A decade ago, "anti-aging" was the domain of supplement marketing and wishful thinking. Today it's Nobel Prize-winning biology, $3 billion venture rounds, and the first FDA-accepted clinical trial targeting aging as a disease. This is what the research actually shows, and what it doesn't.
Engineering WhitepaperRead the Technical Whitepaper →In 2013, López-Otín et al. published a landmark paper in Cell establishing 9 distinct biological mechanisms that drive aging. A decade of evidence led to a 2023 update, adding 3 more hallmarks and cementing this as the field's operating framework. Every serious longevity intervention targets one or more of these. Antiaging Labs's 4-lever protocol addresses 7 of the 12.
Source: López-Otín et al., Cell 2013 (9 hallmarks); López-Otín et al., Cell 2023 (updated to 12 hallmarks)
In 2006, Shinya Yamanaka discovered that four transcription factors, Oct4, Sox2, Klf4, and c-Myc (OSKM), could reprogram any adult cell back to a pluripotent stem cell state. The implication: the genome retains a full "memory" of youth. Aging isn't a one-way ratchet. In 2012, Yamanaka received the Nobel Prize in Medicine. Since then, the question has shifted from "can we reprogram cells?" to "can we do it safely, partially, in living organisms?"
"We can reset the age of a cell, and it remembers its youthful state. Aging has an address, and we've found it."
Professor David Sinclair (Harvard Medical School, Paul F. Glenn Center for Biology of Aging Research) has built the most coherent single theory of aging currently in science. His 2019 book Lifespan brought these ideas to a mainstream audience. The core claim: aging is not wear-and-tear or mutation accumulation, it's the progressive loss of the epigenetic information that tells cells who they are and how to behave.
The genome is like a compact disc, it contains all the information needed for life. The epigenome is like the music encoded on it. With age, the disc gets scratched. The information is still there, but it can't be read correctly. Cells forget who they're supposed to be. Sinclair argues this is the primary cause of aging, not genetic mutation.
The NAD+ space is contested. Blood NAD+ can be raised, the question is whether this translates to meaningful intracellular effects in aged tissues. Phase II trials are ongoing. Exercise remains the most robustly proven method of raising NAD+ and activating sirtuins, which is why it's the backbone of every serious longevity protocol, including Antiaging Labs's.
Sinclair's self-experiments (1g NMN daily) are widely publicized but are n=1 anecdotes, not clinical data. He acknowledges this.
Cellular senescence is one of the most actionable hallmarks of aging. Senescent cells stop dividing after DNA damage, telomere shortening, or oncogene activation, a short-term safety mechanism that prevents cancer. But over time, the immune system fails to clear them efficiently, and they accumulate throughout the body, releasing a toxic cocktail of inflammatory signals (SASP: Senescence-Associated Secretory Phenotype). Senolytics are drugs that selectively kill these cells.
mTOR (mechanistic Target of Rapamycin) is the master regulator of cellular growth, protein synthesis, and autophagy. When it's chronically active, cells prioritize growth over repair, accumulating damage faster. Caloric restriction works partly by reducing mTOR. Rapamycin, a drug originally developed as an immunosuppressant, is its most potent inhibitor, and has the most replicated lifespan-extension data of any drug in mammalian models.
TAME (Targeting Aging with Metformin) is historic not because metformin is the most powerful longevity drug, it probably isn't, but because the FDA accepted "aging" as a therapeutic target for the first time. If TAME produces positive data, it opens a regulatory pathway for every future longevity drug. The trial enrolls adults 65–79 who already have one age-related condition. Primary endpoint: time to development of any additional age-related disease.
You cannot manage what you cannot measure. The epigenetic clock revolution gave aging research its first rigorous outcome metrics, molecular timekeepers that quantify biological age from DNA methylation patterns or blood chemistry. These clocks are why the field can now run clinical trials and produce quantifiable results. They're also what Antiaging Labs uses operationally to measure client outcomes.
Published: Steve Horvath, Genome Biology
353 CpG methylation sites predict biological age across 51 different tissues and cell types. The first pan-tissue epigenetic clock. Measures cumulative epigenetic aging. Slows with caloric restriction; responds to reprogramming experiments.
Published: Lu et al., Aging
The strongest predictor of time-to-death among all existing clocks. Trained on mortality data directly. Integrates smoking history via methylation proxies. Most clinically relevant for longevity interventions. Requires methylation array ($300–500/sample).
Published: Belsky et al., eLife
Measures the pace of aging, not your biological age, but how fast you're aging right now. A DunedinPACE of 0.9 means aging 10% slower than average. The most sensitive clock for detecting short-term intervention effects. Used in the CALERIE trial.
Antiaging Labs calculates PhenoAge (Levine 2018, Nature Medicine) from your 50+ marker blood panel. PhenoAge is a blood-chemistry-based model trained on mortality risk, 9 markers, fully calculable from standard bloodwork, validated across multiple cohorts. It is not an epigenetic clock (that requires methylation arrays at $300–500/sample), but it is the most clinically accessible and well-validated biological age model available at population scale. For protocol follow-up, PhenoAge delta is one useful outcome metric. We are separately building a model to predict epigenetic clock scores from blood chemistry, see Our Research below.
