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The Hidden Factor That Determines How Long You Live | Michael Lustgarten | AFP 57

https://youtu.be/sPCG9ZQf47E

The following is based on a conversation with Michael Lustgarten, a former assistant professor turned longevity consultant.

What are biological aging clocks, and why are organ-specific proteomic clocks more informative than single biological age estimates?

Biological aging clocks measure biochemical processes that accumulate over decades to quantify an individual’s rate of aging relative to their chronological age. While popular systems like Dr. Morgan Levine’s PhenoAge provide a overall systemic estimate, single-number metrics can obscure localized physiological decline. Individual organ systems age at distinct rates; for instance, systemic biomarkers may indicate a biological age 20 years younger than chronological age while cutaneous or ocular tissues show distinct signs of senescence. Recent proteomic research evaluating circulating blood proteins has established organ-specific clocks—such as brain, heart, immune, and kidney clocks. Among these, brain-specific and immune-specific proteomic clocks show the strongest statistical association with overall healthspan and all-cause mortality risk, demonstrating that organ-targeted tracking offers superior predictive value over generalized biological age estimates.

Why are red blood cell parameters like RDW critical markers for tracking systemic aging and mortality risk?

Red blood cells are the most abundant cell type in the human body, yet standard clinical evaluations often overlook subtle shifts within normal reference ranges. During normal aging, total red blood cell count and hemoglobin levels typically decline, impairing systemic oxygen transport and accelerating physical functional decline. Concurrently, mean corpuscular volume (MCV) tends to increase alongside rising Red Cell Distribution Width (RDW), which measures the size variation between mature red blood cells and smaller, immature cells. Elevated RDW reflects systemic physiological dysregulation, chronic low-grade inflammation, and altered erythropoiesis. Clinical studies demonstrate that higher RDW values serve as a robust, independent predictor of all-cause mortality, making longitudinal tracking of red blood cell metrics essential for longevity optimization.

How can heart rate variability (HRV) and resting heart rate (RHR) be used to prevent exercise-induced overtraining and optimize lifespan?

While exercise reliably increases average lifespan, excessive physical exertion without adequate recovery induces chronic overtraining, which generates systemic oxidative stress and may truncate potential maximal lifespan extension. Autonomic nervous system balance can be quantified by monitoring Heart Rate Variability (HRV)—the time variation between consecutive heartbeats—and Resting Heart Rate (RHR). High HRV combined with a low RHR indicates dominant parasympathetic (“rest and digest”) tone and optimal recovery. Conversely, sustained drops in HRV paired with elevated RHR signal sympathetic (“fight or flight”) overload or subclinical immune activation. By using longitudinal HRV and RHR trends to dynamically adjust workout volume and recovery days, individuals can maintain peak physical fitness while minimizing autonomic stress and overtraining risk.

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Cleaned Transcript

Jeroen Schreel: Welcome to Apple Finch Pudding, your gateway into the world of science. Today’s guest scientist is Michael Lustgarten, a former assistant professor who transitioned into a role as a health and longevity consultant. Welcome, Michael.

Michael Lustgarten: Thanks, Jeroen. Thanks for having me, it’s great to be here.

Jeroen Schreel: Michael, longevity research has been booming these last few years. I was wondering: what is the single biggest lie or marketing hype that you have encountered in longevity research?

Michael Lustgarten: I don’t know if it’s marketing hype, but for my specialty—which is biomarkers—aging isn’t something that happens overnight when you view it as a disease process. These are biochemical processes that occur over decades, which means they can be tracked. My forte is tracking and trying to optimize biomarkers across as many organ systems as possible.

As a quick aside, someone could look at my biological age data and say, “Yeah, you seem to be close to 20 years younger using Dr. Morgan Levine’s PhenoAge, great, but your eyes are aging. Look at your skin; you’ve got wrinkles there. You’re not 20 or 30 years younger.” That is true, but I’m not going to die from a loss of eyesight or slightly saggy skin. I am primarily focused on the factors that are actually going to kill me.

