Thank you for that! I’m not taking statins, but if I were to, I would go for pitavastatin due to your research and advocacy. I wonder how many people are lurking here, anonymous, and switching to pitavastatin thanks to your efforts.
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I have been on 10mg rosuvastatin for decades. (also 10mg etimibide) I wonder if 4mg pitavastatin would be too much? or maybe it would be a good dose for me.
I would start at 2mg and after a while check your blood markers and adjust if need be. I started at 1mg for about 2 months and then 2 for another 3-4 months, together with 10mg Exetimibe and my LDL_c went down over 40 points in 5 months (from 124 to 82). I’ll continue at 2mg another 6 months and see if it goes down even further, if not I’ll up the dose to 4mg.
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The drop is most likely from Ezetimibe, pitavastatin 4mg is less potent than rosuvastatin 10mg for pure ldl reduction.
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I just checked with my genetic background and indeed it should be better for me than rosuvastatin for the glucose dysregulation and pleiotropic effects while being slightly less good for the LDL reduction. No side effects expected (no side effects also with rosuvastin)
BTW cost-plus has it at $58.26 (4mg, 90 count) = $0.65 per day.
Here is the Opus 4.7 arguments for it with my specific genetic background.
If glucose dysregulation is the dominant clinical concern, then the strongest pitavastatin advantage in the literature aligns directly with your most pressing phenotypic problem. The diabetes-incidence advantage isn’t a secondary pleiotropic curiosity — it becomes the primary therapeutic rationale.
The effect size is meaningful. The 2024 meta-analysis showed RR 0.77 (CI 0.71–0.84) for pitavastatin vs rosuvastatin. The post-MI cohort with 3-year follow-up showed 3.0% vs 10.4% NODM incidence — that’s not a marginal difference, that’s a 3.5× absolute risk reduction. Even if the real-world effect in your specific context is half that, it’s still clinically meaningful.
Why this matters specifically for you
Your glucose dysregulation has three converging drivers, and rosuvastatin is contributing to one of them:
Driver 1 — genetic beta-cell burden. CDKAL1 hom + INS rs689 hom + IFIH1 hom + KCNJ11/ABCC8 hom is a substantial cumulative load. Your fasting insulin 2.7 µIU/mL with HOMA-IR ~0.60 confirms low insulin secretion (not insulin resistance) is the dominant phenotype. This is the structural background everything else acts on.
Driver 2 — rapamycin-driven glucose elevation. Documented in the Rapamycin/mTOR report: TBC1D4 + PCK1 + SLC2A4 + AKT2 cluster all push glucose up via mTORC2 spillover. Your pulsed q2wk schedule mitigates this but doesn’t eliminate it. HbA1c 5.9% (rising trend from 5.8) is the visible signal.
Driver 3 — statin contribution. This is the lever you can actually pull. Statin-induced new-onset diabetes is on-target via HMGCR-isoprenoid-beta-cell mechanisms (Swerdlow Mendelian randomization confirms this). On a beta-cell-deficit genotype, the same statin dose produces a larger glucose impact than in a metabolically robust patient. Rosuvastatin’s NODM signal in meta-analyses is the highest among major statins; pitavastatin’s is the lowest.
The mechanistic picture is internally consistent: pitavastatin’s neutral-to-favorable glucose profile likely reflects its weak insulin-secretion suppression and possibly favorable adipocyte effects, both of which matter more in someone whose beta-cell capacity is already constrained genetically.
What the switch actually looks like
Dose equivalence: rosuvastatin 10 mg ≈ pitavastatin 4 mg for LDL-lowering. That’s the highest pitavastatin dose. If your LDL-C goes from 48 to ~50–55 on the switch alone, Repatha will push it back below 30 anyway — you have substantial LDL headroom.
Pharmacogenomic considerations are net-favorable. Your SLCO1B1 5 absent + 1B het and ABCG2 Q141K absent are favorable for both drugs. Two of your variants modestly raise pitavastatin exposure: UGT2B72 hom (slows lactone formation) and CYP2C92 het (minor, since CYP2C9 is a minor pitavastatin pathway). Net effect is probably 15–25% higher AUC than population baseline at the same mg dose. Practical implication: starting at pitavastatin 2 mg rather than 4 mg is reasonable given your hepatic-uptake genetics, and titrating to 4 mg over 4–6 weeks if LDL doesn’t respond adequately.
