New Study Confirms What I've Been Saying About Statins for Years
In a Nutshell
A major randomized trial showed that prescribing statins based on calcium scores detected three times fewer patients than traditional risk calculators, but those prescribed statins had much higher adherence (63% vs 23%) with identical cardiac event rates (2.7%) after 4.8 years. The study confirms that statins are overprescribed when using calculator-based approaches, while patients are more likely to take medications when they can see actual plaque evidence. The core message emphasizes that metabolic disease—not just LDL—drives cardiovascular risk, and comprehensive testing beyond standard A1C/fasting glucose is needed to properly identify at-risk patients.
These notes were generated by AI and may contain inaccuracies.
A major randomized clinical trial tested prescribing statins based on plaque presence via calcium score versus the traditional calculator method. The study involved nearly 6,000 people followed for 4.8 years and was published August 31st at the European Society of Cardiology Congress in Munich. The study was funded by internal resources and the Dale Lowy Hansen Foundation with no drug company involvement. Lead author Dr. Molestein reported no financial disclosures.
The theoretical approach involves running a calculator considering age, blood pressure, cholesterol, smoking, and diabetes, with a 10-year cardiovascular risk threshold of 7.5% triggering statin prescription. In practice, doctors typically look at LDL numbers and prescribe if levels exceed 100, 70, or even 50 mg/dL. The American Heart Association standards committee states that if there's no plaque, there's no need for a statin, though this principle isn't always followed in clinical practice.
The disease-based approach examines arterial plaque directly rather than guessing risk through calculators. The process involves first determining if plaque exists, then assessing whether risk factors are present, and evaluating if the plaque is soft. This method treats proven disease rather than the probability of disease. Calcium scoring detects plaque but doesn't reveal information about soft plaque.
Using the calculator method resulted in three times more statin prescriptions compared to the calcium score approach. Among those prescribed statins via calculator, only 23% remained adherent. In contrast, 63% of patients prescribed statins based on calcium score results maintained adherence. The study found that patients demonstrate greater skepticism toward prescriptions based on calculations versus those based on visible disease evidence.
After 4.8 years, cardiac event rates were identical at 2.7% between both groups. The study cannot determine statin efficacy because it lacked a no-statin comparison group. The trial compared two prescribing strategies rather than statin treatment versus no treatment. The low event rate and relatively short duration limit conclusions about long-term outcomes.
Statins demonstrate clear benefit for individuals doing nothing else for cardiovascular prevention. However, evidence remains insufficient to determine whether statins provide additional benefit beyond adequate lifestyle interventions. Multiple studies show lifestyle modifications are more important than medications. The debate centers not on whether statins work in isolation, but on their necessity when patients maintain proper lifestyle measures.
The recommended approach positions statins as having some usefulness without relying on them as the primary prevention strategy. This middle ground acknowledges statin benefits while recognizing overprescription patterns. Standards committees acknowledge statins are overused, similar to stents and stress tests which show 2-10x overuse rates.
For patients on statins considering PCSK9 inhibitors like Repatha, the key question is whether adding these medications provides proven benefit for those already in good control. PCSK9 inhibitors lower LDL substantially and help with inflammation, but evidence supporting their addition to statin therapy in controlled patients remains limited. The approach emphasizes asking doctors for the specific evidence supporting additional LDL lowering rather than accepting it as automatic.
APOB provides more information about metabolic disease than LDL alone. Metabolic disease affects 95% of the population, yet only 5% receive proper diagnosis and treatment. Most physicians fail to diagnose metabolic disease because they rely on A1C and fasting glucose, which miss prediabetes manifestations. Metabolic disease directly contributes to arterial damage, making its identification crucial for cardiovascular prevention.
Patients receive tools to have informed conversations with their physicians rather than medical advice. The goal is providing information about known evidence, clinical approaches, and potential options so patients can make educated decisions about their care.
Dr. Brewer AI has been in development for approximately two years. Its information base consists of Dr. Brewer's videos, lectures, and expertise. The AI is designed to answer questions as Dr. Brewer would, with less deviation from the main topic and more direct responses. Users can access answers without waiting for the Thursday 11 a.m. show. A 14-day free trial is available through a link in the video description.
Dr. Brewer previously stated that evidence for arteriosil was fairly good and tried it himself for a period. He currently does not take arteriosil, prioritizing lifestyle factors including three weekly resistance training sessions, managing weight and body composition, basic statins, and glucose management supplements. He clarifies that not taking arteriosil does not mean it doesn't work, but supplements rank lower than lifestyle interventions. Arteriosil has evidence supporting its use, though lifestyle remains the primary focus. Berberine follows a similar pattern where evidence supports its use but lifestyle takes precedence.
Johan asks about quantifying calcium type during arterial calcium reduction and whether the three known plaque reversal cases were patients using identical methods. Dr. Brewer clarifies that his own reversal was plaque on CIMT, not calcium score reversal. He has had only one calcium score test, multiple CIMTs, and one CCTA. Three people achieved plaque reversal using lifestyle interventions. John lost approximately 40 pounds and Jerry lost 15-20 pounds. Dr. Brewer's BMI started at 21, so weight loss was minimal. Short-term his approach appeared statin-like, with long-term very slow progression from 57 to 58. The primary factors were low-carb diet, improved body composition, decreased body fat, decreased visceral fat, and increased muscle mass.
No AI analysis of CIMT has been performed yet, though Todd and Joe are considering adding AI analysis capabilities. AI analysis is more established in coronary CT angiography. Dr. Brewer's CCTA with AI analysis showed 0.1 cubic millimeters of soft plaque, essentially zero. He has used K2 but does not depend on arterial supplements. He estimates K2 contributed no more than 5% to his improvements, with muscle improvement, fat reduction, and workout intensity being more significant. He distinguishes between body fat (visible as love handles) and visceral fat (measurable only by DEXA scan).