Most people tracking biomarkers for health are casually interested. They test maybe once per year, visit their doctor, check if they are in the reference range, and forget about it until a biomarker falls out of range. To answer your question about the main issue I see: people need to track more frequently.

There are two main camps. I’m in the camp where I want to minimize disease risk, maximize health, and optimize longevity by tracking as much data as possible. The other camp prefers no tracking, choosing instead to wait for rejuvenative therapies like epigenetic reprogramming. People in that second boat want to keep eating and exercising the same way without changing anything today. I hope those future therapies work, but in the meantime, taking one test a year doesn’t give you enough actionable information.

If a star football player scores four goals in one match and then zero goals for the rest of the season, are they the world’s best player or were they just great on that single day? To know, you need to see stats across every game, year after year. Blood testing is the same: we need frequent data. Currently, I do blood draws eight times per year for standard venipuncture panels. For daily metrics like HRV and resting heart rate, I have nearly 3,000 days of continuous data over eight years, allowing me to track and reverse age-related trends. For example, my HRV is 50% higher than when I started, and my resting heart rate has dropped from 51 to 42 beats per minute.

If you want to slow your rate of aging and get ahead of disease, you need more data. The next question is: what do you do with that data? Most people aren’t qualified to run statistical correlations on their own metrics. Standard fitness trackers try to give personalized recommendations based on daily journals, but I’ve found those insights generally uninformative. We are currently developing an app to integrate statistical analysis for biomarker tracking, making it easier for people to gain actionable insights for their personalized diet, exercise, and supplement protocols. So, the long story short is: people should track more.

Jeroen Schreel: One of the things that caught my eye is that you mentioned aging can be linear, but I recently spoke with Michael Snyder, who mentioned two distinct age peaks occurring around ages 44 and 60. Can you explain where you agree or disagree with that perspective?

Michael Lustgarten: I like Michael Snyder a lot—I’m a big fan. His arm is covered in trackers.

Jeroen Schreel: I think he wears eight of them!

Michael Lustgarten: Exactly. In his study, they tracked a small cohort—fewer than 300 people—and evaluated a clinical chemistry panel. Different biomarkers exhibit different trajectory patterns. For instance, albumin, the most abundant protein in blood, shows a linear decline during aging in studies of over one million people. To put its concentration in perspective, blood glucose is measured in milligrams per deciliter, whereas albumin is measured in grams per deciliter—roughly 60-fold higher concentration. Yet most people ignore albumin.

Conversely, markers like LDL cholesterol do not decline linearly. LDL is low in youth, peaks between ages 50 and 60, and then starts to drop. Because of this, a low LDL level in an elderly individual isn’t necessarily a sign of youthfulness; it could reflect reverse causation due to underlying liver disease or cancer. Other markers like uric acid linearly increase as kidney function declines over time.

So rather than generalized systemic waves at specific ages, I evaluate aging through organ-system-specific biomarkers. Trying to map biological aging using purely linear statistical models across nonlinear markers is difficult.

However, there is animal data that supports elements of Michael Snyder’s observations. In calorie-restricted mice—the gold standard in longevity research—body weight remains lower and stable compared to ad-libitum fed mice. But in the second half of life, even calorie-restricted mice exhibit distinct peaks and troughs in body weight as physiological compensatory mechanisms struggle to maintain homeostasis before a rapid final decline. So there are underlying physiological fluctuations that align with those concepts.

Jeroen Schreel: Like you said, it depends heavily on what you measure. If we want to map biological aging effectively, what specific metrics should we be measuring?

Michael Lustgarten: I aim to measure as much as possible to flatten my all-cause mortality risk across all biomarkers. First and foremost, address what is most likely to kill you based on family history. I have a family history of cardiovascular disease, so I prioritize cardiovascular biomarkers. If your family history leans toward Alzheimer’s, stroke, or cancer, start there.