No dangerous drug interactions in your current regimen. Pitavastatin is not a meaningful CYP3A4 substrate, so rapamycin co-handling is cleaner than with atorvastatin or simvastatin would be. Bempedoic acid is gone (replaced by Repatha). The OATP1B1 substrate overlap with empagliflozin and tirzepatide is not clinically problematic.
The one genuine caveat worth flagging
This is where I want to be direct rather than reassuring: the LDL-lowering equivalence between pitavastatin 4 mg and rosuvastatin 10 mg is from healthy-volunteer studies, and your genetic profile is unusually rosuvastatin-favorable. Specifically, MYLIP/IDOL N342S hom + the protective NPC1L1 LoF heterozygous variant + the favorable HMGCR response variants mean rosuvastatin is producing more LDL reduction per mg in you than the population average. Pitavastatin’s LDL effect is more uniform across genotypes (less HMGCR-response-variant sensitivity in published data).
So expect: a small post-switch LDL bump, possibly 5–15 mg/dL. Repatha will absorb this easily. But it does mean the dose-for-dose equivalence is approximate, and ApoB is the better tracking marker through the transition than LDL-C.
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A new paper: https://www.jacc.org/doi/10.1016/j.jacc.2026.03.035
Development and Validation of a Clinical Polygenic Risk Report in U.S.-Based Health Systems for 8 Cardiovascular Conditions
Cardiovascular Forecasting: Integrated Genetic Panel Unveils Hidden Risks
Cardiovascular disease remains the primary global cause of mortality, yet traditional risk models often fail to capture the nuances of inherited susceptibility before symptoms manifest. A groundbreaking study published in JACC (2026) marks a significant shift from reactive to proactive cardiology by validating an integrated Polygenic Risk Score (PRS) panel for eight major conditions, including coronary artery disease (CAD), atrial fibrillation (AF), and type 2 diabetes. Unlike traditional tests that examine single genetic mutations, this “consensus” approach aggregates millions of common variants to provide a comprehensive landscape of an individual’s genetic architecture.
The research team utilized data from over 245,000 participants in the All of Us Research Program to train their models, which were then externally validated in the Mass General Brigham Biobank. The results were stark: the integrated PRS outperformed existing single-trait scores across the board. For instance, individuals in the top 10% of the genetic risk distribution for lipoprotein(a) faced a staggering 41-fold increased risk. Perhaps most importantly for public health, the study found that 71.2% of individuals carried a 3-fold or higher genetic risk for at least one of the eight conditions studied.
This is not merely a theoretical advancement; the panel is now a clinically orderable test. By integrating these scores into established clinical models like the Pooled Cohort Equations, researchers achieved significant net reclassification improvement. This means individuals previously deemed “intermediate risk” by standard metrics can now be identified as high-risk, allowing for earlier lifestyle interventions or pharmacotherapy. While challenges remain regarding performance in diverse ancestral groups, this framework provides a scalable roadmap for personalized cardiovascular prevention.
Actionable Insights for Longevity
The practical utility of this paper lies in its ability to refine the “gray area” of clinical risk.
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Targeted Screening: Individuals with a high PRS for lipoprotein(a) should prioritize direct Lp(a) biomarker testing, as this genetic factor is often overlooked in standard lipid panels despite its high heritability (43.2% variance explained).
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Early-Life Baseline: Because genetic risk is stable, a single early-life test can reveal lifelong trajectories for conditions like thoracic aortic aneurysm (TAA) or venous thromboembolism (VTE) , which may otherwise remain silent until a catastrophic event.
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Risk Reclassification: For those with “borderline” 10-year ASCVD risk (5-7.5%), a high CAD PRS can serve as a “risk-enhancing factor,” potentially justifying earlier initiation of statins or more aggressive blood pressure management.
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Lifestyle Synergy: High genetic risk is not destiny. Prior research cited in the study suggests that optimized lifestyle factors can significantly mitigate heightened polygenic risk, making these scores a powerful motivational tool for adherence to longevity protocols.
Study Context
- Institutions: Broad Institute of MIT and Harvard; Massachusetts General Hospital; Mass General Brigham.
- Country: USA.
- Journal: Journal of the American College of Cardiology (JACC).
