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Proven anti-ageing for me – the metrics

anti-ageing metrics daily tracking

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#1 Zarathrustra

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Posted 23 November 2021 - 03:14 PM


To expand on how I know to I am doing: I have algorithms that convert my daily activity into five health-illness metrics.

 

I presently use a total of 32 biometrics to assess my overall illness-health metric, and also subdivide these into 12 for cardiovascular, 6 for CkD, 12 for cancer, and 3 for body composition.

 

Then my algorithm gives me a single figure for my overall illness-health metric, and similarly for each of the four of my present health concerns. Negative values are equated with increasing health, positive ones with increasing illness.

 

Examples are:

 

Overall (yesterday was -0.562): 

 

 

Cardiovascular (yesterday was -2.1255):

 

 

CKD (yesterday was -0.0508):

 

 

Cancer (yesterday was -1.3933):

 

 

Body Composition (yesterday was 1.5334):

 

 

As overall morbidity is my main concern, I also track that by combining the illness ones (yesterday was -0.5259):

 

p.s. I wished to post the graphs, but that seems to be not allowed. So if you are interested, please see attached document.

Attached Files


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#2 albedo

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Posted 24 November 2021 - 09:02 PM

Against what the algorithm is tested, all causes mortality or  ..? Can you please explain better what are you aiming at? Thank you.



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#3 Zarathrustra

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Posted 24 November 2021 - 09:59 PM

I am aiming at reducing my illnesses: hypertension, CKD3, and cancer.

 

My algorithm is tested against changes in biometrics associated with these: how my lifestyle correlates with these markers and how they change with time - I now use the last 13 years of daily records of lifestyle and various records of the biometrics (some daily, others of decreasing frequency). I change my lifestyle according to these results and test again..

 

In a sense, the mortality figures are built into the biometrics. For example, high blood pressure leads to premature death, so getting that back to about normal (120/80) is good for me. Similarly with all the other biometrics, except the body composition one. For the latter, I assume that increased muscle and bone mass along wit reduced body fat is beneficial. Whilst it may be possible to go too far with such metrics, generally what i have stated is a good thing for those about average or obese or old with sarcopenia.


Edited by Zarathrustra, 24 November 2021 - 10:27 PM.


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#4 albedo

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Posted 25 November 2021 - 10:17 PM

I wonder if you ever thought to compare with aging metrics existing in literature and/or commercialized, say Levine's PhenotypicAge and DNAm PhenoAge or Insilico Medicine's Aging.ai or TruDiagnostic reports. These and others are based on clinically validated health biomarkers regressed on mortality data, or AI driven on similar biomarkers or epigenetics (DNA methylation) in nature. In this Forum some of these measurements are discussed in the "Biological Age" thread.



#5 Zarathrustra

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Posted 26 November 2021 - 12:10 PM

No, Albedo, I hadn't thought of any of that.

 

Thanks for the suggestions. I'll follow them up.


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#6 QuestforLife

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Posted 29 November 2021 - 12:38 PM

You haven't said what your measured biomarkers are (apart from BP). I am guessing they are things like pulse, BP, HRV, weight, fat free BW; or do you take blood tests as well(I assume these wouldn't be daily)?

 

You say that you have found proven anti aging interventions using this data. Can you share any more?

 

There was an interesting paper a while back that suggested that it was useful to look at the variance in a biomarker over a given period, as well as the time it took for a fluctuation in that BM to return to its long term average. Obviously the BM deteriorated over the long term (with aging), but the increase in variance and the increase in the time it took to return to baseline was quicker and could be more actionable. They combined results from whole blood count tests for their BM (in a way they didn't disclose), but also applied it later to step counts and found it equally applicable. 

 

I thought is was a nice way of capturing resiliency change over a sensible time period; rather than looking at daily fluctuations or very long term changes.

 

 Longitudinal analysis of blood markers reveals progressive loss of resilience and predicts human lifespan limit

 

We investigated the dynamic properties of the organism state fluctuations along individual aging trajectories in a large longitudinal database of CBC measurements from a consumer diagnostics laboratory. To simplify the analysis, we used a log-linear mortality estimate from the CBC variables as a single quantitative measure of the aging process, henceforth referred to as dynamic organism state indicator (DOSI). We observed, that the age-dependent population DOSI distribution broadening could be explained by a progressive loss of physiological resilience measured by the DOSI auto-correlation time. Extrapolation of this trend suggested that DOSI recovery time and variance would simultaneously diverge at a critical point of 120 − 150 years of age corresponding to a complete loss of resilience. The observation was immediately confirmed by the independent analysis of correlation properties of intraday physical activity levels fluctuations collected by wearable devices. We conclude that the criticality resulting in the end of life is an intrinsic biological property of an organism that is independent of stress factors and signifies a fundamental or absolute limit of human lifespan.

 

https://www.nature.c...467-021-23014-1

 

 

 


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#7 Zarathrustra

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Posted 29 November 2021 - 03:37 PM

I am happy to share. Just thought I’d be brief in the initial posting.

 

Let me say at the outset regarding the various putative ageing biomarkers, that I have come to regard these, at best, as secondary to the seemingly simplistic  ones already known for decades – resting heart rate, BP, etc – which will anyway be the reference of the gene-based ones. Why? Because if, say, a gene-based one showed a person was getting younger but was frailer, rising resting heart rate, couldn’t walk far, etc, then I’d go with the latter as being a better assessment of longevity than a gene-based one predicting longer life. Having said that, I base my life and research on direct factors – my BP, resting heart rate, hand-grip, creatine levels, CRP, etc.

 

An additional caution of the gene-based ageing measures is that these are necessarily based on averages, and any individual may/will vary from this. So a person needs to know their own ageing – probably dubiously based on this average. Whereas said individual can tell if they are walking more slowly, cannot get up off the chair quickly, etc.

 

I looked at your link to the loss of resilience as a putative predictor. Too complex for an individual to easily access.

 

However, to my own approach>

 

I measure over a hundred biomarkers, many via serum and urine analysis – as you correctly thought, not daily but about monthly. I have taken these up to about 100 times over the last 13 years, giving me fairly robust correlations.

I have reduced them to about 30, in three groups, for lifestyle action – for cardio-vascular, kidney, and cancer. I have a further 3 for body composition, done daily – muscle-mass, bone-mass and body-fat (trunk and visceral fat correlates highly with the overall body-fat, so I no longer analysis this separately). And a host of others: sleep, temperature, oximeter, morning HRV. I use an iterative approach – check the correlations with my present lifestyle; change what I do in the light of these, and test again in about a month.

 

My CV ones are: BP, resting heart rate, morning PNN50 when exercising, CRP, LDL, Total Cholesteral:HDL, Apolipoprotein-B, Migraines, Total Cholesterol, and HbA1ac.

 

My kidney ones are: creatinine, urea, cystatin, potassium, albumin, bilirubin, albumin:globulim.

 

My cancer ones are: Cxbladder, NMP22, CA19-9, CEA, CRP, AST:ALT, Neutrophil:Lymphocytes, Hb:PLT, Basophils, Eosinophils, CRP: Albumin

 

I look to see whether I’ve improved on all of these over time, and have a combined score for each health/illness group (based on normalising each variable).

 

Please see attachment for graphs and tables.

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