Hacking Aging with AI

GERO.AI uses Whole-Exome Sequencing data for in-human AI Drug Discovery, while GeroSense is the arm of Gero creating Digital Biomarkers. GERO.AI models are taken from the physics of complex dynamic systems. They have presented their unique approach in the journal Frontiers in Genetics. They combine deep neural networks with the physical models to study dynamical processes and undersend what drives a disease.

Currently, biopharma and gene editing is curing single gene diseases called monogenic diseases. Those are a substantial market of about $60 billion. However, our main health problems are from more complex diseases and going beyond and cracking aging will be even more complex.

Monogenic diseases will be extinguished by genetic therapies. Most of us will suffer from much more prevalent and lucrative complex diseases, those that are not caused by a single gene.

Common Gene Variants (FREQUENT MUTATIONS or SNPs) cover only 0.5% of the whole genome length. They are mostly responsible for hair and eye color, height, ancestry. Whole-Exome (and eventually GENOME) Sequencing reveal rare mutations, with Gero technology they can discover ideas for drugs in the remaining 99.5% of the genetic.

GERO.AI has successfully identified and validated a human target for treating senescence-associated disorders and aging. They achieved systemic rejuvenation in experiments with 100-weeks old animals by intervention vs. protein predicted by the AI. Their discovery is even a little better than the antiaging drug Rapamycin.

A Gero co-founder, Peter Fedichev, published a study that counts blood cells and footsteps to predict a hard limit of 120-150 to longevity unless there is intervention. They created the dynamic organism state indicator (DOSI).

GERO did a systematic investigation of aging, organism state fluctuations, and gradual loss of resilience in a dataset involving multiple Complete Blood Counts (CBC) measured over short periods of time (a few months) from the same person along the individual aging trajectory. Neutrophil to Lymphocyte Ratio (NLR) and Red cell distribution width have been already suggested and characterized as biomarkers of aging. Instead of focusing on individual factors, to simplify the matters, they followed and described the organism state by means of a single variable, henceforth referred to as the dynamic organism state indicator (DOSI) in the form of the log-transformed proportional all-cause mortality model predictor.

The average line demonstrates nearly linear growth after age of 40. In younger ages the dependence of age is different and consistent with the universal curve suggested by the general model for ontogenetic growth. Looking at many people they have found that key aging really starts at 40.

In the most healthy subjects, i.e., those with no diagnosed diseases at the time of assessment, the DOSI predicted the future incidence of chronic age-related diseases observed during 10-year follow-up in the UB study.

They have an app gerosense analyzes acceleration patterns and provides insight into health changes. They use the app to provide data, development and tracking for the DOSI aging indicator.

SOURCEs- Gero, Nature
Written By Brian Wang, Nextbigfuture.com

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