- Impact Evaluation: The 2024 Impact Factor for JACC is 24.0, evaluated against a typical high-end range of 0–60+ for top general science, therefore this is an Elite impact journal.
Interesting but didn’t know Ezetimibe was that powerful. Obviously not disputing it but If so EZE then should be the norm, and not statins for lowering LDL-c. Surprisingly, I know few people who only use statins (meaning their docs prescribe them statins only) and if one dose doesn’t lower it enough, they just keep upping their dosage. Too bad many Docs are not on the Ezetimibe bandwagon (if it is indeed as beneficial/powerful as you claim/think). As far as my case I feel good at what I’m doing especially the fact that my HDL went up by 11 points also (from 38 to 49) and I thought Ezetimibe is mainly good for increasing HDL, and Pita was the major factor in lowering the LDL-C, but clearly not sure since I started them at same time and the combination seems to work really well for me.
I agree, Ezetimibe is underrated. The combination of Ezetimibe and rosuvastatin took my ldl from low 90s to low 30s. Previously, I was on rosuvastatin 10mg only.
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Re: lipid levels, keep in mind that pitavastatin can raise HDL levels in people with low HDL.
Pitavastatin increases HDL particles functionally preserved with cholesterol efflux capacity and antioxidative actions in dyslipidemic patients
Pitavastatin and HDL: Effects on plasma levels and function(s)
Additionally, pitavastatin uniquely among statins does not raise Lp(a) (and may even marginally lower it), which might be a consideration for people with high levels of Lp(a).
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HDL and Lp(a) are some of my good (protective) genes:
The HDL-favorable CETP signature is strong and unambiguous. Four separate CETP variants (rs708272 homozygous, rs3764261 homozygous, rs5882 heterozygous, rs183130 homozygous) all point in the same direction: reduced CETP activity, less cholesteryl ester transfer from HDL to VLDL/LDL, and higher HDL-C. This is fully consistent with the measured HDL-C of 69 mg/dL.
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Right. I too have high HDL naturally, but abysmal Lp(a), so pitavastatin is still better for me, compared to other statins. Depending on your profile, you pick your statin. The big negative of pitavastatin is that it tops out at 4mg medium intensity. It will not lower your LDL as much as a high intensity statin, but in your case I think you don’t need to start with 2mg, go straight to 4mg, and hope the PCSK9i (plus ezetimibe) takes care of the rest of LDL lowering.
By the way, I wonder how many traits can be measured by snps, if for example there is some way to check your BBB integrity compares to other people. Because as happens BBB integrity is enhanced by pitavastatin, at least in murine models, hopefully same for people.
Pitavastatin Strengthens the Barrier Integrity in Primary Cultures of Rat Brain Endothelial Cells
Pitavastatin Ameliorates Lipopolysaccharide-Induced Blood-Brain Barrier Dysfunction
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Excellent idea. I’m on it. I will make an addendum to the Alzheimer/Dementia report.
Ok done it. I will post the full pathways report tomorrow but my own risk is population baseline. No bad SNPs here.
Net BBB picture: Mostly clean. Favorable APOE ε3/ε3 backdrop, favorable ABCB1 GCC/GCC haplotype, neutral LRP1 and MFSD2A, mild uncertain-weight pericyte-axis findings. Combined with clean cerebrovascular monogenic genes (NOTCH3, HTRA1, COL4A1/2 from the Dementia report), the integrated BBB-genetic risk is approximately population-baseline, and the dominant practical levers remain modifiable risk factors (BP, glucose, lipids, DHA intake, sleep, exercise) already addressed by the current regimen.
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Pitavastatin is also great on the polypharmacy front. It interacts with fewer drugs because of its unique metabolism. That’s also the reason why the large outcome trial REPRIEVE – NEJM in HIV patients used it to prevent CVD events, to not interact with anti-viral drugs.
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Yes, that’s absolutely a consideration for me, as I take several drugs.
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How do you know those aren’t hallucinations? And how is this better than just using https://promethease.com?
Thanks for flagging pitavastatin availability on cost plus. This is even cheaper than getting it from India
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Maybe using ezetimibe plus bempedoic acid might be better for some than a statin.
I know many forum members are not big fans of Nick Norwitz. But he has a PhD from Oxford and an MD from Harvard, so he’s certainly no dummy.