Clinical evidence in longevity is still early but moving fast. These are the most significant active or recently completed trials targeting biological aging in humans.
| Trial | Intervention | N | Duration | Primary Endpoint | Status |
|---|---|---|---|---|---|
| TAME Barzilai, Albert Einstein College of Medicine | Metformin 1500mg/day | 3,000 | 6 years | Composite of 5 age-related conditions (CVD, cancer, dementia, disability, death) | Enrolling |
| TRIIM-X Fahy, Intervene Immune | GH + DHEA + metformin (replication of TRIIM) | ~100 | 12 months | Epigenetic clock reversal (Horvath, GrimAge) | Ongoing |
| Dog Aging Project / TRIAD Kaeberlein, Univ. of Washington | Rapamycin (low-dose, intermittent) | 580 dogs → human trial planning | 4 years (dogs) | All-cause mortality, healthspan, cardiac function | Active |
| CALERIE II Duke, Tufts, Washington Univ. | 25% caloric restriction | 220 | 2 years | DunedinPACE, cardiometabolic risk | Completed · Nature Aging 2023 |
| PEARL (NMN) Procter & Gamble / Keio University | NMN 250mg/day | ~30 per arm | 12 weeks | Muscle function, insulin sensitivity, NAD+ levels | Completed · Science 2021 |
| Senolytic D+Q Kirkland, Mayo Clinic, multiple studies | Dasatinib + Quercetin (intermittent) | 11–100 (varies by arm) | 3–6 months | Senescent cell markers (p16, p21), physical function | Phase II ongoing |
| Rapamycin in Healthy Adults Multiple academic sites, 2024–2025 | Low-dose rapamycin (weekly) | ~250 | 12 months | Biological age clocks, immune function, cardiometabolic markers | Enrolling 2024–25 |
Longevity has moved from fringe to the most-funded area in biotechnology. These are the companies that matter, not supplement brands or wellness influencers, but organizations with serious scientific leadership and institutional capital.
"We are at the beginning of a new era in medicine. Not treating diseases of aging, but treating aging itself, before those diseases emerge."
Beyond running the Antiaging Labs program, we are building the computational core that models the body as a single high-dimensional dynamical system. Below is a summary of that architecture. The full derivations, tables and equations live in our Technical Whitepaper.
A multi-omic neural architecture for real-time biological age decoupling and closed-loop optimization. This is the engineering thesis behind the Antiaging Labs engine. What follows is a summary; the full derivations, coefficient tables and equations are in the Technical Whitepaper.
Traditional diagnostics rely on isolated, static reference ranges and episodic panels that treat the body as a set of compartmentalized organs. That model misses the multi-system feedback loops that actually drive aging. We instead model the human organism as a highly integrated, non-linear dynamical system. At any moment, a person's complete physiological state is a single point, a Dynamic State Vector, navigating a high-dimensional Physiological State Space.
The trajectory of that vector over time is the true biological velocity of aging. In a resilient state, the system sits in a deep basin of attraction, the Healthy Optimization Attractor, and snaps back after stress. As allostatic load accumulates, those basins flatten, the state vector drifts into high-entropy regions, and in extreme strain the system crosses a separatrix into a self-degrading limit-cycle associated with chronic disease.
The engine constructs the state vector by compiling data across five biological layers, each at a different temporal resolution:
Simply concatenating these tiers obscures cross-modal interactions and overfits, and real users always have gaps, the "Missing Modality Problem." The core resolves this with a dual-engine design:
Open-source linear clocks (Levine PhenoAge, KDM-BA) are trained on population mortality and offer no proprietary moat. We calculate them as transparent industry benchmarks, then route true optimization through a non-linear Deep Multi-Modal Clock built in three stages:
The daily protocol compiler is a Closed-Loop Reinforcement Learning system (Contextual Bandits with Proximal Policy Optimization). The aligned latent state is the context; the action space is exact dosing of co-factors, macros, fasting windows and training load; the reward minimizes the displacement from the Healthy Optimization Attractor while maximizing biological-age reversal. If an intervention moves the trajectory the right way, the policy reinforces it for that physiological archetype, for example deploying methylfolate against an MTHFR vascular-inflammation profile, or PQQ and ubiquinol against a SOD2 mitochondrial-decay profile.
The platform operates as an Explainable Expert Co-Pilot, not an unsupervised diagnostic engine. Every automated recommendation passes a four-stage pipeline, algorithmic synthesis, a hard-coded clinical safety filter, an explainable summary for the physician dashboard, and final clinical authorization. Protein and metabolic guardrails are aligned to KDIGO guidelines using the race-free 2021 CKD-EPI Creatinine-Cystatin C equation, with explicit overrides for frail, sarcopenic and pediatric populations.
Read the full Technical Whitepaper → for the complete state-space formulation, the genomic and biomarker tables, the PhenoAge and Klemera-Doubal proofs, and every equation.