Freeflow Ventures’ Post

🌟 N = 1 Medicine: A Paradigm Shift in Healthcare 🌟 🔍 The Problem: Modern medical research relies heavily on Randomized Controlled Trials (RCTs), but is this method truly meeting the needs of individual patients? 💡 A Startling Reality: Did you know that the top 10 highest-grossing drugs only benefit between 1 in 25 to 1 in 4 people who take them? This raises questions about the effectiveness of our current approach. 🔬 Reinforcing a Key Point: Our current medical research system—and by extension, clinical practice—is grounded in this idea of “lumping” people together. This approach sacrifices specificity and individuality, leading to a mix of responders and non-responders, strong and weak responders. For pharmaceutical companies, all that matters is achieving a “statistically significant” result in the population. But for patients, what truly matters is whether they, as individuals, are likely to respond to the treatment. Of course, clinicians know this. That’s part of the “art” of medicine. But, what if we made the “art” more bioindividial. More scientific… 🛤️ The Path to N = 1 Medicine: We need a new paradigm: N = 1 Medicine, where the focus is on specificity and recognizing individuality. The key to this future lies in data — YOUR data. We are entering an era where vast datasets can be collected on each individual, encompassing everything from your microbiome to your genome to your proteome to your transcriptome and so on… (this is multi-omics). By integrating these datasets and leveraging machine learning and AI, we can target the root causes of metabolic dysfunction with unprecedented precision. 🚀 The Way Forward: Imagine a future where 100% of the population enjoys metabolic health. Achieving this goal is possible through N = 1 Medicine. Let's embark on this journey together towards a healthier tomorrow. Watch: https://lnkd.in/eWg3kS3C #Healthcare #PrecisionMedicine #PersonalizedCare #MetabolicHealth #Nequals1Medicine Cc Dominic D'Agostino Adrian Soto Mota Benjamin Rolnik Enovone and big hat tip to Snyderlab Stanford

🩺 The Illusion of ''EVIDENCE BASED MEDICINE'' 📓 No denying that RCTs are an excellent type of evidence, but it is still a reductionist way of practising medicine in my opinion. An RCT done on a bunch of 70 kg healthy male volunteers might produce a statistically significant benefit that generate an n number, but how much of that can we extrapolate to a real life patient with many chronic problems? Here are some problems with RCTs in medicine ➡ A reductionist, 'one-size-fits all' approach that cannot be applied to real life where we see complex patients with multiple co-morbidities ➡ Lack of consideration of individual genetics, lifestyles, body types, race and sex. For example, most trials are done in men, and it is difficult to apply the results to women whose physiologies are vastly different. ➡ Most RCTs are funded by pharmaceutical companies themselves, and there is no independent data analysis done, therefore there is huge bias. Studies have shown that pharma-funded medical trials are much more likely to produce a positive result. The ''science'' is where the money is. ➡ Many trials done by Pharma are not released into the public, they only publish positive trials. For example, if there are 7 negative trials, and 1 positive trial, they will publish just the one. ➡ Trials are often stopped early as soon as a statistically significant benefit is achieved. This is a perfect way to market a new drug, without EVER knowing the long term effects. ➡ 'A pill for each symptom' is again reductionist because each pill targets one specific physiological target, as if the disease is caused by one factor. In real life, disease is multifactorial with many root causes. Different hormones, enzymes and proteins communicate with each other, and disease is a manifestation of the disruption of homeostasis, which often takes many years to develop. 🤔 A few other limitations in medical practice: ❌ Most doctors don't have the time to review the latest research and evidence, therefore stick to guidelines. This is not their fault. ❌ Most guidelines are based on conclusions of studies. It is easy to hide study findings by not including them in the study, and drug benefits are exaggerated using statistical manipulation (For eg. relative risk reduction vs absolute risk reduction). ❌Guidelines are based on 'evidence' produced by the pharmaceutical industry. They do not even look at evidence based on lifestyle interventions or supplements, even though there is plenty. ❓ What is the point of valuing the clinical experience of doctors, if these experiences are just a series of 'anecdotes'? ❗ My clinical experience after seeing THOUSANDS of patients is telling me that these drugs worsen chronic disease, and have some value only in emergencies. If anything, the more drugs they take, the more likely they will get worse. #Pharmaceuticalindustry #ChronicDisease #Type2Diabetes #EvidenceBasedMedicine #Research #RandomisedControlTrials #FunctionalMedicine

