Results show equally sustainable given the right conditions, but...
Researchers designed study that during first 4 weeks of each diet, food was provided & during the next 8 they had to buy their own. Baseline&followup adherence scores were also collected. All this allowed researchers to determine how sustainable diets were under diff conditions
baseline #keto adherence score was lower than Medi score. BUT during the time period when food was provided AND when they had to buy their own food, mean keto score was equal to or higher than Medi score. At the follow-up #keto score had dropped again. From this I conclude...
keto diet was not hard to adhere to itself, but social enviornment pushed ppl away from it b4&after study
In other words, not a diet issue but a social acceptability issue (at the population level)
Can see no difference (or a slight edge to keto) when food was made available.
Was also interesting to see some reasons individuals prefered one option over the other. IMO "better glucose numbers" is a better reason than "keto is for weight loss" / "I had no energy" (discouraged mineral supp during adapation), bc the latter 2 are misconceptions/misleading
Overall good study&both diets were strong-ish. Both <40% CHO reduced refined grains/ simple sugars. Chronometer was used to track food intake & ketone blood meters confirmed nutritional ketosis.
In Sum, keto is not a hard diet to which to adhere. It's our darn food ecosystem :(
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Have High Lp(a)? You Need to See Today's Video covering a new 2025 study on Lp(a) and waist-to-hip ratio
1/5) Here's a quick breakdown...
The goal of this new study was to determine whether a measure of adiposity—waist-to-hip ratio—modifies the relationship between Lp(a) and cardiovascular disease risk.
2/5) To explore this, researchers analyzed data from 4,652 participants in the Multi-Ethnic Study of Atherosclerosis (MESA), following them over a median of 17.4 years.
The study stratified individuals based on Lp(a) levels defined as >50 mg/dL and investigated how this risk interacted with waist-to-hip ratio as a marker of central adiposity and visceral fat
*Lp(a) (nmol/L) = Lp(a) (mg/dL) x 2.15
3/5) And—remarkably—in individuals with a ↓ waist-to-hip ratio, Lp(a) levels were not significantly associated with increased cardiovascular risk…
New Study: Person Study Finds Statin Use Associated Decline in Muscle Mass
1/6) A colleague of mine—a medical doctor—texted me recently: “I’m stopping my statin.”
The new paper referenced concludes: “Continuous statin use is associated with a decline in muscle function and mass over time (25% decline in grip strength and 73% decline in appendicular lean mass compared to never- users).”
Let’s discuss. (links in 5/6 and 6/6)
2/6) We’ll break this up by discussing the cross-sectional (single time point) and longitudinal (over time) results.
Cross-sectional analysis: In the fully adjusted model, adjusting for age, sex, education, smoking, BMI, activity score, diet quality score, high blood pressure, diabetes, and so on, statin use was associated with lower grip strength and lower appendicular lean mass.
3/6) Longitudinal Analysis: Statin Use Associated with Steeper Muscle Decline
Echoing the prior analysis, continuous statin use was significantly associated with accelerated declining grip strength and lean mass.
*Nuance note: For those with keen eyes, we discuss the y-intercept in the full letter
TL;DR: Statins use is linked to fasting muscular decline.
2/8) As a quick recap, I recently did a self-experiment that can be summarized in just two words: Sardine Diet. After a couple weeks on this extremely high omega-3 diet, I became conspicuously cold-resistant.
This was weird.
And I wanted to understand what might be happening. So, I dug into the literature.
3/8) Here's what could be happening...
Metabolically active brown fat can convert omega-3 fatty acids into a hormone called 12-HEPE.
12-HEPE stimulates thermogenesis in brown fat and ramps up glucose uptake into muscle as well.
This ultimately promotes heat production, cold adaptation, and energy expenditure.
(1/8) Alzheimer’s disease is personal for me. In my early 20s, I discovered I carry the ApoE4/4 genotype—placing me at the highest genetic risk. I was terrified. But over time, that fear shifted to a realization:
👉A genetic predisposition is a vulnerability, not a destiny.