Diet is the most significant factor in risk improvement through elimination of glycemic carbs that raise blood sugar. Dr. Brewer discovered he had plaque and pre-diabetes that progressed to full diabetes within a year. He had no prior indication of metabolic disease, similar to approximately 90% of people unaware of their condition.
Dr. Brewer AI was developed over two years rather than minutes to avoid the bias found in frontier AI systems like ChatGPT, Claude, Grok, and Gemini. These systems, trained on internet content, default to recommending LDL monitoring and statin use. The Dr. Brewer AI system was designed to provide deeper analysis of heart attack, stroke, and vascular disease prevention beyond standard LDL-focused approaches. The "yellow water" analogy illustrates how AI systems become biased by their training data, similar to fish unable to perceive yellow in yellow water.
Bernard, age 58, was diagnosed with high cholesterol at age 19 and began Lipitor treatment 15 years ago, progressing from 10mg to 20mg to 40mg without significant LDL movement. At age 50, his calcium score was 44. He increased to 80mg Lipitor, then switched to pitavastatin 4mg plus Repatha for the past 18 months. His highest LDL was in the 110 range. His A1C is 5.3 with a reference range of 5.7%. He has fibroscan results indicating possible mild fatty liver.
Fasting glucose has approximately 50% probability of being wrong and misses 50% of metabolic disease cases according to ADA research. A1C is more often wrong than right for diabetes diagnosis. The American Association of Clinical Endocrinologists acknowledges that most doctors incorrectly use A1C for pre-diabetes diagnosis. People with A1Cs in the low fives can have full-blown diabetes if not consuming carbs. CGM monitoring is recommended for accurate assessment.
Bernard is offered an at-home oral glucose tolerance test. Dr. Brewer plans to demonstrate the test process in an upcoming video.
Side effects from atorvastatin and rosuvastatin do not necessarily occur with all statins. Pitavastatin is described as one of the kinder, gentler statins and is often tolerated by those who cannot tolerate rosuvastatin. Genetic predisposition affects statin metabolism and liver enzyme response. Some people are more sensitive to certain statins than others.
Rob has been pre-diabetic for several years except for 2023. He has a mildly dilated aorta and previously high cholesterol now at normal values. Recent tests show apolipoprotein at 0.4 and Lp(a) at 276. He has atherosclerosis throughout his body.
Mildly dilated aorta indicates aneurysm that is being monitored rather than representing an emergency. Most aortic aneurysms do not progress to dangerous levels. Lp(a) is an apolipoprotein similar to the protein component of LDL particles. It functions to transport fats through the bloodstream, preventing oil from forming large blobs in the 98% water environment of blood. Genetic variation causes some individuals to produce significantly more Lp(a), increasing cardiovascular disease risk.
People with elevated Lp(a) levels have been increasingly identified as more individuals become informed about this marker. Previously, most physicians operated under the assumption that elevated Lp(a) increased cardiovascular risk but nothing could be done about it, leading to statin prescriptions while managing other risk factors. Clinical experience indicates concern typically begins when Lp(a) values exceed 300, with values between 200-300 representing an area where risk exists but may not be the primary concern.
For patients with Lp(a) levels between 200-400, the recommended approach involves first assessing the biggest risk factor: undiagnosed metabolic disease. This requires comprehensive testing including OGTT with one-hour and two-hour glucose measurements, one-hour and two-hour insulin levels, continuous glucose monitoring, and lipid fractionation analysis to understand HDL and LDL particle distribution.
Statins do not meaningfully reduce Lp(a) levels. PCSK9 inhibitors like Repatha show some indication of potential benefit. Antisense drugs represent a newer therapeutic category specifically developed for Lp(a) reduction, with clinical trial participants experiencing dramatic reductions from levels of 400 down to 40.
However, the recent Horizon trial testing pelacarsen demonstrated that despite significant Lp(a) reduction, there was no corresponding decrease in heart attack and stroke risk. This outcome suggests that focusing primarily on Lp(a) while neglecting metabolic disease may be addressing the wrong primary risk factor.
Lp(a) is measured in nanomoles per liter as the standard unit for this marker.
A patient case involving a 6'4" male weighing 210 pounds with abdominal weight gain presented with joint pain preventing statin tolerance. Family history included paternal double bypass surgery and carotid artery stent placement. Previous live scan from 10 years prior indicated mild plaque presence.
Triglyceride to HDL ratio measured 3.4, indicating metabolic disease presence. A1C of 5.5 represents borderline prediabetes assuming high carbohydrate intake, though this interpretation varies by genetic background, with Middle Eastern descent being one factor that may affect A1C reliability as a metabolic marker.
Standard cardiology approaches often focus on LDL levels and flow problems while inadequately assessing metabolic health status. Missing assessments include one-hour and two-hour glucose and insulin measurements from OGTT, fasting insulin levels, and continuous glucose monitoring data. Plaque stability assessment is frequently overlooked, with no determination of whether existing plaque represents stabilized or unstable forms.
A1C values require contextual interpretation based on dietary carbohydrate intake and genetic background. European and Mediterranean descent populations may have different baseline A1C relationships to metabolic status. Middle Eastern genetic background, as demonstrated in clinical examples, can show A1C values between 4-5 despite significant prediabetes presence.
Rather than attempting to interpret ambiguous markers like A1C, direct testing through CGM and the previously mentioned OGTT with insulin measurements provides definitive metabolic health assessment.
A comprehensive cardiovascular risk assessment panel valued at $500 is offered as a resource for identifying true cardiovascular risk through targeted testing protocols.
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