At the foundational tier, it comes down to the basics: eat real food, exercise, and sleep properly. A key component of this is body composition—maintaining high lean mass, solid bone density, low overall body fat, and visceral fat as close to zero as possible. Visceral fat surrounds your internal organs; surgically removing visceral fat in rat models increases their lifespan by 20%.

Under the hood, we can look at organ-specific risk factors. A recent proteomic study analyzing circulating blood proteins created organ-specific biological aging clocks (brain, heart, muscle, immune, kidney, pancreas, etc.). The two organ clocks most strongly associated with healthspan and longevity were the brain clock and the immune clock.

Most people don’t think about their immune system until they get sick. Advanced cardiovascular disease or cancer takes months or decades to kill you, but severe immune failure can kill you in days. On standard blood panels, people only check total white blood cell count. However, total white blood cells can remain constant while underlying cell compositions shift dramatically. Neutrophils and monocytes increase with age, while lymphocytes decrease. Tracking white blood cell differentials is therefore essential.

Another key component is red blood cells. They are the most abundant cell type in the body, yet they are widely ignored on standard panels. Red blood cell count and total hemoglobin decline with age. Lower hemoglobin reduces oxygen delivery, causing fatigue and functional decline. Furthermore, mean red blood cell volume increases with age, which correlates with higher all-cause mortality risk. Red Cell Distribution Width (RDW), which measures size variability between mature and young red blood cells, is a potent predictor of biological age, systemic inflammation, and adverse health outcomes.

Finally, tracking Heart Rate Variability (HRV) and Resting Heart Rate (RHR) is essential. These metrics reflect autonomic nervous system balance between the sympathetic (“fight or flight”) and parasympathetic (“rest and digest”) systems, providing key insights into stress resilience and recovery.

Jeroen Schreel: We previously had an episode with Dr. Roger Seheult discussing the effects of sunlight on mitochondria. Does getting outdoor sunlight exposure directly improve longevity?

Michael Lustgarten: Sunlight is great; I’m pro-sunlight. I lived in Boston for 15 years, but recently moved to Austin where I can step outside for 15 to 20 minutes of daily sunlight for most of the year.

How much that directly improves mitochondrial function compared to dietary interventions is hard to quantify. Calorie restriction is well documented to optimize mitochondrial efficiency by preventing substrate overload. Mitochondria from calorie-restricted mice function significantly better than those from ad-libitum fed mice.

Jeroen Schreel: You mentioned exercise is central to your routine. Does exercise also directly improve mitochondrial function?

Michael Lustgarten: Absolutely, but up to a physiological limit. Chronic overtraining increases oxidative stress. Some advocates suggest pushing VO2​ max as high as humanly possible, but if pushing VO2​ max from 50 to 60 causes my HRV and resting heart rate to signal overtraining, I won’t do it. It’s about finding that optimal U-shaped curve: the dose that maximizes functional fitness while minimizing unnecessary physiological stress.

Jeroen Schreel: How do you determine that optimal exercise balance between cardio, resistance training, and rest days?

Michael Lustgarten: I let HRV and resting heart rate data dictate my schedule. Recently, my data showed my best monthly average ever—HRV around 77–80 ms and resting heart rate around 38–39 bpm. Then, my HRV suddenly dropped to 60 ms and resting heart rate rose to 40 bpm for several consecutive days.

I also track my nighttime respiratory rate via my Whoop strap. Nighttime breaths per minute serve as a sensitive marker for immune activation. When I received my COVID-19 vaccinations in the past, my baseline nighttime respiratory rate spiked from 14 to over 16 breaths per minute alongside a sharp drop in HRV.