👉 Our choices shape our health trajectory more than our genes ever could.
Today, I want to share a piece of that puzzle: The Omega-3 Paradox.
👉The Signal: Data clearly shows eating fatty fish lowers Alzheimer’s rates and boosts cognitive longevity.
👉The Failure: Yet, large clinical trials using Omega-3 supplements often fail to protect the brain.
👉The Question: Why?
One answer lies in a specific delivery mechanism most people—and many researchers—overlook.
Here is the science of getting Omega-3s into the brain. 🧵👇
(2/8) So, why do supplements often miss the mark? The answer is likely the form in which the Omega-3s are packaged.
When you eat seafood, you ingest Omega-3s in diverse forms, including phospholipids. However, most supplements on the shelf provide them in other forms, like triglycerides.
The Form Matters…
(3/8) The Form of Omega-3 Matters.
Think of it like this: Consuming DHA as a free fatty acid triglyceride is like mailing a letter with no address. It enters your system, but it doesn't know where to go. It rarely reaches the brain.
But if you have phospholipid-bound DHA? That’s like sending a letter via express courier, straight to the correct neuron. More specifically, the “express courier” form is called Lyso-DHA.
This specific form has special access to the brain through a transporter called MFSD2A.
Without the phospholipid "address," the DHA gets lost in transit.
How Metabolic Disease Feeds Emotional Eating 🧠🍩
(link at the end)
1/8) A brand new study (Dec 10, 2025) reveals how poor metabolic health can drive emotional eating.
Why this is important: There’s a known link between metabolic disease (obesity, diabetes, etc.) and mental health conditions (eating disorders, anxiety, depression).
But the causal relationships remain murky.
In uncovering the “how” we lay the groundwork for innovative solutions.
cc @Metabolic_Mind @janellison @TuitNutrition @ChrisPalmerMD @MitoPsychoBio @WilliamFurness @drjenunwin
2/8) The researchers behind the experiments took interest in ImP, which is known to be elevated in patients with metabolic conditions like diabetes (below)—and is linked to cardiometabolic disease.
*ImP levels are elevated in humans with type 2 diabetes (red) vs healthy controls (blue).
3/8) Given the link between metabolic diseases and mental health, the researchers set out to test a new hypothesis:
If you increase ImP, does that change the brain and behavior?
To do this, they fitted mice with a tiny pump that continuously delivered ImP at levels designed to mimic what’s seen in people with diabetes.
Afterward, they looked for neural changes and found a large shift in gene-expression programs within neurons tied to the stress response in the hypothalamus.
When The “Cholesterol Drop” Misses the Mark
(Links in 6/7 and 7/7)
1/7) Can we assume that how much LDL drops tells us how much cardiovascular risk is reduced?
A new meta-analysis in the European Heart Journal says, “No.”
In fact, it suggests the link between LDL-C reduction and actual cardiovascular outcomes is incredibly weak.
So, have we built a multi-billion-dollar industry on the assumption that hot chocolate equals real illness?
Let’s unpack that…
cc @realDaveFeldman @AdrianSotoMota @ApoDudz @DrEricRodgers @LDLSkeptic @AKoutnik @janellison @bschermd
2/7) This was an umbrella review of meta-analyses of randomized controlled trials.
In total, the review included 20 RCTs comprising 194,686 participants, with a median follow-up of 4.85 years.
So, what did they find?
In this study, the r² for LDL-C on major adverse cardiovascular events ranged from 0 to 0.1.
In other words, this calls into serious question whether LDL-C can be used as a surrogate for clinical outcomes in statin trials.
3/7) To better define r2 (pronounced “R-squared”)… it’s a number that tells you how well one thing predicts another. It ranges from 0 to 1 (or 0% to 100%):
r² = 1 means perfect prediction — knowing the first number tells you exactly what the second will be.
r² = 0 means no prediction — the first number tells you nothing about the second.
r² <0.1 ... is terrible!
It’s like trying to predict who will win the marathon based on who tied their shoes the tightest.