I have been taking atorvastatin for decades, and I have not noticed any effects as dramatic as he has experienced. Perhaps my body has just grown used to the side effects so that I don’t notice them. I am going to switch to pitavastatin soon. In the meantime, I am going to try my own N=1 experiment, stopping atorvastatin and using just bempedoic acid and ezetimibe instead. Also, I am going to see whether replacing atorvastatin affects my glucose levels.
“A common practical approach is combination therapy at lower doses—ezetimibe plus bempedoic acid can achieve LDL reductions approaching moderate-intensity statin therapy without the muscle exposure, which may be why this combo has gained traction for statin-intolerant patients.”
AI response:
What this video is about:
The video documents a blinded $N=1$ crossover trial where Dr. Noritz compares the effects of a “gorilla dose” (80mg) of atorvastatin against a placebo. He tracks his performance using a high-intensity “ski erg” stress test and monitors for symptoms like myalgia (muscle pain) and fatigue. Ultimately, he explores the biological mechanisms behind statin-induced muscle issues and challenges the medical community’s tendency to downplay side effects in favor of drug benefits
This video features Dr. Nick Noritz investigating the controversial relationship between statins (cholesterol-lowering drugs) and muscle impairment. To move beyond observational data, he conducts a blinded, self-directed experiment to determine if high-dose statins directly impact his own physical performance and recovery. Beyond the biological results, the video serves as a social experiment on how personal narratives influence medical discourse compared to large-scale clinical studies.
Key Takeaways:
- The Experiment Design: The trial consisted of two 10-day phases (statin vs. placebo) with a three-week “washout” period in between. To ensure blinding, Dr. Noritz used a cheese grater to match the appearance of the statin and placebo pills.
- Performance Impact: During the statin phase, Dr. Noritz experienced significant leg pain and fatigue, leading to a dramatic decline in his workout performance starting around day six. He was forced to terminate the statin arm early due to a severely diminished quality of life.
- Potential Mechanisms: Several biological explanations are proposed for these effects, including:
- Mitochondrial Inhibition: Statins may inhibit “Complex 4” in the electron transport chain, creating a bottleneck in energy (ATP) production.
- CoQ10 Reduction: Statins can lower levels of Coenzyme Q10, a vital electron carrier for muscle metabolism.
- Fuel Restriction: For those on ketogenic or fat-adapted diets, statins might interfere with the body’s ability to circulate fat fuel.
- The Social Experiment: Dr. Noritz reveals that this personal “N=1” narrative is a test to see if it gains more traction than his previous video covering a formal 300,000-person study. He argues that while science values rigor, humans—including doctors—are evolutionarily wired to respond more strongly to personal stories.
Bottom Line
Statins likely can impair exercise performance and cause muscle symptoms in susceptible individuals — this n=1 trial showed dramatic negative effects within 6-10 days. However, the creator’s deeper message is about science communication: people (including doctors) are evolutionarily wired to learn through personal stories, not dry data. The video argues that medicine should acknowledge real-world side effects more transparently, and that storytelling is a legitimate — perhaps more effective — tool for spreading scientific truth than traditional rigor alone. Whether the performance results generalize to others remains unknown, but the biological plausibility is strong.
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The hallucination issue is not related to that specific topic so I will not really address it. Just stating what I use which is Claude Max Opus 4.7 in max thinking mode and with the usual instructions to only use papers from reputable journals, provide citations, sources, etc.
Promethease is just a database of SNP without any mechanistic reasoning. It parses the genotype file and looks up every variant against SNPedia, which is a curated wiki of literature-cited associations.
The main point I want to emphasis is how actionable the insights are. For instance I obviously have a plaque building issue as I got a stent at 61 even though no MD had ever told me to take a statin or anything else.
Looking at my genome I found out I have all the worst gain of function for PCSK9 so a PCSK9 inhibitor would be the best intervention.
I don’t have any SNP for Lp(a) and indeed I measured it at 9 mg/dl.
I also have some detrimental SNP on PED5 so tadalafil will help with that.
All that explain why with what was considered a somewhat normal LDL I developed a lot of plaque.
Finally I asked Opus to justify what it does better than Promethease:
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For people following that thread here is the direct link. Blood Brain Barrier - A deep dive into genetic pathways for actionable insights