Is #precisionmedicine facing a 𝐫𝐞𝐚𝐥𝐢𝐭𝐲 𝐜𝐡𝐞𝐜𝐤?🧬🔬 The vision of precision medicine, tailoring treatments to #individual patient profiles, has been a beacon of hope. #Machinelearning, touted as the catalyst for accelerating precision medicine, sifts through vast and intricate data to 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐦𝐚𝐫𝐤𝐞𝐫𝐬 predicting the right treatment at the right time. 🌐💻 However, a recent study by Chekroud et al. reveals a 𝒄𝒓𝒖𝒄𝒊𝒂𝒍 𝒓𝒆𝒂𝒍𝒊𝒕𝒚 𝒄𝒉𝒆𝒄𝒌. Machine learning models predicting treatment response in #schizophrenia clinical trials failed to generalize to new trials, prompting a call for stricter methodological standards. 📉🤖 These findings not only underscore the need for refined approaches but also signal a reevaluation of 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 in precision medicine. As the field matures, addressing these challenges head-on becomes paramount, ensuring that the lofty promises of personalized healthcare translate seamlessly into #clinical reality. 💡🔍 #PrecisionMedicine #MachineLearning #HealthcareChallenges #ResearchInsights https://lnkd.in/dC8ZhNKJ

The book covers how to build models that can be applied with minimal risk in high-stakes settings. It discusses how to integrate clinical and molecular analysis and modeling in medicine and healthcare. Full-text download: https://lnkd.in/dSeQE8yV

10,000 Genome Project In this episode of All Things Policy, Shambhavi Naik and Saurabh Todi discuss the recent completion of the 10,000 Genome Project and the importance of collecting such data for medical research. Listen here: https://shorturl.at/qrS16

AI-Driven Precision Medicine: The Future of Personalized Healthcare Our health landscape is rapidly transforming, courtesy of AI-driven Precision Medicine. Tapping into patient-specific data and predictive algorithms, scientists can now offer bespoke diagnostic and treatment plans – breakthrough! These advances promise therapy strategies tailored to individual genetic and biometric data translating to more effective, less harmful medical intervention practices. Soon, one-size-fits-all approaches in medicare will become ancient history. What are your thoughts on this?

4 min to learn something new watch to learn about The Evolution Of Evidence based Medicine: Past, Present, And Future https://buff.ly/3L1w7w7 #EvidenceBasedmesicine

🔬💡 #Culture and #experience drive continuous development in the medical profession. Each day, studying cutting-edge research, harnessing over 35 years of #expertise, or listening to invaluable patient #feedback fuels progress. At Isokinetic's Education and Research Department, we strive to leverage these insights for every patient's benefit. Annually, we publish numerous internationally renowned scientific works, aiming for constant advancement in care and rehabilitation. 💪📚 #Medicine #Research #Isokinetic

I'm happy to report that my Precision Medicine course has been successfully finished! 🎓💼 Through the customization of medicines to each patient's specific genetic composition, lifestyle, and environmental circumstances, precision medicine is transforming healthcare. I have acquired priceless knowledge about the guiding ideas, innovations, and practical uses of this game-changing medical approach thanks to this course. My knowledge and abilities have grown, and I can now contribute to the progress of precision medicine in healthcare, from comprehending the analysis of genetic data to investigating tailored treatment plans. awaiting the opportunity to put these lessons into practice to improve patient outcomes and care. #Medicine Precision #InnovationInHealthcare #ContinuingEducation 🧬💉

Dive into fixed-effect vs random-effects models in meta-analysis and understand their differences and implications for clinical research! Being able to use increasingly large data bases to improve insights into medical conditions and treatments, systematic reviews and—condensed from there—meta-analysis have become a mainstay. But some caution is needed. Editor-in-Chief of the Global Spine Journal Jens Chapman delves into what to look out for in this statistical methodology 👉 https://brnw.ch/21wLurj #MetaAnalysis #ResearchMethods #AOGuestBlog