When my recent HRV dropped to 60 ms, my respiratory rate remained baseline at 13 breaths per minute, ruling out an active infection. Instead, it signaled physical overtraining. I had added three to six minutes of post-meal jumping exercises to my routine to manage postprandial glucose. While the jumping was extremely effective at dropping postprandial glucose by 15–20 mg/dL, doing it continuously alongside my intense four-day split workouts induced overtraining. As soon as I removed the extra jumping, my recovery metrics rebounded toward my baseline mean.

My general training routine follows a four-day cycle splitting upper and lower body resistance work, mobility, and flexibility, punctuated by lower-intensity walking days where my heart rate averages around 80 bpm to allow full autonomic recovery.

Jeroen Schreel: Carrying muscle mass also acts as a metabolic sink for glucose storage. How do you optimize body weight and muscle mass for longevity?

Michael Lustgarten: Carrying lean muscle while staying lean is key. In 2015, I weighed 160 lbs (~73 kg). I was active and fit, but my blood biomarkers were sub-optimal. Over time, I systematically trimmed my intake and dropped 20 lbs (~9 kg). Across more than 70 blood tests over 11 years, average daily caloric intake shows the strongest net correlation with favorable biomarker shifts. Lower caloric intake within my personal range (~2,300 kcal/day) correlates with healthier biomarker profiles.

However, there is a lower limit. When my weight dips below 140 lbs (~63.5 kg), my physical strength declines. Currently, at ~142 lbs, I maintain low body fat with visible abdominal muscles while preserving full functional strength.

Jeroen Schreel: What are your recommendations regarding diet, whole foods versus ultra-processed foods (UPFs), and cheat meals?

Michael Lustgarten: A diet that maintains lean body mass, physical function, and optimal internal biomarkers is top tier. Historically, I struggled with binge eating patterns where I would consume massive amounts of hyper-palatable junk food. Going cold turkey indefinitely doesn’t work for my psychological profile; it leads to eventual massive binges.

Instead, I follow a strict, whole-food, nutrient-dense diet for ~39 consecutive days between blood tests. Immediately following a blood draw, I allow a structured 3-day period where I incorporate controlled cheat meals. This keeps me fully satiated and compliant over the long term.

Modern ultra-processed foods contain additives like mono- and diglycerides, industrial seed oils, and artificial dyes. Additionally, roasting nuts or cooking foods at high temperatures creates Advanced Glycation End Products (AGEs) and acrylamide. In animal models, high-AGE diets shorten lifespan even under calorie restriction, while human trials show low-AGE diets improve insulin sensitivity and lower systemic inflammation. Because of this, I consume raw rather than roasted nuts during my strict periods.

Regarding oils: whole foods are always superior to their refined extracts. Eating whole olives provides fiber and micro-nutrients in natural matrix ratios that isolated olive oil shots cannot replicate. Whole-food nutrition consistently outperforms processed versions.

Jeroen Schreel: Does consuming junk food cause immediate spikes in blood glucose and markers of inflammation?

Michael Lustgarten: Yes, dramatically. I track glucose using finger-prick testing—over 700 tests since November—to optimize my HbA1c down toward 5.0%.

During my strict whole-food periods, my post-meal glucose averages 95 mg/dL. Following a high-calorie cheat meal containing processed ice cream, my postprandial glucose spiked to 133 mg/dL four hours later, and remained elevated for days.

However, managing postprandial glucose isn’t as simple as going strictly ketogenic. When I cut net carbs too low (~140g/day) and increased dietary fat, my postprandial glucose response actually worsened due to fat-induced insulin resistance. Finding the optimal individual ratio of complex carbohydrates to healthy fats—combined with light physical activity like post-meal movement—keeps baseline fasting glucose low (~80 mg/dL) while eliminating postprandial spikes.

Long-term adherence is what ultimately dictates success. Whether through strict routine or structured flexibility, finding the dietary protocol that optimizes your internal biomarkers while enabling multi-year consistency is the true key to healthspan.

Jeroen Schreel: That is extremely insightful, Michael. Thank you for sharing your data and expertise with us today on Apple Finch Pudding.

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