SystemAge [is it possible to reduce the differential of your fastest aging system to your slowest aging systems if you have major differentials?]

#1

So
if you insert your CpG file of trudiagnostic into phylo biomni.

You can basically figure out a rough approximate (even if there are some missing sites that are subject to disagreeing imputation methods). You don’t even need the $1000 SystemAge CpG site test to get an approximate readout of your systemage [not 100% confirmed, just looking]

#2

But can SystemAge specifically in the brain be almost as malleable as DunedinPACE is?

If you have cPTSD and your brain systemage is higher than other organs, you might as well do ketamine, bc Albert higgins chen showed that ketamine decreased it…

#3

FYI, brain and heart systemage are said to be the least reliable… (bc you can only measure so much in blood, PARTICUALRLY CPG sites)… and there is probably way more variability between CpG sites in brain and those in blood…

  • More validation: SYMPHONYAge organ clocks have been validated to be superior to presently available clocks when evaluating 16 different health outcomes across 5,129 samples (three different datasets). Validation is set to be published, but is currently online and under review, by bioRxiv. The old organ age clock (Systems Age) has shown to only be comparable to four different outcomes in just 2,167 samples (1 dataset only), in unpublished data.
#4

Our results also highlighted that advanced DNAm biomarkers, particularly those from
Generation 2+ (Reliable and mortality or rate of aging predictors) such as GrimAgeV2,
PCGrimAge, PCPhenoAge, SystemAge and DunedinPACE, exhibit substantial responsiveness
to these interventions. Specifically, these biomarkers showed profound changes in individuals
undergoing longevity treatments, reinforcing their potential utility in both research and clinical
settings for monitoring the effectiveness of anti-aging strategies. The discriminative power of
these advanced biomarkers makes them essential tools in personalized medicine for tracking
biological age and intervention efficacy.
A key observation from our analysis was the variability in responsiveness among DNAm
biomarkers, influenced significantly by the type of intervention. For instance, while PCPhenoAge
showed variable results across different studies, GrimAgeV2, SystemsAge, PCGrimAge and
DunedinPACE consistently indicated significant decreases in epigenetic age, regardless of the
intervention type (Lifestyle or Pharmacological). This consistency highlights the crucial role of
selecting suitable biomarkers for specific interventions, ensuring that measurements of
biological aging remain reliable and accurate. These findings suggest that reliable Gen 2+
DNAm biomarkers should be prioritized in clinical trials to provide robust insights.