily heart rate.
  • Skin temperature has been correlated inversely with heart rate variability, where higher skin temperatures correspond to lower heart rate variability.
  • Conversely, higher skin temperatures are associated with higher resting heart rates, indicating a potential negative impact on cardiovascular health.
  • The type of mattress used may affect skin temperature, thereby influencing heart rate metrics.

Impact of Body Weight

  • Body weight has a significant inverse correlation with heart rate variability, with higher body weight associated with lower heart rate variability.
  • At a current weight of approximately 141 pounds, the presenter is positioned toward the lower end of body weight, correlating with higher heart rate variability.
  • In contrast, a positive correlation exists between body weight and resting heart rate, where higher body weight is linked to higher resting heart rates.
  • Maintaining an optimal body weight is essential for balancing heart rate metrics without compromising strength or performance.

Role of Physical Activity

  • Physical activity significantly impacts both heart rate variability and resting heart rate, with higher activity levels generally leading to improved metrics.
  • The average daily heart rate is a more comprehensive measure than step count as it incorporates all forms of physical activity and emotional stress responses.
  • A significant inverse correlation is observed between average daily heart rate and next-day heart rate variability, indicating that higher daily exertion typically results in lower heart rate variability the following day.
  • Conversely, a positive correlation exists between average daily heart rate and next-day resting heart rate, suggesting that intense workouts can lead to elevated resting heart rates.

Collective Influence of Identified Factors

  • A multivariate analysis indicates that skin temperature, body weight, and average daily heart rate collectively explain 63 percent of the variability in resting heart rate.
  • For heart rate variability, these three factors account for 36 percent of the variability observed.
  • The remaining percentages highlight areas for further research and understanding of other potential influences on these cardiovascular metrics.
  • The presenter expresses a goal to quantify these influences further and enhance predictive accuracy regarding heart rate metrics.
#28

Diet Composition That Corresponds To A 17y Younger Biological Age

AI Summary:

Biological Age and Blood Tests

  • In 2025, the speaker’s blood test number six revealed that their biological age was 17 years younger than their chronological age.
  • The video references previous content regarding supplements and focuses on diet composition related to the recent blood test.
  • The analysis covers the average daily dietary intake recorded over 56 days between blood tests five and six.
  • The speaker meticulously tracks their diet using a food scale and inputs the data into a diet tracking application called Chronometer.
  • The dietary intake data is presented in a ranked format, with exceptions for cheesecake (measured in calories) and green tea (measured in ounces).

Interventions for Biomarker Optimization

  • The speaker discusses three main interventions undertaken to optimize biomarker results without adversely affecting other biomarkers.
  • One intervention included a significant reduction in saturated fat sources, primarily coconut butter and cacao beans, to examine their correlation with cistatin C levels.
  • Coconut butter intake was reduced from 26 grams to 3 grams per day, and cacao beans from 10 grams to 1 gram per day.
  • Cistatin C is noted not only as a kidney function biomarker but also as a contributor to epigenetic aging, particularly relevant to the GrimAge epigenetic clock.
  • The speaker emphasizes the importance of tracking cistatin C levels and their correlation with chronological age over an 18-month period, noting a strong linear relationship.

Optimal Levels and Biomarker Tracking

  • The speaker introduces a new Patreon tier that focuses on optimal levels for various biomarkers, including cistatin C, which has been documented with extensive references.
  • The literature suggests that optimal cistatin C levels should be less than 0.7 milligrams per liter, and the speaker notes their readings have consistently been above this threshold.
  • The speaker expresses uncertainty about how to effectively reduce cistatin C levels and discusses the role of diet and tracking in this process.
  • The speaker has been tracking their food intake since 2015, allowing for detailed analysis of correlations between diet and biomarkers.
  • The video highlights correlations between various macro and micronutrients and their potential impacts on cistatin C levels.

Food Correlations and Dietary Adjustments

  • The speaker elaborates on the significance of tracking individual foods rather than just macronutrients, advocating for a comprehensive approach to optimize biomarkers.
  • A correlation analysis reveals that saturated fatty acids, calories, and body weight have notable relationships with cistatin C levels.
  • The speaker points out that cacao beans and coconut butter, despite positive correlations with cistatin C, fall outside the threshold for statistical significance.
  • Lycopene, primarily sourced from watermelon and tomatoes, is also positively correlated with cistatin C but does not meet statistical significance.
  • The speaker plans to reduce intake of saturated fatty acids and lycopene to observe potential impacts on cistatin C levels in future tests.

