Meta-analysis & systematic review
Pooled RCTs — the most robust evidence we can find in biohacking topics. Examples: creatine monohydrate for strength output, NMN for plasma NAD+ levels.
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Biohacking · Study database
The largest biohacking study database
Over 670,000 scientific studies on biohacking, longevity, supplements and performance — 94% with abstract, organized into 453 topics. Searchable, evidence-classified, with a clickable PubMed source per claim instead of influencer hype.
Evidence-based · PubMed-verified
What's inside the database?
The base is over 670,000 scientific papers, aggregated from PubMed, OpenAlex and Europe PMC — focused on the fields that actually make up biohacking: longevity, sleep, cognition, hormones, peptides, performance and metabolic health. 94% of studies have a full-text abstract, the dataset spans 1855 to 2026, and 54% come from the last decade. 399,592 studies are sorted into 453 thematic clusters — from autophagy through caffeine to dementia prevention — so for each substance or method you can see how thick the actual evidence base is. It's the honest counter to the single cherry-picked study that happens to back an influencer post.
How we classify evidence
Not every study carries equal weight. We classify by evidence tier (meta-analysis > RCT > cohort > case series > in-vitro/animal) and enrich where possible: study type, sample size, effect direction, safety signals (adverse effects, contraindications, interactions) and AI-assisted structured extracts. Important — and we say this openly: deep enrichment is not finished for every one of the 670,000 studies. Safety screening already covers around 198,000 studies, AI structured extracts around 38,000, and full A→F grading a smaller, growing core. Where a layer is missing, we flag it instead of claiming completeness. Evidence honesty is the core — including about our own gaps.
Why an open study index?
Most biohacking content online is opinion- and sales-driven: an anecdote, an affiliate link, a single cherry-picked study. A searchable index across the whole evidence base flips that — you don't see the one convenient study, you see the distribution: strong evidence, thin evidence, or contradictory. That's why this index is free to access and citable. If you research, write or need to back up an article, you can reference the per-topic study counts and sources directly — link to the relevant topic page or to this overview.
The database in numbers
The largest evidence-based biohacking study index
671,516
studies indexed
PubMed · OpenAlex · Europe PMC
94 %
with abstract
full-text searchable
453
topic clusters
399,592 studies mapped
1855–2026
publication years
54% since 2015
198,582
safety-screened
adverse effects · interactions
38,330
AI-structured
extraction growing
As of May 2026. The corpus is broad; deep enrichment (A→F evidence grading, AI extraction, safety) currently covers a growing share and expands continuously. We flag gaps transparently instead of claiming completeness.
Most-researched topics
| # | Topic | Studies |
|---|---|---|
| 1 | AMPK Activators | 5,146 |
| 2 | Autophagy | 4,526 |
| 3 | Circadian Rhythm | 4,143 |
| 4 | Curcumin | 3,958 |
| 5 | Depression | 3,662 |
| 6 | Alcohol | 3,524 |
| 7 | Amphetamines | 3,493 |
| 8 | ApoB | 3,443 |
| 9 | Cellular Senescence | 3,442 |
| 10 | Carbohydrates | 3,420 |
| 11 | Anxiety | 3,408 |
| 12 | Caffeine | 3,335 |
| 13 | Acetylcholine | 3,327 |
| 14 | Dopamine | 3,300 |
| 15 | Mitochondria | 3,219 |
| 16 | Microbiome Modulation | 3,077 |
| 17 | Dementia Prevention | 3,035 |
| 18 | Apigenin | 3,003 |
Excerpt from 453 topics. Each links to its evidence-sorted topic hub.
Evidence, not hallucination
Evidence-based biohacking — how we rank studies
Evidence-based biohacking means every claim about sleep, supplements, longevity or performance stands or falls with the study it cites. Biohacking AI makes that study trail visible — with clickable PubMed links, transparent evidence tiers and honest labeling where research is still thin. Every biohacker should know whether they're following a meta-analysis or a mouse paper.
Randomized controlled trial (RCT)
Gold standard for single studies. Causal claims are possible, but effect sizes vary widely. Examples: magnesium for cramps, ashwagandha for cortisol-driven stress.
Observational / cohort study
Large population data, but no causality — useful hypothesis generators. Examples: vitamin D levels and mortality, sleep duration and dementia risk.
Mechanistic & animal model
Plausibility yes, clinical proof no. We label this transparently so no one reads a mouse result as "proven." Examples: peptides like BPC-157, red-light therapy at the cell level.
Those four tiers underpin every answer on the platform — no study is cited without a tier label, and when the evidence is thin the AI says so openly.
FAQ
Frequently asked questions
- How many studies does the biohacking database contain?
- Currently over 670,000 scientific studies (as of May 2026), aggregated from PubMed, OpenAlex and Europe PMC. 94% have an abstract, and 399,592 are sorted into 453 topic clusters. The index grows continuously via automated harvests.
- Where do the studies come from?
- From the major open scientific literature databases: PubMed (NIH/NLM), OpenAlex and Europe PMC. Every study keeps its original source including PubMed ID and DOI, so you can trace any claim back to the primary source.
- What does the A→F evidence classification mean?
- It ranks each study by methodological strength: A is the strongest evidence (meta-analyses, systematic reviews of large RCTs), descending through individual RCTs, cohort and case-control studies, down to F for the weakest (in-vitro, animal models, case reports). So you can see at a glance whether a recommendation rests on robust or thin evidence.
- Are all studies already AI-analyzed?
- No — and we flag that transparently. The corpus is broad (670,000 studies, 94% with abstract), while deep enrichment grows step by step: safety screening covers around 198,000 studies, structured AI extracts around 38,000, and full A→F grading a smaller, growing core. Where a layer is still missing, the platform shows it instead of faking an analysis.
- How current is the data?
- New publications are added continuously via automated daily harvests, so fresh work in fast-moving fields (longevity, GLP-1 agonists, peptides) lands in the index promptly. The aggregate figures shown here are a dated snapshot (as of May 2026) and are refreshed periodically.
- May I cite this data?
- Yes, explicitly. The per-topic study counts and the headline figures are free to access and intended for editorial, scientific or journalistic use. Please link to biohacking-ai.com as the source — either to this overview or to the specific topic page whose study count you reference.
Related
- Explore topic worlds
Ten curated hubs for sleep, longevity, hormones, peptides and more — each sorted by the most robust studies.
- Knowledge: answers with sources
Direct, evidence-based answers to specific biohacking questions — every claim with a PubMed citation.
- The Biohacking app in detail
How the app makes the database usable: live search, A→F evidence tiers, clickable sources instead of hallucinations.
- AI comparison: best AI for biohacking
Why a specialized study AI hallucinates less on health questions than ChatGPT, Claude or Gemini.
Ask your first evidence-based question
Search the evidence base for your topic — with a clickable PubMed source per claim, free and without an account.