How is DunedinPACE rate of aging calculated? How is physiological aging quantified? How is it affected by rapamycin usage?

DunedinPACE tracks changes in 19 biomarkers of organ-system integrity in the 1000-member Dunedin Study birth cohort, who were first enrolled in the study at birth in 1972-1973 and have been followed up ever since, most recently at the time of their 45th birthday. This study used data collected from the participants when they were all aged 26, 32, 38, and 45 years.

I mean, they did a longitudinal study, and that longitudinal study mostly looked at markers of strength or capacity rather than markers of damage.

And if it’s strength/capacity changes with age, then [a] calorie restriction may prevent an increase, [b] they can be easily corrected with follistatin or GDF15 or whatever.

So DunedinPACE was taken out of this study

Ok, so you have to differentiate between the biomarkers that don’t change with age in super-healthy people (eg Mike Lustgarten or CRONites) [and the blood tests/blood pressure/waist to hip ratio of CRONites pretty much does not change for that entire range] from those that do change with age (which is, like, uh, just FEV1/FVC, leukocyte telomere length, and… uhh… that’s it?) [I don’t know if they used these markers to detect pace of aging change because, like, the vast majority don’t change in super-healthy people]… There are way better ones

We used data from the Dunedin Study 1972-1973 birth cohort tracking within-individual decline in 19 indicators of organ-system integrity across four time points spanning two decades to model Pace of Aging

(well, if you look at the above traits HERE, then yes, most of these DO change with age over that timecourse, even in ultra-healthy people [eg even with the best possible diet, a 35 year old is unlikely to be a grandmaster starcraft player or youthful olympic athlete]). These things are harder for someone to measure longitudinally from afar except in the context of a clinic, however. Even routine doctor visits fail to measure these…



Random notes on fraility and GDF15 below::


  • coders of longevity variants have lower GDF15 ()on effect of longevity variants on molecular aging rate)
  • Several proteomic studies using the SOMAscan platform have
    investigated the proteomic profiles of aging-related outcomes and
    reported associated observation of GDF-15, PTN, spondin-1, URB, FSTL3,
    and SMOC1 with frailty and mobility disability, and mortality, in older
    people (Osawa et al., 2020; Sathyan, Ayers, Gao, Milman, et al., 2020; Sathyan, Ayers, Gao, Weiss, et al., 2020).
    Of these, GDF-15, a stress-induced cytokine and a divergent member of
    the transforming growth factor (TGF)-β superfamily, was the protein most
    strongly associated with physical function decline in our study. Recent
    studies have consistently demonstrated the upregulation of GDF-15 in
    aging (Tanaka et al., 2018)
    and showed that higher GDF-15 relates to higher risk of diabetes,
    cancer, cognitive impairment, cardiovascular diseases, and mortality
    (Justice et al., 2018).
    It has been proposed that GDF-15 is a stress-induced cytokine in
    response to tissue injury and can be utilized as a biomarker for various
    diseases (Emmerson et al., 2018).
    An Italian cohort study showed that GDF-15 is associated with the
    development of mobility disability in community-dwelling older adults
    (Osawa et al., 2020).
    Furthermore, investigators from the Baltimore Longitudinal Study of
    Aging (BLSA) found that elevated plasma GDF-15 was associated with
    slower gait speed and low physical performance but not with muscle
    strength in community-dwelling adults (Semba et al., 2020).
    However, this study included very healthy adults and adopted a
    cross-sectional design and therefore is limited in revealing biomarkers
    for longitudinal changes in physical function. More work is needed to
    further elucidate the underlying molecular mechanisms of GDF-15 in
    functional decline.

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Additionally, a whole-body predictive model was estab-lished using all body phenotypes, irrespective of organ grouping. Keymeasures used to assess individual organ function are as follows (alsosee Fig. 1a):• Cardiovascular system: pulse rate, systolic blood pressure anddiastolic blood pressure.• Pulmonary system: FVC, FEV1, PEF and FEV1 :FVC ratio.• Musculoskeletal system: handgrip strength, standing height,weight, BMI, waist and hip circumference, waist:hip circumfer-ence ratio, heal bone mineral density, ankle spacing width,blood biochemical markers such as phosphatase, calcium,phosphate and vitamin D.• Immune system: C-reactive protein and blood hematology testsof leukocytes, erythrocytes, thrombocytes and hemoglobin.• Renal system: biomarkers associated with glomerular filtrationand electrolyte regulation, including creatinine (enzymatic),potassium and sodium in urine, albumin, urea, urate, creatinine,cystatin C, phosphate and total protein in blood.• Hepatic system: alanine aminotransferase, aspartate ami-notransferase, γ-glutamyl transferase, direct and total bilirubin,albumin, alkaline phosphatase and total protein in blood.• Metabolic system: blood biomarkers associated with lipids andglucose metabolism, including apolipoprotein A, apolipopro-tein B, cholesterol, glucose, glycated hemoglobin, high-densitylipoprotein cholesterol, direct low-density lipoprotein choles-terol, lipoprotein A and triglyceride

[I don’t think these are the best panels, they’re very low resolution and unlikely to find aging in calorie-restricted individuals)

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Well mine is 0.59… Supposedly it goes up with age…

Among people who have the “slow ager” phenotype, I do wonder if some parts of their system do still age and that (while they don’t affect much when people are young) at the end these parts of the system slowly catch up with them and destroy them, even if they’re otherwise super-fit at 100 [amyloidosis, or maybe DNA damage, or maybe some parts of aging, like aspartic acid racemization, that are less “modified” by changes like dietary restriction].

HannumAge is MORE associated with adapt-age than damage-age, as is HorvathAge. DNAm that contribute to HorvathAge/HannumAge have little functional significance

GrimAge outperforms phenoage… (there is also a phenoage2 that is just trained on more data than phenoage)

Then there’s GrimAge2, which supposedly outperforms GrimAge (trained on more data). A source says that GrimAge outperforms PhenoAge (p-value is several OOM different) and all other clocks.


How do you actually get tested on GrimAge? Is there a website to get the test?

You get the raw methylation values from the idat file (you can request from TruDiagnostic) then run the age calculators through one of many code repositories (or get someone to run the code for you). GrimAge is not open-source but some variations of it are



I am assuming they meant ‘reliable clocks’ in line 2.

Or they really like roosters.

Or maybe the BBC.

What a typo! :open_mouth: