Rejuve.AI partners with FLT to advance personalized longevity
Published: October 17, 2025 | Updated: October 17, 2025
The longevity gap isn't science-it's translation. Rejuve.AI and FLT are linking AI-driven insights with evidence-led supplementation to make healthspan gains measurable, not theoretical.
Why this matters for researchers
FLT is built on nearly a decade of literature tracking in aging science. Its formulations are evidence-led, manufactured in the US under GMP, and tied to peer-reviewed references for transparency.
Rejuve.AI focuses on individualized analytics and N-of-1 experimentation, combining self-reported outcomes, wearable data and biomarkers. The partnership tests a practical model: match interventions to people, then close the loop with measurement at scale.
This is an attempt to reduce claims without data. Interventions are linked to hypotheses, outcomes and feedback.
From data to decisions
FLT's product line targets functional goals: Cognition (clarity), Muscle (strength and recovery), and Longevity (cellular resilience and metabolic support). Ingredients include ErgoActive (ergothioneine), L-theanine, PeptiStrong, creatine, Niagen (nicotinamide riboside), spermidine and fisetin-compounds with clinical literature in energy metabolism, neuroprotection and cellular maintenance.
- Cognition: working memory, processing speed, reaction time; tests such as Symbol Digit Modalities or n-back.
- Muscle: grip strength, vertical jump, isometric mid-thigh pull; recovery via HRV and DOMS scores.
- Longevity: resting HRV, VO2max estimate, fasting insulin/HOMA-IR, lipid subfractions, hs-CRP, epigenetic clocks where applicable.
- Mechanistic proxies: NAD+ adjacents for NR; autophagy-related markers for spermidine (where feasible in research settings).
- Adherence and tolerability: dosing logs, GI tolerance, sleep disruption, perceived energy.
AI as a feedback engine
Rejuve.AI ingests lifestyle inputs, wearable streams and lab markers to generate adaptive recommendations. The community-driven N-of-1 approach fits an area with high heterogeneity of response and practical constraints on RCTs for every stack component. See an overview of N-of-1 methods in clinical care at the BMJ.
"The future of longevity isn't just about discovering new molecules - it's about how individuals respond to them," said Dev Heaton, Head of FLT. "Partnering with Rejuve.AI connects our formulations with real-world data, helping people see how cellular, cognitive and physical performance can evolve over time."
Practical study design notes
- Pre-specify primary and secondary outcomes, effect direction, and time horizons (e.g., 6-12 weeks per phase).
- Use designs that fit daily life: A-B, A-B-A, or alternating sequences with wash-in/wash-out where biologically appropriate.
- Standardize measurement cadence: morning fasting labs; same-time daily cognitive tasks; weekly strength tests.
- Control confounding: stable training volume, sleep targets, caffeine windows, and step counts during phases.
- Quantify adherence objectively (pill counts, smart caps) and subjectively (daily check-ins).
- Report uncertainty: within-person effect sizes, confidence/credible intervals; adjust for multiplicity if testing many endpoints.
- Track adverse events and discontinuations; define stop rules.
- Privacy by design: consent flows, de-identification, and governance for data reuse.
What to watch in the data
- Responder analysis: identify characteristics linked to positive, null or negative responses.
- Dose-response and timing: morning vs evening dosing, fed vs fasted state.
- Concordance: do biomarker shifts align with performance and patient-reported outcomes?
- Device and batch effects: wearable firmware changes; supplement lot traceability.
- Generalizability: replicate across demographics, training status and baseline health.
Guardrails and limits
Supplements are not drugs; effects tend to be modest and context-dependent. Many endpoints are noisy, and publication bias is common. Maintain restraint, publish nulls and prioritize clinically meaningful effects over small statistical wins.
For example, creatine has a well-characterized safety and efficacy profile in performance and some cognitive contexts; see the NIH ODS fact sheet for a concise overview: Creatine - Health Professional.
Why this collaboration is interesting for the field
It moves supplementation from static "stacks" to living protocols informed by data. Less promise, more proof: open references, measurable endpoints and iteration based on individual response.
If executed with rigor-pre-registration, transparent methods, and sharable aggregate outcomes-this model can help align consumer practice with standards familiar to clinical and translational researchers.
If you're building your own protocol
- Define one clear goal (e.g., improve working memory or lower fasting insulin).
- Select 1-2 interventions; avoid stacking changes that mask signal.
- Run a structured N-of-1 for 8-12 weeks with a stable routine and fixed measurement windows.
- Track 3-5 high-signal metrics (one primary), plus adherence and adverse events.
- Review quarterly; keep what shows a meaningful effect, retire what doesn't.
For researchers and teams upgrading AI skills for biomarker and wearable analytics, curated training can help operationalize these workflows: AI courses by job role.
Bottom line: FLT supplies evidence-led interventions; Rejuve.AI supplies the measurement and adaptation layer. If the feedback loop holds, consumer longevity gets more accountable-and measurably useful.
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