Impact of Dietary Changes on Cistatin C

  • Following the dietary adjustments, the speaker notes a decrease in cistatin C levels, suggesting a potential correlation with the reduced saturated fat intake.
  • While the speaker cannot definitively claim causation due to the variability of biomarker data, they acknowledge the positive trend observed.
  • The speaker discusses the variability of cistatin C readings across multiple tests, noting that only one test previously recorded a value below 75.
  • In comparing correlations before and after the dietary changes, the speaker observes some strengthening and weakening of correlations, indicating the need for ongoing adjustments.
  • The speaker plans to explore the effects of blueberry and cinnamon intake on cistatin C levels in future tests.

Additional Experiments and Biomarker Monitoring

  • The speaker discusses monitoring other biomarkers, including RDW and HBA1C, alongside their dietary interventions.
  • RDW levels showed an increase over five tests, prompting the speaker to cut fructose intake from fruits to test its correlation with RDW.
  • After reducing fruit intake, RDW levels decreased slightly, although the speaker acknowledges the uncertainty of causation.
  • HBA1C levels have remained stable at 5.3 to 5.4%, leading the speaker to cut total fat intake to see if it affects glucose levels.
  • The speaker expresses hopes that future dietary adjustments, particularly with cinnamon, will impact both HBA1C and cistatin C levels positively.

Cheat Meals and Dietary Philosophy

  • The speaker shares their approach to cheat meals, noting that strict adherence to a clean diet can lead to binge eating.
  • Post-blood test, the speaker indulges in some junk food while maintaining calorie goals, which helps mitigate cravings.
  • The speaker had cheesecake and peanut butter mixed with chocolate as their cheat meals, accounting for a small percentage of overall calorie intake.
  • The speaker emphasizes the importance of finding a personal balance between indulgence and maintaining a nutrient-dense diet.
  • The overall diet consisted of 98.5% clean eating, with only 1.5% attributed to junk food over the 56-day period.

Dietary Composition and Macronutrient Breakdown

  • The speaker presents data on average daily calorie intake, which was approximately 2274 calories for the test period.
  • Protein intake was recorded at 113 grams per day, constituting about 20% of total calories, slightly higher than the previous test.
  • Fat intake averaged 84 grams per day, making up about 33% of total calories, with a noted reduction from the prior test.
  • Carbohydrate intake totaled 312 grams, with net carbs calculated at 225 grams after accounting for fiber intake.
  • The speaker discusses the role of soluble fiber in calorie contribution and its fermentation into short-chain fatty acids.

Micronutrient Intake and Optimization

  • The speaker reviews their micronutrient intake, emphasizing the importance of full RDA coverage for optimal health.
  • Most vitamins in the speaker’s diet exceeded RDA levels, with particular attention drawn to riboflavin and niacin intake, which were significantly higher than recommended levels.
  • The speaker highlights the correlation between carotenoid intake and biological age, referencing studies that link higher intake with younger biological markers.
  • The speaker notes the importance of personalizing dietary intake based on individual biomarker correlations and ongoing testing.
  • The speaker plans to continue monitoring micronutrient levels and their effects on biomarkers in future tests.

Sodium Intake and Future Testing Plans

  • The speaker discusses their sodium intake, which has increased over recent tests, with a focus on its potential effects on heart health and aging.
  • The speaker is exploring the balance between sodium intake and its effects on blood pressure and heart rate variability.
2 Likes
#29

He doesn’t mention whether the Niacin he took was IR. Since it was 350 mg one time thing, probably was not IR. That would burn. IR does not cause the problem with 2PY, 4PY which is where this stuff comes from.

#30

Oops, I just looked it up and actually got that one backwards. IR is bad for 2py 4py. Slow release is good for those, but bad for your liver. I may quit the Niacin.

#31

I would be very cautious supplementing with niacin in any form, unless you are frankly deficient. It’s a very double edged vitamin.

3 Likes