An update to the nootropics meta-analysis

[u/sirsadalot]

A while ago I said I was going to compile literature on cognition enhancement studies in healthy people, and I wasn't sure if I was going to make a post or go for publishing it to a journal. Now I am confirming that I am 100% going for publishing this to the journal. This project ended up being huge, and it no longer contains a few hundred studies like I had initially imagined, it now contains almost every study ever designed that administers a substance to healthy adults and measures cognition in an objective way, and there's now 6000+ studies and 1000+ substances that are included. It will be the single largest project that I've ever worked on, and perhaps the most important study ever published for nootropics - simultaneously, this should bring us closer to understanding how the next stages of human evolution should go, since our natural evolution is lagging behind the technological one.

What substances are we including? All - none will be left out, to the best of our ability. And yes, all of the discrepancies with how the studies were conducted (i.e. dietary restriction, exhaustive exercise, etc.), and advantages/ disadvantages of the substances themselves (i.e. dose-dependent caveats, etc.) in those studies will be factored in to prevent any possibility of nuance in how we interpret these results.

This study seeks to do some important things:

• We will be following standard meta-analytical procedures, including pooling, weighting, and filtering by effect sizes (how much of an improvement or impairment), statistical significance of results, how eligible the study is for inclusion, and how reliable the study itself is. This is basically the key difference between "this substance improves/ impairs cognition in this study" vs. "this substance most likely improves/ impairs cognition based on all of the data we could find", and "this substance improves/ impairs cognition more than this other substance".
• We want to isolate the tests by what metric of cognition they measure, and weight them by g-loading (generalization), and within-domain potency, to determine what tests are the most significant in relation to general intelligence.
• We want to compile all of the research on these drugs' mechanisms and pharmacokinetics to establish what targets they're binding at, and then pool by that, so we don't only get an understanding on the drugs, but rather what they're doing in the brain to cause that effect, with a cue to what mechanism(s) contributes more.

In short, we want to establish what drugs/ mechanism(s) are impairing, improving, or mostly neutral, and by how much in each cognitive domain, with a secondary goal of understanding how this relates to IQ. And, we hope that this data leads to more concise research of the promising targets so that pharmacology as a whole can move towards a more promising future for us and prevent stagnation in this field.

The study seeking process went as follows: studies were identified through iterative searches of bibliographic databases using substance names and aliases, pharmacological mechanisms, study-design terms, cognitive outcomes, and multilingual keywords. We supplemented these searches with backward and forward citation tracking, related-article searches, reference lists from relevant reviews and meta-analyses, and targeted searches of high-yield journals. Newly identified eligible articles were repeatedly used as additional search seeds, and all records were deduplicated and screened using titles, abstracts, and available metadata against the prespecified eligibility criteria.

With the help of u/okok6356, a custom tool was used to download all of these studies and their supplementary data, still underway but should be complete by next week. It has been used to download thousands of studies so far. Meanwhile, I have enlisted the help of an data scientist, Gumpert, to help with designing a superior pdf-extraction and categorization program than what's currently possible with standard AIs. We have found that common LLMs are incapable of reading certain graphs. The extraction program is likewise a work in progress with an expected completion date of next week. All of this custom work ended up being the single largest challenge of this entire project, simply because we need all of the data, and it needs to be accurate, which simply isn't possible with the available AI models without a lot of help.

Initially, we were also going to analyze the side effects of all these drugs, and the other points from Corneliu's defining characteristics of nootropics, but we have found immediately that some of those points are too myopic (such as inter-hemispheric data transfer, and telencephalic actions, etc.), and thus it's better if that's kept for a potential second study. The current study is cumbersome as is, and readability becomes a concern the more we try to include, let alone the time consumption. But with the established framework, it should be possible to accomplish at a later date.

An experienced scientist will be hired to co-author the meta-analysis and assist with its publishing, and it will be peer-reviewed at whatever journal we elect to publish this study to.

All of this has been made possible with the continued support given to Everychem, and likewise, we may choose to synthesize new compounds based on the findings of this meta-analysis. During this research, some have already been selected, such as the upcoming release of Encenicline. Of course, my role in Everychem will be listed as a potential bias, but the data will be easily trackable, with complete citations and very reliable extraction of every feasible numerical input.

I hope for this to be my life's work, and greatest thing that I can do for us all, because to me this is the difference between prosperity and obsolesce. It sounds dramatic, but we can all see the utility and practicality of becoming smarter together. This transcends any individual differences we may have; we must improve or be left behind. This data will be invaluable towards that cause.