This is a really interesting effort by Michael Lustgarten (@ConquerAging ) and Sergey Vlasov (@SergeyVlasov ). Not sure why we haven’t talked about it here more. Seems like a very good idea… Sergey - are you guys still supporting this (it may be an old effort and not something you’re currently working on - please update us!
Biomarker Optimizers is a community of health enthusiasts striving to live long and well. We consider periodic biomarker assessments the foundation for a truly scientific approach to health.
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Yes, I like it. I’ve used the Test Results Analyzer for things like ApoB, Cystatin C, HbA1c, Homocysteine and Testosterone that aren’t on the Levine’s Phenotypic Age. I joined the Reddit group. Thinking about creating a profile. What do people here think of the AnthropoAge measure?
Ah - so its working for most people and most browsers, it seems.
I use the Apple Safari browser and I was always getting this error message, so I thought the site wasn’t working, but it appears to be working fine with other browsers…
Yes, I’ve only done a quick scan but it looks good. Any extra info on Biomarkers and how to measure for results is super helpful. Too bad the commercially available epigenetic clock tests aren’t more useful (and cheaper) yet.
Here’s something I just picked up from the Reddit group that says that it improves on the Levine PhenoAge model by increasing the blood test inputs to 25 (including Cystatin C) for Biological age estimation.
I think he’s talking about the “25 selected biomarkers” mentioned in this paper:
Biological age captures physiological deterioration better than chronological age and is amenable to interventions. Blood-based biomarkers have been identified as suitable candidates for biological age estimation. This study aims to improve biological age estimation using machine learning models and a feature-set of 60 circulating biomarkers available from the UK Biobank (n = 306,116). We implement an Elastic-Net derived Cox model with 25 selected biomarkers to predict mortality risk (C-Index = 0.778; 95% CI [0.767–0.788]), which outperforms the well-known blood-biomarker based PhenoAge model (C-Index = 0.750; 95% CI [0.739–0.761]), providing a C-Index lift of 0.028 representing an 11% relative increase in predictive value. Importantly, we then show that using common clinical assay panels, with few biomarkers, alongside imputation and the model derived on the full set of biomarkers, does not substantially degrade predictive accuracy from the theoretical maximum achievable for the available biomarkers. Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual’s mortality risk. Values ranged between 20-years younger and 20-years older than individuals’ chronological age, exposing the magnitude of ageing signals contained in blood markers. Thus, we demonstrate a practical and cost-efficient method of estimating an improved measure of Biological Age, available to the general population.
It does look like Sergey may be working on an Excel calculator for BioAge based on the 25 blood biomarkers. Sergey’s comment on Reddit:
“They used 25 blood biomarkers instead of 9 in PhenoAge:
Apart from age and sex, cystatin C appears to have the strongest effect size.
The paper includes the full algorithm, it should be possible to create an Excel calculator similar to what we have for Levine’s Phenotypic Age.”
Hi Sergey, Hopefully we’ll get a chance to test out a calculator. What’s your opinion of improvements to the Levine BioAge calculator? Are the 25 inputs well chosen? Are there any others from blood tests that you would have added? And besides blood tests, what about blood pressure, resting heart rate, heart rate variation, functional tests, etc. that would give us a more complete picture of the effectiveness of interventions such as rapamycin? Wouldn’t adding things like ApoB, Cystatin C, A1c, testosterone, be an improvement? Can you tell us why you included AnthropoAge on the Biomarkers Optimizers site?
Adding cystatin c and removing WBC are definitely improvements from my perspective.
Including AnthropoAge was Michael’s idea.
More testing certainly helps at individual level. But to compare results with each other it is valuable to have standardized set of most important markers.
Daily HRV/RHR measurements are indispensable to ensure proper recovery from exercise.
AIUI the theory behind biomarker clocks is to link the data to known mortality figures. Hence the development of such a clock is a statistical process.
My personal view is that separate to the development of such clocks we should identify key biomarkers.
Cystatin-C for example is far better than Creatinine as it does not metabolise in the sample and is not as dependent on muscle mass and exercise. However, AIUI it still have some variability other than that linked to Kidney function.
I don’t know why WBC is in the Levine formula, but I would like to have a good reason to ignore it.
